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Screening High-Risk Women Veterans for Breast Cancer
The number of women seeking care from the Veterans Health Administration (VHA) is increasing.1 In 2015, there were 2 million women veterans in the United States, which is 9.4% of the total veteran population. This group is expected to increase at an average of about 18,000 women per year for the next 10 years.2 The percentage of women veterans who are US Department of Veterans Affairs (VA) users aged 45 to 64 years rose 46% from 2000 to 2015.1,3-4 It is estimated that 15% of veterans who used VA services in 2020 were women.1 Nineteen percent of women veterans are Black.1 The median age of women veterans in 2015 was 50 years.5 Breast cancer is the leading cancer affecting female veterans, and data suggest they have an increased risk of breast cancer based on unique service-related exposures.1,6-9
In the US, about 10 million women are eligible for breast cancer preventive therapy, including, but not limited to, medications, surgery, or lifestyle changes.10 Secondary prevention options include change in surveillance that can reduce their risk or identify cancer at an earlier stage when treatment is more effective. The United States Preventive Services Task Force, the National Comprehensive Cancer Network, the American Society for Clinical Oncology, the National Institute for Health and Care Excellence, and the Oncology Nursing Society recommend screening women aged ≥ 35 years to assess breast cancer risk.11-18 If a woman is at increased risk, she may be a candidate for chemoprevention, prozphylactic surgery, and possibly an enhanced screening regimen.
Urban and minority women are an understudied population. Most veterans (75%) live in urban or suburban settings.19,20 Urban veteran women constitute an important potential study population.
Chemoprevention measures have been underused because of factors involving both women and their health care providers. A large proportion of women are unaware of their higher risk status due to lack of adequate screening and risk assessment.21,22 In addition to patient lack of awareness of their high-risk status, primary care physicians are also reluctant to prescribe chemopreventive agents due to a lack of comfort or familiarity with the risks and benefits.23-26 The STAR2015, BCPT2005, IBIS2014, MAP3 2011, IBIS-I 2014, and IBIS II 2014 studies clearly demonstrate a 49 to 62% reduction in risk for women using chemoprevention such as selective estrogen receptor modulators or aromatase inhibitors, respectively.27-32 Yet only 4 to 9% of high-risk women not enrolled in a clinical trial are using chemoprevention.33-39
The possibility of developing breast cancer also may be increased because of a positive family history or being a member of a family in which there is a known susceptibility gene mutation.40 Based on these risk factors, women may be eligible for tailored follow-up and genetic counseling.41-44
Nationally, 7 to 10% of the civilian US population will experience posttraumatic stress disorder (PTSD).45 The rates are remarkably higher for women veterans, with roughly 20% diagnosed with PTSD.46,47 Anxiety and PTSD have been implicated in poor adherence to medical advice.48,49
In 2014, a national VA multidisciplinary group focused on breast cancer prevention, detection, treatment, and research to address breast health in the growing population of women veterans. High-risk breast cancer screenings are not routinely carried out by the VA in primary care, women’s health, or oncology services. Furthermore, the recording of screening questionnaire results was not synchronized until a standard questionnaire was created and approved as a template by this group in the VA electronic medical record (EMR) in 2015.
Several prediction models can identify which women are at an increased risk of developing breast cancer. The most commonly used risk assessment model, the Gail breast cancer risk assessment tool (BCRAT), has been refined to include women of additional ethnicities (https://www.cancer.gov/bcrisktool).
This pilot project was launched to identify an effective manner to screen women veterans regarding their risk of developing breast cancer and refer them for chemoprevention education or genetic counseling as appropriate.
Methods
A high-risk breast cancer screening questionnaire based on the Gail BCRAT and including lifestyle questions was developed and included as a note template in the VA EMR. The James J. Peters VA Medical Center, Bronx, NY (JJPVAMC) and the Washington DC VA Medical Center (DCVAMC) ran a pilot study between 2015 and 2018 using this breast cancer screening questionnaire to collect data from women veterans. Quality Executive Committee and institutional review board approvals were granted respectively.
Eligibility criteria included women aged ≥ 35 years with no personal history of breast cancer. Most patients were self-referred, but participants also were recruited during VA Breast Cancer Awareness month events, health fairs, or at informational tables in the hospital lobbies. After completing the 20 multiple choice questionnaire with a study team member, either in person or over the phone, a 5-year and lifetime risk of invasive breast cancer was calculated using the Gail BCRAT. A woman is considered high risk and eligible for chemoprevention if her 5-year risk is > 1.66% or her lifetime risk is ≥ 20%. Eligibility for genetic counseling is based on the Breast Cancer Referral Screening Tool, which includes a personal or family history of breast or ovarian cancer and Jewish ancestry.
All patients were notified of their average or high risk status by a clinician. Those who were deemed to be average risk received a follow-up letter in the mail with instructions (eg, to follow-up with a yearly mammogram). Those who were deemed to be high risk for developing breast cancer were asked to come in for an appointment with the study principal investigator (a VA oncologist/breast cancer specialist) to discuss prevention options, further screening, or referrals to genetic counseling. Depending on a patient’s other health factors, a woman at high risk for developing breast cancer also may be a candidate for chemoprevention with tamoxifen, raloxifene, exemestane, anastrozole, or letrozole.
Data on the participant’s lifestyle, including exercise, diet, and smoking, were evaluated to determine whether these factors had an impact on risk status.
Results
The JJP and DC VAMCs screened 103 women veterans between 2015 and 2018. Four patients were excluded for nonveteran (spousal) status, leaving 99 women veterans with a mean age of 54 years. The most common self-reported races were Black (60%), non-Hispanic White (14%), and Hispanic or Latino (13%) (Table 1).
Women veterans in our study were nearly 3-times more likely than the general population were to receive a high-risk Gail Score/BCRAT (35% vs 13%, respectively).50,51 Of this subset, 46% had breast biopsies, and 86% had a positive family history. Thirty-one percent of Black women in our study were high risk, while nationally, 8.2 to 13.3% of Black women aged 50 to 59 years are considered high risk.50,51 Of the Black high-risk group with a high Gail/BCRAT score, 94% had a positive family history, and 33% had a history of breast biopsy (Table 2).
Of the 35 high-risk patients 26 (74%) patients accepted consultations for chemoprevention and 5 (19%) started chemoprevention. Of this high-risk group, 13 (37%) patients were referred for genetic counseling (Table 3).44 The prevalence of PTSD was present in 31% of high-risk women and 29% of the cohort (Figure).The lifestyle questions indicated that, among all participants, 79% had an overweight or obese body mass index; 58% exercised weekly; 51% consumed alcohol; 14% were smokers; and 21% consumed 3 to 4 servings of fruits/vegetables daily.
Discussion
Breast cancer is the most common cancer in women.52 The number of women with breast cancer in the VHA has more than tripled from 1995 to 2012.1 The lifetime risk of developing breast cancer in the general population is about 13%.50 This rate can be affected by risk factors including age, hormone exposure, family history, radiation exposure, and lifestyle factors, such as weight and alcohol use.6,52-56 In the United States, invasive breast cancer affects 1 in 8 women.50,52,57
Our screened population showed nearly 3 times as many women veterans were at an increased risk for breast cancer when compared with historical averages in US women. This difference may be based on a high rate of prior breast biopsies or positive family history, although a provocative study using the Surveillance, Epidemiology, and End Results database showed military women to have higher rates of breast cancer as well.9 Historically, Blacks are vastly understudied in clinical research with only 5% representation on a national level.5,58 The urban locations of both pilot sites (Washington, DC and Bronx, NY) allowed for the inclusion of minority patients in our study. We found that the rates of breast cancer in Black women veterans to be higher than seen nationally, possibly prompting further screening initiatives for this understudied population.
Our pilot study’s chemoprevention utilization (19%) was double the < 10% seen in the national population.33-35 The presence of a knowledgeable breast health practitioner to recruit study participants and offer personalized counseling to women veterans is a likely factor in overcoming barriers to chemopreventive acceptance. These participants may have been motivated to seek care for their high-risk status given a strong family history and prior breast biopsies.
Interestingly, a 3-fold higher PTSD rate was seen in this pilot population (29%) when compared with PTSD rates in the general female population (7-10%) and still one-third higher than the general population of women veterans (20%).45-47 Mental health, anxiety, and PTSD have been barriers to patients who sought treatment and have been implicated in poor adherence to medical advice.48,49 Cancer screening can induce anxiety in patients, and it may be amplified in patients with PTSD. It was remarkable that although adherence with screening recommendations is decreased when PTSD is present, our patient population demonstrated a higher rate of screening adherence.
Women who are seen at the VA often use multiple clinical specialties, and their EMR can be accessed across VA medical centers nationwide. Therefore, identifying women veterans who meet screening criteria is easily attainable within the VA.
When comparing high-risk with average risk women, the lifestyle results (BMI, smoking history, exercise and consumption of fruits, vegetables and alcohol) were essentially the same. Lifestyle factors were similar to national population rates and were unlikely to impact risk levels.
Limitations
Study limitations included a high number of self-referrals and the large percentage of patients with a family history of breast cancer, making them more likely to seek screening. The higher-than-average risk of breast cancer may be driven by a high rate of breast biopsies and a strong family history. Lifestyle metrics could not be accurately compared to other national assessments of lifestyle factors due to the difference in data points that we used or the format of our questions.
Conclusions
As the number of women veterans increases and the incidence of breast cancer in women veterans rise, chemoprevention options should follow national guidelines. To our knowledge, this is the only oncology study with 60% Black women veterans. This study had a higher participation rate for Black women veterans than is typically seen in national research studies and shows the VA to be a germane source for further understanding of an understudied population that may benefit from increased screening for breast cancer.
A team-based, multidisciplinary model that meets the unique healthcare needs of women veterans results in a patient-centric delivery of care for assessing breast cancer risk status and prevention options. This model can be replicated nationally by directing primary care physicians and women’s health practitioners to a risk-assessment questionnaire and referring high-risk women for appropriate preventative care. Given that these results show chemoprevention adherence rates doubled those seen nationally, perhaps techniques used within this VA pilot study may be adapted to decrease breast cancer incidence nationally.
Since the rate of PTSD among women veterans is triple the national average, we would expect adherence rates to be lower in our patient cohort. However, the multidisciplinary approach we used in this study (eg, 1:1 consultation with oncologist; genetic counseling referrals; mental health support available), may have improved adherence rates. Perhaps the high rates of PTSD seen in the VA patient population can be a useful way to explore patient adherence rates in those with mental illness and medical conditions.
Future research with a larger cohort may lead to greater insight into the correlation between PTSD and adherence to treatment. Exploring the connection between breast cancer, epigenetics, and specific military service-related exposures could be an area of analysis among this veteran population exhibiting increased breast cancer rates. VAMCs are situated in rural, suburban, and urban locations across the United States and offers a diverse socioeconomic and ethnic patient population for inclusion in clinical investigations. Women veterans make up a small subpopulation of women in the United States, but it is worth considering VA patients as an untapped resource for research collaboration.
Acknowledgements
The authors thank Steven Sanchez and Marissa Vallette, PhD, Breast Health Research Group. This research project was approved by the James J. Peters VA Medical Center Quality Executive Committee and the Washington, DC VA Medical Center Institutional Review Board. This work was supported by the US Department of Veterans Affairs. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.
1. US Department of Veterans Affairs. National Center for Veterans Analysis and Statistics. The past, present and future of women veterans. Published February 2017. Accessed April 28, 2021. https://www.va.gov/vetdata/docs/specialreports/women_veterans_2015_final.pdf.
2. Frayne SM, Carney DV, Bastian L, et al. The VA Women’s Health Practice-Based Research Network: amplifying women veterans’ voices in VA research. J Gen Intern Med. 2013;28 Suppl 2(Suppl 2):S504-S509. doi:10.1007/s11606-013-2476-3
3. US Department of Veterans Affairs, Veterans Health Administration, Women’s Health Evaluation Initiative, Women Veterans Health Strategic Health Care Group. Sourcebook: women veterans in the Veterans Health Administration. Volume 1: Sociodemographic characteristics and use of VHA care. Published December 2010. Accessed April 12, 2021. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2455
4. Bean-Mayberry B, Yano EM, Bayliss N, Navratil J, Weisman CS, Scholle SH. Federally funded comprehensive women’s health centers: leading innovation in women’s healthcare delivery. J Womens Health (Larchmt). 2007;16(9):1281-1290. doi:10.1089/jwh.2006.0284
5. US Department of Veterans Affairs. National Center for Veterans Analysis and Statistics.VA utilization profile FY 2016. Published November 2017. Accessed April 12, 2021. https://www.va.gov/vetdata/docs/QuickFacts/VA_Utilization_Profile.PDF
6. Ekenga CC, Parks CG, Sandler DP. Chemical exposures in the workplace and breast cancer risk: a prospective cohort study. Int J Cancer. 2015;137(7):1765-1774. doi:10.1002/ijc.29545
7. Rennix CP, Quinn MM, Amoroso PJ, Eisen EA, Wegman DH. Risk of breast cancer among enlisted Army women occupationally exposed to volatile organic compounds. Am J Ind Med. 2005;48(3):157-167. doi:10.1002/ajim.20201
8. Ritz B. Cancer mortality among workers exposed to chemicals during uranium processing. J Occup Environ Med. 1999;41(7):556-566. doi:10.1097/00043764-199907000-00004
9. Zhu K, Devesa SS, Wu H, et al. Cancer incidence in the U.S. military population: comparison with rates from the SEER program. Cancer Epidemiol Biomarkers Prev. 2009;18(6):1740-1745. doi:10.1158/1055-9965.EPI-09-0041
10. Freedman AN, Yu B, Gail MH, et al. Benefit/risk assessment for breast cancer chemoprevention with raloxifene or tamoxifen for women age 50 years or older [published correction appears in J Clin Oncol. 2013 Nov 10;31(32):4167]. J Clin Oncol. 2011;29(17):2327-2333. doi:10.1200/JCO.2010.33.0258
11. Greene, H. Cancer prevention, screening and early detection. In: Gobel BH, Triest-Robertson S, Vogel WH, eds. Advanced Oncology Nursing Certification Review and Resource Manual. 3rd ed. Oncology Nursing Society; 2016:1-34. https://www.ons.org/sites/default/files/publication_pdfs/2%20ADVPrac%20chapter%201.pdf
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14. Moyer VA; U.S. Preventive Services Task Force. Medications to decrease the risk for breast cancer in women: recommendations from the U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2013;159(10):698-708. doi:10.7326/0003-4819-159-10-201311190-00717
15. Boucher JE. Chemoprevention: an overview of pharmacologic agents and nursing considerations. Clin J Oncol Nurs. 2018;22(3):350-353. doi:10.1188/18.CJON.350-353
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17. Bevers TB, Helvie M, Bonaccio E, et al. Breast cancer screening and diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2018;16(11):1362-1389. doi:10.6004/jnccn.2018.0083
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19. Sealy-Jefferson S, Roseland ME, Cote ML, et al. rural-urban residence and stage at breast cancer diagnosis among postmenopausal women: The Women’s Health Initiative. J Womens Health (Larchmt). 2019;28(2):276-283. doi:10.1089/jwh.2017.6884
20. Holder KA. Veterans in rural America: 2011-2015. Published January 25, 2017. Accessed April 12, 2021. https://www.census.gov/library/publications/2017/acs/acs-36.html
21. Owens WL, Gallagher TJ, Kincheloe MJ, Ruetten VL. Implementation in a large health system of a program to identify women at high risk for breast cancer. J Oncol Pract. 2011;7(2):85-88. doi:10.1200/JOP.2010.000107
2. Pivot X, Viguier J, Touboul C, et al. Breast cancer screening controversy: too much or not enough?. Eur J Cancer Prev. 2015;24 Suppl:S73-S76. doi:10.1097/CEJ.0000000000000145
23. Bidassie B, Kovach A, Vallette MA, et al. Breast Cancer risk assessment and chemoprevention use among veterans affairs primary care providers: a national online survey. Mil Med. 2020;185(3-4):512-518. doi:10.1093/milmed/usz291
24. Brewster AM, Davidson NE, McCaskill-Stevens W. Chemoprevention for breast cancer: overcoming barriers to treatment. Am Soc Clin Oncol Educ Book. 2012;85-90. doi:10.14694/EdBook_AM.2012.32.152
25. Meyskens FL Jr, Curt GA, Brenner DE, et al. Regulatory approval of cancer risk-reducing (chemopreventive) drugs: moving what we have learned into the clinic. Cancer Prev Res (Phila). 2011;4(3):311-323. doi:10.1158/1940-6207.CAPR-09-0014
26. Tice JA, Kerlikowske K. Screening and prevention of breast cancer in primary care. Prim Care. 2009;36(3):533-558. doi:10.1016/j.pop.2009.04.003
27. Vogel VG. Selective estrogen receptor modulators and aromatase inhibitors for breast cancer chemoprevention. Curr Drug Targets. 2011;12(13):1874-1887. doi:10.2174/138945011798184164
28. Vogel VG, Costantino JP, Wickerham DL, et al. Effects of tamoxifen vs raloxifene on the risk of developing invasive breast cancer and other disease outcomes: the NSABP Study of Tamoxifen and Raloxifene (STAR) P-2 trial [published correction appears in JAMA. 2006 Dec 27;296(24):2926] [published correction appears in JAMA. 2007 Sep 5;298(9):973]. JAMA. 2006;295(23):2727-2741. doi:10.1001/jama.295.23.joc60074
29. Pruthi S, Heisey RE, Bevers TB. Chemoprevention for breast cancer. Ann Surg Oncol. 2015;22(10):3230-3235. doi:10.1245/s10434-015-4715-9
30. Cuzick J, Sestak I, Forbes JF, et al. Anastrozole for prevention of breast cancer in high-risk postmenopausal women (IBIS-II): an international, double-blind, randomised placebo-controlled trial [published correction appears in Lancet. 2014 Mar 22;383(9922):1040] [published correction appears in Lancet. 2017 Mar 11;389(10073):1010]. Lancet. 2014;383(9922):1041-1048. doi:10.1016/S0140-6736(13)62292-8
31. Bozovic-Spasojevic I, Azambuja E, McCaskill-Stevens W, Dinh P, Cardoso F. Chemoprevention for breast cancer. Cancer Treat Rev. 2012;38(5):329-339. doi:10.1016/j.ctrv.2011.07.005
32. Gabriel EM, Jatoi I. Breast cancer chemoprevention. Expert Rev Anticancer Ther. 2012;12(2):223-228. doi:10.1586/era.11.206

33. Crew KD, Albain KS, Hershman DL, Unger JM, Lo SS. How do we increase uptake of tamoxifen and other anti-estrogens for breast cancer prevention?. NPJ Breast Cancer. 2017;3:20. Published 2017 May 19. doi:10.1038/s41523-017-0021-y
34. Ropka ME, Keim J, Philbrick JT. Patient decisions about breast cancer chemoprevention: a systematic review and meta-analysis. J Clin Oncol. 2010;28(18):3090-3095. doi:10.1200/JCO.2009.27.8077
35. Smith SG, Sestak I, Forster A, et al. Factors affecting uptake and adherence to breast cancer chemoprevention: a systematic review and meta-analysis. Ann Oncol. 2016;27(4):575-590. doi:10.1093/annonc/mdv590
36. Grann VR, Patel PR, Jacobson JS, et al. Comparative effectiveness of screening and prevention strategies among BRCA1/2-affected mutation carriers. Breast Cancer Res Treat. 2011 Feb;125(3):837-847. doi:10.1007/s10549-010-1043-4
37. Goss PE, Ingle JN, Alés-Martínez JE, et al. Exemestane for breast-cancer prevention in postmenopausal women [published correction appears in N Engl J Med. 2011 Oct 6;365(14):1361]. N Engl J Med. 2011;364(25):2381-2391. doi:10.1056/NEJMoa1103507
38. Kmietowicz Z. Five in six women reject drugs that could reduce their risk of breast cancer. BMJ. 2015;351:h6650. Published 2015 Dec 8. doi:10.1136/bmj.h6650
39. Nelson HD, Fu R, Griffin JC, Nygren P, Smith ME, Humphrey L. Systematic review: comparative effectiveness of medications to reduce risk for primary breast cancer. Ann Intern Med. 2009;151(10):703-235. doi:10.7326/0003-4819-151-10-200911170-00147
40. Dahabreh IJ, Wieland LS, Adam GP, Halladay C, Lau J, Trikalinos TA. Core needle and open surgery biopsy for diagnosis of breast lesions: an update to the 2009 report. Published September 2014. Accessed April 12, 2021. https://www.ncbi.nlm.nih.gov/books/NBK246878
41. National Cancer Institute. Genetics of breast and ovarian cancer (PDQ)—health profession version. Updated February 12, 2021. Accessed April 12, 2021. http://www.cancer.gov/cancertopics/pdq/genetics/breast-and-ovarian/HealthProfessional
42. US Department of Health and Human Services. National Institutes of Health, National Institute of Environmental Health Sciences The sister study. Accessed April 12, 2021. https://sisterstudy.niehs.nih.gov/english/NIEHS.htm
43. Tutt A, Ashworth A. Can genetic testing guide treatment in breast cancer?. Eur J Cancer. 2008;44(18):2774-2780. doi:10.1016/j.ejca.2008.10.009
44. Katz SJ, Ward KC, Hamilton AS, et al. Gaps in receipt of clinically indicated genetic counseling after diagnosis of breast cancer. J Clin Oncol. 2018;36(12):1218-1224. doi:10.1200/JCO.2017.76.2369
45. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in adults? Updated October 17, 2019. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_adults.asp
46. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in women? Updated October 16, 2019. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_women.asp
47. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in veterans? Updated September 24, 2018. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_veterans.asp
48. Lindberg NM, Wellisch D. Anxiety and compliance among women at high risk for breast cancer. Ann Behav Med. 2001;23(4):298-303. doi:10.1207/S15324796ABM2304_9
49. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med. 2000;160(14):2101-2107. doi:10.1001/archinte.160.14.2101
50. Centers for Disease Control and Prevention. MMWR appendix: breast cancer rates among black women and white women. Updated October 13, 2016. Accessed April 12, 2021. https://www.cdc.gov/cancer/breast/statistics/trends_invasive.htm
51. Richardson LC, Henley SJ, Miller JW, Massetti G, Thomas CC. Patterns and trends in age-specific black-white differences in breast cancer incidence and mortality - United States, 1999-2014. MMWR Morb Mortal Wkly Rep. 2016;65(40):1093-1098. Published 2016 Oct 14. doi:10.15585/mmwr.mm6540a1
52. Brody JG, Moysich KB, Humblet O, Attfield KR, Beehler GP, Rudel RA. Environmental pollutants and breast cancer: epidemiologic studies. Cancer. 2007;109(12 Suppl):2667-2711. doi:10.1002/cncr.22655
53. Brophy JT, Keith MM, Watterson A, et al. Breast cancer risk in relation to occupations with exposure to carcinogens and endocrine disruptors: a Canadian case-control study. Environ Health. 2012;11:87. Published 2012 Nov 19. doi:10.1186/1476-069X-11-87
54. Labrèche F, Goldberg MS, Valois MF, Nadon L. Postmenopausal breast cancer and occupational exposures. Occup Environ Med. 2010;67(4):263-269. doi:10.1136/oem.2009.049817
55. National Institute of Environmental Health Sciences, Interagency Breast Cancer & Environmental Research Coordinating Committee. Breast cancer and the environment: prioritizing prevention. Updated March 8, 2013. Accessed April 12, 2021. https://www.niehs.nih.gov/about/boards/ibcercc/index.cfm
56. Gail MH, Costantino JP, Pee D, et al. Projecting individualized absolute invasive breast cancer risk in African American women [published correction appears in J Natl Cancer Inst. 2008 Aug 6;100(15):1118] [published correction appears in J Natl Cancer Inst. 2008 Mar 5;100(5):373]. J Natl Cancer Inst. 2007;99(23):1782-1792. doi:10.1093/jnci/djm223
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The number of women seeking care from the Veterans Health Administration (VHA) is increasing.1 In 2015, there were 2 million women veterans in the United States, which is 9.4% of the total veteran population. This group is expected to increase at an average of about 18,000 women per year for the next 10 years.2 The percentage of women veterans who are US Department of Veterans Affairs (VA) users aged 45 to 64 years rose 46% from 2000 to 2015.1,3-4 It is estimated that 15% of veterans who used VA services in 2020 were women.1 Nineteen percent of women veterans are Black.1 The median age of women veterans in 2015 was 50 years.5 Breast cancer is the leading cancer affecting female veterans, and data suggest they have an increased risk of breast cancer based on unique service-related exposures.1,6-9
In the US, about 10 million women are eligible for breast cancer preventive therapy, including, but not limited to, medications, surgery, or lifestyle changes.10 Secondary prevention options include change in surveillance that can reduce their risk or identify cancer at an earlier stage when treatment is more effective. The United States Preventive Services Task Force, the National Comprehensive Cancer Network, the American Society for Clinical Oncology, the National Institute for Health and Care Excellence, and the Oncology Nursing Society recommend screening women aged ≥ 35 years to assess breast cancer risk.11-18 If a woman is at increased risk, she may be a candidate for chemoprevention, prozphylactic surgery, and possibly an enhanced screening regimen.
Urban and minority women are an understudied population. Most veterans (75%) live in urban or suburban settings.19,20 Urban veteran women constitute an important potential study population.
Chemoprevention measures have been underused because of factors involving both women and their health care providers. A large proportion of women are unaware of their higher risk status due to lack of adequate screening and risk assessment.21,22 In addition to patient lack of awareness of their high-risk status, primary care physicians are also reluctant to prescribe chemopreventive agents due to a lack of comfort or familiarity with the risks and benefits.23-26 The STAR2015, BCPT2005, IBIS2014, MAP3 2011, IBIS-I 2014, and IBIS II 2014 studies clearly demonstrate a 49 to 62% reduction in risk for women using chemoprevention such as selective estrogen receptor modulators or aromatase inhibitors, respectively.27-32 Yet only 4 to 9% of high-risk women not enrolled in a clinical trial are using chemoprevention.33-39
The possibility of developing breast cancer also may be increased because of a positive family history or being a member of a family in which there is a known susceptibility gene mutation.40 Based on these risk factors, women may be eligible for tailored follow-up and genetic counseling.41-44
Nationally, 7 to 10% of the civilian US population will experience posttraumatic stress disorder (PTSD).45 The rates are remarkably higher for women veterans, with roughly 20% diagnosed with PTSD.46,47 Anxiety and PTSD have been implicated in poor adherence to medical advice.48,49
In 2014, a national VA multidisciplinary group focused on breast cancer prevention, detection, treatment, and research to address breast health in the growing population of women veterans. High-risk breast cancer screenings are not routinely carried out by the VA in primary care, women’s health, or oncology services. Furthermore, the recording of screening questionnaire results was not synchronized until a standard questionnaire was created and approved as a template by this group in the VA electronic medical record (EMR) in 2015.
Several prediction models can identify which women are at an increased risk of developing breast cancer. The most commonly used risk assessment model, the Gail breast cancer risk assessment tool (BCRAT), has been refined to include women of additional ethnicities (https://www.cancer.gov/bcrisktool).
This pilot project was launched to identify an effective manner to screen women veterans regarding their risk of developing breast cancer and refer them for chemoprevention education or genetic counseling as appropriate.
Methods
A high-risk breast cancer screening questionnaire based on the Gail BCRAT and including lifestyle questions was developed and included as a note template in the VA EMR. The James J. Peters VA Medical Center, Bronx, NY (JJPVAMC) and the Washington DC VA Medical Center (DCVAMC) ran a pilot study between 2015 and 2018 using this breast cancer screening questionnaire to collect data from women veterans. Quality Executive Committee and institutional review board approvals were granted respectively.
Eligibility criteria included women aged ≥ 35 years with no personal history of breast cancer. Most patients were self-referred, but participants also were recruited during VA Breast Cancer Awareness month events, health fairs, or at informational tables in the hospital lobbies. After completing the 20 multiple choice questionnaire with a study team member, either in person or over the phone, a 5-year and lifetime risk of invasive breast cancer was calculated using the Gail BCRAT. A woman is considered high risk and eligible for chemoprevention if her 5-year risk is > 1.66% or her lifetime risk is ≥ 20%. Eligibility for genetic counseling is based on the Breast Cancer Referral Screening Tool, which includes a personal or family history of breast or ovarian cancer and Jewish ancestry.
All patients were notified of their average or high risk status by a clinician. Those who were deemed to be average risk received a follow-up letter in the mail with instructions (eg, to follow-up with a yearly mammogram). Those who were deemed to be high risk for developing breast cancer were asked to come in for an appointment with the study principal investigator (a VA oncologist/breast cancer specialist) to discuss prevention options, further screening, or referrals to genetic counseling. Depending on a patient’s other health factors, a woman at high risk for developing breast cancer also may be a candidate for chemoprevention with tamoxifen, raloxifene, exemestane, anastrozole, or letrozole.
Data on the participant’s lifestyle, including exercise, diet, and smoking, were evaluated to determine whether these factors had an impact on risk status.
Results
The JJP and DC VAMCs screened 103 women veterans between 2015 and 2018. Four patients were excluded for nonveteran (spousal) status, leaving 99 women veterans with a mean age of 54 years. The most common self-reported races were Black (60%), non-Hispanic White (14%), and Hispanic or Latino (13%) (Table 1).
Women veterans in our study were nearly 3-times more likely than the general population were to receive a high-risk Gail Score/BCRAT (35% vs 13%, respectively).50,51 Of this subset, 46% had breast biopsies, and 86% had a positive family history. Thirty-one percent of Black women in our study were high risk, while nationally, 8.2 to 13.3% of Black women aged 50 to 59 years are considered high risk.50,51 Of the Black high-risk group with a high Gail/BCRAT score, 94% had a positive family history, and 33% had a history of breast biopsy (Table 2).
Of the 35 high-risk patients 26 (74%) patients accepted consultations for chemoprevention and 5 (19%) started chemoprevention. Of this high-risk group, 13 (37%) patients were referred for genetic counseling (Table 3).44 The prevalence of PTSD was present in 31% of high-risk women and 29% of the cohort (Figure).The lifestyle questions indicated that, among all participants, 79% had an overweight or obese body mass index; 58% exercised weekly; 51% consumed alcohol; 14% were smokers; and 21% consumed 3 to 4 servings of fruits/vegetables daily.
Discussion
Breast cancer is the most common cancer in women.52 The number of women with breast cancer in the VHA has more than tripled from 1995 to 2012.1 The lifetime risk of developing breast cancer in the general population is about 13%.50 This rate can be affected by risk factors including age, hormone exposure, family history, radiation exposure, and lifestyle factors, such as weight and alcohol use.6,52-56 In the United States, invasive breast cancer affects 1 in 8 women.50,52,57
Our screened population showed nearly 3 times as many women veterans were at an increased risk for breast cancer when compared with historical averages in US women. This difference may be based on a high rate of prior breast biopsies or positive family history, although a provocative study using the Surveillance, Epidemiology, and End Results database showed military women to have higher rates of breast cancer as well.9 Historically, Blacks are vastly understudied in clinical research with only 5% representation on a national level.5,58 The urban locations of both pilot sites (Washington, DC and Bronx, NY) allowed for the inclusion of minority patients in our study. We found that the rates of breast cancer in Black women veterans to be higher than seen nationally, possibly prompting further screening initiatives for this understudied population.
Our pilot study’s chemoprevention utilization (19%) was double the < 10% seen in the national population.33-35 The presence of a knowledgeable breast health practitioner to recruit study participants and offer personalized counseling to women veterans is a likely factor in overcoming barriers to chemopreventive acceptance. These participants may have been motivated to seek care for their high-risk status given a strong family history and prior breast biopsies.
Interestingly, a 3-fold higher PTSD rate was seen in this pilot population (29%) when compared with PTSD rates in the general female population (7-10%) and still one-third higher than the general population of women veterans (20%).45-47 Mental health, anxiety, and PTSD have been barriers to patients who sought treatment and have been implicated in poor adherence to medical advice.48,49 Cancer screening can induce anxiety in patients, and it may be amplified in patients with PTSD. It was remarkable that although adherence with screening recommendations is decreased when PTSD is present, our patient population demonstrated a higher rate of screening adherence.
Women who are seen at the VA often use multiple clinical specialties, and their EMR can be accessed across VA medical centers nationwide. Therefore, identifying women veterans who meet screening criteria is easily attainable within the VA.
When comparing high-risk with average risk women, the lifestyle results (BMI, smoking history, exercise and consumption of fruits, vegetables and alcohol) were essentially the same. Lifestyle factors were similar to national population rates and were unlikely to impact risk levels.
Limitations
Study limitations included a high number of self-referrals and the large percentage of patients with a family history of breast cancer, making them more likely to seek screening. The higher-than-average risk of breast cancer may be driven by a high rate of breast biopsies and a strong family history. Lifestyle metrics could not be accurately compared to other national assessments of lifestyle factors due to the difference in data points that we used or the format of our questions.
Conclusions
As the number of women veterans increases and the incidence of breast cancer in women veterans rise, chemoprevention options should follow national guidelines. To our knowledge, this is the only oncology study with 60% Black women veterans. This study had a higher participation rate for Black women veterans than is typically seen in national research studies and shows the VA to be a germane source for further understanding of an understudied population that may benefit from increased screening for breast cancer.
A team-based, multidisciplinary model that meets the unique healthcare needs of women veterans results in a patient-centric delivery of care for assessing breast cancer risk status and prevention options. This model can be replicated nationally by directing primary care physicians and women’s health practitioners to a risk-assessment questionnaire and referring high-risk women for appropriate preventative care. Given that these results show chemoprevention adherence rates doubled those seen nationally, perhaps techniques used within this VA pilot study may be adapted to decrease breast cancer incidence nationally.
Since the rate of PTSD among women veterans is triple the national average, we would expect adherence rates to be lower in our patient cohort. However, the multidisciplinary approach we used in this study (eg, 1:1 consultation with oncologist; genetic counseling referrals; mental health support available), may have improved adherence rates. Perhaps the high rates of PTSD seen in the VA patient population can be a useful way to explore patient adherence rates in those with mental illness and medical conditions.
Future research with a larger cohort may lead to greater insight into the correlation between PTSD and adherence to treatment. Exploring the connection between breast cancer, epigenetics, and specific military service-related exposures could be an area of analysis among this veteran population exhibiting increased breast cancer rates. VAMCs are situated in rural, suburban, and urban locations across the United States and offers a diverse socioeconomic and ethnic patient population for inclusion in clinical investigations. Women veterans make up a small subpopulation of women in the United States, but it is worth considering VA patients as an untapped resource for research collaboration.
Acknowledgements
The authors thank Steven Sanchez and Marissa Vallette, PhD, Breast Health Research Group. This research project was approved by the James J. Peters VA Medical Center Quality Executive Committee and the Washington, DC VA Medical Center Institutional Review Board. This work was supported by the US Department of Veterans Affairs. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.
The number of women seeking care from the Veterans Health Administration (VHA) is increasing.1 In 2015, there were 2 million women veterans in the United States, which is 9.4% of the total veteran population. This group is expected to increase at an average of about 18,000 women per year for the next 10 years.2 The percentage of women veterans who are US Department of Veterans Affairs (VA) users aged 45 to 64 years rose 46% from 2000 to 2015.1,3-4 It is estimated that 15% of veterans who used VA services in 2020 were women.1 Nineteen percent of women veterans are Black.1 The median age of women veterans in 2015 was 50 years.5 Breast cancer is the leading cancer affecting female veterans, and data suggest they have an increased risk of breast cancer based on unique service-related exposures.1,6-9
In the US, about 10 million women are eligible for breast cancer preventive therapy, including, but not limited to, medications, surgery, or lifestyle changes.10 Secondary prevention options include change in surveillance that can reduce their risk or identify cancer at an earlier stage when treatment is more effective. The United States Preventive Services Task Force, the National Comprehensive Cancer Network, the American Society for Clinical Oncology, the National Institute for Health and Care Excellence, and the Oncology Nursing Society recommend screening women aged ≥ 35 years to assess breast cancer risk.11-18 If a woman is at increased risk, she may be a candidate for chemoprevention, prozphylactic surgery, and possibly an enhanced screening regimen.
Urban and minority women are an understudied population. Most veterans (75%) live in urban or suburban settings.19,20 Urban veteran women constitute an important potential study population.
Chemoprevention measures have been underused because of factors involving both women and their health care providers. A large proportion of women are unaware of their higher risk status due to lack of adequate screening and risk assessment.21,22 In addition to patient lack of awareness of their high-risk status, primary care physicians are also reluctant to prescribe chemopreventive agents due to a lack of comfort or familiarity with the risks and benefits.23-26 The STAR2015, BCPT2005, IBIS2014, MAP3 2011, IBIS-I 2014, and IBIS II 2014 studies clearly demonstrate a 49 to 62% reduction in risk for women using chemoprevention such as selective estrogen receptor modulators or aromatase inhibitors, respectively.27-32 Yet only 4 to 9% of high-risk women not enrolled in a clinical trial are using chemoprevention.33-39
The possibility of developing breast cancer also may be increased because of a positive family history or being a member of a family in which there is a known susceptibility gene mutation.40 Based on these risk factors, women may be eligible for tailored follow-up and genetic counseling.41-44
Nationally, 7 to 10% of the civilian US population will experience posttraumatic stress disorder (PTSD).45 The rates are remarkably higher for women veterans, with roughly 20% diagnosed with PTSD.46,47 Anxiety and PTSD have been implicated in poor adherence to medical advice.48,49
In 2014, a national VA multidisciplinary group focused on breast cancer prevention, detection, treatment, and research to address breast health in the growing population of women veterans. High-risk breast cancer screenings are not routinely carried out by the VA in primary care, women’s health, or oncology services. Furthermore, the recording of screening questionnaire results was not synchronized until a standard questionnaire was created and approved as a template by this group in the VA electronic medical record (EMR) in 2015.
Several prediction models can identify which women are at an increased risk of developing breast cancer. The most commonly used risk assessment model, the Gail breast cancer risk assessment tool (BCRAT), has been refined to include women of additional ethnicities (https://www.cancer.gov/bcrisktool).
This pilot project was launched to identify an effective manner to screen women veterans regarding their risk of developing breast cancer and refer them for chemoprevention education or genetic counseling as appropriate.
Methods
A high-risk breast cancer screening questionnaire based on the Gail BCRAT and including lifestyle questions was developed and included as a note template in the VA EMR. The James J. Peters VA Medical Center, Bronx, NY (JJPVAMC) and the Washington DC VA Medical Center (DCVAMC) ran a pilot study between 2015 and 2018 using this breast cancer screening questionnaire to collect data from women veterans. Quality Executive Committee and institutional review board approvals were granted respectively.
Eligibility criteria included women aged ≥ 35 years with no personal history of breast cancer. Most patients were self-referred, but participants also were recruited during VA Breast Cancer Awareness month events, health fairs, or at informational tables in the hospital lobbies. After completing the 20 multiple choice questionnaire with a study team member, either in person or over the phone, a 5-year and lifetime risk of invasive breast cancer was calculated using the Gail BCRAT. A woman is considered high risk and eligible for chemoprevention if her 5-year risk is > 1.66% or her lifetime risk is ≥ 20%. Eligibility for genetic counseling is based on the Breast Cancer Referral Screening Tool, which includes a personal or family history of breast or ovarian cancer and Jewish ancestry.
All patients were notified of their average or high risk status by a clinician. Those who were deemed to be average risk received a follow-up letter in the mail with instructions (eg, to follow-up with a yearly mammogram). Those who were deemed to be high risk for developing breast cancer were asked to come in for an appointment with the study principal investigator (a VA oncologist/breast cancer specialist) to discuss prevention options, further screening, or referrals to genetic counseling. Depending on a patient’s other health factors, a woman at high risk for developing breast cancer also may be a candidate for chemoprevention with tamoxifen, raloxifene, exemestane, anastrozole, or letrozole.
Data on the participant’s lifestyle, including exercise, diet, and smoking, were evaluated to determine whether these factors had an impact on risk status.
Results
The JJP and DC VAMCs screened 103 women veterans between 2015 and 2018. Four patients were excluded for nonveteran (spousal) status, leaving 99 women veterans with a mean age of 54 years. The most common self-reported races were Black (60%), non-Hispanic White (14%), and Hispanic or Latino (13%) (Table 1).
Women veterans in our study were nearly 3-times more likely than the general population were to receive a high-risk Gail Score/BCRAT (35% vs 13%, respectively).50,51 Of this subset, 46% had breast biopsies, and 86% had a positive family history. Thirty-one percent of Black women in our study were high risk, while nationally, 8.2 to 13.3% of Black women aged 50 to 59 years are considered high risk.50,51 Of the Black high-risk group with a high Gail/BCRAT score, 94% had a positive family history, and 33% had a history of breast biopsy (Table 2).
Of the 35 high-risk patients 26 (74%) patients accepted consultations for chemoprevention and 5 (19%) started chemoprevention. Of this high-risk group, 13 (37%) patients were referred for genetic counseling (Table 3).44 The prevalence of PTSD was present in 31% of high-risk women and 29% of the cohort (Figure).The lifestyle questions indicated that, among all participants, 79% had an overweight or obese body mass index; 58% exercised weekly; 51% consumed alcohol; 14% were smokers; and 21% consumed 3 to 4 servings of fruits/vegetables daily.
Discussion
Breast cancer is the most common cancer in women.52 The number of women with breast cancer in the VHA has more than tripled from 1995 to 2012.1 The lifetime risk of developing breast cancer in the general population is about 13%.50 This rate can be affected by risk factors including age, hormone exposure, family history, radiation exposure, and lifestyle factors, such as weight and alcohol use.6,52-56 In the United States, invasive breast cancer affects 1 in 8 women.50,52,57
Our screened population showed nearly 3 times as many women veterans were at an increased risk for breast cancer when compared with historical averages in US women. This difference may be based on a high rate of prior breast biopsies or positive family history, although a provocative study using the Surveillance, Epidemiology, and End Results database showed military women to have higher rates of breast cancer as well.9 Historically, Blacks are vastly understudied in clinical research with only 5% representation on a national level.5,58 The urban locations of both pilot sites (Washington, DC and Bronx, NY) allowed for the inclusion of minority patients in our study. We found that the rates of breast cancer in Black women veterans to be higher than seen nationally, possibly prompting further screening initiatives for this understudied population.
Our pilot study’s chemoprevention utilization (19%) was double the < 10% seen in the national population.33-35 The presence of a knowledgeable breast health practitioner to recruit study participants and offer personalized counseling to women veterans is a likely factor in overcoming barriers to chemopreventive acceptance. These participants may have been motivated to seek care for their high-risk status given a strong family history and prior breast biopsies.
Interestingly, a 3-fold higher PTSD rate was seen in this pilot population (29%) when compared with PTSD rates in the general female population (7-10%) and still one-third higher than the general population of women veterans (20%).45-47 Mental health, anxiety, and PTSD have been barriers to patients who sought treatment and have been implicated in poor adherence to medical advice.48,49 Cancer screening can induce anxiety in patients, and it may be amplified in patients with PTSD. It was remarkable that although adherence with screening recommendations is decreased when PTSD is present, our patient population demonstrated a higher rate of screening adherence.
Women who are seen at the VA often use multiple clinical specialties, and their EMR can be accessed across VA medical centers nationwide. Therefore, identifying women veterans who meet screening criteria is easily attainable within the VA.
When comparing high-risk with average risk women, the lifestyle results (BMI, smoking history, exercise and consumption of fruits, vegetables and alcohol) were essentially the same. Lifestyle factors were similar to national population rates and were unlikely to impact risk levels.
Limitations
Study limitations included a high number of self-referrals and the large percentage of patients with a family history of breast cancer, making them more likely to seek screening. The higher-than-average risk of breast cancer may be driven by a high rate of breast biopsies and a strong family history. Lifestyle metrics could not be accurately compared to other national assessments of lifestyle factors due to the difference in data points that we used or the format of our questions.
Conclusions
As the number of women veterans increases and the incidence of breast cancer in women veterans rise, chemoprevention options should follow national guidelines. To our knowledge, this is the only oncology study with 60% Black women veterans. This study had a higher participation rate for Black women veterans than is typically seen in national research studies and shows the VA to be a germane source for further understanding of an understudied population that may benefit from increased screening for breast cancer.
A team-based, multidisciplinary model that meets the unique healthcare needs of women veterans results in a patient-centric delivery of care for assessing breast cancer risk status and prevention options. This model can be replicated nationally by directing primary care physicians and women’s health practitioners to a risk-assessment questionnaire and referring high-risk women for appropriate preventative care. Given that these results show chemoprevention adherence rates doubled those seen nationally, perhaps techniques used within this VA pilot study may be adapted to decrease breast cancer incidence nationally.
Since the rate of PTSD among women veterans is triple the national average, we would expect adherence rates to be lower in our patient cohort. However, the multidisciplinary approach we used in this study (eg, 1:1 consultation with oncologist; genetic counseling referrals; mental health support available), may have improved adherence rates. Perhaps the high rates of PTSD seen in the VA patient population can be a useful way to explore patient adherence rates in those with mental illness and medical conditions.
Future research with a larger cohort may lead to greater insight into the correlation between PTSD and adherence to treatment. Exploring the connection between breast cancer, epigenetics, and specific military service-related exposures could be an area of analysis among this veteran population exhibiting increased breast cancer rates. VAMCs are situated in rural, suburban, and urban locations across the United States and offers a diverse socioeconomic and ethnic patient population for inclusion in clinical investigations. Women veterans make up a small subpopulation of women in the United States, but it is worth considering VA patients as an untapped resource for research collaboration.
Acknowledgements
The authors thank Steven Sanchez and Marissa Vallette, PhD, Breast Health Research Group. This research project was approved by the James J. Peters VA Medical Center Quality Executive Committee and the Washington, DC VA Medical Center Institutional Review Board. This work was supported by the US Department of Veterans Affairs. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.
1. US Department of Veterans Affairs. National Center for Veterans Analysis and Statistics. The past, present and future of women veterans. Published February 2017. Accessed April 28, 2021. https://www.va.gov/vetdata/docs/specialreports/women_veterans_2015_final.pdf.
2. Frayne SM, Carney DV, Bastian L, et al. The VA Women’s Health Practice-Based Research Network: amplifying women veterans’ voices in VA research. J Gen Intern Med. 2013;28 Suppl 2(Suppl 2):S504-S509. doi:10.1007/s11606-013-2476-3
3. US Department of Veterans Affairs, Veterans Health Administration, Women’s Health Evaluation Initiative, Women Veterans Health Strategic Health Care Group. Sourcebook: women veterans in the Veterans Health Administration. Volume 1: Sociodemographic characteristics and use of VHA care. Published December 2010. Accessed April 12, 2021. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2455
4. Bean-Mayberry B, Yano EM, Bayliss N, Navratil J, Weisman CS, Scholle SH. Federally funded comprehensive women’s health centers: leading innovation in women’s healthcare delivery. J Womens Health (Larchmt). 2007;16(9):1281-1290. doi:10.1089/jwh.2006.0284
5. US Department of Veterans Affairs. National Center for Veterans Analysis and Statistics.VA utilization profile FY 2016. Published November 2017. Accessed April 12, 2021. https://www.va.gov/vetdata/docs/QuickFacts/VA_Utilization_Profile.PDF
6. Ekenga CC, Parks CG, Sandler DP. Chemical exposures in the workplace and breast cancer risk: a prospective cohort study. Int J Cancer. 2015;137(7):1765-1774. doi:10.1002/ijc.29545
7. Rennix CP, Quinn MM, Amoroso PJ, Eisen EA, Wegman DH. Risk of breast cancer among enlisted Army women occupationally exposed to volatile organic compounds. Am J Ind Med. 2005;48(3):157-167. doi:10.1002/ajim.20201
8. Ritz B. Cancer mortality among workers exposed to chemicals during uranium processing. J Occup Environ Med. 1999;41(7):556-566. doi:10.1097/00043764-199907000-00004
9. Zhu K, Devesa SS, Wu H, et al. Cancer incidence in the U.S. military population: comparison with rates from the SEER program. Cancer Epidemiol Biomarkers Prev. 2009;18(6):1740-1745. doi:10.1158/1055-9965.EPI-09-0041
10. Freedman AN, Yu B, Gail MH, et al. Benefit/risk assessment for breast cancer chemoprevention with raloxifene or tamoxifen for women age 50 years or older [published correction appears in J Clin Oncol. 2013 Nov 10;31(32):4167]. J Clin Oncol. 2011;29(17):2327-2333. doi:10.1200/JCO.2010.33.0258
11. Greene, H. Cancer prevention, screening and early detection. In: Gobel BH, Triest-Robertson S, Vogel WH, eds. Advanced Oncology Nursing Certification Review and Resource Manual. 3rd ed. Oncology Nursing Society; 2016:1-34. https://www.ons.org/sites/default/files/publication_pdfs/2%20ADVPrac%20chapter%201.pdf
12. National Comprehensive Cancer Network. NCCN Breast Cancer Risk Reduction. Version 1.2021 NCCN Clinical Practice Guidelines in Oncology. Updated March 24, 2021 Accessed April 12, 2021. https://www.nccn.org/professionals/physician_gls/pdf/breast_risk.pdf
13. US Preventive Services Task Force. Breast cancer: Medications use to reduce risk. Updated September 3, 2019. Accessed April 12, 2021. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/breast-cancer-medications-for-risk-reduction
14. Moyer VA; U.S. Preventive Services Task Force. Medications to decrease the risk for breast cancer in women: recommendations from the U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2013;159(10):698-708. doi:10.7326/0003-4819-159-10-201311190-00717
15. Boucher JE. Chemoprevention: an overview of pharmacologic agents and nursing considerations. Clin J Oncol Nurs. 2018;22(3):350-353. doi:10.1188/18.CJON.350-353
16. Nichols HB, Stürmer T, Lee VS, et al. Breast cancer chemoprevention in an integrated health care setting. JCO Clin Cancer Inform. 2017;1:1-12. doi:10.1200/CCI.16.00059
17. Bevers TB, Helvie M, Bonaccio E, et al. Breast cancer screening and diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2018;16(11):1362-1389. doi:10.6004/jnccn.2018.0083
18. Visvanathan K, Hurley P, Bantug E, et al. Use of pharmacologic interventions for breast cancer risk reduction: American Society of Clinical Oncology clinical practice guideline [published correction appears in J Clin Oncol. 2013 Dec 1;31(34):4383]. J Clin Oncol. 2013;31(23):2942-2962. doi:10.1200/JCO.2013.49.3122
19. Sealy-Jefferson S, Roseland ME, Cote ML, et al. rural-urban residence and stage at breast cancer diagnosis among postmenopausal women: The Women’s Health Initiative. J Womens Health (Larchmt). 2019;28(2):276-283. doi:10.1089/jwh.2017.6884
20. Holder KA. Veterans in rural America: 2011-2015. Published January 25, 2017. Accessed April 12, 2021. https://www.census.gov/library/publications/2017/acs/acs-36.html
21. Owens WL, Gallagher TJ, Kincheloe MJ, Ruetten VL. Implementation in a large health system of a program to identify women at high risk for breast cancer. J Oncol Pract. 2011;7(2):85-88. doi:10.1200/JOP.2010.000107
2. Pivot X, Viguier J, Touboul C, et al. Breast cancer screening controversy: too much or not enough?. Eur J Cancer Prev. 2015;24 Suppl:S73-S76. doi:10.1097/CEJ.0000000000000145
23. Bidassie B, Kovach A, Vallette MA, et al. Breast Cancer risk assessment and chemoprevention use among veterans affairs primary care providers: a national online survey. Mil Med. 2020;185(3-4):512-518. doi:10.1093/milmed/usz291
24. Brewster AM, Davidson NE, McCaskill-Stevens W. Chemoprevention for breast cancer: overcoming barriers to treatment. Am Soc Clin Oncol Educ Book. 2012;85-90. doi:10.14694/EdBook_AM.2012.32.152
25. Meyskens FL Jr, Curt GA, Brenner DE, et al. Regulatory approval of cancer risk-reducing (chemopreventive) drugs: moving what we have learned into the clinic. Cancer Prev Res (Phila). 2011;4(3):311-323. doi:10.1158/1940-6207.CAPR-09-0014
26. Tice JA, Kerlikowske K. Screening and prevention of breast cancer in primary care. Prim Care. 2009;36(3):533-558. doi:10.1016/j.pop.2009.04.003
27. Vogel VG. Selective estrogen receptor modulators and aromatase inhibitors for breast cancer chemoprevention. Curr Drug Targets. 2011;12(13):1874-1887. doi:10.2174/138945011798184164
28. Vogel VG, Costantino JP, Wickerham DL, et al. Effects of tamoxifen vs raloxifene on the risk of developing invasive breast cancer and other disease outcomes: the NSABP Study of Tamoxifen and Raloxifene (STAR) P-2 trial [published correction appears in JAMA. 2006 Dec 27;296(24):2926] [published correction appears in JAMA. 2007 Sep 5;298(9):973]. JAMA. 2006;295(23):2727-2741. doi:10.1001/jama.295.23.joc60074
29. Pruthi S, Heisey RE, Bevers TB. Chemoprevention for breast cancer. Ann Surg Oncol. 2015;22(10):3230-3235. doi:10.1245/s10434-015-4715-9
30. Cuzick J, Sestak I, Forbes JF, et al. Anastrozole for prevention of breast cancer in high-risk postmenopausal women (IBIS-II): an international, double-blind, randomised placebo-controlled trial [published correction appears in Lancet. 2014 Mar 22;383(9922):1040] [published correction appears in Lancet. 2017 Mar 11;389(10073):1010]. Lancet. 2014;383(9922):1041-1048. doi:10.1016/S0140-6736(13)62292-8
31. Bozovic-Spasojevic I, Azambuja E, McCaskill-Stevens W, Dinh P, Cardoso F. Chemoprevention for breast cancer. Cancer Treat Rev. 2012;38(5):329-339. doi:10.1016/j.ctrv.2011.07.005
32. Gabriel EM, Jatoi I. Breast cancer chemoprevention. Expert Rev Anticancer Ther. 2012;12(2):223-228. doi:10.1586/era.11.206

33. Crew KD, Albain KS, Hershman DL, Unger JM, Lo SS. How do we increase uptake of tamoxifen and other anti-estrogens for breast cancer prevention?. NPJ Breast Cancer. 2017;3:20. Published 2017 May 19. doi:10.1038/s41523-017-0021-y
34. Ropka ME, Keim J, Philbrick JT. Patient decisions about breast cancer chemoprevention: a systematic review and meta-analysis. J Clin Oncol. 2010;28(18):3090-3095. doi:10.1200/JCO.2009.27.8077
35. Smith SG, Sestak I, Forster A, et al. Factors affecting uptake and adherence to breast cancer chemoprevention: a systematic review and meta-analysis. Ann Oncol. 2016;27(4):575-590. doi:10.1093/annonc/mdv590
36. Grann VR, Patel PR, Jacobson JS, et al. Comparative effectiveness of screening and prevention strategies among BRCA1/2-affected mutation carriers. Breast Cancer Res Treat. 2011 Feb;125(3):837-847. doi:10.1007/s10549-010-1043-4
37. Goss PE, Ingle JN, Alés-Martínez JE, et al. Exemestane for breast-cancer prevention in postmenopausal women [published correction appears in N Engl J Med. 2011 Oct 6;365(14):1361]. N Engl J Med. 2011;364(25):2381-2391. doi:10.1056/NEJMoa1103507
38. Kmietowicz Z. Five in six women reject drugs that could reduce their risk of breast cancer. BMJ. 2015;351:h6650. Published 2015 Dec 8. doi:10.1136/bmj.h6650
39. Nelson HD, Fu R, Griffin JC, Nygren P, Smith ME, Humphrey L. Systematic review: comparative effectiveness of medications to reduce risk for primary breast cancer. Ann Intern Med. 2009;151(10):703-235. doi:10.7326/0003-4819-151-10-200911170-00147
40. Dahabreh IJ, Wieland LS, Adam GP, Halladay C, Lau J, Trikalinos TA. Core needle and open surgery biopsy for diagnosis of breast lesions: an update to the 2009 report. Published September 2014. Accessed April 12, 2021. https://www.ncbi.nlm.nih.gov/books/NBK246878
41. National Cancer Institute. Genetics of breast and ovarian cancer (PDQ)—health profession version. Updated February 12, 2021. Accessed April 12, 2021. http://www.cancer.gov/cancertopics/pdq/genetics/breast-and-ovarian/HealthProfessional
42. US Department of Health and Human Services. National Institutes of Health, National Institute of Environmental Health Sciences The sister study. Accessed April 12, 2021. https://sisterstudy.niehs.nih.gov/english/NIEHS.htm
43. Tutt A, Ashworth A. Can genetic testing guide treatment in breast cancer?. Eur J Cancer. 2008;44(18):2774-2780. doi:10.1016/j.ejca.2008.10.009
44. Katz SJ, Ward KC, Hamilton AS, et al. Gaps in receipt of clinically indicated genetic counseling after diagnosis of breast cancer. J Clin Oncol. 2018;36(12):1218-1224. doi:10.1200/JCO.2017.76.2369
45. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in adults? Updated October 17, 2019. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_adults.asp
46. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in women? Updated October 16, 2019. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_women.asp
47. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in veterans? Updated September 24, 2018. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_veterans.asp
48. Lindberg NM, Wellisch D. Anxiety and compliance among women at high risk for breast cancer. Ann Behav Med. 2001;23(4):298-303. doi:10.1207/S15324796ABM2304_9
49. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med. 2000;160(14):2101-2107. doi:10.1001/archinte.160.14.2101
50. Centers for Disease Control and Prevention. MMWR appendix: breast cancer rates among black women and white women. Updated October 13, 2016. Accessed April 12, 2021. https://www.cdc.gov/cancer/breast/statistics/trends_invasive.htm
51. Richardson LC, Henley SJ, Miller JW, Massetti G, Thomas CC. Patterns and trends in age-specific black-white differences in breast cancer incidence and mortality - United States, 1999-2014. MMWR Morb Mortal Wkly Rep. 2016;65(40):1093-1098. Published 2016 Oct 14. doi:10.15585/mmwr.mm6540a1
52. Brody JG, Moysich KB, Humblet O, Attfield KR, Beehler GP, Rudel RA. Environmental pollutants and breast cancer: epidemiologic studies. Cancer. 2007;109(12 Suppl):2667-2711. doi:10.1002/cncr.22655
53. Brophy JT, Keith MM, Watterson A, et al. Breast cancer risk in relation to occupations with exposure to carcinogens and endocrine disruptors: a Canadian case-control study. Environ Health. 2012;11:87. Published 2012 Nov 19. doi:10.1186/1476-069X-11-87
54. Labrèche F, Goldberg MS, Valois MF, Nadon L. Postmenopausal breast cancer and occupational exposures. Occup Environ Med. 2010;67(4):263-269. doi:10.1136/oem.2009.049817
55. National Institute of Environmental Health Sciences, Interagency Breast Cancer & Environmental Research Coordinating Committee. Breast cancer and the environment: prioritizing prevention. Updated March 8, 2013. Accessed April 12, 2021. https://www.niehs.nih.gov/about/boards/ibcercc/index.cfm
56. Gail MH, Costantino JP, Pee D, et al. Projecting individualized absolute invasive breast cancer risk in African American women [published correction appears in J Natl Cancer Inst. 2008 Aug 6;100(15):1118] [published correction appears in J Natl Cancer Inst. 2008 Mar 5;100(5):373]. J Natl Cancer Inst. 2007;99(23):1782-1792. doi:10.1093/jnci/djm223
57. Corbie-Smith G, Thomas SB, Williams MV, Moody-Ayers S. Attitudes and beliefs of African Americans toward participation in medical research. J Gen Intern Med. 1999;14(9):537-546. doi:10.1046/j.1525-1497.1999.07048.x
58. Braunstein JB, Sherber NS, Schulman SP, Ding EL, Powe NR. Race, medical researcher distrust, perceived harm, and willingness to participate in cardiovascular prevention trials. Medicine (Baltimore). 2008;87(1):1-9. doi:10.1097/MD.0b013e3181625d78
1. US Department of Veterans Affairs. National Center for Veterans Analysis and Statistics. The past, present and future of women veterans. Published February 2017. Accessed April 28, 2021. https://www.va.gov/vetdata/docs/specialreports/women_veterans_2015_final.pdf.
2. Frayne SM, Carney DV, Bastian L, et al. The VA Women’s Health Practice-Based Research Network: amplifying women veterans’ voices in VA research. J Gen Intern Med. 2013;28 Suppl 2(Suppl 2):S504-S509. doi:10.1007/s11606-013-2476-3
3. US Department of Veterans Affairs, Veterans Health Administration, Women’s Health Evaluation Initiative, Women Veterans Health Strategic Health Care Group. Sourcebook: women veterans in the Veterans Health Administration. Volume 1: Sociodemographic characteristics and use of VHA care. Published December 2010. Accessed April 12, 2021. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2455
4. Bean-Mayberry B, Yano EM, Bayliss N, Navratil J, Weisman CS, Scholle SH. Federally funded comprehensive women’s health centers: leading innovation in women’s healthcare delivery. J Womens Health (Larchmt). 2007;16(9):1281-1290. doi:10.1089/jwh.2006.0284
5. US Department of Veterans Affairs. National Center for Veterans Analysis and Statistics.VA utilization profile FY 2016. Published November 2017. Accessed April 12, 2021. https://www.va.gov/vetdata/docs/QuickFacts/VA_Utilization_Profile.PDF
6. Ekenga CC, Parks CG, Sandler DP. Chemical exposures in the workplace and breast cancer risk: a prospective cohort study. Int J Cancer. 2015;137(7):1765-1774. doi:10.1002/ijc.29545
7. Rennix CP, Quinn MM, Amoroso PJ, Eisen EA, Wegman DH. Risk of breast cancer among enlisted Army women occupationally exposed to volatile organic compounds. Am J Ind Med. 2005;48(3):157-167. doi:10.1002/ajim.20201
8. Ritz B. Cancer mortality among workers exposed to chemicals during uranium processing. J Occup Environ Med. 1999;41(7):556-566. doi:10.1097/00043764-199907000-00004
9. Zhu K, Devesa SS, Wu H, et al. Cancer incidence in the U.S. military population: comparison with rates from the SEER program. Cancer Epidemiol Biomarkers Prev. 2009;18(6):1740-1745. doi:10.1158/1055-9965.EPI-09-0041
10. Freedman AN, Yu B, Gail MH, et al. Benefit/risk assessment for breast cancer chemoprevention with raloxifene or tamoxifen for women age 50 years or older [published correction appears in J Clin Oncol. 2013 Nov 10;31(32):4167]. J Clin Oncol. 2011;29(17):2327-2333. doi:10.1200/JCO.2010.33.0258
11. Greene, H. Cancer prevention, screening and early detection. In: Gobel BH, Triest-Robertson S, Vogel WH, eds. Advanced Oncology Nursing Certification Review and Resource Manual. 3rd ed. Oncology Nursing Society; 2016:1-34. https://www.ons.org/sites/default/files/publication_pdfs/2%20ADVPrac%20chapter%201.pdf
12. National Comprehensive Cancer Network. NCCN Breast Cancer Risk Reduction. Version 1.2021 NCCN Clinical Practice Guidelines in Oncology. Updated March 24, 2021 Accessed April 12, 2021. https://www.nccn.org/professionals/physician_gls/pdf/breast_risk.pdf
13. US Preventive Services Task Force. Breast cancer: Medications use to reduce risk. Updated September 3, 2019. Accessed April 12, 2021. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/breast-cancer-medications-for-risk-reduction
14. Moyer VA; U.S. Preventive Services Task Force. Medications to decrease the risk for breast cancer in women: recommendations from the U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2013;159(10):698-708. doi:10.7326/0003-4819-159-10-201311190-00717
15. Boucher JE. Chemoprevention: an overview of pharmacologic agents and nursing considerations. Clin J Oncol Nurs. 2018;22(3):350-353. doi:10.1188/18.CJON.350-353
16. Nichols HB, Stürmer T, Lee VS, et al. Breast cancer chemoprevention in an integrated health care setting. JCO Clin Cancer Inform. 2017;1:1-12. doi:10.1200/CCI.16.00059
17. Bevers TB, Helvie M, Bonaccio E, et al. Breast cancer screening and diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2018;16(11):1362-1389. doi:10.6004/jnccn.2018.0083
18. Visvanathan K, Hurley P, Bantug E, et al. Use of pharmacologic interventions for breast cancer risk reduction: American Society of Clinical Oncology clinical practice guideline [published correction appears in J Clin Oncol. 2013 Dec 1;31(34):4383]. J Clin Oncol. 2013;31(23):2942-2962. doi:10.1200/JCO.2013.49.3122
19. Sealy-Jefferson S, Roseland ME, Cote ML, et al. rural-urban residence and stage at breast cancer diagnosis among postmenopausal women: The Women’s Health Initiative. J Womens Health (Larchmt). 2019;28(2):276-283. doi:10.1089/jwh.2017.6884
20. Holder KA. Veterans in rural America: 2011-2015. Published January 25, 2017. Accessed April 12, 2021. https://www.census.gov/library/publications/2017/acs/acs-36.html
21. Owens WL, Gallagher TJ, Kincheloe MJ, Ruetten VL. Implementation in a large health system of a program to identify women at high risk for breast cancer. J Oncol Pract. 2011;7(2):85-88. doi:10.1200/JOP.2010.000107
2. Pivot X, Viguier J, Touboul C, et al. Breast cancer screening controversy: too much or not enough?. Eur J Cancer Prev. 2015;24 Suppl:S73-S76. doi:10.1097/CEJ.0000000000000145
23. Bidassie B, Kovach A, Vallette MA, et al. Breast Cancer risk assessment and chemoprevention use among veterans affairs primary care providers: a national online survey. Mil Med. 2020;185(3-4):512-518. doi:10.1093/milmed/usz291
24. Brewster AM, Davidson NE, McCaskill-Stevens W. Chemoprevention for breast cancer: overcoming barriers to treatment. Am Soc Clin Oncol Educ Book. 2012;85-90. doi:10.14694/EdBook_AM.2012.32.152
25. Meyskens FL Jr, Curt GA, Brenner DE, et al. Regulatory approval of cancer risk-reducing (chemopreventive) drugs: moving what we have learned into the clinic. Cancer Prev Res (Phila). 2011;4(3):311-323. doi:10.1158/1940-6207.CAPR-09-0014
26. Tice JA, Kerlikowske K. Screening and prevention of breast cancer in primary care. Prim Care. 2009;36(3):533-558. doi:10.1016/j.pop.2009.04.003
27. Vogel VG. Selective estrogen receptor modulators and aromatase inhibitors for breast cancer chemoprevention. Curr Drug Targets. 2011;12(13):1874-1887. doi:10.2174/138945011798184164
28. Vogel VG, Costantino JP, Wickerham DL, et al. Effects of tamoxifen vs raloxifene on the risk of developing invasive breast cancer and other disease outcomes: the NSABP Study of Tamoxifen and Raloxifene (STAR) P-2 trial [published correction appears in JAMA. 2006 Dec 27;296(24):2926] [published correction appears in JAMA. 2007 Sep 5;298(9):973]. JAMA. 2006;295(23):2727-2741. doi:10.1001/jama.295.23.joc60074
29. Pruthi S, Heisey RE, Bevers TB. Chemoprevention for breast cancer. Ann Surg Oncol. 2015;22(10):3230-3235. doi:10.1245/s10434-015-4715-9
30. Cuzick J, Sestak I, Forbes JF, et al. Anastrozole for prevention of breast cancer in high-risk postmenopausal women (IBIS-II): an international, double-blind, randomised placebo-controlled trial [published correction appears in Lancet. 2014 Mar 22;383(9922):1040] [published correction appears in Lancet. 2017 Mar 11;389(10073):1010]. Lancet. 2014;383(9922):1041-1048. doi:10.1016/S0140-6736(13)62292-8
31. Bozovic-Spasojevic I, Azambuja E, McCaskill-Stevens W, Dinh P, Cardoso F. Chemoprevention for breast cancer. Cancer Treat Rev. 2012;38(5):329-339. doi:10.1016/j.ctrv.2011.07.005
32. Gabriel EM, Jatoi I. Breast cancer chemoprevention. Expert Rev Anticancer Ther. 2012;12(2):223-228. doi:10.1586/era.11.206

33. Crew KD, Albain KS, Hershman DL, Unger JM, Lo SS. How do we increase uptake of tamoxifen and other anti-estrogens for breast cancer prevention?. NPJ Breast Cancer. 2017;3:20. Published 2017 May 19. doi:10.1038/s41523-017-0021-y
34. Ropka ME, Keim J, Philbrick JT. Patient decisions about breast cancer chemoprevention: a systematic review and meta-analysis. J Clin Oncol. 2010;28(18):3090-3095. doi:10.1200/JCO.2009.27.8077
35. Smith SG, Sestak I, Forster A, et al. Factors affecting uptake and adherence to breast cancer chemoprevention: a systematic review and meta-analysis. Ann Oncol. 2016;27(4):575-590. doi:10.1093/annonc/mdv590
36. Grann VR, Patel PR, Jacobson JS, et al. Comparative effectiveness of screening and prevention strategies among BRCA1/2-affected mutation carriers. Breast Cancer Res Treat. 2011 Feb;125(3):837-847. doi:10.1007/s10549-010-1043-4
37. Goss PE, Ingle JN, Alés-Martínez JE, et al. Exemestane for breast-cancer prevention in postmenopausal women [published correction appears in N Engl J Med. 2011 Oct 6;365(14):1361]. N Engl J Med. 2011;364(25):2381-2391. doi:10.1056/NEJMoa1103507
38. Kmietowicz Z. Five in six women reject drugs that could reduce their risk of breast cancer. BMJ. 2015;351:h6650. Published 2015 Dec 8. doi:10.1136/bmj.h6650
39. Nelson HD, Fu R, Griffin JC, Nygren P, Smith ME, Humphrey L. Systematic review: comparative effectiveness of medications to reduce risk for primary breast cancer. Ann Intern Med. 2009;151(10):703-235. doi:10.7326/0003-4819-151-10-200911170-00147
40. Dahabreh IJ, Wieland LS, Adam GP, Halladay C, Lau J, Trikalinos TA. Core needle and open surgery biopsy for diagnosis of breast lesions: an update to the 2009 report. Published September 2014. Accessed April 12, 2021. https://www.ncbi.nlm.nih.gov/books/NBK246878
41. National Cancer Institute. Genetics of breast and ovarian cancer (PDQ)—health profession version. Updated February 12, 2021. Accessed April 12, 2021. http://www.cancer.gov/cancertopics/pdq/genetics/breast-and-ovarian/HealthProfessional
42. US Department of Health and Human Services. National Institutes of Health, National Institute of Environmental Health Sciences The sister study. Accessed April 12, 2021. https://sisterstudy.niehs.nih.gov/english/NIEHS.htm
43. Tutt A, Ashworth A. Can genetic testing guide treatment in breast cancer?. Eur J Cancer. 2008;44(18):2774-2780. doi:10.1016/j.ejca.2008.10.009
44. Katz SJ, Ward KC, Hamilton AS, et al. Gaps in receipt of clinically indicated genetic counseling after diagnosis of breast cancer. J Clin Oncol. 2018;36(12):1218-1224. doi:10.1200/JCO.2017.76.2369
45. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in adults? Updated October 17, 2019. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_adults.asp
46. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in women? Updated October 16, 2019. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_women.asp
47. US Department of Veterans Affairs. PTSD: National Center for PTSD. How common is PTSD in veterans? Updated September 24, 2018. Accessed April 12, 2021. https://www.ptsd.va.gov/understand/common/common_veterans.asp
48. Lindberg NM, Wellisch D. Anxiety and compliance among women at high risk for breast cancer. Ann Behav Med. 2001;23(4):298-303. doi:10.1207/S15324796ABM2304_9
49. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med. 2000;160(14):2101-2107. doi:10.1001/archinte.160.14.2101
50. Centers for Disease Control and Prevention. MMWR appendix: breast cancer rates among black women and white women. Updated October 13, 2016. Accessed April 12, 2021. https://www.cdc.gov/cancer/breast/statistics/trends_invasive.htm
51. Richardson LC, Henley SJ, Miller JW, Massetti G, Thomas CC. Patterns and trends in age-specific black-white differences in breast cancer incidence and mortality - United States, 1999-2014. MMWR Morb Mortal Wkly Rep. 2016;65(40):1093-1098. Published 2016 Oct 14. doi:10.15585/mmwr.mm6540a1
52. Brody JG, Moysich KB, Humblet O, Attfield KR, Beehler GP, Rudel RA. Environmental pollutants and breast cancer: epidemiologic studies. Cancer. 2007;109(12 Suppl):2667-2711. doi:10.1002/cncr.22655
53. Brophy JT, Keith MM, Watterson A, et al. Breast cancer risk in relation to occupations with exposure to carcinogens and endocrine disruptors: a Canadian case-control study. Environ Health. 2012;11:87. Published 2012 Nov 19. doi:10.1186/1476-069X-11-87
54. Labrèche F, Goldberg MS, Valois MF, Nadon L. Postmenopausal breast cancer and occupational exposures. Occup Environ Med. 2010;67(4):263-269. doi:10.1136/oem.2009.049817
55. National Institute of Environmental Health Sciences, Interagency Breast Cancer & Environmental Research Coordinating Committee. Breast cancer and the environment: prioritizing prevention. Updated March 8, 2013. Accessed April 12, 2021. https://www.niehs.nih.gov/about/boards/ibcercc/index.cfm
56. Gail MH, Costantino JP, Pee D, et al. Projecting individualized absolute invasive breast cancer risk in African American women [published correction appears in J Natl Cancer Inst. 2008 Aug 6;100(15):1118] [published correction appears in J Natl Cancer Inst. 2008 Mar 5;100(5):373]. J Natl Cancer Inst. 2007;99(23):1782-1792. doi:10.1093/jnci/djm223
57. Corbie-Smith G, Thomas SB, Williams MV, Moody-Ayers S. Attitudes and beliefs of African Americans toward participation in medical research. J Gen Intern Med. 1999;14(9):537-546. doi:10.1046/j.1525-1497.1999.07048.x
58. Braunstein JB, Sherber NS, Schulman SP, Ding EL, Powe NR. Race, medical researcher distrust, perceived harm, and willingness to participate in cardiovascular prevention trials. Medicine (Baltimore). 2008;87(1):1-9. doi:10.1097/MD.0b013e3181625d78
Clinical Use of a Diagnostic Gene Expression Signature for Melanocytic Neoplasms
According to National Institutes of Health estimates, more than 90,000 new cases of melanoma were diagnosed in 2018.1 Overall 5-year survival for patients with melanoma exceeds 90%, but individual survival estimates are highly dependent on stage at diagnosis, and survival decreases markedly with metastasis. Therefore, early and accurate diagnosis is critical.
Diagnosis of melanocytic neoplasms usually is performed by dermatopathologists through microscopic examination of stained tissue biopsy sections, a technically simple and effective method that enables a definitive diagnosis of benign nevus or malignant melanoma to be made in most cases. However, approximately 15% of all biopsied melanocytic lesions will exhibit some degree of histopathologic ambiguity,2-4 meaning that some of their microscopic features will be characteristic of a benign nevus while others will suggest the possibility of malignant melanoma. Diagnostic interpretations often vary in these cases, even among experts, and a definitive diagnosis of benign or malignant may be difficult to achieve by microscopy alone.2-4 Because of the marked reduction in survival once a melanoma has metastasized, these diagnostically ambiguous lesions often are treated as possible malignant melanomas with complete surgical excision (or re-excision). However, some experts suggest that many histopathologically ambiguous melanocytic neoplasms are, in fact, benign,5 a notion supported by epidemiologic evidence.6,7 Therefore, excision of many ambiguous melanocytic neoplasms might be avoided if definitive diagnosis could be achieved.
A gene expression signature was developed and validated for use as an adjunct to traditional methods of differentiating malignant melanocytic neoplasms from their benign counterparts.8-11 This test quantifies the RNA transcripts produced by 14 genes known to be overexpressed in malignant melanomas by comparison to benign nevi. These values are then combined algorithmically with measurements of 9 reference genes to produce an objective numerical score that is classified as benign, malignant, or indeterminate. When used by board-certified dermatopathologists and dermatologists confronting ambiguous melanocytic lesions, the test produces substantial increases in definitive diagnoses and prompts changes in treatment recommendations.12,13 However, the long-term consequences of foregoing surgical excision of melanocytic neoplasms that are diagnostically ambiguous but classified as benign by this test have not yet been formally assessed. In the current study, prospectively tested patients whose ambiguous melanocytic neoplasms were classified as benign by the gene expression signature were followed for up to 4.5 years to evaluate the long-term safety of treatment decisions aligned with benign test results.
Methods
Study Population
As part of a prior study,12 US-based dermatopathologists submitted tissue sections from biopsied melanocytic neoplasms determined to be diagnostically ambiguous by histopathology for analysis with the gene expression signature (Myriad Genetics, Inc). Diagnostically ambiguous lesions were those lesions that were described as ambiguous, uncertain, equivocal, indeterminate, or other synonymous terms by the submitting dermatopathologist and therefore lacked a confident diagnosis of benign or malignant prior to testing. Patients initially were tested between May 2014 and August 2014, with samples submitted through a prospective clinical experience study designed to assess the impact of the test on diagnosis and treatment decisions. This study was performed under an institutional review board waiver of consent (Quorum #33403/1).
Patients were eligible for inclusion in the current study if their biopsy specimens (1) had an uncertain preliminary diagnosis according to the submitting dermatopathologist (pretest diagnosis of indeterminate); (2) received a negative (benign) score from the gene expression test; (3) were treated as benign by the dermatologist(s) involved in follow-up care; and (4) were submitted by a single site (St. Joseph Medical Center, Houston, Texas). Although a single dermatopathology site was used for this study, multiple dermatologists were involved in the final treatment of these patients. Patients with benign scores who received additional intervention were excluded, as they may have a lower rate of adverse events (ie, metastasis) than those who did not receive intervention and would therefore skew the analysis population. A total of 25 patients from the prior study met these inclusion criteria. The previously collected12 pretest and posttest de-identified data were compiled from the commercial laboratory databases, and the patients were followed from the time of testing via medical record review performed by the dermatology providers at participating sites. Clinical follow-up data were collected using study-specific case report forms (CRFs) that captured the following: (1) the dates and results of clinical follow-up visits; (2) the type(s) of treatment and interventions (if any) performed at those visits; (3) the specific indication for any intervention performed; (4) any evidence of persistent, locally recurrent, and/or distant melanocytic neoplasia (whether definitively attributable to the tested lesion or not); and (5) death from any cause. The CRF assigned interventions to 1 of 5 categories: excision, excision with sentinel lymph node biopsy, referral to dermatologic or other surgeon, examination only (without surgical intervention), and other. Selection of other required a free-text description of the treatment and indications. Pertinent information not otherwise captured by the CRF also was recordable as free text.
Gene Expression Testing
Gene expression testing was carried out at the time of specimen submission in the prior study12 as described previously.14 Briefly, formalin-fixed, paraffin-embedded, unstained tissue sections and/or tissue blocks were submitted for testing along with a single hematoxylin and eosin–stained slide used to identify and designate the representative portion(s) of the lesion to be tested. These areas were macrodissected from unstained tissue sections and pooled for RNA extraction. Expression of 14 biomarker genes and 9 reference genes was measured via
Statistical Analysis
Demographic and other baseline characteristics of the patient population were summarized. Follow-up time was calculated as the interval between the date a patient’s gene expression test result was first issued to the provider and the date of the patient’s last recorded visit during the study period. All patient dermatology office visits within the designated follow-up period were documented, with a nonstandard number of visits and follow-up time across all study patients. Statistical analyses were conducted using SAS software (SAS Institute Inc), R software version 3.5.0 (R Foundation for Statistical Computing), and IBM SPSS Statistics software (IBM SPSS Statistics for Windows, Version 25).
Results
Patient Sample
A total of 25 ambiguous melanocytic neoplasms from 25 patients met the study inclusion criteria of a benign gene expression result with subsequent treatment as a benign neoplasm during follow-up. The patient sample statistics are summarized in Table 1. Most patients were younger than 65 years, with an average age at the time of biopsy of 48.4 years. All 25 neoplasms produced negative (benign) gene expression signature scores, all were diagnosed as benign nevi posttest by the submitting dermatopathologist, and all patients were initially treated in accordance with the benign diagnosis by the dermatologist(s) involved in clinical follow-up care. Prior to testing with the gene expression signature, most of these histopathologically indeterminate lesions received differential diagnoses, the most common of which were dysplastic nevus (84%), melanoma arising from a nevus (72%), and superficial spreading melanoma (64%; eTable). After testing with the gene expression signature and receiving a benign score, most lesions received a single differential diagnosis of dysplastic nevus (88%).
Follow-up and Survival
Clinical follow-up time ranged from 0.6 to 53.3 months, with a mean duration (SD) of 38.5 (16.6) months, and patients attended an average of 4 postbiopsy dermatology appointments (mean [SD], 4.6 [3.6]). According to the participating dermatology care providers, none of the 25 patients developed any indication during follow-up that the diagnosis of benign nevus was inaccurate. No patient had evidence of locally recurrent or metastatic melanoma, and none died during the study period.
Treatment/Interventions
The treatment recorded in the CRF was examination only for 21 of 25 patients, excision for 3, and other for 1 (Table 2). Because the explanation for the selection of other in this case described an excision performed at the same anatomic location as the biopsy, this treatment also was considered an excision for purposes of the study analyses. The 3 excisions all occurred at the first postbiopsy dermatology encounter. Across all follow-up visits, no additional surgical interventions occurred (Table 2).
The first excision (case 1) involved a 67-year-old woman with a lesion on the mid pubic region described clinically as an atypical nevus that generated a pretest histopathologic differential diagnosis including dysplastic nevus, superficial spreading melanoma, and melanoma arising within a nevus (Table 3; Figure, A and B). The gene expression test result was benign (score, −5.4), and the final pathology report diagnosis was nevus with junctional dysplasia, moderate. Surgical excision was performed at the patient’s first return visit, 505 days after initial diagnosis, with moderately dysplastic nevus as the recorded indication for removal. No repigmentation or other evidence of local recurrence or progression was detected, and the treating dermatologist indicated no suspicion that the original diagnosis of benign nevus was incorrect during the 23-month follow-up period.
The second excision (case 2) involved a 27-year-old woman with a pigmented neoplasm on the mid upper back (Figure, C and D) biopsied to rule out dysplastic nevus that resulted in a pretest histopathologic differential diagnosis of dysplastic nevus vs superficial spreading melanoma or melanoma arising within a nevus. The gene expression test result classified the lesion as benign (score, −2.9), and the final pathology diagnosis was nevus, compound, with moderate dysplasia. Despite the benign diagnosis, residual neoplasm (or pigmentation) at the biopsy site prompted the patient to request excision at her first postbiopsy visit, 22 days after testing (Table 3). The CRF completed by the dermatologist reported no indication that the benign diagnosis was inaccurate, but the patient was subsequently lost to follow-up.
The third excision (case 3) involved a 32-year-old woman with a pigmented lesion on the abdomen (Table 3; Figure, E and F). The clinical description was irregular-appearing black papule, nevus with atypia, and the histopathologic differential diagnosis again included dysplastic nevus, superficial spreading melanoma, and melanoma arising within a preexisting nevus. The gene expression signature result was benign (score, −7.2), and the final diagnosis issued within the accompanying pathology report was nevus with moderate junctional dysplasia. Despite the benign diagnosis, excision was performed 89 days after test result availability, with apparent residual pigmentation as the specified indication. As with the other 2 cases, the treating dermatologist confirmed that neither clinical features nor follow-up events suggested malignancy.
Comment
This study followed a cohort of 25 patients with histopathologically ambiguous melanocytic neoplasms that were classified as benign by a diagnostic gene expression test with the intent of determining the outcomes of patients whose treatment aligned with their benign test result. All patients initially were managed according to their test result. During an average posttest clinical follow-up time of more than 3 years (38.5 months), the 25 biopsied lesions, most of which received a differential diagnosis of dysplastic nevus, were regarded as benign nevi by their dermatologists, and the vast majority (88%) received no further surgical intervention. Three patients underwent subsequent excision of the biopsied lesion, with patient or physician preference as the indication in each instance. None of the 25 patients developed evidence of local recurrence, metastasis, or other findings that prompted doubt of the benign diagnosis. The absence of adverse events during clinical follow-up, particularly given that most lesions were not subjected to further intervention, supports use of the gene expression test as a safe and effective adjunct to the diagnosis and treatment of ambiguous melanocytic neoplasms by dermatologists and dermatopathologists.
Ambiguous melanocytic neoplasms evaluated without the aid of molecular adjuncts often result in equivocal or less-than-definitive diagnoses, and further surgical intervention is commonly undertaken to mitigate against the possibility of a missed melanoma.13 In this study, treatment that was aligned with the benign test result allowed most patients to avoid further surgical intervention, which suggests that adjunctive use of the gene signature can contribute to reductions in the physical and economic burdens imposed by unnecessary surgical interventions.15,16 Moreover, any means of increasing accurate and definitive diagnoses may produce an immediate impact on health outcomes by reducing the anxiety that uncertainty often provokes in patients and health care providers alike.
Study Limitations
This study must be interpreted within the context of its limitations. Obtaining meaningful patient outcome data is a common challenge in health care research due to the requisite length of follow-up and sometimes the lack of definitive evidence of adverse events. This is particularly difficult for melanocytic neoplasms because of an apparent inclination for patients with benign diagnoses to abandon follow-up and an increasing tendency for even minimal diagnostic uncertainty to prompt complete excision. Additionally, the only definitive clinical outcome for melanocytic neoplasms is distant metastasis, which (fortunately for patients) is relatively rare. Not surprisingly, studies documenting clinical outcomes of patients with ambiguous melanocytic neoplasms tested prospectively with diagnostic adjuncts are scarce, and this study’s sample size and clinical follow-up compare favorably with the few that exist.17,18 Although most melanomas declare themselves through recurrence or metastasis within several years of initial biopsy,1,19 some are clinically dormant for as long as 10 years after initial detection.20,21 This may be particularly true for the small or early-stage lesions that now comprise the majority of biopsied neoplasms, and such events would go undetected by this study and many others. It also must be recognized that uneventful follow-up, regardless of duration, cannot prove that a biopsied melanocytic neoplasm was benign. Although only 5 patients had a follow-up time of less than 2 years (the time frame in which most recurrence or metastasis will occur), it cannot be definitively proven that a minimum of 2 years recurrence- or metastasis-free survival indicates a benign lesion. Many early-stage malignant melanomas are eradicated by complete excision or even by the initial biopsy if margins are uninvolved.
Because these limitations are intrinsic to melanocytic neoplasms and current management strategies, they pertain to all investigations seeking insights into biological potential through clinical outcomes. Similarly, all current diagnostic tools and procedures have the potential for sampling error, including histopathology. The rarity of adverse outcomes (recurrence and metastasis) in patients with benign test results within this cohort indicates that false-negative results are uncommon, which is further evidenced by a similar rarity of adverse events in prior studies of the gene expression signature.8-10,22 A particular strength of this study is that most of the ambiguous melanocytic neoplasms followed did not undergo excision after the initial biopsy, an increasingly uncommon situation that may increase their likelihood to be informative.
It must be emphasized that the gene expression test, similar to other diagnostic adjuncts, is neither a replacement for histopathologic interpretation nor a substitute for judgment. As with all tests, it can produce false-positive and false-negative results. Therefore, it should always be interpreted within the constellation of the many other data points that must be considered when making a distinction between benign nevus and malignant melanoma, including but not limited to patient age, family and personal history of melanoma, anatomic location, clinical features, and histopathologic findings. As is the case for many diseases, careful consideration of all relevant input is necessary to minimize the risk of misdiagnosis that might occur should any single data point prove inaccurate, including the results of adjunctive molecular tests.
Conclusion
Ancillary methods are emerging as useful tools for the diagnostic evaluation of melanocytic neoplasms that cannot be assigned definitive diagnoses using traditional techniques alone. This study suggests that patients with ambiguous melanocytic neoplasms may benefit from diagnoses and treatment decisions aligned with the results of a gene expression test, and that for those with a benign result, simple observation may be a safe alternative to surgical excision. This expands upon prior observations of the test’s influence on diagnoses and treatment decisions and supports its role as part of dermatopathologists’ and dermatologists’ decision-making process for histopathologically ambiguous melanocytic lesions.
- Noone AM, Howlander N, Krapcho M, et al, eds. SEER Cancer Statistics Review, 1975-2015. National Cancer Institute website. Updated September 10, 2018. Accessed April 21, 2021. https://seer.cancer.gov/archive/csr/1975_2015/
- Shoo BA, Sagebiel RW, Kashani-Sabet M. Discordance in the histopathologic diagnosis of melanoma at a melanoma referral center. J Am Acad Dermatol. 2010;62:751-756.
- Veenhuizen KC, De Wit PE, Mooi WJ, et al. Quality assessment by expert opinion in melanoma pathology: experience of the pathology panel of the Dutch Melanoma Working Party. J Pathol. 1997;182:266-272.
- Elmore JG, Barnhill RL, Elder DE, et al. Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study. BMJ. 2017;357:j2813. doi:10.1136/bmj.j2813
- Glusac EJ. The melanoma ‘epidemic’, a dermatopathologist’s perspective. J Cutan Pathol. 2011;38:264-267.
- Welch HG, Woloshin S, Schwartz LM. Skin biopsy rates and incidence of melanoma: population based ecological study. BMJ. 2005;331:481.
- Swerlick RA, Chen S. The melanoma epidemic. Is increased surveillance the solution or the problem? Arch Dermatol. 1996;132:881-884.
- Ko JS, Matharoo-Ball B, Billings SD, et al. Diagnostic distinction of malignant melanoma and benign nevi by a gene expression signature and correlation to clinical outcomes. Cancer Epidemiol Biomarkers Prev. 2017;26:1107-1113.
- Clarke LE, Flake DD 2nd, Busam K, et al. An independent validation of a gene expression signature to differentiate malignant melanoma from benign melanocytic nevi. Cancer. 2017;123:617-628.
- Clarke LE, Warf BM, Flake DD 2nd, et al. Clinical validation of a gene expression signature that differentiates benign nevi from malignant melanoma. J Cutan Pathol. 2015;42:244-252.
- Minca EC, Al-Rohil RN, Wang M, et al. Comparison between melanoma gene expression score and fluorescence in situ hybridization for the classification of melanocytic lesions. Mod Pathol. 2016;29:832-843.
- Cockerell CJ, Tschen J, Evans B, et al. The influence of a gene expression signature on the diagnosis and recommended treatment of melanocytic tumors by dermatopathologists. Medicine (Baltimore). 2016;95:e4887. doi:10.1097/MD.0000000000004887
- Cockerell C, Tschen J, Billings SD, et al. The influence of a gene-expression signature on the treatment of diagnostically challenging melanocytic lesions. Per Med. 2017;14:123-130.
- Warf MB, Flake DD 2nd, Adams D, et al. Analytical validation of a melanoma diagnostic gene signature using formalin-fixed paraffin-embedded melanocytic lesions. Biomark Med. 2015;9:407-416.
- Guy GP Jr, Ekwueme DU, Tangka FK, et al. Melanoma treatment costs: a systematic review of the literature, 1990-2011. Am J Prev Med. 2012;43:537-545.
- Guy GP Jr, Machlin SR, Ekwueme DU, et al. Prevalence and costs of skin cancer treatment in the U.S., 2002-2006 and 2007-2011. Am J Prev Med. 2015;48:183-187.
- Egnatios GL, Ferringer TC. Clinical follow-up of atypical spitzoid tumors analyzed by fluorescence in situ hybridization. Am J Dermatopathol. 2016;38:289-296.
- Fischer AS, High WA. The difficulty in interpreting gene expression profiling in BAP-negative melanocytic tumors. J Cutan Pathol. 2018;45:659-666. doi:10.1111/cup.13277
- Vollmer RT. The dynamics of death in melanoma. J Cutan Pathol. 2012;39:1075-1082.
- Osella-Abate S, Ribero S, Sanlorenzo M, et al. Risk factors related to late metastases in 1,372 melanoma patients disease free more than 10 years. Int J Cancer. 2015;136:2453-2457.
- Faries MB, Steen S, Ye X, et al. Late recurrence in melanoma: clinical implications of lost dormancy. J Am Coll Surg. 2013;217:27-34.
- Ko JS, Clarke LE, Minca EC, et al. Correlation of melanoma gene expression score with clinical outcomes on a series of melanocytic lesions. Hum Pathol. 2019;86:213-221.
According to National Institutes of Health estimates, more than 90,000 new cases of melanoma were diagnosed in 2018.1 Overall 5-year survival for patients with melanoma exceeds 90%, but individual survival estimates are highly dependent on stage at diagnosis, and survival decreases markedly with metastasis. Therefore, early and accurate diagnosis is critical.
Diagnosis of melanocytic neoplasms usually is performed by dermatopathologists through microscopic examination of stained tissue biopsy sections, a technically simple and effective method that enables a definitive diagnosis of benign nevus or malignant melanoma to be made in most cases. However, approximately 15% of all biopsied melanocytic lesions will exhibit some degree of histopathologic ambiguity,2-4 meaning that some of their microscopic features will be characteristic of a benign nevus while others will suggest the possibility of malignant melanoma. Diagnostic interpretations often vary in these cases, even among experts, and a definitive diagnosis of benign or malignant may be difficult to achieve by microscopy alone.2-4 Because of the marked reduction in survival once a melanoma has metastasized, these diagnostically ambiguous lesions often are treated as possible malignant melanomas with complete surgical excision (or re-excision). However, some experts suggest that many histopathologically ambiguous melanocytic neoplasms are, in fact, benign,5 a notion supported by epidemiologic evidence.6,7 Therefore, excision of many ambiguous melanocytic neoplasms might be avoided if definitive diagnosis could be achieved.
A gene expression signature was developed and validated for use as an adjunct to traditional methods of differentiating malignant melanocytic neoplasms from their benign counterparts.8-11 This test quantifies the RNA transcripts produced by 14 genes known to be overexpressed in malignant melanomas by comparison to benign nevi. These values are then combined algorithmically with measurements of 9 reference genes to produce an objective numerical score that is classified as benign, malignant, or indeterminate. When used by board-certified dermatopathologists and dermatologists confronting ambiguous melanocytic lesions, the test produces substantial increases in definitive diagnoses and prompts changes in treatment recommendations.12,13 However, the long-term consequences of foregoing surgical excision of melanocytic neoplasms that are diagnostically ambiguous but classified as benign by this test have not yet been formally assessed. In the current study, prospectively tested patients whose ambiguous melanocytic neoplasms were classified as benign by the gene expression signature were followed for up to 4.5 years to evaluate the long-term safety of treatment decisions aligned with benign test results.
Methods
Study Population
As part of a prior study,12 US-based dermatopathologists submitted tissue sections from biopsied melanocytic neoplasms determined to be diagnostically ambiguous by histopathology for analysis with the gene expression signature (Myriad Genetics, Inc). Diagnostically ambiguous lesions were those lesions that were described as ambiguous, uncertain, equivocal, indeterminate, or other synonymous terms by the submitting dermatopathologist and therefore lacked a confident diagnosis of benign or malignant prior to testing. Patients initially were tested between May 2014 and August 2014, with samples submitted through a prospective clinical experience study designed to assess the impact of the test on diagnosis and treatment decisions. This study was performed under an institutional review board waiver of consent (Quorum #33403/1).
Patients were eligible for inclusion in the current study if their biopsy specimens (1) had an uncertain preliminary diagnosis according to the submitting dermatopathologist (pretest diagnosis of indeterminate); (2) received a negative (benign) score from the gene expression test; (3) were treated as benign by the dermatologist(s) involved in follow-up care; and (4) were submitted by a single site (St. Joseph Medical Center, Houston, Texas). Although a single dermatopathology site was used for this study, multiple dermatologists were involved in the final treatment of these patients. Patients with benign scores who received additional intervention were excluded, as they may have a lower rate of adverse events (ie, metastasis) than those who did not receive intervention and would therefore skew the analysis population. A total of 25 patients from the prior study met these inclusion criteria. The previously collected12 pretest and posttest de-identified data were compiled from the commercial laboratory databases, and the patients were followed from the time of testing via medical record review performed by the dermatology providers at participating sites. Clinical follow-up data were collected using study-specific case report forms (CRFs) that captured the following: (1) the dates and results of clinical follow-up visits; (2) the type(s) of treatment and interventions (if any) performed at those visits; (3) the specific indication for any intervention performed; (4) any evidence of persistent, locally recurrent, and/or distant melanocytic neoplasia (whether definitively attributable to the tested lesion or not); and (5) death from any cause. The CRF assigned interventions to 1 of 5 categories: excision, excision with sentinel lymph node biopsy, referral to dermatologic or other surgeon, examination only (without surgical intervention), and other. Selection of other required a free-text description of the treatment and indications. Pertinent information not otherwise captured by the CRF also was recordable as free text.
Gene Expression Testing
Gene expression testing was carried out at the time of specimen submission in the prior study12 as described previously.14 Briefly, formalin-fixed, paraffin-embedded, unstained tissue sections and/or tissue blocks were submitted for testing along with a single hematoxylin and eosin–stained slide used to identify and designate the representative portion(s) of the lesion to be tested. These areas were macrodissected from unstained tissue sections and pooled for RNA extraction. Expression of 14 biomarker genes and 9 reference genes was measured via
Statistical Analysis
Demographic and other baseline characteristics of the patient population were summarized. Follow-up time was calculated as the interval between the date a patient’s gene expression test result was first issued to the provider and the date of the patient’s last recorded visit during the study period. All patient dermatology office visits within the designated follow-up period were documented, with a nonstandard number of visits and follow-up time across all study patients. Statistical analyses were conducted using SAS software (SAS Institute Inc), R software version 3.5.0 (R Foundation for Statistical Computing), and IBM SPSS Statistics software (IBM SPSS Statistics for Windows, Version 25).
Results
Patient Sample
A total of 25 ambiguous melanocytic neoplasms from 25 patients met the study inclusion criteria of a benign gene expression result with subsequent treatment as a benign neoplasm during follow-up. The patient sample statistics are summarized in Table 1. Most patients were younger than 65 years, with an average age at the time of biopsy of 48.4 years. All 25 neoplasms produced negative (benign) gene expression signature scores, all were diagnosed as benign nevi posttest by the submitting dermatopathologist, and all patients were initially treated in accordance with the benign diagnosis by the dermatologist(s) involved in clinical follow-up care. Prior to testing with the gene expression signature, most of these histopathologically indeterminate lesions received differential diagnoses, the most common of which were dysplastic nevus (84%), melanoma arising from a nevus (72%), and superficial spreading melanoma (64%; eTable). After testing with the gene expression signature and receiving a benign score, most lesions received a single differential diagnosis of dysplastic nevus (88%).
Follow-up and Survival
Clinical follow-up time ranged from 0.6 to 53.3 months, with a mean duration (SD) of 38.5 (16.6) months, and patients attended an average of 4 postbiopsy dermatology appointments (mean [SD], 4.6 [3.6]). According to the participating dermatology care providers, none of the 25 patients developed any indication during follow-up that the diagnosis of benign nevus was inaccurate. No patient had evidence of locally recurrent or metastatic melanoma, and none died during the study period.
Treatment/Interventions
The treatment recorded in the CRF was examination only for 21 of 25 patients, excision for 3, and other for 1 (Table 2). Because the explanation for the selection of other in this case described an excision performed at the same anatomic location as the biopsy, this treatment also was considered an excision for purposes of the study analyses. The 3 excisions all occurred at the first postbiopsy dermatology encounter. Across all follow-up visits, no additional surgical interventions occurred (Table 2).
The first excision (case 1) involved a 67-year-old woman with a lesion on the mid pubic region described clinically as an atypical nevus that generated a pretest histopathologic differential diagnosis including dysplastic nevus, superficial spreading melanoma, and melanoma arising within a nevus (Table 3; Figure, A and B). The gene expression test result was benign (score, −5.4), and the final pathology report diagnosis was nevus with junctional dysplasia, moderate. Surgical excision was performed at the patient’s first return visit, 505 days after initial diagnosis, with moderately dysplastic nevus as the recorded indication for removal. No repigmentation or other evidence of local recurrence or progression was detected, and the treating dermatologist indicated no suspicion that the original diagnosis of benign nevus was incorrect during the 23-month follow-up period.
The second excision (case 2) involved a 27-year-old woman with a pigmented neoplasm on the mid upper back (Figure, C and D) biopsied to rule out dysplastic nevus that resulted in a pretest histopathologic differential diagnosis of dysplastic nevus vs superficial spreading melanoma or melanoma arising within a nevus. The gene expression test result classified the lesion as benign (score, −2.9), and the final pathology diagnosis was nevus, compound, with moderate dysplasia. Despite the benign diagnosis, residual neoplasm (or pigmentation) at the biopsy site prompted the patient to request excision at her first postbiopsy visit, 22 days after testing (Table 3). The CRF completed by the dermatologist reported no indication that the benign diagnosis was inaccurate, but the patient was subsequently lost to follow-up.
The third excision (case 3) involved a 32-year-old woman with a pigmented lesion on the abdomen (Table 3; Figure, E and F). The clinical description was irregular-appearing black papule, nevus with atypia, and the histopathologic differential diagnosis again included dysplastic nevus, superficial spreading melanoma, and melanoma arising within a preexisting nevus. The gene expression signature result was benign (score, −7.2), and the final diagnosis issued within the accompanying pathology report was nevus with moderate junctional dysplasia. Despite the benign diagnosis, excision was performed 89 days after test result availability, with apparent residual pigmentation as the specified indication. As with the other 2 cases, the treating dermatologist confirmed that neither clinical features nor follow-up events suggested malignancy.
Comment
This study followed a cohort of 25 patients with histopathologically ambiguous melanocytic neoplasms that were classified as benign by a diagnostic gene expression test with the intent of determining the outcomes of patients whose treatment aligned with their benign test result. All patients initially were managed according to their test result. During an average posttest clinical follow-up time of more than 3 years (38.5 months), the 25 biopsied lesions, most of which received a differential diagnosis of dysplastic nevus, were regarded as benign nevi by their dermatologists, and the vast majority (88%) received no further surgical intervention. Three patients underwent subsequent excision of the biopsied lesion, with patient or physician preference as the indication in each instance. None of the 25 patients developed evidence of local recurrence, metastasis, or other findings that prompted doubt of the benign diagnosis. The absence of adverse events during clinical follow-up, particularly given that most lesions were not subjected to further intervention, supports use of the gene expression test as a safe and effective adjunct to the diagnosis and treatment of ambiguous melanocytic neoplasms by dermatologists and dermatopathologists.
Ambiguous melanocytic neoplasms evaluated without the aid of molecular adjuncts often result in equivocal or less-than-definitive diagnoses, and further surgical intervention is commonly undertaken to mitigate against the possibility of a missed melanoma.13 In this study, treatment that was aligned with the benign test result allowed most patients to avoid further surgical intervention, which suggests that adjunctive use of the gene signature can contribute to reductions in the physical and economic burdens imposed by unnecessary surgical interventions.15,16 Moreover, any means of increasing accurate and definitive diagnoses may produce an immediate impact on health outcomes by reducing the anxiety that uncertainty often provokes in patients and health care providers alike.
Study Limitations
This study must be interpreted within the context of its limitations. Obtaining meaningful patient outcome data is a common challenge in health care research due to the requisite length of follow-up and sometimes the lack of definitive evidence of adverse events. This is particularly difficult for melanocytic neoplasms because of an apparent inclination for patients with benign diagnoses to abandon follow-up and an increasing tendency for even minimal diagnostic uncertainty to prompt complete excision. Additionally, the only definitive clinical outcome for melanocytic neoplasms is distant metastasis, which (fortunately for patients) is relatively rare. Not surprisingly, studies documenting clinical outcomes of patients with ambiguous melanocytic neoplasms tested prospectively with diagnostic adjuncts are scarce, and this study’s sample size and clinical follow-up compare favorably with the few that exist.17,18 Although most melanomas declare themselves through recurrence or metastasis within several years of initial biopsy,1,19 some are clinically dormant for as long as 10 years after initial detection.20,21 This may be particularly true for the small or early-stage lesions that now comprise the majority of biopsied neoplasms, and such events would go undetected by this study and many others. It also must be recognized that uneventful follow-up, regardless of duration, cannot prove that a biopsied melanocytic neoplasm was benign. Although only 5 patients had a follow-up time of less than 2 years (the time frame in which most recurrence or metastasis will occur), it cannot be definitively proven that a minimum of 2 years recurrence- or metastasis-free survival indicates a benign lesion. Many early-stage malignant melanomas are eradicated by complete excision or even by the initial biopsy if margins are uninvolved.
Because these limitations are intrinsic to melanocytic neoplasms and current management strategies, they pertain to all investigations seeking insights into biological potential through clinical outcomes. Similarly, all current diagnostic tools and procedures have the potential for sampling error, including histopathology. The rarity of adverse outcomes (recurrence and metastasis) in patients with benign test results within this cohort indicates that false-negative results are uncommon, which is further evidenced by a similar rarity of adverse events in prior studies of the gene expression signature.8-10,22 A particular strength of this study is that most of the ambiguous melanocytic neoplasms followed did not undergo excision after the initial biopsy, an increasingly uncommon situation that may increase their likelihood to be informative.
It must be emphasized that the gene expression test, similar to other diagnostic adjuncts, is neither a replacement for histopathologic interpretation nor a substitute for judgment. As with all tests, it can produce false-positive and false-negative results. Therefore, it should always be interpreted within the constellation of the many other data points that must be considered when making a distinction between benign nevus and malignant melanoma, including but not limited to patient age, family and personal history of melanoma, anatomic location, clinical features, and histopathologic findings. As is the case for many diseases, careful consideration of all relevant input is necessary to minimize the risk of misdiagnosis that might occur should any single data point prove inaccurate, including the results of adjunctive molecular tests.
Conclusion
Ancillary methods are emerging as useful tools for the diagnostic evaluation of melanocytic neoplasms that cannot be assigned definitive diagnoses using traditional techniques alone. This study suggests that patients with ambiguous melanocytic neoplasms may benefit from diagnoses and treatment decisions aligned with the results of a gene expression test, and that for those with a benign result, simple observation may be a safe alternative to surgical excision. This expands upon prior observations of the test’s influence on diagnoses and treatment decisions and supports its role as part of dermatopathologists’ and dermatologists’ decision-making process for histopathologically ambiguous melanocytic lesions.
According to National Institutes of Health estimates, more than 90,000 new cases of melanoma were diagnosed in 2018.1 Overall 5-year survival for patients with melanoma exceeds 90%, but individual survival estimates are highly dependent on stage at diagnosis, and survival decreases markedly with metastasis. Therefore, early and accurate diagnosis is critical.
Diagnosis of melanocytic neoplasms usually is performed by dermatopathologists through microscopic examination of stained tissue biopsy sections, a technically simple and effective method that enables a definitive diagnosis of benign nevus or malignant melanoma to be made in most cases. However, approximately 15% of all biopsied melanocytic lesions will exhibit some degree of histopathologic ambiguity,2-4 meaning that some of their microscopic features will be characteristic of a benign nevus while others will suggest the possibility of malignant melanoma. Diagnostic interpretations often vary in these cases, even among experts, and a definitive diagnosis of benign or malignant may be difficult to achieve by microscopy alone.2-4 Because of the marked reduction in survival once a melanoma has metastasized, these diagnostically ambiguous lesions often are treated as possible malignant melanomas with complete surgical excision (or re-excision). However, some experts suggest that many histopathologically ambiguous melanocytic neoplasms are, in fact, benign,5 a notion supported by epidemiologic evidence.6,7 Therefore, excision of many ambiguous melanocytic neoplasms might be avoided if definitive diagnosis could be achieved.
A gene expression signature was developed and validated for use as an adjunct to traditional methods of differentiating malignant melanocytic neoplasms from their benign counterparts.8-11 This test quantifies the RNA transcripts produced by 14 genes known to be overexpressed in malignant melanomas by comparison to benign nevi. These values are then combined algorithmically with measurements of 9 reference genes to produce an objective numerical score that is classified as benign, malignant, or indeterminate. When used by board-certified dermatopathologists and dermatologists confronting ambiguous melanocytic lesions, the test produces substantial increases in definitive diagnoses and prompts changes in treatment recommendations.12,13 However, the long-term consequences of foregoing surgical excision of melanocytic neoplasms that are diagnostically ambiguous but classified as benign by this test have not yet been formally assessed. In the current study, prospectively tested patients whose ambiguous melanocytic neoplasms were classified as benign by the gene expression signature were followed for up to 4.5 years to evaluate the long-term safety of treatment decisions aligned with benign test results.
Methods
Study Population
As part of a prior study,12 US-based dermatopathologists submitted tissue sections from biopsied melanocytic neoplasms determined to be diagnostically ambiguous by histopathology for analysis with the gene expression signature (Myriad Genetics, Inc). Diagnostically ambiguous lesions were those lesions that were described as ambiguous, uncertain, equivocal, indeterminate, or other synonymous terms by the submitting dermatopathologist and therefore lacked a confident diagnosis of benign or malignant prior to testing. Patients initially were tested between May 2014 and August 2014, with samples submitted through a prospective clinical experience study designed to assess the impact of the test on diagnosis and treatment decisions. This study was performed under an institutional review board waiver of consent (Quorum #33403/1).
Patients were eligible for inclusion in the current study if their biopsy specimens (1) had an uncertain preliminary diagnosis according to the submitting dermatopathologist (pretest diagnosis of indeterminate); (2) received a negative (benign) score from the gene expression test; (3) were treated as benign by the dermatologist(s) involved in follow-up care; and (4) were submitted by a single site (St. Joseph Medical Center, Houston, Texas). Although a single dermatopathology site was used for this study, multiple dermatologists were involved in the final treatment of these patients. Patients with benign scores who received additional intervention were excluded, as they may have a lower rate of adverse events (ie, metastasis) than those who did not receive intervention and would therefore skew the analysis population. A total of 25 patients from the prior study met these inclusion criteria. The previously collected12 pretest and posttest de-identified data were compiled from the commercial laboratory databases, and the patients were followed from the time of testing via medical record review performed by the dermatology providers at participating sites. Clinical follow-up data were collected using study-specific case report forms (CRFs) that captured the following: (1) the dates and results of clinical follow-up visits; (2) the type(s) of treatment and interventions (if any) performed at those visits; (3) the specific indication for any intervention performed; (4) any evidence of persistent, locally recurrent, and/or distant melanocytic neoplasia (whether definitively attributable to the tested lesion or not); and (5) death from any cause. The CRF assigned interventions to 1 of 5 categories: excision, excision with sentinel lymph node biopsy, referral to dermatologic or other surgeon, examination only (without surgical intervention), and other. Selection of other required a free-text description of the treatment and indications. Pertinent information not otherwise captured by the CRF also was recordable as free text.
Gene Expression Testing
Gene expression testing was carried out at the time of specimen submission in the prior study12 as described previously.14 Briefly, formalin-fixed, paraffin-embedded, unstained tissue sections and/or tissue blocks were submitted for testing along with a single hematoxylin and eosin–stained slide used to identify and designate the representative portion(s) of the lesion to be tested. These areas were macrodissected from unstained tissue sections and pooled for RNA extraction. Expression of 14 biomarker genes and 9 reference genes was measured via
Statistical Analysis
Demographic and other baseline characteristics of the patient population were summarized. Follow-up time was calculated as the interval between the date a patient’s gene expression test result was first issued to the provider and the date of the patient’s last recorded visit during the study period. All patient dermatology office visits within the designated follow-up period were documented, with a nonstandard number of visits and follow-up time across all study patients. Statistical analyses were conducted using SAS software (SAS Institute Inc), R software version 3.5.0 (R Foundation for Statistical Computing), and IBM SPSS Statistics software (IBM SPSS Statistics for Windows, Version 25).
Results
Patient Sample
A total of 25 ambiguous melanocytic neoplasms from 25 patients met the study inclusion criteria of a benign gene expression result with subsequent treatment as a benign neoplasm during follow-up. The patient sample statistics are summarized in Table 1. Most patients were younger than 65 years, with an average age at the time of biopsy of 48.4 years. All 25 neoplasms produced negative (benign) gene expression signature scores, all were diagnosed as benign nevi posttest by the submitting dermatopathologist, and all patients were initially treated in accordance with the benign diagnosis by the dermatologist(s) involved in clinical follow-up care. Prior to testing with the gene expression signature, most of these histopathologically indeterminate lesions received differential diagnoses, the most common of which were dysplastic nevus (84%), melanoma arising from a nevus (72%), and superficial spreading melanoma (64%; eTable). After testing with the gene expression signature and receiving a benign score, most lesions received a single differential diagnosis of dysplastic nevus (88%).
Follow-up and Survival
Clinical follow-up time ranged from 0.6 to 53.3 months, with a mean duration (SD) of 38.5 (16.6) months, and patients attended an average of 4 postbiopsy dermatology appointments (mean [SD], 4.6 [3.6]). According to the participating dermatology care providers, none of the 25 patients developed any indication during follow-up that the diagnosis of benign nevus was inaccurate. No patient had evidence of locally recurrent or metastatic melanoma, and none died during the study period.
Treatment/Interventions
The treatment recorded in the CRF was examination only for 21 of 25 patients, excision for 3, and other for 1 (Table 2). Because the explanation for the selection of other in this case described an excision performed at the same anatomic location as the biopsy, this treatment also was considered an excision for purposes of the study analyses. The 3 excisions all occurred at the first postbiopsy dermatology encounter. Across all follow-up visits, no additional surgical interventions occurred (Table 2).
The first excision (case 1) involved a 67-year-old woman with a lesion on the mid pubic region described clinically as an atypical nevus that generated a pretest histopathologic differential diagnosis including dysplastic nevus, superficial spreading melanoma, and melanoma arising within a nevus (Table 3; Figure, A and B). The gene expression test result was benign (score, −5.4), and the final pathology report diagnosis was nevus with junctional dysplasia, moderate. Surgical excision was performed at the patient’s first return visit, 505 days after initial diagnosis, with moderately dysplastic nevus as the recorded indication for removal. No repigmentation or other evidence of local recurrence or progression was detected, and the treating dermatologist indicated no suspicion that the original diagnosis of benign nevus was incorrect during the 23-month follow-up period.
The second excision (case 2) involved a 27-year-old woman with a pigmented neoplasm on the mid upper back (Figure, C and D) biopsied to rule out dysplastic nevus that resulted in a pretest histopathologic differential diagnosis of dysplastic nevus vs superficial spreading melanoma or melanoma arising within a nevus. The gene expression test result classified the lesion as benign (score, −2.9), and the final pathology diagnosis was nevus, compound, with moderate dysplasia. Despite the benign diagnosis, residual neoplasm (or pigmentation) at the biopsy site prompted the patient to request excision at her first postbiopsy visit, 22 days after testing (Table 3). The CRF completed by the dermatologist reported no indication that the benign diagnosis was inaccurate, but the patient was subsequently lost to follow-up.
The third excision (case 3) involved a 32-year-old woman with a pigmented lesion on the abdomen (Table 3; Figure, E and F). The clinical description was irregular-appearing black papule, nevus with atypia, and the histopathologic differential diagnosis again included dysplastic nevus, superficial spreading melanoma, and melanoma arising within a preexisting nevus. The gene expression signature result was benign (score, −7.2), and the final diagnosis issued within the accompanying pathology report was nevus with moderate junctional dysplasia. Despite the benign diagnosis, excision was performed 89 days after test result availability, with apparent residual pigmentation as the specified indication. As with the other 2 cases, the treating dermatologist confirmed that neither clinical features nor follow-up events suggested malignancy.
Comment
This study followed a cohort of 25 patients with histopathologically ambiguous melanocytic neoplasms that were classified as benign by a diagnostic gene expression test with the intent of determining the outcomes of patients whose treatment aligned with their benign test result. All patients initially were managed according to their test result. During an average posttest clinical follow-up time of more than 3 years (38.5 months), the 25 biopsied lesions, most of which received a differential diagnosis of dysplastic nevus, were regarded as benign nevi by their dermatologists, and the vast majority (88%) received no further surgical intervention. Three patients underwent subsequent excision of the biopsied lesion, with patient or physician preference as the indication in each instance. None of the 25 patients developed evidence of local recurrence, metastasis, or other findings that prompted doubt of the benign diagnosis. The absence of adverse events during clinical follow-up, particularly given that most lesions were not subjected to further intervention, supports use of the gene expression test as a safe and effective adjunct to the diagnosis and treatment of ambiguous melanocytic neoplasms by dermatologists and dermatopathologists.
Ambiguous melanocytic neoplasms evaluated without the aid of molecular adjuncts often result in equivocal or less-than-definitive diagnoses, and further surgical intervention is commonly undertaken to mitigate against the possibility of a missed melanoma.13 In this study, treatment that was aligned with the benign test result allowed most patients to avoid further surgical intervention, which suggests that adjunctive use of the gene signature can contribute to reductions in the physical and economic burdens imposed by unnecessary surgical interventions.15,16 Moreover, any means of increasing accurate and definitive diagnoses may produce an immediate impact on health outcomes by reducing the anxiety that uncertainty often provokes in patients and health care providers alike.
Study Limitations
This study must be interpreted within the context of its limitations. Obtaining meaningful patient outcome data is a common challenge in health care research due to the requisite length of follow-up and sometimes the lack of definitive evidence of adverse events. This is particularly difficult for melanocytic neoplasms because of an apparent inclination for patients with benign diagnoses to abandon follow-up and an increasing tendency for even minimal diagnostic uncertainty to prompt complete excision. Additionally, the only definitive clinical outcome for melanocytic neoplasms is distant metastasis, which (fortunately for patients) is relatively rare. Not surprisingly, studies documenting clinical outcomes of patients with ambiguous melanocytic neoplasms tested prospectively with diagnostic adjuncts are scarce, and this study’s sample size and clinical follow-up compare favorably with the few that exist.17,18 Although most melanomas declare themselves through recurrence or metastasis within several years of initial biopsy,1,19 some are clinically dormant for as long as 10 years after initial detection.20,21 This may be particularly true for the small or early-stage lesions that now comprise the majority of biopsied neoplasms, and such events would go undetected by this study and many others. It also must be recognized that uneventful follow-up, regardless of duration, cannot prove that a biopsied melanocytic neoplasm was benign. Although only 5 patients had a follow-up time of less than 2 years (the time frame in which most recurrence or metastasis will occur), it cannot be definitively proven that a minimum of 2 years recurrence- or metastasis-free survival indicates a benign lesion. Many early-stage malignant melanomas are eradicated by complete excision or even by the initial biopsy if margins are uninvolved.
Because these limitations are intrinsic to melanocytic neoplasms and current management strategies, they pertain to all investigations seeking insights into biological potential through clinical outcomes. Similarly, all current diagnostic tools and procedures have the potential for sampling error, including histopathology. The rarity of adverse outcomes (recurrence and metastasis) in patients with benign test results within this cohort indicates that false-negative results are uncommon, which is further evidenced by a similar rarity of adverse events in prior studies of the gene expression signature.8-10,22 A particular strength of this study is that most of the ambiguous melanocytic neoplasms followed did not undergo excision after the initial biopsy, an increasingly uncommon situation that may increase their likelihood to be informative.
It must be emphasized that the gene expression test, similar to other diagnostic adjuncts, is neither a replacement for histopathologic interpretation nor a substitute for judgment. As with all tests, it can produce false-positive and false-negative results. Therefore, it should always be interpreted within the constellation of the many other data points that must be considered when making a distinction between benign nevus and malignant melanoma, including but not limited to patient age, family and personal history of melanoma, anatomic location, clinical features, and histopathologic findings. As is the case for many diseases, careful consideration of all relevant input is necessary to minimize the risk of misdiagnosis that might occur should any single data point prove inaccurate, including the results of adjunctive molecular tests.
Conclusion
Ancillary methods are emerging as useful tools for the diagnostic evaluation of melanocytic neoplasms that cannot be assigned definitive diagnoses using traditional techniques alone. This study suggests that patients with ambiguous melanocytic neoplasms may benefit from diagnoses and treatment decisions aligned with the results of a gene expression test, and that for those with a benign result, simple observation may be a safe alternative to surgical excision. This expands upon prior observations of the test’s influence on diagnoses and treatment decisions and supports its role as part of dermatopathologists’ and dermatologists’ decision-making process for histopathologically ambiguous melanocytic lesions.
- Noone AM, Howlander N, Krapcho M, et al, eds. SEER Cancer Statistics Review, 1975-2015. National Cancer Institute website. Updated September 10, 2018. Accessed April 21, 2021. https://seer.cancer.gov/archive/csr/1975_2015/
- Shoo BA, Sagebiel RW, Kashani-Sabet M. Discordance in the histopathologic diagnosis of melanoma at a melanoma referral center. J Am Acad Dermatol. 2010;62:751-756.
- Veenhuizen KC, De Wit PE, Mooi WJ, et al. Quality assessment by expert opinion in melanoma pathology: experience of the pathology panel of the Dutch Melanoma Working Party. J Pathol. 1997;182:266-272.
- Elmore JG, Barnhill RL, Elder DE, et al. Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study. BMJ. 2017;357:j2813. doi:10.1136/bmj.j2813
- Glusac EJ. The melanoma ‘epidemic’, a dermatopathologist’s perspective. J Cutan Pathol. 2011;38:264-267.
- Welch HG, Woloshin S, Schwartz LM. Skin biopsy rates and incidence of melanoma: population based ecological study. BMJ. 2005;331:481.
- Swerlick RA, Chen S. The melanoma epidemic. Is increased surveillance the solution or the problem? Arch Dermatol. 1996;132:881-884.
- Ko JS, Matharoo-Ball B, Billings SD, et al. Diagnostic distinction of malignant melanoma and benign nevi by a gene expression signature and correlation to clinical outcomes. Cancer Epidemiol Biomarkers Prev. 2017;26:1107-1113.
- Clarke LE, Flake DD 2nd, Busam K, et al. An independent validation of a gene expression signature to differentiate malignant melanoma from benign melanocytic nevi. Cancer. 2017;123:617-628.
- Clarke LE, Warf BM, Flake DD 2nd, et al. Clinical validation of a gene expression signature that differentiates benign nevi from malignant melanoma. J Cutan Pathol. 2015;42:244-252.
- Minca EC, Al-Rohil RN, Wang M, et al. Comparison between melanoma gene expression score and fluorescence in situ hybridization for the classification of melanocytic lesions. Mod Pathol. 2016;29:832-843.
- Cockerell CJ, Tschen J, Evans B, et al. The influence of a gene expression signature on the diagnosis and recommended treatment of melanocytic tumors by dermatopathologists. Medicine (Baltimore). 2016;95:e4887. doi:10.1097/MD.0000000000004887
- Cockerell C, Tschen J, Billings SD, et al. The influence of a gene-expression signature on the treatment of diagnostically challenging melanocytic lesions. Per Med. 2017;14:123-130.
- Warf MB, Flake DD 2nd, Adams D, et al. Analytical validation of a melanoma diagnostic gene signature using formalin-fixed paraffin-embedded melanocytic lesions. Biomark Med. 2015;9:407-416.
- Guy GP Jr, Ekwueme DU, Tangka FK, et al. Melanoma treatment costs: a systematic review of the literature, 1990-2011. Am J Prev Med. 2012;43:537-545.
- Guy GP Jr, Machlin SR, Ekwueme DU, et al. Prevalence and costs of skin cancer treatment in the U.S., 2002-2006 and 2007-2011. Am J Prev Med. 2015;48:183-187.
- Egnatios GL, Ferringer TC. Clinical follow-up of atypical spitzoid tumors analyzed by fluorescence in situ hybridization. Am J Dermatopathol. 2016;38:289-296.
- Fischer AS, High WA. The difficulty in interpreting gene expression profiling in BAP-negative melanocytic tumors. J Cutan Pathol. 2018;45:659-666. doi:10.1111/cup.13277
- Vollmer RT. The dynamics of death in melanoma. J Cutan Pathol. 2012;39:1075-1082.
- Osella-Abate S, Ribero S, Sanlorenzo M, et al. Risk factors related to late metastases in 1,372 melanoma patients disease free more than 10 years. Int J Cancer. 2015;136:2453-2457.
- Faries MB, Steen S, Ye X, et al. Late recurrence in melanoma: clinical implications of lost dormancy. J Am Coll Surg. 2013;217:27-34.
- Ko JS, Clarke LE, Minca EC, et al. Correlation of melanoma gene expression score with clinical outcomes on a series of melanocytic lesions. Hum Pathol. 2019;86:213-221.
- Noone AM, Howlander N, Krapcho M, et al, eds. SEER Cancer Statistics Review, 1975-2015. National Cancer Institute website. Updated September 10, 2018. Accessed April 21, 2021. https://seer.cancer.gov/archive/csr/1975_2015/
- Shoo BA, Sagebiel RW, Kashani-Sabet M. Discordance in the histopathologic diagnosis of melanoma at a melanoma referral center. J Am Acad Dermatol. 2010;62:751-756.
- Veenhuizen KC, De Wit PE, Mooi WJ, et al. Quality assessment by expert opinion in melanoma pathology: experience of the pathology panel of the Dutch Melanoma Working Party. J Pathol. 1997;182:266-272.
- Elmore JG, Barnhill RL, Elder DE, et al. Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study. BMJ. 2017;357:j2813. doi:10.1136/bmj.j2813
- Glusac EJ. The melanoma ‘epidemic’, a dermatopathologist’s perspective. J Cutan Pathol. 2011;38:264-267.
- Welch HG, Woloshin S, Schwartz LM. Skin biopsy rates and incidence of melanoma: population based ecological study. BMJ. 2005;331:481.
- Swerlick RA, Chen S. The melanoma epidemic. Is increased surveillance the solution or the problem? Arch Dermatol. 1996;132:881-884.
- Ko JS, Matharoo-Ball B, Billings SD, et al. Diagnostic distinction of malignant melanoma and benign nevi by a gene expression signature and correlation to clinical outcomes. Cancer Epidemiol Biomarkers Prev. 2017;26:1107-1113.
- Clarke LE, Flake DD 2nd, Busam K, et al. An independent validation of a gene expression signature to differentiate malignant melanoma from benign melanocytic nevi. Cancer. 2017;123:617-628.
- Clarke LE, Warf BM, Flake DD 2nd, et al. Clinical validation of a gene expression signature that differentiates benign nevi from malignant melanoma. J Cutan Pathol. 2015;42:244-252.
- Minca EC, Al-Rohil RN, Wang M, et al. Comparison between melanoma gene expression score and fluorescence in situ hybridization for the classification of melanocytic lesions. Mod Pathol. 2016;29:832-843.
- Cockerell CJ, Tschen J, Evans B, et al. The influence of a gene expression signature on the diagnosis and recommended treatment of melanocytic tumors by dermatopathologists. Medicine (Baltimore). 2016;95:e4887. doi:10.1097/MD.0000000000004887
- Cockerell C, Tschen J, Billings SD, et al. The influence of a gene-expression signature on the treatment of diagnostically challenging melanocytic lesions. Per Med. 2017;14:123-130.
- Warf MB, Flake DD 2nd, Adams D, et al. Analytical validation of a melanoma diagnostic gene signature using formalin-fixed paraffin-embedded melanocytic lesions. Biomark Med. 2015;9:407-416.
- Guy GP Jr, Ekwueme DU, Tangka FK, et al. Melanoma treatment costs: a systematic review of the literature, 1990-2011. Am J Prev Med. 2012;43:537-545.
- Guy GP Jr, Machlin SR, Ekwueme DU, et al. Prevalence and costs of skin cancer treatment in the U.S., 2002-2006 and 2007-2011. Am J Prev Med. 2015;48:183-187.
- Egnatios GL, Ferringer TC. Clinical follow-up of atypical spitzoid tumors analyzed by fluorescence in situ hybridization. Am J Dermatopathol. 2016;38:289-296.
- Fischer AS, High WA. The difficulty in interpreting gene expression profiling in BAP-negative melanocytic tumors. J Cutan Pathol. 2018;45:659-666. doi:10.1111/cup.13277
- Vollmer RT. The dynamics of death in melanoma. J Cutan Pathol. 2012;39:1075-1082.
- Osella-Abate S, Ribero S, Sanlorenzo M, et al. Risk factors related to late metastases in 1,372 melanoma patients disease free more than 10 years. Int J Cancer. 2015;136:2453-2457.
- Faries MB, Steen S, Ye X, et al. Late recurrence in melanoma: clinical implications of lost dormancy. J Am Coll Surg. 2013;217:27-34.
- Ko JS, Clarke LE, Minca EC, et al. Correlation of melanoma gene expression score with clinical outcomes on a series of melanocytic lesions. Hum Pathol. 2019;86:213-221.
Practice Point
- Implementation of a gene expression signature in the diagnosis of histopathologically ambiguous lesions can safely increase diagnostic accuracy and optimize treatment.
Tanning Attitudes and Behaviors in Adolescents and Young Adults
Intentional tanning—through sun exposure and tanning beds—is an easily avoidable contributor to skin cancer development and an important area for public education. Since the advent of social media, a correlation between social media use and increased indoor tanning behaviors has been reported.1 In 2010, 11.3% of US adults aged 18 to 29 years reported using a tanning bed in the last 12 months.2 The American Academy of Dermatology first published their “Position Statement on Indoor Tanning” in 1998, endorsing a ban on the sale of indoor tanning equipment for nonmedical purposes.3
Although there has been no outright ban on indoor tanning, regulations have been put in place in many states—including Texas, where (as of 2013) a person younger than 18 years must have written consent from their parent(s) to use a tanning bed. Despite efforts of organizations including the American Academy of Dermatology and the government to educate the public on skin cancer prevention and sun safety, the skin cancer rate has been steadily increasing over the last 20 years.
There is a constant campaign among dermatologists to educate their patients on how to reduce or avoid the risk for skin cancer, including the use of sunscreen and avoidance of tanning. Adolescents and young adults are an especially important demographic to reach and educate because increased UV light exposure during these years leads to a greatly increased risk for skin cancer later in life.4 Data on the overall prevalence of tanning and the demographics of participation in tanning activities are important to capture and can be used to efficiently target higher-risk populations.
In this study, we aimed to investigate the attitudes and behaviors of adolescents and young adults regarding sun protection and tanning. We also aimed to determine which avenues, including social media, would be most effective at educating about skin cancer awareness and sun protection to the higher-risk younger population.
Materials and Methods
We developed an institutional review board–approved protocol for the prospective collection of data from registered patients at the dermatology clinic of the Mays Cancer Center at the University of Texas Health at San Antonio. A paper survey containing 15 rating-scale questions was administered to 60 patients aged 13 to 27 years. Surveys were administered during intake, prior to the patients’ visit with a dermatologist; all visits were of a functional (not cosmetic) nature. Data collection spanned June to August 2018. Survey results were entered into Research Electronic Data Capture (REDCap) software for qualitative analysis.
Results
Sixty patients responded to the survey. The mean age of respondents was 19.5 years. No surveys were excluded from the data set. Table 1 provides baseline characteristics of respondents. Some respondents left questions unanswered, resulting in questions with fewer than 60 responses.
Among respondents to the survey, 70% (42/60) reported it is very important to protect their skin from sun exposure, and 30% (18/60) reported it is somewhat important. Regarding sunscreen use, 70% (42/60) indicated they use sunscreen only before outdoor activities, 12% (7/60) use sunscreen daily, and 17% (10/60) never use sunscreen. Of those who use sunscreen, 52% (28/54) do so to prevent skin damage and aging and 44% (24/54) to prevent skin cancer. Twenty-three percent (13/56) of respondents reported finding tanned skin attractive; 26% (14/55) reported wanting to be tan. Looking at race, 28% (10/36) of Whites, 25% (5/20) of Spanish/Hispanic/Latinos, and 22% (2/9) of Asians found tanned skin attractive; no Black respondents found tanned skin attractive.
Regarding tanning, 12% (7/57) reported using a tanning bed in their lifetime and 4% (2/57) in the last year; 34% (19/56) reported deliberately tanning outdoors; and 9% (5/56) reported using sunless or spray-on tanning. Dermatologists (75% [42/56]), primary care physicians (69.6% [39/56]), and parents (46.4% [26/56]) were perceived as more effective sources of skin care education; among media modalities, television (33.9% [19/56]), Instagram (30.4% [17/60]), and YouTube (23.2% [13/60]) were perceived as more effective sources of skin care education (Table 2).
Comment
Perceptions of Tanning
Almost one-quarter of respondents found tanned skin attractive, which might reflect a shift from prior generations. Compared to the 11% of respondents in the 2010 survey,2 only 3.5% (2/57) of our respondents reported using a tanning bed in the last year, which could reflect the results of recent Texas legislation restricting the use of tanning beds by adolescents.
An alarming number of respondents reported going outdoors with the intention of tanning; although it appears that indoor tanning education has been successful, this finding shows that there is still a need for sun protection education because outdoor tanning is not a suitable alternative. A small number of respondents reported getting a sunless or spray-on tan, which is a risk-free alternative to indoor tanning.
Despite all respondents stating that protecting skin from the sun is important, most respondents surveyed do not use sunscreen daily. More respondents use sunscreen to prevent damage and aging than to prevent skin cancer. Young people might be more alarmed by the threat of early aging and losing their “youthful appearance” than by the possibility of developing skin cancer in the distant future. This discrepancy might indicate a lack of knowledge and be an important focus for future education efforts.
Perceptions of Trustworthiness of Education Sources
Our findings show dermatologists and primary care physicians are important educators on skin protection. Primary care physicians should remain vigilant to recognize at-risk patients who would benefit from skin protection education, especially those who do not see a dermatologist. Education of young people focusing on their concern over maintaining a youthful appearance instead of the possibility of developing skin cancer in the future might be more effective.
Although education provided by a physician is effective, using media—particularly social media—might be more efficient. Television, Instagram, and YouTube were listed by respondents as the 3 most preferred media outlets for skin health education, which shows important areas of focus for future advertising. Facebook was listed at a surprisingly low level, possibly showing the change in use of certain social media websites among this age group. According to the Pew Research Center, the most widely used social media apps among young adults aged 18 to 29 years are YouTube (91%), Facebook (63%), Instagram (67%), and Snapchat (62%). More than half of the same demographic visit Facebook (74%), Instagram (63%), Snapchat (61%), and YouTube (51%) daily.5 Although respondents to our survey were not specifically asked about the frequency of their use of social media and our data set includes patients younger than 18 years, we know that social media use has been increasing over the last decade among adolescents.1 Therefore, we assume that more than one-half of respondents to our survey use their reported social media platforms daily.
Social media is an underused medium for skin cancer prevention education and can reach those who do not regularly see a dermatologist. Unlike printed pamphlets and posters, advertisements through social media can use metrics such as age, race, gender, and interests to target high-risk individuals.
Study Limitations
This was a single-site study of currently enrolled dermatology patients who might be more aware of skin protection than the general population because they are being treated by a dermatologist. Survey questions regarding demographics, required by our institution, could not effectively differentiate Hispanic and White patients. Respondents could have been subject to the Hawthorne effect—awareness that their behavior is being observed—when responding to the survey because it was administered in the office prior to being seen by a dermatologist.
- Falzone AE, Brindis CD, Chren M-M, et al. Teens, tweets, and tanning beds: rethinking the use of social media for skin cancer prevention. Am J Prev Med. 2017;53(3 suppl 1):S86-S94.
- Centers for Disease Control and Prevention. Use of indoor tanning devices by adults—United States, 2010. MMWR Morb Mortal Wkly Rep. 2012;61:323-326.
- American Academy of Dermatology. Position statement on indoor tanning. Amended November 14, 2009. Accessed January 10, 2021. https://server.aad.org/Forms/Policies/Uploads/PS/PS-Indoor%20Tanning%2011-16-09.pdf?
- American Academy of Dermatology. Indoor tanning. Accessed January 10, 2020. https://www.aad.org/media/stats-indoor-tanning
- Perrin A, Anderson M. Share of U.S. adults using social media, including Facebook, is mostly unchanged since 2018. Pew Research Center; April 10, 2019. Accessed April 16, 2021. https://www.pewresearch.org/fact-tank/2019/04/10/share-of-u-s-adults-using-social-media-including-facebook-is-mostly-unchanged-since-2018/
Intentional tanning—through sun exposure and tanning beds—is an easily avoidable contributor to skin cancer development and an important area for public education. Since the advent of social media, a correlation between social media use and increased indoor tanning behaviors has been reported.1 In 2010, 11.3% of US adults aged 18 to 29 years reported using a tanning bed in the last 12 months.2 The American Academy of Dermatology first published their “Position Statement on Indoor Tanning” in 1998, endorsing a ban on the sale of indoor tanning equipment for nonmedical purposes.3
Although there has been no outright ban on indoor tanning, regulations have been put in place in many states—including Texas, where (as of 2013) a person younger than 18 years must have written consent from their parent(s) to use a tanning bed. Despite efforts of organizations including the American Academy of Dermatology and the government to educate the public on skin cancer prevention and sun safety, the skin cancer rate has been steadily increasing over the last 20 years.
There is a constant campaign among dermatologists to educate their patients on how to reduce or avoid the risk for skin cancer, including the use of sunscreen and avoidance of tanning. Adolescents and young adults are an especially important demographic to reach and educate because increased UV light exposure during these years leads to a greatly increased risk for skin cancer later in life.4 Data on the overall prevalence of tanning and the demographics of participation in tanning activities are important to capture and can be used to efficiently target higher-risk populations.
In this study, we aimed to investigate the attitudes and behaviors of adolescents and young adults regarding sun protection and tanning. We also aimed to determine which avenues, including social media, would be most effective at educating about skin cancer awareness and sun protection to the higher-risk younger population.
Materials and Methods
We developed an institutional review board–approved protocol for the prospective collection of data from registered patients at the dermatology clinic of the Mays Cancer Center at the University of Texas Health at San Antonio. A paper survey containing 15 rating-scale questions was administered to 60 patients aged 13 to 27 years. Surveys were administered during intake, prior to the patients’ visit with a dermatologist; all visits were of a functional (not cosmetic) nature. Data collection spanned June to August 2018. Survey results were entered into Research Electronic Data Capture (REDCap) software for qualitative analysis.
Results
Sixty patients responded to the survey. The mean age of respondents was 19.5 years. No surveys were excluded from the data set. Table 1 provides baseline characteristics of respondents. Some respondents left questions unanswered, resulting in questions with fewer than 60 responses.
Among respondents to the survey, 70% (42/60) reported it is very important to protect their skin from sun exposure, and 30% (18/60) reported it is somewhat important. Regarding sunscreen use, 70% (42/60) indicated they use sunscreen only before outdoor activities, 12% (7/60) use sunscreen daily, and 17% (10/60) never use sunscreen. Of those who use sunscreen, 52% (28/54) do so to prevent skin damage and aging and 44% (24/54) to prevent skin cancer. Twenty-three percent (13/56) of respondents reported finding tanned skin attractive; 26% (14/55) reported wanting to be tan. Looking at race, 28% (10/36) of Whites, 25% (5/20) of Spanish/Hispanic/Latinos, and 22% (2/9) of Asians found tanned skin attractive; no Black respondents found tanned skin attractive.
Regarding tanning, 12% (7/57) reported using a tanning bed in their lifetime and 4% (2/57) in the last year; 34% (19/56) reported deliberately tanning outdoors; and 9% (5/56) reported using sunless or spray-on tanning. Dermatologists (75% [42/56]), primary care physicians (69.6% [39/56]), and parents (46.4% [26/56]) were perceived as more effective sources of skin care education; among media modalities, television (33.9% [19/56]), Instagram (30.4% [17/60]), and YouTube (23.2% [13/60]) were perceived as more effective sources of skin care education (Table 2).
Comment
Perceptions of Tanning
Almost one-quarter of respondents found tanned skin attractive, which might reflect a shift from prior generations. Compared to the 11% of respondents in the 2010 survey,2 only 3.5% (2/57) of our respondents reported using a tanning bed in the last year, which could reflect the results of recent Texas legislation restricting the use of tanning beds by adolescents.
An alarming number of respondents reported going outdoors with the intention of tanning; although it appears that indoor tanning education has been successful, this finding shows that there is still a need for sun protection education because outdoor tanning is not a suitable alternative. A small number of respondents reported getting a sunless or spray-on tan, which is a risk-free alternative to indoor tanning.
Despite all respondents stating that protecting skin from the sun is important, most respondents surveyed do not use sunscreen daily. More respondents use sunscreen to prevent damage and aging than to prevent skin cancer. Young people might be more alarmed by the threat of early aging and losing their “youthful appearance” than by the possibility of developing skin cancer in the distant future. This discrepancy might indicate a lack of knowledge and be an important focus for future education efforts.
Perceptions of Trustworthiness of Education Sources
Our findings show dermatologists and primary care physicians are important educators on skin protection. Primary care physicians should remain vigilant to recognize at-risk patients who would benefit from skin protection education, especially those who do not see a dermatologist. Education of young people focusing on their concern over maintaining a youthful appearance instead of the possibility of developing skin cancer in the future might be more effective.
Although education provided by a physician is effective, using media—particularly social media—might be more efficient. Television, Instagram, and YouTube were listed by respondents as the 3 most preferred media outlets for skin health education, which shows important areas of focus for future advertising. Facebook was listed at a surprisingly low level, possibly showing the change in use of certain social media websites among this age group. According to the Pew Research Center, the most widely used social media apps among young adults aged 18 to 29 years are YouTube (91%), Facebook (63%), Instagram (67%), and Snapchat (62%). More than half of the same demographic visit Facebook (74%), Instagram (63%), Snapchat (61%), and YouTube (51%) daily.5 Although respondents to our survey were not specifically asked about the frequency of their use of social media and our data set includes patients younger than 18 years, we know that social media use has been increasing over the last decade among adolescents.1 Therefore, we assume that more than one-half of respondents to our survey use their reported social media platforms daily.
Social media is an underused medium for skin cancer prevention education and can reach those who do not regularly see a dermatologist. Unlike printed pamphlets and posters, advertisements through social media can use metrics such as age, race, gender, and interests to target high-risk individuals.
Study Limitations
This was a single-site study of currently enrolled dermatology patients who might be more aware of skin protection than the general population because they are being treated by a dermatologist. Survey questions regarding demographics, required by our institution, could not effectively differentiate Hispanic and White patients. Respondents could have been subject to the Hawthorne effect—awareness that their behavior is being observed—when responding to the survey because it was administered in the office prior to being seen by a dermatologist.
Intentional tanning—through sun exposure and tanning beds—is an easily avoidable contributor to skin cancer development and an important area for public education. Since the advent of social media, a correlation between social media use and increased indoor tanning behaviors has been reported.1 In 2010, 11.3% of US adults aged 18 to 29 years reported using a tanning bed in the last 12 months.2 The American Academy of Dermatology first published their “Position Statement on Indoor Tanning” in 1998, endorsing a ban on the sale of indoor tanning equipment for nonmedical purposes.3
Although there has been no outright ban on indoor tanning, regulations have been put in place in many states—including Texas, where (as of 2013) a person younger than 18 years must have written consent from their parent(s) to use a tanning bed. Despite efforts of organizations including the American Academy of Dermatology and the government to educate the public on skin cancer prevention and sun safety, the skin cancer rate has been steadily increasing over the last 20 years.
There is a constant campaign among dermatologists to educate their patients on how to reduce or avoid the risk for skin cancer, including the use of sunscreen and avoidance of tanning. Adolescents and young adults are an especially important demographic to reach and educate because increased UV light exposure during these years leads to a greatly increased risk for skin cancer later in life.4 Data on the overall prevalence of tanning and the demographics of participation in tanning activities are important to capture and can be used to efficiently target higher-risk populations.
In this study, we aimed to investigate the attitudes and behaviors of adolescents and young adults regarding sun protection and tanning. We also aimed to determine which avenues, including social media, would be most effective at educating about skin cancer awareness and sun protection to the higher-risk younger population.
Materials and Methods
We developed an institutional review board–approved protocol for the prospective collection of data from registered patients at the dermatology clinic of the Mays Cancer Center at the University of Texas Health at San Antonio. A paper survey containing 15 rating-scale questions was administered to 60 patients aged 13 to 27 years. Surveys were administered during intake, prior to the patients’ visit with a dermatologist; all visits were of a functional (not cosmetic) nature. Data collection spanned June to August 2018. Survey results were entered into Research Electronic Data Capture (REDCap) software for qualitative analysis.
Results
Sixty patients responded to the survey. The mean age of respondents was 19.5 years. No surveys were excluded from the data set. Table 1 provides baseline characteristics of respondents. Some respondents left questions unanswered, resulting in questions with fewer than 60 responses.
Among respondents to the survey, 70% (42/60) reported it is very important to protect their skin from sun exposure, and 30% (18/60) reported it is somewhat important. Regarding sunscreen use, 70% (42/60) indicated they use sunscreen only before outdoor activities, 12% (7/60) use sunscreen daily, and 17% (10/60) never use sunscreen. Of those who use sunscreen, 52% (28/54) do so to prevent skin damage and aging and 44% (24/54) to prevent skin cancer. Twenty-three percent (13/56) of respondents reported finding tanned skin attractive; 26% (14/55) reported wanting to be tan. Looking at race, 28% (10/36) of Whites, 25% (5/20) of Spanish/Hispanic/Latinos, and 22% (2/9) of Asians found tanned skin attractive; no Black respondents found tanned skin attractive.
Regarding tanning, 12% (7/57) reported using a tanning bed in their lifetime and 4% (2/57) in the last year; 34% (19/56) reported deliberately tanning outdoors; and 9% (5/56) reported using sunless or spray-on tanning. Dermatologists (75% [42/56]), primary care physicians (69.6% [39/56]), and parents (46.4% [26/56]) were perceived as more effective sources of skin care education; among media modalities, television (33.9% [19/56]), Instagram (30.4% [17/60]), and YouTube (23.2% [13/60]) were perceived as more effective sources of skin care education (Table 2).
Comment
Perceptions of Tanning
Almost one-quarter of respondents found tanned skin attractive, which might reflect a shift from prior generations. Compared to the 11% of respondents in the 2010 survey,2 only 3.5% (2/57) of our respondents reported using a tanning bed in the last year, which could reflect the results of recent Texas legislation restricting the use of tanning beds by adolescents.
An alarming number of respondents reported going outdoors with the intention of tanning; although it appears that indoor tanning education has been successful, this finding shows that there is still a need for sun protection education because outdoor tanning is not a suitable alternative. A small number of respondents reported getting a sunless or spray-on tan, which is a risk-free alternative to indoor tanning.
Despite all respondents stating that protecting skin from the sun is important, most respondents surveyed do not use sunscreen daily. More respondents use sunscreen to prevent damage and aging than to prevent skin cancer. Young people might be more alarmed by the threat of early aging and losing their “youthful appearance” than by the possibility of developing skin cancer in the distant future. This discrepancy might indicate a lack of knowledge and be an important focus for future education efforts.
Perceptions of Trustworthiness of Education Sources
Our findings show dermatologists and primary care physicians are important educators on skin protection. Primary care physicians should remain vigilant to recognize at-risk patients who would benefit from skin protection education, especially those who do not see a dermatologist. Education of young people focusing on their concern over maintaining a youthful appearance instead of the possibility of developing skin cancer in the future might be more effective.
Although education provided by a physician is effective, using media—particularly social media—might be more efficient. Television, Instagram, and YouTube were listed by respondents as the 3 most preferred media outlets for skin health education, which shows important areas of focus for future advertising. Facebook was listed at a surprisingly low level, possibly showing the change in use of certain social media websites among this age group. According to the Pew Research Center, the most widely used social media apps among young adults aged 18 to 29 years are YouTube (91%), Facebook (63%), Instagram (67%), and Snapchat (62%). More than half of the same demographic visit Facebook (74%), Instagram (63%), Snapchat (61%), and YouTube (51%) daily.5 Although respondents to our survey were not specifically asked about the frequency of their use of social media and our data set includes patients younger than 18 years, we know that social media use has been increasing over the last decade among adolescents.1 Therefore, we assume that more than one-half of respondents to our survey use their reported social media platforms daily.
Social media is an underused medium for skin cancer prevention education and can reach those who do not regularly see a dermatologist. Unlike printed pamphlets and posters, advertisements through social media can use metrics such as age, race, gender, and interests to target high-risk individuals.
Study Limitations
This was a single-site study of currently enrolled dermatology patients who might be more aware of skin protection than the general population because they are being treated by a dermatologist. Survey questions regarding demographics, required by our institution, could not effectively differentiate Hispanic and White patients. Respondents could have been subject to the Hawthorne effect—awareness that their behavior is being observed—when responding to the survey because it was administered in the office prior to being seen by a dermatologist.
- Falzone AE, Brindis CD, Chren M-M, et al. Teens, tweets, and tanning beds: rethinking the use of social media for skin cancer prevention. Am J Prev Med. 2017;53(3 suppl 1):S86-S94.
- Centers for Disease Control and Prevention. Use of indoor tanning devices by adults—United States, 2010. MMWR Morb Mortal Wkly Rep. 2012;61:323-326.
- American Academy of Dermatology. Position statement on indoor tanning. Amended November 14, 2009. Accessed January 10, 2021. https://server.aad.org/Forms/Policies/Uploads/PS/PS-Indoor%20Tanning%2011-16-09.pdf?
- American Academy of Dermatology. Indoor tanning. Accessed January 10, 2020. https://www.aad.org/media/stats-indoor-tanning
- Perrin A, Anderson M. Share of U.S. adults using social media, including Facebook, is mostly unchanged since 2018. Pew Research Center; April 10, 2019. Accessed April 16, 2021. https://www.pewresearch.org/fact-tank/2019/04/10/share-of-u-s-adults-using-social-media-including-facebook-is-mostly-unchanged-since-2018/
- Falzone AE, Brindis CD, Chren M-M, et al. Teens, tweets, and tanning beds: rethinking the use of social media for skin cancer prevention. Am J Prev Med. 2017;53(3 suppl 1):S86-S94.
- Centers for Disease Control and Prevention. Use of indoor tanning devices by adults—United States, 2010. MMWR Morb Mortal Wkly Rep. 2012;61:323-326.
- American Academy of Dermatology. Position statement on indoor tanning. Amended November 14, 2009. Accessed January 10, 2021. https://server.aad.org/Forms/Policies/Uploads/PS/PS-Indoor%20Tanning%2011-16-09.pdf?
- American Academy of Dermatology. Indoor tanning. Accessed January 10, 2020. https://www.aad.org/media/stats-indoor-tanning
- Perrin A, Anderson M. Share of U.S. adults using social media, including Facebook, is mostly unchanged since 2018. Pew Research Center; April 10, 2019. Accessed April 16, 2021. https://www.pewresearch.org/fact-tank/2019/04/10/share-of-u-s-adults-using-social-media-including-facebook-is-mostly-unchanged-since-2018/
PRACTICE POINTS
- Dermatologists are the preferred educators of skin care for adolescents and young adults.
- Social media is an underused medium for skin cancer prevention education and can reach those who do not regularly see a dermatologist.
- Education of young people focusing on their concerns about maintaining a youthful appearance instead of the possibility of developing skin cancer in the future might be more effective.
Communication Strategies in Mohs Micrographic Surgery: A Survey of Methods, Time Savings, and Perceived Patient Satisfaction
Mohs micrographic surgery (MMS) entails multiple time-consuming surgical and histological examinations for each patient. As surgical stages are performed and histological sections are processed, an efficient communication method among providers, medical assistants, histotechnologists, and patients is necessary to avoid delays. To address these and other communication issues, providers have focused on ways to increase clinic efficiency and improve patient-reported outcomes by utilizing new or repurposed communication technologies in their Mohs practice.
Prior reports have highlighted the utility of hands-free headsets that allow real-time communication among staff members as a means of increasing clinic efficiency and decreasing patient wait times.1-4 These systems may mediate a more rapid turnover between stages by mitigating the need for surgeons and support staff to assemble within a designated workspace.1,3,4 However, there is no single or standardized communication method that best suits all surgical suites and MMS practices. Our study aimed to identify the current communication strategies employed by Mohs surgeons and thereby ascertain which method(s) portend(s) the highest benefit in average daily time savings and provider-perceived patient satisfaction.
Materials and Methods
Survey Instrument
A new 10-question electronic survey was published on the SurveyMonkey website, and a link to the survey was provided in a quarterly email that originated from the American College of Mohs Surgery and was distributed to all 1735 active members. Responses were obtained from January 2019 to February 2019.
Statistical Analysis
A statistical analysis was done to determine any significant associations among the providers’ responses. P<.05 was used to determine statistical significance. A Cochran-Armitage test for trend was used to identify significant associations between the number of rooms and the communication systems that were used. Thus, 7 total tests—1 for each device (whiteboard, light system, flag system, wired intercom, wireless intercom, walkie-talkie, or headset)—were conducted. The Cochran-Armitage test also was used to determine whether the probability of using the device was affected by the number of stations/surgical rooms that were attended by the Mohs surgeons. To determine whether the communication devices used were associated with higher patient satisfaction, a χ2 test was conducted for each device (7 total tests), testing the categories of using that device (yes/no) and patient satisfaction (yes/no). A Fisher exact test of independence was used in any case where the proportion for the device and patient satisfaction was 25% or higher. To determine whether the communication method was associated with increased time savings, 7 total Cochran-Armitage tests were conducted, 1 for each device. A logistic regression model was used to determine whether there was a significant association between the number of stations and the likelihood of reporting patient satisfaction.
Results
Eighty-eight surgeons responded to the survey, with a response rate of 5% (88/1735). A total of 55 surgeons completed the survey in its entirety and were included in the data analysis. The most commonly used communication mediums were whiteboards (29/55 [53%]), followed by a flag system (16/55 [29%]) and a light system (13/55 [24%]). Most Mohs surgeons (52/55 [95%]) used the communication media to communicate with their staff only, and 76% (42/55) of Mohs surgeons believed that their communication media contributed to higher patient satisfaction. Overall, 58% (32/55) of Mohs surgeons stated that their communication media saved more than 15 minutes (on average) per day. The use of a whiteboard and/or flag system was reported as the least efficient method, with average daily time savings of 13 minutes. With the introduction of newer technology (wired or wireless intercoms, headsets, walkie-talkies, or internal messaging systems such as Skype) to the whiteboard and/or flag system, the time savings increased by 10 minutes per day. Nearly 25% (14/55) of surgeons utilized more than 1 communication system.
As the number of stations in an MMS suite increased, the probability of using a whiteboard to track the progress of the cases decreased. There were no statistically significant associations identified between the number of stations and the use of other communication devices (ie, flag system, light system, wireless intercom, wired intercom, walkie-talkie, headset). The stratified percentages of the amount of time savings for each communication modality are presented in the Figure (whiteboards and headsets were excluded because they did not increase time savings). The use of a light system was the only communication modality found to be statistically associated with an increase in provider-reported time savings (P=.0482; Figure). In addition, our analysis did not show an improvement in provider-reported patient satisfaction with any of the current systems used in MMS clinics.
Comment
The process of transmitting information among the medical team during MMS is a complex interplay involving the relay of crucial information, with many opportunities for the introduction of distraction and error. Despite numerous improvements in the efficiency of the preparation of histological specimens and implementation of various time-saving and tissue-saving surgical interventions, relatively little attention has been given to address the sometimes chaotic and challenging process of organizing results from each stage of multiple patients in an MMS surgical suite.5
As demonstrated by our survey, incorporation of a light-based system into an MMS clinic may improve workplace efficiency by decreasing the redundant use of support staff and allowing Mohs surgeons to transition from one station to the next seamlessly. Light-based communication systems provide an immediate notification for support staff via color-coded and/or numerically coded indicators on input switches located outside and inside the examination/surgery rooms. The switch indicators can be depressed with minimal disruption from station to station, thereby foregoing the need to interrupt an ongoing excision or closure to convey the status of the case. These systems may then permit enhanced clinic and workflow efficiency, which may help to shorten patient wait times.
Study Limitation
Although all members of the American College of Mohs Surgery were invited to participate in this online survey, only a small number (N=55) completed it in its entirety. Moreover, sample sizes for some of the communication devices were small. As a result, many of the tests might be lacking sufficient power to detect possible relationships, which might be identified in future larger-scale studies.
Conclusion
Our study supports the use of light-based communication systems in MMS suites to improve efficiency in the clinic. Based on our analysis, light-based communication methods were significantly associated with improved time savings (P=.0482). Our study did not show an improvement in provider-reported satisfaction with any of the current systems used in MMS clinics. We hope that this information will help guide providers in implementing new communication techniques to improve clinic efficiency.
Acknowledgments
The authors would like to thank Ms. Kathy Kyler (Oklahoma City, Oklahoma) for her assistance in preparing this manuscript. Support for Dr. Chen and Mr. Stubblefield was provided through National Institutes of Health, National Institute of General Medical Sciences [Grant 2U54GM104938-06, PI Judith James].
- Chen T, Vines L, Wanitphakdeedecha R, et al. Electronically linked: wireless, discrete, hands-free communication to improve surgical workflow in Mohs and dermasurgery clinic. Dermatol Surg. 2009;35:248-252.
- Lanto AB, Yano EM, Fink A, et al. Anatomy of an outpatient visit. An evaluation of clinic efficiency in general and subspecialty clinics. Med Group Manage J. 1995;42:18-25.
- Kantor J. Application of Google Glass to Mohs micrographic surgery: a pilot study in 120 patients. Dermatol Surg. 2015;41:288-289.
- Spurk PA, Mohr ML, Seroka AM, et al. The impact of a wireless telecommunication system on efficiency. J Nurs Admin. 1995;25:21-26.
- Dietert JB, MacFarlane DF. A survey of Mohs tissue tracking practices. Dermatol Surg. 2019;45:514-518.
Mohs micrographic surgery (MMS) entails multiple time-consuming surgical and histological examinations for each patient. As surgical stages are performed and histological sections are processed, an efficient communication method among providers, medical assistants, histotechnologists, and patients is necessary to avoid delays. To address these and other communication issues, providers have focused on ways to increase clinic efficiency and improve patient-reported outcomes by utilizing new or repurposed communication technologies in their Mohs practice.
Prior reports have highlighted the utility of hands-free headsets that allow real-time communication among staff members as a means of increasing clinic efficiency and decreasing patient wait times.1-4 These systems may mediate a more rapid turnover between stages by mitigating the need for surgeons and support staff to assemble within a designated workspace.1,3,4 However, there is no single or standardized communication method that best suits all surgical suites and MMS practices. Our study aimed to identify the current communication strategies employed by Mohs surgeons and thereby ascertain which method(s) portend(s) the highest benefit in average daily time savings and provider-perceived patient satisfaction.
Materials and Methods
Survey Instrument
A new 10-question electronic survey was published on the SurveyMonkey website, and a link to the survey was provided in a quarterly email that originated from the American College of Mohs Surgery and was distributed to all 1735 active members. Responses were obtained from January 2019 to February 2019.
Statistical Analysis
A statistical analysis was done to determine any significant associations among the providers’ responses. P<.05 was used to determine statistical significance. A Cochran-Armitage test for trend was used to identify significant associations between the number of rooms and the communication systems that were used. Thus, 7 total tests—1 for each device (whiteboard, light system, flag system, wired intercom, wireless intercom, walkie-talkie, or headset)—were conducted. The Cochran-Armitage test also was used to determine whether the probability of using the device was affected by the number of stations/surgical rooms that were attended by the Mohs surgeons. To determine whether the communication devices used were associated with higher patient satisfaction, a χ2 test was conducted for each device (7 total tests), testing the categories of using that device (yes/no) and patient satisfaction (yes/no). A Fisher exact test of independence was used in any case where the proportion for the device and patient satisfaction was 25% or higher. To determine whether the communication method was associated with increased time savings, 7 total Cochran-Armitage tests were conducted, 1 for each device. A logistic regression model was used to determine whether there was a significant association between the number of stations and the likelihood of reporting patient satisfaction.
Results
Eighty-eight surgeons responded to the survey, with a response rate of 5% (88/1735). A total of 55 surgeons completed the survey in its entirety and were included in the data analysis. The most commonly used communication mediums were whiteboards (29/55 [53%]), followed by a flag system (16/55 [29%]) and a light system (13/55 [24%]). Most Mohs surgeons (52/55 [95%]) used the communication media to communicate with their staff only, and 76% (42/55) of Mohs surgeons believed that their communication media contributed to higher patient satisfaction. Overall, 58% (32/55) of Mohs surgeons stated that their communication media saved more than 15 minutes (on average) per day. The use of a whiteboard and/or flag system was reported as the least efficient method, with average daily time savings of 13 minutes. With the introduction of newer technology (wired or wireless intercoms, headsets, walkie-talkies, or internal messaging systems such as Skype) to the whiteboard and/or flag system, the time savings increased by 10 minutes per day. Nearly 25% (14/55) of surgeons utilized more than 1 communication system.
As the number of stations in an MMS suite increased, the probability of using a whiteboard to track the progress of the cases decreased. There were no statistically significant associations identified between the number of stations and the use of other communication devices (ie, flag system, light system, wireless intercom, wired intercom, walkie-talkie, headset). The stratified percentages of the amount of time savings for each communication modality are presented in the Figure (whiteboards and headsets were excluded because they did not increase time savings). The use of a light system was the only communication modality found to be statistically associated with an increase in provider-reported time savings (P=.0482; Figure). In addition, our analysis did not show an improvement in provider-reported patient satisfaction with any of the current systems used in MMS clinics.
Comment
The process of transmitting information among the medical team during MMS is a complex interplay involving the relay of crucial information, with many opportunities for the introduction of distraction and error. Despite numerous improvements in the efficiency of the preparation of histological specimens and implementation of various time-saving and tissue-saving surgical interventions, relatively little attention has been given to address the sometimes chaotic and challenging process of organizing results from each stage of multiple patients in an MMS surgical suite.5
As demonstrated by our survey, incorporation of a light-based system into an MMS clinic may improve workplace efficiency by decreasing the redundant use of support staff and allowing Mohs surgeons to transition from one station to the next seamlessly. Light-based communication systems provide an immediate notification for support staff via color-coded and/or numerically coded indicators on input switches located outside and inside the examination/surgery rooms. The switch indicators can be depressed with minimal disruption from station to station, thereby foregoing the need to interrupt an ongoing excision or closure to convey the status of the case. These systems may then permit enhanced clinic and workflow efficiency, which may help to shorten patient wait times.
Study Limitation
Although all members of the American College of Mohs Surgery were invited to participate in this online survey, only a small number (N=55) completed it in its entirety. Moreover, sample sizes for some of the communication devices were small. As a result, many of the tests might be lacking sufficient power to detect possible relationships, which might be identified in future larger-scale studies.
Conclusion
Our study supports the use of light-based communication systems in MMS suites to improve efficiency in the clinic. Based on our analysis, light-based communication methods were significantly associated with improved time savings (P=.0482). Our study did not show an improvement in provider-reported satisfaction with any of the current systems used in MMS clinics. We hope that this information will help guide providers in implementing new communication techniques to improve clinic efficiency.
Acknowledgments
The authors would like to thank Ms. Kathy Kyler (Oklahoma City, Oklahoma) for her assistance in preparing this manuscript. Support for Dr. Chen and Mr. Stubblefield was provided through National Institutes of Health, National Institute of General Medical Sciences [Grant 2U54GM104938-06, PI Judith James].
Mohs micrographic surgery (MMS) entails multiple time-consuming surgical and histological examinations for each patient. As surgical stages are performed and histological sections are processed, an efficient communication method among providers, medical assistants, histotechnologists, and patients is necessary to avoid delays. To address these and other communication issues, providers have focused on ways to increase clinic efficiency and improve patient-reported outcomes by utilizing new or repurposed communication technologies in their Mohs practice.
Prior reports have highlighted the utility of hands-free headsets that allow real-time communication among staff members as a means of increasing clinic efficiency and decreasing patient wait times.1-4 These systems may mediate a more rapid turnover between stages by mitigating the need for surgeons and support staff to assemble within a designated workspace.1,3,4 However, there is no single or standardized communication method that best suits all surgical suites and MMS practices. Our study aimed to identify the current communication strategies employed by Mohs surgeons and thereby ascertain which method(s) portend(s) the highest benefit in average daily time savings and provider-perceived patient satisfaction.
Materials and Methods
Survey Instrument
A new 10-question electronic survey was published on the SurveyMonkey website, and a link to the survey was provided in a quarterly email that originated from the American College of Mohs Surgery and was distributed to all 1735 active members. Responses were obtained from January 2019 to February 2019.
Statistical Analysis
A statistical analysis was done to determine any significant associations among the providers’ responses. P<.05 was used to determine statistical significance. A Cochran-Armitage test for trend was used to identify significant associations between the number of rooms and the communication systems that were used. Thus, 7 total tests—1 for each device (whiteboard, light system, flag system, wired intercom, wireless intercom, walkie-talkie, or headset)—were conducted. The Cochran-Armitage test also was used to determine whether the probability of using the device was affected by the number of stations/surgical rooms that were attended by the Mohs surgeons. To determine whether the communication devices used were associated with higher patient satisfaction, a χ2 test was conducted for each device (7 total tests), testing the categories of using that device (yes/no) and patient satisfaction (yes/no). A Fisher exact test of independence was used in any case where the proportion for the device and patient satisfaction was 25% or higher. To determine whether the communication method was associated with increased time savings, 7 total Cochran-Armitage tests were conducted, 1 for each device. A logistic regression model was used to determine whether there was a significant association between the number of stations and the likelihood of reporting patient satisfaction.
Results
Eighty-eight surgeons responded to the survey, with a response rate of 5% (88/1735). A total of 55 surgeons completed the survey in its entirety and were included in the data analysis. The most commonly used communication mediums were whiteboards (29/55 [53%]), followed by a flag system (16/55 [29%]) and a light system (13/55 [24%]). Most Mohs surgeons (52/55 [95%]) used the communication media to communicate with their staff only, and 76% (42/55) of Mohs surgeons believed that their communication media contributed to higher patient satisfaction. Overall, 58% (32/55) of Mohs surgeons stated that their communication media saved more than 15 minutes (on average) per day. The use of a whiteboard and/or flag system was reported as the least efficient method, with average daily time savings of 13 minutes. With the introduction of newer technology (wired or wireless intercoms, headsets, walkie-talkies, or internal messaging systems such as Skype) to the whiteboard and/or flag system, the time savings increased by 10 minutes per day. Nearly 25% (14/55) of surgeons utilized more than 1 communication system.
As the number of stations in an MMS suite increased, the probability of using a whiteboard to track the progress of the cases decreased. There were no statistically significant associations identified between the number of stations and the use of other communication devices (ie, flag system, light system, wireless intercom, wired intercom, walkie-talkie, headset). The stratified percentages of the amount of time savings for each communication modality are presented in the Figure (whiteboards and headsets were excluded because they did not increase time savings). The use of a light system was the only communication modality found to be statistically associated with an increase in provider-reported time savings (P=.0482; Figure). In addition, our analysis did not show an improvement in provider-reported patient satisfaction with any of the current systems used in MMS clinics.
Comment
The process of transmitting information among the medical team during MMS is a complex interplay involving the relay of crucial information, with many opportunities for the introduction of distraction and error. Despite numerous improvements in the efficiency of the preparation of histological specimens and implementation of various time-saving and tissue-saving surgical interventions, relatively little attention has been given to address the sometimes chaotic and challenging process of organizing results from each stage of multiple patients in an MMS surgical suite.5
As demonstrated by our survey, incorporation of a light-based system into an MMS clinic may improve workplace efficiency by decreasing the redundant use of support staff and allowing Mohs surgeons to transition from one station to the next seamlessly. Light-based communication systems provide an immediate notification for support staff via color-coded and/or numerically coded indicators on input switches located outside and inside the examination/surgery rooms. The switch indicators can be depressed with minimal disruption from station to station, thereby foregoing the need to interrupt an ongoing excision or closure to convey the status of the case. These systems may then permit enhanced clinic and workflow efficiency, which may help to shorten patient wait times.
Study Limitation
Although all members of the American College of Mohs Surgery were invited to participate in this online survey, only a small number (N=55) completed it in its entirety. Moreover, sample sizes for some of the communication devices were small. As a result, many of the tests might be lacking sufficient power to detect possible relationships, which might be identified in future larger-scale studies.
Conclusion
Our study supports the use of light-based communication systems in MMS suites to improve efficiency in the clinic. Based on our analysis, light-based communication methods were significantly associated with improved time savings (P=.0482). Our study did not show an improvement in provider-reported satisfaction with any of the current systems used in MMS clinics. We hope that this information will help guide providers in implementing new communication techniques to improve clinic efficiency.
Acknowledgments
The authors would like to thank Ms. Kathy Kyler (Oklahoma City, Oklahoma) for her assistance in preparing this manuscript. Support for Dr. Chen and Mr. Stubblefield was provided through National Institutes of Health, National Institute of General Medical Sciences [Grant 2U54GM104938-06, PI Judith James].
- Chen T, Vines L, Wanitphakdeedecha R, et al. Electronically linked: wireless, discrete, hands-free communication to improve surgical workflow in Mohs and dermasurgery clinic. Dermatol Surg. 2009;35:248-252.
- Lanto AB, Yano EM, Fink A, et al. Anatomy of an outpatient visit. An evaluation of clinic efficiency in general and subspecialty clinics. Med Group Manage J. 1995;42:18-25.
- Kantor J. Application of Google Glass to Mohs micrographic surgery: a pilot study in 120 patients. Dermatol Surg. 2015;41:288-289.
- Spurk PA, Mohr ML, Seroka AM, et al. The impact of a wireless telecommunication system on efficiency. J Nurs Admin. 1995;25:21-26.
- Dietert JB, MacFarlane DF. A survey of Mohs tissue tracking practices. Dermatol Surg. 2019;45:514-518.
- Chen T, Vines L, Wanitphakdeedecha R, et al. Electronically linked: wireless, discrete, hands-free communication to improve surgical workflow in Mohs and dermasurgery clinic. Dermatol Surg. 2009;35:248-252.
- Lanto AB, Yano EM, Fink A, et al. Anatomy of an outpatient visit. An evaluation of clinic efficiency in general and subspecialty clinics. Med Group Manage J. 1995;42:18-25.
- Kantor J. Application of Google Glass to Mohs micrographic surgery: a pilot study in 120 patients. Dermatol Surg. 2015;41:288-289.
- Spurk PA, Mohr ML, Seroka AM, et al. The impact of a wireless telecommunication system on efficiency. J Nurs Admin. 1995;25:21-26.
- Dietert JB, MacFarlane DF. A survey of Mohs tissue tracking practices. Dermatol Surg. 2019;45:514-518.
Practice Points
- There are limited studies evaluating the efficacy of different communication methods in Mohs micrographic surgery (MMS) clinics.
- This study suggests that incorporation of a light-based system into an MMS clinic improves workplace efficiency.
Applying a Text-Search Algorithm to Radiology Reports Can Find More Patients With Pulmonary Nodules Than Radiology Coding Alone (FULL)
Rapid advances in imaging technology have led to better spatial resolution with lower radiation doses to patients. These advances have helped to increase the use of diagnostic chest imaging, particularly in emergency departments and oncology centers, and in screening for coronary artery disease. As a result, there has been an explosion of incidental findings on chest imaging—including indeterminate lung nodules.1,2
Lung nodules are rounded and well-circumscribed lung opacities (≤ 3 cm in diameter) that may present as solitary or multiple lesions in usually asymptomatic patients. Most lung nodules are benign, the result of an infectious or inflammatory process. Nodules that are ≤ 8 mm in diameter, unless they show increase in size over time, often can be safely followed with imaging surveillance. In contrast, lung nodules > 8 mm could represent an early-stage lung cancer, especially among patients with high-risk for developing lung cancer (ie, those with advanced age, heavy tobacco abuse, or emphysema) and should be further assessed with close imaging surveillance, either chest computed tomography (CT) alone or positron-emission tomography (PET)/CT, or tissue biopsy, based on the underlying likelihood of malignancy.
Patients who receive an early-stage lung cancer diagnosis can be offered curative treatments leading to improved 5-year survival rates.3,4 Consequently, health care systems need to be able to identify these nodules accurately, in order to categorize and manage them accordingly to the Fleischner radiographic and American College of Chest Physicians clinical guidelines.5,6 Unfortunately, many hospitals struggle to identify patients with incidental lung nodules found during diagnostic chest and abdominal imaging, due in part to poor adherence to Fleischner guidelines among radiologists for categorizing pulmonary nodules.7,8
The Veterans Health Administration (VHA) system is interested in effectively detecting patients with incidental lung nodules. Veterans have a higher risk of developing lung cancer when compared with the entire US population, mainly due to a higher incidence of tobacco use.6 The prevalence of lung nodules among veterans with significant risk factors for lung cancer is about 60% nationwide, and up to 85% in the Midwest, due to the high prevalence of histoplasmosis.7 However, only a small percentage of these nodules represent an early stage primary lung cancer.
Several Veterans Integrated Service Networks (VISNs) in the VHA use a radiology diagnostic code to systematically identify imaging studies with presence of lung nodules. In VISN 23, which includes Minnesota, North Dakota, South Dakota, Iowa, and portions of neighboring states, the code used to identify these radiology studies is 44. However, there is high variability in the reporting and coding of imaging studies among radiologists, which could lead to misclassifying patients with lung nodules.8
Some studies suggest that using an automated text search algorithm within radiology reports can be a highly effective strategy to identify patients with lung nodules.9,10 In this study, we compared the diagnostic performance of a newly developed text search algorithm applied to radiology reports with the current standard practice of using a radiology diagnostic code for identifying patients with lung nodules at the Iowa City US Department of Veterans Affairs (VA) Health Care System (ICVAHCS) hospital in Iowa.
Methods
Since 2014, The ICVAHCS has used a radiology diagnostic code to identify any imaging studies with lung nodules. The radiologist enters “44” at the end of the reading process using the Nuance Powerscribe 360 radiation reporting system. The code is uploaded into the VHA Corporate Data Warehouse (CDW), and it is located within the radiology exam domain. This strategy was created and implemented by the Minneapolis VA Health Care System in Minnesota for all the VA hospitals in VISN 23. A lung nodule registry nurse was provided with a list of radiology studies flagged with this radiology diagnostic code every 2 weeks. A chart review was then performed for all these studies to determine the presence of a lung nodule. When detected, the ordering health care provider was alerted and given recommendations for managing the nodule.
We initially searched for the radiology studies with a presumptive lung nodule using the radiology code 44 within the CDW. Separately, we applied the text search strategy only to radiology reports from chest and abdomen studies (ie, X-rays, CT, magnetic resonance imaging [MRI], and PET) that contained any of the keyword phrases. The text search strategy was modeled based on a natural language processing (NLP) algorithm developed by the Puget Sound VA Healthcare System in Seattle, Washington to identify lung nodules on radiology reports.9 Our algorithm included a series of text searches using Microsoft SQL. After several simulations using a random group of radiology reports, we chose the keywords: “lung AND nodul”; “pulm AND nodul”; “pulm AND mass”; “lung AND mass”; and “ground glass”. We selected only chest and abdomen studies because on several simulations using a random group of radiology reports, the vast majority of lung nodules were identified on chest and abdomen imaging studies. Also, it would not have been feasible to chart review the approximately 30,000 total radiology reports that were generated during the study period.
From January 1, 2016 through November 30, 2016, we applied both search strategies independently: radiology diagnostic code for lung nodules to all imaging studies, and text search to all radiology reports of chest and abdomen imaging studies in the CDW (Figure). We also collected demographic (eg, age, sex, race, rurality) and clinical (eg, medical comorbidities, tobacco use) information that were uploaded to the database automatically from CDW using International Statistical Classification of Diseases, Tenth Edition and demographic codes. The VHA uses the Rural-Urban Commuting Areas (RUCA) system to define rurality, which takes into account population density and how closely a community is linked socioeconomically to larger urban centers.11 The protocol was reviewed and approved by the institutional review board of ICVAHCS and the University of Iowa.
The presence of a lung nodule was established by having the lung nodule registry nurse manually review the charts of every patient with a radiology report identified by either code 44 or the text search algorithm. The goal was to ensure that our text search strategy identified all reports with a code 44 to be compliant with VISN expectations. Cases in which a lung nodule was described in the radiology report were considered true positives, and those without a lung nodule description were considered false positives.
We compared the sociodemographic and clinical characteristics of patients with lung nodules between those identified with both code 44 and the text search and those identified with the text search alone. We used χ2 tests for categorical variables (eg, age, gender, RUCA, chronic obstructive pulmonary disease (COPD), smoking status) and t tests for continuous variables (eg, Charlson comorbidity score). A P value ≤ .05 was considered statistically significant. To assess the yield of each search strategy, we determined the number of patients with lung nodules detected by the text search and the radiology diagnostic code. We also calculated the positive predictive value (PPV) and 95% CI of each search strategy.
Results
We identified 12,983 radiology studies that required manual review during the study period. We confirmed that 8,516 imaging studies had lung nodules, representing 2,912 patients. Subjects with lung nodules were predominantly male (96%), aged between 60 and 79 years (71%), and lived in a rural area (72%). More than 50% of these patients had COPD and over a third were current smokers (Table 1). The text search algorithm identified all of the patients identified by the radiology diagnostic code (n = 1,251). It also identified an additional 1,661 patients with lung nodules that otherwise would have been missed by the radiology code. Compared with those identified only by the text search, those identified by both the radiology coding and text search were older, had lower Charlson comorbidity scores, and were more likely to be a current smoker.
The text search algorithm identified more than twice as many patients with potential lung nodules compared with the radiology diagnostic code (4,071 vs 1,363) (Table 2). However, the text search algorithm was associated with a much higher number of false positives than was the diagnostic code (1,159 vs 112) and a lower PPV (72% [95% CI, 70.6-73.4] vs 92% [95% CI, 90.6-93.4], respectively). The text search algorithm identified 130 patients with lung nodules of moderate to high risk for malignancy (> 8 mm diameter) that were not identified by the radiology code. When the PPV of each search strategy was calculated based on imaging studies with nodules (most patients had > 1 imaging study), the results remained similar (98% for radiology code and 66% for text search). A larger proportion of the lung nodules detected by code 44 vs the text search algorithm were from CT chest studies.
Discussion
In a population of predominantly older male veterans with significant risk factors for lung cancer and high incidence of incidental lung nodules, applying a text search algorithm on radiology reports identified a substantial number of patients with lung nodules, including some with nodules > 8 mm, that were missed by the radiologist-generated code.9,10 Improving the yield of detection for lung nodules in a population with high risk for lung cancer would increase the likelihood of detecting patients with potentially curable early-stage lung cancers, decreasing lung cancer mortality.
The reasons for the high number of patients with lung nodules missed by the radiology code are unclear. Potential explanations may include the lack of standardization of imaging reports by the radiologists (ie, only 21% of chest CTs used a standardized template describing a lung nodule in our study), a problem well recognized both within and outside VHA.8,12
The text search algorithm identified more patients with lung nodules but had a higher rate of false positives when compared with the diagnostic code. The high rate of false positives resulted in more charts to review and an increased workload for the lung nodule registry team. The challenges presented by an increased workload should be balanced against the potential harms of missing nodules that develop into advanced cancer.
Text Search Adjustments
Refining the text search criteria algorithm and the chart review process may decrease the rate of false positives significantly without affecting detection of lung nodules. In subsequent simulations, we found that by adding an exclusion criteria to text search algorithm to remove reports with specific keywords we could substantially reduce the number of false positive reports without affecting the detection rate of the lung nodules. These exclusion criteria would exclude any reports that: (1) contain “nodul” within the next 8 words after mentioning “no”; (2) contain “clear” within the next 8 words after mentioning “lung” in the text (eg, “lungs appear to be clear”); (3) contain “clear” within the next 4 words after mentioning “otherwise” in the text (eg, “otherwise appear to be clear”). Based on our study results, we further refined the text search strategy by limiting the search to only chest imaging studies. When we applied the revised algorithm to a random sample of imaging reports, we found all the code 44 radiology reports were still captured, but we were able to reduce the number of radiology reports needing review by about 80%.
Although classification approaches are being refined to improve radiology performance in multiple categories of nodules, this study suggests that alternative approaches based on text algorithms can improve the capture of pulmonary nodules that require surveillance. These algorithms also can be used to augment radiologist reporting systems. This represents an investment in resources to build a team that should include a bioinformatics specialist, lung nodule registry personnel (review charts of the detected imaging studies with lung nodules, populating the lung nodule database, and determining and tracking the need of imaging follow up), a lung nodule clinic nurse coordinator, and a dedicated lung nodule clinic pulmonologist.
Radiology departments could employ this text search approach to identify missed nodules and use an audit and feedback system to train radiologists to code lung nodules consistently at the time of the initial reading to avoid delays in identifying patients with nodules. Alternatively, the more widespread use of a standardized CT chest radiology reports using Fleischner or the American College of Radiology Lung Imaging Reporting and Data System (Lung RADS) templates might improve the detection of patients with lung nodules.5,13,14
The VHA system should have an effective strategy for identifying incidental lung nodules during routine radiology examinations. Relying only on radiologists to identify and code pulmonary nodules can lead to missing a significant number of patients with lung nodules and some patients with early stage lung cancer who could receive curative therapy.12,14-16 The use of a standardized algorithm, like a text search strategy, might decrease the risk of variation in the execution and result in a more sensitive detection of patients with lung nodules. The text search strategy might be easily implemented and shared with other hospitals both within and outside the VHA.
Limitations
This study was performed in a single VHA hospital and the findings may not be generalizable to other settings of care. Second, our study design is susceptible to work-up bias because the results of a diagnostic test (eg, chest or abdomen imaging) affected whether the chart review was used to verify the test result. It was not feasible to review the patient records of all radiology studies done at the facility during the study period, consequently complete 2 × 2 tables could not be created to calculate sensitivity, specificity, and negative predictive value.
Conclusion
A text search algorithm of radiology reports increased the detection of patients with lung nodules when compared with radiology diagnostic coding alone. However, the improved detection was associated with a higher rate of false positives, which requires manually reviewing a larger number of patient’s chart reports. Future research and quality improvement should focus on standardizing the radiology reporting process and improving the efficiency and reliability of follow up and tracking of incidental lung nodules.
Acknowledgments
The work reported here was supported by a grant from the Office of Rural Health (N32-FY16Q1-S1-P01577), US Department of Veterans Affairs, Veterans Health Administration. We also had the support from the Veterans Rural Health Resource Center-Iowa City, and the Health Services Research and Development (HSR&D) Service through the Comprehensive Access and Delivery Research and Evaluation (CADRE) Center (REA 09-220).
1. Jacobs PC, Mali WP, Grobbee DE, van der Graaf Y. Prevalence of incidental findings in computed tomographic screening of the chest: a systematic review. Journal of computer assisted tomography. 2008;32(2):214-221.
2. Frank L, Quint LE. Chest CT incidentalomas: thyroid lesions, enlarged mediastinal lymph nodes, and lung nodules. Cancer Imaging. 2012;12(1):41-48.
3. National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Cancer stat facts: lung and bronchus cancer. https://seer.cancer.gov/statfacts/html/lungb.html. Accessed April 8, 2020.
4. Alberg AJ, Brock MV, Ford JG, Samet JM, Spivack SD. Epidemiology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e1S-e29S.
5. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
6. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701.
7. Kinsinger LS, Anderson C, Kim J, et al. Implementation of lung cancer screening in the Veterans Health Administration. JAMA Intern Med. 2017;177(3):399-406.
8. Iqbal MN, Stott E, Huml AM, et al. What’s in a name? Factors associated with documentation and evaluation of incidental pulmonary nodules. Ann Am Thorac Soc. 2016;13(10):1704-1711.
9. Farjah F, Halgrim S, Buist DS, et al. An automated method for identifying individuals with a lung nodule can be feasibly implemented across health systems. Egems (Wash DC). 2016;4(1):1254.
10. Danforth KN, Early MI, Ngan S, Kosco AE, Zheng C, Gould MK. Automated identification of patients with pulmonary nodules in an integrated health system using administrative health plan data, radiology reports, and natural language processing. J Thorac Oncol. 2012;7(8):1257-1262.
11. US Department of Veterans Affairs, Office of Rural Health. https://www.ruralhealth.va.gov/aboutus/ruralvets.asp. Updated January 28, 2020. Accessed April 8, 2020.
12. Blagev DP, Lloyd JF, Conner K, et al. Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol. 2016;13(2 suppl):R18-R24.
13. Eisenberg RL, Fleischner S. Ways to improve radiologists’ adherence to Fleischner Society guidelines for management of pulmonary nodules. J Am Coll Radiol. 2013;10(6):439-441.
14. Aberle DR. Implementing lung cancer screening: the US experience. Clin Radiol. 2017;72(5):401-406.
15. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e93S-e120S.
16. Callister ME, Baldwin DR. How should pulmonary nodules be optimally investigated and managed? Lung Cancer. 2016;91:48-55.
Rapid advances in imaging technology have led to better spatial resolution with lower radiation doses to patients. These advances have helped to increase the use of diagnostic chest imaging, particularly in emergency departments and oncology centers, and in screening for coronary artery disease. As a result, there has been an explosion of incidental findings on chest imaging—including indeterminate lung nodules.1,2
Lung nodules are rounded and well-circumscribed lung opacities (≤ 3 cm in diameter) that may present as solitary or multiple lesions in usually asymptomatic patients. Most lung nodules are benign, the result of an infectious or inflammatory process. Nodules that are ≤ 8 mm in diameter, unless they show increase in size over time, often can be safely followed with imaging surveillance. In contrast, lung nodules > 8 mm could represent an early-stage lung cancer, especially among patients with high-risk for developing lung cancer (ie, those with advanced age, heavy tobacco abuse, or emphysema) and should be further assessed with close imaging surveillance, either chest computed tomography (CT) alone or positron-emission tomography (PET)/CT, or tissue biopsy, based on the underlying likelihood of malignancy.
Patients who receive an early-stage lung cancer diagnosis can be offered curative treatments leading to improved 5-year survival rates.3,4 Consequently, health care systems need to be able to identify these nodules accurately, in order to categorize and manage them accordingly to the Fleischner radiographic and American College of Chest Physicians clinical guidelines.5,6 Unfortunately, many hospitals struggle to identify patients with incidental lung nodules found during diagnostic chest and abdominal imaging, due in part to poor adherence to Fleischner guidelines among radiologists for categorizing pulmonary nodules.7,8
The Veterans Health Administration (VHA) system is interested in effectively detecting patients with incidental lung nodules. Veterans have a higher risk of developing lung cancer when compared with the entire US population, mainly due to a higher incidence of tobacco use.6 The prevalence of lung nodules among veterans with significant risk factors for lung cancer is about 60% nationwide, and up to 85% in the Midwest, due to the high prevalence of histoplasmosis.7 However, only a small percentage of these nodules represent an early stage primary lung cancer.
Several Veterans Integrated Service Networks (VISNs) in the VHA use a radiology diagnostic code to systematically identify imaging studies with presence of lung nodules. In VISN 23, which includes Minnesota, North Dakota, South Dakota, Iowa, and portions of neighboring states, the code used to identify these radiology studies is 44. However, there is high variability in the reporting and coding of imaging studies among radiologists, which could lead to misclassifying patients with lung nodules.8
Some studies suggest that using an automated text search algorithm within radiology reports can be a highly effective strategy to identify patients with lung nodules.9,10 In this study, we compared the diagnostic performance of a newly developed text search algorithm applied to radiology reports with the current standard practice of using a radiology diagnostic code for identifying patients with lung nodules at the Iowa City US Department of Veterans Affairs (VA) Health Care System (ICVAHCS) hospital in Iowa.
Methods
Since 2014, The ICVAHCS has used a radiology diagnostic code to identify any imaging studies with lung nodules. The radiologist enters “44” at the end of the reading process using the Nuance Powerscribe 360 radiation reporting system. The code is uploaded into the VHA Corporate Data Warehouse (CDW), and it is located within the radiology exam domain. This strategy was created and implemented by the Minneapolis VA Health Care System in Minnesota for all the VA hospitals in VISN 23. A lung nodule registry nurse was provided with a list of radiology studies flagged with this radiology diagnostic code every 2 weeks. A chart review was then performed for all these studies to determine the presence of a lung nodule. When detected, the ordering health care provider was alerted and given recommendations for managing the nodule.
We initially searched for the radiology studies with a presumptive lung nodule using the radiology code 44 within the CDW. Separately, we applied the text search strategy only to radiology reports from chest and abdomen studies (ie, X-rays, CT, magnetic resonance imaging [MRI], and PET) that contained any of the keyword phrases. The text search strategy was modeled based on a natural language processing (NLP) algorithm developed by the Puget Sound VA Healthcare System in Seattle, Washington to identify lung nodules on radiology reports.9 Our algorithm included a series of text searches using Microsoft SQL. After several simulations using a random group of radiology reports, we chose the keywords: “lung AND nodul”; “pulm AND nodul”; “pulm AND mass”; “lung AND mass”; and “ground glass”. We selected only chest and abdomen studies because on several simulations using a random group of radiology reports, the vast majority of lung nodules were identified on chest and abdomen imaging studies. Also, it would not have been feasible to chart review the approximately 30,000 total radiology reports that were generated during the study period.
From January 1, 2016 through November 30, 2016, we applied both search strategies independently: radiology diagnostic code for lung nodules to all imaging studies, and text search to all radiology reports of chest and abdomen imaging studies in the CDW (Figure). We also collected demographic (eg, age, sex, race, rurality) and clinical (eg, medical comorbidities, tobacco use) information that were uploaded to the database automatically from CDW using International Statistical Classification of Diseases, Tenth Edition and demographic codes. The VHA uses the Rural-Urban Commuting Areas (RUCA) system to define rurality, which takes into account population density and how closely a community is linked socioeconomically to larger urban centers.11 The protocol was reviewed and approved by the institutional review board of ICVAHCS and the University of Iowa.
The presence of a lung nodule was established by having the lung nodule registry nurse manually review the charts of every patient with a radiology report identified by either code 44 or the text search algorithm. The goal was to ensure that our text search strategy identified all reports with a code 44 to be compliant with VISN expectations. Cases in which a lung nodule was described in the radiology report were considered true positives, and those without a lung nodule description were considered false positives.
We compared the sociodemographic and clinical characteristics of patients with lung nodules between those identified with both code 44 and the text search and those identified with the text search alone. We used χ2 tests for categorical variables (eg, age, gender, RUCA, chronic obstructive pulmonary disease (COPD), smoking status) and t tests for continuous variables (eg, Charlson comorbidity score). A P value ≤ .05 was considered statistically significant. To assess the yield of each search strategy, we determined the number of patients with lung nodules detected by the text search and the radiology diagnostic code. We also calculated the positive predictive value (PPV) and 95% CI of each search strategy.
Results
We identified 12,983 radiology studies that required manual review during the study period. We confirmed that 8,516 imaging studies had lung nodules, representing 2,912 patients. Subjects with lung nodules were predominantly male (96%), aged between 60 and 79 years (71%), and lived in a rural area (72%). More than 50% of these patients had COPD and over a third were current smokers (Table 1). The text search algorithm identified all of the patients identified by the radiology diagnostic code (n = 1,251). It also identified an additional 1,661 patients with lung nodules that otherwise would have been missed by the radiology code. Compared with those identified only by the text search, those identified by both the radiology coding and text search were older, had lower Charlson comorbidity scores, and were more likely to be a current smoker.
The text search algorithm identified more than twice as many patients with potential lung nodules compared with the radiology diagnostic code (4,071 vs 1,363) (Table 2). However, the text search algorithm was associated with a much higher number of false positives than was the diagnostic code (1,159 vs 112) and a lower PPV (72% [95% CI, 70.6-73.4] vs 92% [95% CI, 90.6-93.4], respectively). The text search algorithm identified 130 patients with lung nodules of moderate to high risk for malignancy (> 8 mm diameter) that were not identified by the radiology code. When the PPV of each search strategy was calculated based on imaging studies with nodules (most patients had > 1 imaging study), the results remained similar (98% for radiology code and 66% for text search). A larger proportion of the lung nodules detected by code 44 vs the text search algorithm were from CT chest studies.
Discussion
In a population of predominantly older male veterans with significant risk factors for lung cancer and high incidence of incidental lung nodules, applying a text search algorithm on radiology reports identified a substantial number of patients with lung nodules, including some with nodules > 8 mm, that were missed by the radiologist-generated code.9,10 Improving the yield of detection for lung nodules in a population with high risk for lung cancer would increase the likelihood of detecting patients with potentially curable early-stage lung cancers, decreasing lung cancer mortality.
The reasons for the high number of patients with lung nodules missed by the radiology code are unclear. Potential explanations may include the lack of standardization of imaging reports by the radiologists (ie, only 21% of chest CTs used a standardized template describing a lung nodule in our study), a problem well recognized both within and outside VHA.8,12
The text search algorithm identified more patients with lung nodules but had a higher rate of false positives when compared with the diagnostic code. The high rate of false positives resulted in more charts to review and an increased workload for the lung nodule registry team. The challenges presented by an increased workload should be balanced against the potential harms of missing nodules that develop into advanced cancer.
Text Search Adjustments
Refining the text search criteria algorithm and the chart review process may decrease the rate of false positives significantly without affecting detection of lung nodules. In subsequent simulations, we found that by adding an exclusion criteria to text search algorithm to remove reports with specific keywords we could substantially reduce the number of false positive reports without affecting the detection rate of the lung nodules. These exclusion criteria would exclude any reports that: (1) contain “nodul” within the next 8 words after mentioning “no”; (2) contain “clear” within the next 8 words after mentioning “lung” in the text (eg, “lungs appear to be clear”); (3) contain “clear” within the next 4 words after mentioning “otherwise” in the text (eg, “otherwise appear to be clear”). Based on our study results, we further refined the text search strategy by limiting the search to only chest imaging studies. When we applied the revised algorithm to a random sample of imaging reports, we found all the code 44 radiology reports were still captured, but we were able to reduce the number of radiology reports needing review by about 80%.
Although classification approaches are being refined to improve radiology performance in multiple categories of nodules, this study suggests that alternative approaches based on text algorithms can improve the capture of pulmonary nodules that require surveillance. These algorithms also can be used to augment radiologist reporting systems. This represents an investment in resources to build a team that should include a bioinformatics specialist, lung nodule registry personnel (review charts of the detected imaging studies with lung nodules, populating the lung nodule database, and determining and tracking the need of imaging follow up), a lung nodule clinic nurse coordinator, and a dedicated lung nodule clinic pulmonologist.
Radiology departments could employ this text search approach to identify missed nodules and use an audit and feedback system to train radiologists to code lung nodules consistently at the time of the initial reading to avoid delays in identifying patients with nodules. Alternatively, the more widespread use of a standardized CT chest radiology reports using Fleischner or the American College of Radiology Lung Imaging Reporting and Data System (Lung RADS) templates might improve the detection of patients with lung nodules.5,13,14
The VHA system should have an effective strategy for identifying incidental lung nodules during routine radiology examinations. Relying only on radiologists to identify and code pulmonary nodules can lead to missing a significant number of patients with lung nodules and some patients with early stage lung cancer who could receive curative therapy.12,14-16 The use of a standardized algorithm, like a text search strategy, might decrease the risk of variation in the execution and result in a more sensitive detection of patients with lung nodules. The text search strategy might be easily implemented and shared with other hospitals both within and outside the VHA.
Limitations
This study was performed in a single VHA hospital and the findings may not be generalizable to other settings of care. Second, our study design is susceptible to work-up bias because the results of a diagnostic test (eg, chest or abdomen imaging) affected whether the chart review was used to verify the test result. It was not feasible to review the patient records of all radiology studies done at the facility during the study period, consequently complete 2 × 2 tables could not be created to calculate sensitivity, specificity, and negative predictive value.
Conclusion
A text search algorithm of radiology reports increased the detection of patients with lung nodules when compared with radiology diagnostic coding alone. However, the improved detection was associated with a higher rate of false positives, which requires manually reviewing a larger number of patient’s chart reports. Future research and quality improvement should focus on standardizing the radiology reporting process and improving the efficiency and reliability of follow up and tracking of incidental lung nodules.
Acknowledgments
The work reported here was supported by a grant from the Office of Rural Health (N32-FY16Q1-S1-P01577), US Department of Veterans Affairs, Veterans Health Administration. We also had the support from the Veterans Rural Health Resource Center-Iowa City, and the Health Services Research and Development (HSR&D) Service through the Comprehensive Access and Delivery Research and Evaluation (CADRE) Center (REA 09-220).
Rapid advances in imaging technology have led to better spatial resolution with lower radiation doses to patients. These advances have helped to increase the use of diagnostic chest imaging, particularly in emergency departments and oncology centers, and in screening for coronary artery disease. As a result, there has been an explosion of incidental findings on chest imaging—including indeterminate lung nodules.1,2
Lung nodules are rounded and well-circumscribed lung opacities (≤ 3 cm in diameter) that may present as solitary or multiple lesions in usually asymptomatic patients. Most lung nodules are benign, the result of an infectious or inflammatory process. Nodules that are ≤ 8 mm in diameter, unless they show increase in size over time, often can be safely followed with imaging surveillance. In contrast, lung nodules > 8 mm could represent an early-stage lung cancer, especially among patients with high-risk for developing lung cancer (ie, those with advanced age, heavy tobacco abuse, or emphysema) and should be further assessed with close imaging surveillance, either chest computed tomography (CT) alone or positron-emission tomography (PET)/CT, or tissue biopsy, based on the underlying likelihood of malignancy.
Patients who receive an early-stage lung cancer diagnosis can be offered curative treatments leading to improved 5-year survival rates.3,4 Consequently, health care systems need to be able to identify these nodules accurately, in order to categorize and manage them accordingly to the Fleischner radiographic and American College of Chest Physicians clinical guidelines.5,6 Unfortunately, many hospitals struggle to identify patients with incidental lung nodules found during diagnostic chest and abdominal imaging, due in part to poor adherence to Fleischner guidelines among radiologists for categorizing pulmonary nodules.7,8
The Veterans Health Administration (VHA) system is interested in effectively detecting patients with incidental lung nodules. Veterans have a higher risk of developing lung cancer when compared with the entire US population, mainly due to a higher incidence of tobacco use.6 The prevalence of lung nodules among veterans with significant risk factors for lung cancer is about 60% nationwide, and up to 85% in the Midwest, due to the high prevalence of histoplasmosis.7 However, only a small percentage of these nodules represent an early stage primary lung cancer.
Several Veterans Integrated Service Networks (VISNs) in the VHA use a radiology diagnostic code to systematically identify imaging studies with presence of lung nodules. In VISN 23, which includes Minnesota, North Dakota, South Dakota, Iowa, and portions of neighboring states, the code used to identify these radiology studies is 44. However, there is high variability in the reporting and coding of imaging studies among radiologists, which could lead to misclassifying patients with lung nodules.8
Some studies suggest that using an automated text search algorithm within radiology reports can be a highly effective strategy to identify patients with lung nodules.9,10 In this study, we compared the diagnostic performance of a newly developed text search algorithm applied to radiology reports with the current standard practice of using a radiology diagnostic code for identifying patients with lung nodules at the Iowa City US Department of Veterans Affairs (VA) Health Care System (ICVAHCS) hospital in Iowa.
Methods
Since 2014, The ICVAHCS has used a radiology diagnostic code to identify any imaging studies with lung nodules. The radiologist enters “44” at the end of the reading process using the Nuance Powerscribe 360 radiation reporting system. The code is uploaded into the VHA Corporate Data Warehouse (CDW), and it is located within the radiology exam domain. This strategy was created and implemented by the Minneapolis VA Health Care System in Minnesota for all the VA hospitals in VISN 23. A lung nodule registry nurse was provided with a list of radiology studies flagged with this radiology diagnostic code every 2 weeks. A chart review was then performed for all these studies to determine the presence of a lung nodule. When detected, the ordering health care provider was alerted and given recommendations for managing the nodule.
We initially searched for the radiology studies with a presumptive lung nodule using the radiology code 44 within the CDW. Separately, we applied the text search strategy only to radiology reports from chest and abdomen studies (ie, X-rays, CT, magnetic resonance imaging [MRI], and PET) that contained any of the keyword phrases. The text search strategy was modeled based on a natural language processing (NLP) algorithm developed by the Puget Sound VA Healthcare System in Seattle, Washington to identify lung nodules on radiology reports.9 Our algorithm included a series of text searches using Microsoft SQL. After several simulations using a random group of radiology reports, we chose the keywords: “lung AND nodul”; “pulm AND nodul”; “pulm AND mass”; “lung AND mass”; and “ground glass”. We selected only chest and abdomen studies because on several simulations using a random group of radiology reports, the vast majority of lung nodules were identified on chest and abdomen imaging studies. Also, it would not have been feasible to chart review the approximately 30,000 total radiology reports that were generated during the study period.
From January 1, 2016 through November 30, 2016, we applied both search strategies independently: radiology diagnostic code for lung nodules to all imaging studies, and text search to all radiology reports of chest and abdomen imaging studies in the CDW (Figure). We also collected demographic (eg, age, sex, race, rurality) and clinical (eg, medical comorbidities, tobacco use) information that were uploaded to the database automatically from CDW using International Statistical Classification of Diseases, Tenth Edition and demographic codes. The VHA uses the Rural-Urban Commuting Areas (RUCA) system to define rurality, which takes into account population density and how closely a community is linked socioeconomically to larger urban centers.11 The protocol was reviewed and approved by the institutional review board of ICVAHCS and the University of Iowa.
The presence of a lung nodule was established by having the lung nodule registry nurse manually review the charts of every patient with a radiology report identified by either code 44 or the text search algorithm. The goal was to ensure that our text search strategy identified all reports with a code 44 to be compliant with VISN expectations. Cases in which a lung nodule was described in the radiology report were considered true positives, and those without a lung nodule description were considered false positives.
We compared the sociodemographic and clinical characteristics of patients with lung nodules between those identified with both code 44 and the text search and those identified with the text search alone. We used χ2 tests for categorical variables (eg, age, gender, RUCA, chronic obstructive pulmonary disease (COPD), smoking status) and t tests for continuous variables (eg, Charlson comorbidity score). A P value ≤ .05 was considered statistically significant. To assess the yield of each search strategy, we determined the number of patients with lung nodules detected by the text search and the radiology diagnostic code. We also calculated the positive predictive value (PPV) and 95% CI of each search strategy.
Results
We identified 12,983 radiology studies that required manual review during the study period. We confirmed that 8,516 imaging studies had lung nodules, representing 2,912 patients. Subjects with lung nodules were predominantly male (96%), aged between 60 and 79 years (71%), and lived in a rural area (72%). More than 50% of these patients had COPD and over a third were current smokers (Table 1). The text search algorithm identified all of the patients identified by the radiology diagnostic code (n = 1,251). It also identified an additional 1,661 patients with lung nodules that otherwise would have been missed by the radiology code. Compared with those identified only by the text search, those identified by both the radiology coding and text search were older, had lower Charlson comorbidity scores, and were more likely to be a current smoker.
The text search algorithm identified more than twice as many patients with potential lung nodules compared with the radiology diagnostic code (4,071 vs 1,363) (Table 2). However, the text search algorithm was associated with a much higher number of false positives than was the diagnostic code (1,159 vs 112) and a lower PPV (72% [95% CI, 70.6-73.4] vs 92% [95% CI, 90.6-93.4], respectively). The text search algorithm identified 130 patients with lung nodules of moderate to high risk for malignancy (> 8 mm diameter) that were not identified by the radiology code. When the PPV of each search strategy was calculated based on imaging studies with nodules (most patients had > 1 imaging study), the results remained similar (98% for radiology code and 66% for text search). A larger proportion of the lung nodules detected by code 44 vs the text search algorithm were from CT chest studies.
Discussion
In a population of predominantly older male veterans with significant risk factors for lung cancer and high incidence of incidental lung nodules, applying a text search algorithm on radiology reports identified a substantial number of patients with lung nodules, including some with nodules > 8 mm, that were missed by the radiologist-generated code.9,10 Improving the yield of detection for lung nodules in a population with high risk for lung cancer would increase the likelihood of detecting patients with potentially curable early-stage lung cancers, decreasing lung cancer mortality.
The reasons for the high number of patients with lung nodules missed by the radiology code are unclear. Potential explanations may include the lack of standardization of imaging reports by the radiologists (ie, only 21% of chest CTs used a standardized template describing a lung nodule in our study), a problem well recognized both within and outside VHA.8,12
The text search algorithm identified more patients with lung nodules but had a higher rate of false positives when compared with the diagnostic code. The high rate of false positives resulted in more charts to review and an increased workload for the lung nodule registry team. The challenges presented by an increased workload should be balanced against the potential harms of missing nodules that develop into advanced cancer.
Text Search Adjustments
Refining the text search criteria algorithm and the chart review process may decrease the rate of false positives significantly without affecting detection of lung nodules. In subsequent simulations, we found that by adding an exclusion criteria to text search algorithm to remove reports with specific keywords we could substantially reduce the number of false positive reports without affecting the detection rate of the lung nodules. These exclusion criteria would exclude any reports that: (1) contain “nodul” within the next 8 words after mentioning “no”; (2) contain “clear” within the next 8 words after mentioning “lung” in the text (eg, “lungs appear to be clear”); (3) contain “clear” within the next 4 words after mentioning “otherwise” in the text (eg, “otherwise appear to be clear”). Based on our study results, we further refined the text search strategy by limiting the search to only chest imaging studies. When we applied the revised algorithm to a random sample of imaging reports, we found all the code 44 radiology reports were still captured, but we were able to reduce the number of radiology reports needing review by about 80%.
Although classification approaches are being refined to improve radiology performance in multiple categories of nodules, this study suggests that alternative approaches based on text algorithms can improve the capture of pulmonary nodules that require surveillance. These algorithms also can be used to augment radiologist reporting systems. This represents an investment in resources to build a team that should include a bioinformatics specialist, lung nodule registry personnel (review charts of the detected imaging studies with lung nodules, populating the lung nodule database, and determining and tracking the need of imaging follow up), a lung nodule clinic nurse coordinator, and a dedicated lung nodule clinic pulmonologist.
Radiology departments could employ this text search approach to identify missed nodules and use an audit and feedback system to train radiologists to code lung nodules consistently at the time of the initial reading to avoid delays in identifying patients with nodules. Alternatively, the more widespread use of a standardized CT chest radiology reports using Fleischner or the American College of Radiology Lung Imaging Reporting and Data System (Lung RADS) templates might improve the detection of patients with lung nodules.5,13,14
The VHA system should have an effective strategy for identifying incidental lung nodules during routine radiology examinations. Relying only on radiologists to identify and code pulmonary nodules can lead to missing a significant number of patients with lung nodules and some patients with early stage lung cancer who could receive curative therapy.12,14-16 The use of a standardized algorithm, like a text search strategy, might decrease the risk of variation in the execution and result in a more sensitive detection of patients with lung nodules. The text search strategy might be easily implemented and shared with other hospitals both within and outside the VHA.
Limitations
This study was performed in a single VHA hospital and the findings may not be generalizable to other settings of care. Second, our study design is susceptible to work-up bias because the results of a diagnostic test (eg, chest or abdomen imaging) affected whether the chart review was used to verify the test result. It was not feasible to review the patient records of all radiology studies done at the facility during the study period, consequently complete 2 × 2 tables could not be created to calculate sensitivity, specificity, and negative predictive value.
Conclusion
A text search algorithm of radiology reports increased the detection of patients with lung nodules when compared with radiology diagnostic coding alone. However, the improved detection was associated with a higher rate of false positives, which requires manually reviewing a larger number of patient’s chart reports. Future research and quality improvement should focus on standardizing the radiology reporting process and improving the efficiency and reliability of follow up and tracking of incidental lung nodules.
Acknowledgments
The work reported here was supported by a grant from the Office of Rural Health (N32-FY16Q1-S1-P01577), US Department of Veterans Affairs, Veterans Health Administration. We also had the support from the Veterans Rural Health Resource Center-Iowa City, and the Health Services Research and Development (HSR&D) Service through the Comprehensive Access and Delivery Research and Evaluation (CADRE) Center (REA 09-220).
1. Jacobs PC, Mali WP, Grobbee DE, van der Graaf Y. Prevalence of incidental findings in computed tomographic screening of the chest: a systematic review. Journal of computer assisted tomography. 2008;32(2):214-221.
2. Frank L, Quint LE. Chest CT incidentalomas: thyroid lesions, enlarged mediastinal lymph nodes, and lung nodules. Cancer Imaging. 2012;12(1):41-48.
3. National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Cancer stat facts: lung and bronchus cancer. https://seer.cancer.gov/statfacts/html/lungb.html. Accessed April 8, 2020.
4. Alberg AJ, Brock MV, Ford JG, Samet JM, Spivack SD. Epidemiology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e1S-e29S.
5. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
6. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701.
7. Kinsinger LS, Anderson C, Kim J, et al. Implementation of lung cancer screening in the Veterans Health Administration. JAMA Intern Med. 2017;177(3):399-406.
8. Iqbal MN, Stott E, Huml AM, et al. What’s in a name? Factors associated with documentation and evaluation of incidental pulmonary nodules. Ann Am Thorac Soc. 2016;13(10):1704-1711.
9. Farjah F, Halgrim S, Buist DS, et al. An automated method for identifying individuals with a lung nodule can be feasibly implemented across health systems. Egems (Wash DC). 2016;4(1):1254.
10. Danforth KN, Early MI, Ngan S, Kosco AE, Zheng C, Gould MK. Automated identification of patients with pulmonary nodules in an integrated health system using administrative health plan data, radiology reports, and natural language processing. J Thorac Oncol. 2012;7(8):1257-1262.
11. US Department of Veterans Affairs, Office of Rural Health. https://www.ruralhealth.va.gov/aboutus/ruralvets.asp. Updated January 28, 2020. Accessed April 8, 2020.
12. Blagev DP, Lloyd JF, Conner K, et al. Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol. 2016;13(2 suppl):R18-R24.
13. Eisenberg RL, Fleischner S. Ways to improve radiologists’ adherence to Fleischner Society guidelines for management of pulmonary nodules. J Am Coll Radiol. 2013;10(6):439-441.
14. Aberle DR. Implementing lung cancer screening: the US experience. Clin Radiol. 2017;72(5):401-406.
15. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e93S-e120S.
16. Callister ME, Baldwin DR. How should pulmonary nodules be optimally investigated and managed? Lung Cancer. 2016;91:48-55.
1. Jacobs PC, Mali WP, Grobbee DE, van der Graaf Y. Prevalence of incidental findings in computed tomographic screening of the chest: a systematic review. Journal of computer assisted tomography. 2008;32(2):214-221.
2. Frank L, Quint LE. Chest CT incidentalomas: thyroid lesions, enlarged mediastinal lymph nodes, and lung nodules. Cancer Imaging. 2012;12(1):41-48.
3. National Institutes of Health, National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Cancer stat facts: lung and bronchus cancer. https://seer.cancer.gov/statfacts/html/lungb.html. Accessed April 8, 2020.
4. Alberg AJ, Brock MV, Ford JG, Samet JM, Spivack SD. Epidemiology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e1S-e29S.
5. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
6. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701.
7. Kinsinger LS, Anderson C, Kim J, et al. Implementation of lung cancer screening in the Veterans Health Administration. JAMA Intern Med. 2017;177(3):399-406.
8. Iqbal MN, Stott E, Huml AM, et al. What’s in a name? Factors associated with documentation and evaluation of incidental pulmonary nodules. Ann Am Thorac Soc. 2016;13(10):1704-1711.
9. Farjah F, Halgrim S, Buist DS, et al. An automated method for identifying individuals with a lung nodule can be feasibly implemented across health systems. Egems (Wash DC). 2016;4(1):1254.
10. Danforth KN, Early MI, Ngan S, Kosco AE, Zheng C, Gould MK. Automated identification of patients with pulmonary nodules in an integrated health system using administrative health plan data, radiology reports, and natural language processing. J Thorac Oncol. 2012;7(8):1257-1262.
11. US Department of Veterans Affairs, Office of Rural Health. https://www.ruralhealth.va.gov/aboutus/ruralvets.asp. Updated January 28, 2020. Accessed April 8, 2020.
12. Blagev DP, Lloyd JF, Conner K, et al. Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol. 2016;13(2 suppl):R18-R24.
13. Eisenberg RL, Fleischner S. Ways to improve radiologists’ adherence to Fleischner Society guidelines for management of pulmonary nodules. J Am Coll Radiol. 2013;10(6):439-441.
14. Aberle DR. Implementing lung cancer screening: the US experience. Clin Radiol. 2017;72(5):401-406.
15. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e93S-e120S.
16. Callister ME, Baldwin DR. How should pulmonary nodules be optimally investigated and managed? Lung Cancer. 2016;91:48-55.
Incidental Findings of Pulmonary and Hilar Malignancy by Low-Resolution Computed Tomography Used in Myocardial Perfusion Imaging (FULL)
Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for the evaluation of coronary artery disease (CAD).1 To improve image quality, low-resolution computed tomography (CT) is used commonly for anatomical correct and artifact attenuation during SPECT MPI.2 The low resolution, unenhanced CT images are considered low quality and are, therefore, labeled by the manufacturer as nondiagnostic. The CT portion of the MPI in many centers is used only for image fusion and attenuation correction, and these images are not routinely reviewed or reported by cardiologists.
Incidental findings by these low-resolution CT were frequent. However, clinically significant findings, including lung cancer, although relatively infrequent, were serious enough for major clinical management.3-5 Currently, there are no consensus recommendations for reviewing low-resolution CT images or the interpretation of such incidental findings during cardiac MPI.6 Clinically, low-dose CT were used for early detection and screening of lung cancer and were associated with reduced lung-cancer and any cause mortality in National Lung Screening Trial (NLST).7,8 Therefore, low-dose CT is recommended for lung cancer screening of high-risk patients by the US Preventive Service Task Force (USPSTF).9 In the veteran population, current and past smoking history are more common when compared with the general population; therefore, veterans are potentially at increased risk of lung cancer.10 In this study, we did not intend to use low-resolution CT for lung cancer screening or detection but rather to identify and report incidental findings of pulmonary/hilar malignancy detected during cardiac MPI.
Methods
The Siemens’ (Munich, Germany) Symbia Intevo Excel SPECT/CT MPI cameras with dedicated cardiac collimators were used at both the Dwight D. Eisenhower VA Medical Center (VAMC) in Leavenworth, Kansas and Colmery-O'Neil VAMC in Topeka, Kansas. The integrated CT scanner (x-ray tube current 30 to 240 mA; voltage 110 Kv with a 40 kW power generator) has the capability to image up to a 2-slice/rotation, each of 5.0 mm per slice with a scan time of about 30 seconds. The SPECT/CT gamma camera has a low energy (140 KeV), high resolution, parallel hole collimator with IQ SPECT capabilities.
The radiation dose received by the patients were expressed in dose length product (DLP), which reflects the total energy absorbed by the patient and represents integrated dose in terms of the total scan length. Additionally, each patients received 2 injections of Technetium Tc 99m sestamibi (1-day Protocol: 10 mCi rest injection, 30 mCi stress injection: 2-day Protocol for patients weighing > 350 pounds: 30 mCi at rest injection and 30 mCi at stress injection) for myocardial perfusion imaging.
All CT images and cardiac MPI findings were reviewed and reported contemporaneously by 1 of 2 experienced, board-certified radiologists who were blinded to patients’ clinical information except the indication for the cardiac stress testing. When suspicious pulmonary/hilar nodules or masses were detected, these findings and recommendations for further evaluation were conveyed to primary care provider or ordering physician via the electronic health record system.
All CT images were reviewed with cardiac MPI from September 1, 2017 to August 31, 2018. When pulmonary/hilar malignancies were identified, the health records were reviewed. Patients with known history of prior pulmonary malignancy were excluded from the study.
Results
A total of 1,098 patients underwent cardiac MPI during the study period. When the CT imaging and cardiac MPI were reviewed, incidental findings led to the diagnosis of lung cancer in 5 patients and hilar mantle cell lymphoma in 1 patient. Their clinical characteristics, CT findings, and types of malignancies for these 6 patients are summarized in the Table and Figure. Only 0.55% (6 of 1,098) patients were found to have incidental pulmonary/hilar malignancy with the cardiac evaluation low-resolution CT. Four patients with prior, known history of lung cancer were excluded from the study.
For the 6 patients found to have cancer, the average CT radiation dose during the cardiac MPI was 100 mGy-cm (range, 77 -133 mCy-cm). The subsequent chest CT with or without contrast delivered a radiation dose of 726.4 mGy-cm (range, 279.4 - 1,075 mGy-cm).
A total of 79 (7.2%) patients were found to have significant pulmonary nodules that required further evaluation; after CT examination, 32 patients had findings of benign nature and required no further follow-up; the other 47 patients are being followed according to the Fleischner Society 2017 guidelines for pulmonary nodules.11 The follow-up findings on these patients are not within the scope of this report.
Discussion
Although incidental findings on low-resolution CT during cardiac MPI are frequent, clinically significant findings are less common. However, some incidental findings may be of important clinical significance.3-5 A multicenter analysis by Coward and colleagues reported that 2.4% findings on low-resolution CT were significant enough to warrant follow-up tests, but only 0.2% were deemed potentially detrimental to patient outcomes (ie, pathology confirmed malignancies).12 Thus, the authors suggested that routine reporting of incidental findings on low-dose CT images was not beneficial.12,13
Currently, the majority of cardiac MPIs are reviewed and interpreted by nuclear cardiologists, the use of hybrid SPECT/CT for attenuation correction give rise of issue of reviewing and interpreting these CT images during cardiac MPI. Since low-dose, low-resolution CT are considered nondiagnostic, these images are not routinely and readily reviewed by cardiologists who are not trained or skilled in CT interpretations.
Studies of high-resolution cardiac CT (including multidetector CT with contrast) suggest that incidental extracardiac findings should always be reported as there was a 0.7% incidence of previously unknown malignancies, while others have argued against “performing large field reconstructionsfor the explicit purpose of screening as it will lead to additional cost, liability and anxiety without proven benefits.”14-16 A review of incidental findings of cardiac CT by Earls suggested that all cardiac CT should be reconstructed in the maximal field of view available and images should be adequately reviewed to detect pathological findings.17 This led to an interesting discussion by Douglas and colleagues regarding the role of cardiologists and radiologists in this issue.18 Currently there is no uniform or consensus recommendations regarding incidental findings during cardiac CT imaging. Guidances range from no recommendations to optional reporting or mandatory reporting.19-23
Risk Factors for Veterans
Lung cancer is the second most common cancer and the leading cause of cancer-related death in the US.24 Smoking is the most important risk factor for lung cancer and CAD.25 Current or past smoking are more common among the veterans.10 According to a report for the US Centers for Disease Control and Prevention report, about 29.2% US veterans use tobacco products between 2010-2015, which is similar to the rate reported in 1997.26
When low-dose CT was used for lung cancer screening, it was associated with a 20.0% reduction in lung cancer mortality and a 6.7% reduction in any cause mortality.7 Currently, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT screening for lung cancer in high-risk adults that includes patients aged 55 to 80 years who have a 30-pack-year smoking history and currently smoke or have quit within the past 15 years.8
It is likely that the cardiac patients in this study might have pulmonary malignancy mortality similar to those reported in the NLST. While other studies have shown a low incidence (0.2%) of detection of malignancy by low-resolution CT during cardiac MPI,12,13 in this study we found pulmonary or hilar malignancy in 0.55% of patients.The higher incidence of malignancy in our study might be due in part to differences in the patient population studied (ie, our veterans patients have a higher proportion of current or past smoking history).10
The CT used in this study is part of the cardiac imaging process. Therefore, there was no additional radiation exposure besides that of the cardiac MPI for patients. Despite the limitations of low-resolution CT, which may miss small lesions, this study showed 0.55% incidence of incidental detection of pulmonary/hilar malignancy. This is comparable with 0.65%/year of diagnosing lung cancer using low-dose CT for lung cancer screening in NLST.8
Two of the 5 study patients who were found to have lung cancer, had quit smoking > 15 years previously and thus would not be considered as high-risk for lung cancer screening according to USPSTF guideline. These patients would not have been candidates for annual low-dose CT lung cancer screening. This study suggests that it is appropriate and necessary to review the low-resolution CT images for incidental findings during cardiac MPI.
Limitations
The study was retrospective in nature and limited by its small number of patients. The CT modality used in the study also has limitations, including low resolution, respiratory motion artifacts, and scans that did not include the entire chest area. Therefore, small and apical lesions may have been missed. However, both sets of CT at rest and after stress were reviewed to reduce or minimize the effects of respiratory motion artifacts. The true prevalence or incidence of pulmonary/hilar malignancies may have been higher than reported here. Our study population of veterans may not be representative of the general population with regards to gender (as most of our veteran patient population are of male gender, vs general population), smoking history, or lung cancer risk, thus the results should be interpreted with caution.
Conclusion
Low-resolution CTs used for attenuation correction during cardiac MPI should be routinely reviewed and interpreted by a physician or radiologist skilled in CT interpretation in order to identify incidental findings of pulmonary/hilar malignancy. This would require close collaboration between cardiologists and radiologists in the field to ensure unfragmented and high-quality patient care.
Acknowledgements
We want to thank all the staffs in cardiology and radiology department on both campuses for their dedication for our patients. Special thanks to Laura Knox, Radiation Safety Officer, Nuclear Medicine Supervisor for her technical assistance.
1. Hendel RC, Berman DS, Di Carli MF, et al. ACCF/ASNC/ACR/AHA/ASE/SCCT/SCMR/SNM 2009 appropriate use criteria for cardiac radionuclide imaging: a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the American Society of Nuclear Cardiology, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the Society of Cardiovascular Computed Tomography, the Society for Cardiovascular Magnetic Resonance, and the Society of Nuclear Medicine. Circulation. 2009;119(22):e561-e587.
2. Hendel RC, Corbett JR, Cullom SJ, DePuey EG, Garcia EV, Bateman TM. The value and practice of attenuation correction for myocardial perfusion SPECT imaging: a joint position statement from the American Society of Nuclear Cardiology and the Society of Nuclear Medicine. J Nucl Cardiol. 2002;9(1):135–143.
3. Coward J, Nightingale J, Hogg P. The clinical dilemma of incidental findings on the low-resolution CT images from SPECT/CT MPI studies. J Nucl Med Technol. 2016;44(3):167-172.
4. Osman MM, Cohade C, Fishman E, Wahl RL. Clinically significant incidental findings on the unenhanced CT portion of PET/CT studies: frequency in 250 patients. J Nucl Med. 2005;46(8):1352-1355.
5. Goetze S, Pannu HK, Wahl RL. Clinically significant abnormal findings on the “nondiagnostic” CT portion of low-amperage-CT attenuation-corrected myocardial perfusion SPECT/CT studies. J Nucl Med. 2006;47(8):1312-1318.
6. American College of Cardiology Foundation Task Force on Expert Consensus Documents, Mark DB, Berman DS, et al. ACCF/ACR/AHA/NASCI/SAIP/SCAI/SCCT 2010 expert consensus document on coronary computed tomographic angiography: a report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents. J Am Coll Cardiol. 2010;55(23):2663-2699.
7. Diederich S, Wormanns D, Semik M, et al. Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology. 2002;222(3):773-781.
8. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409.
9. Moyer VA; U.S. Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338.
10. McKinney WP, McIntire DD, Carmody TJ, Joseph A. Comparing the smoking behavior of veterans and nonveterans. Public Health Rep. 1997;112(3):212-218.
11. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
12. Coward J, Lawson R, Kane T, et al. Multi-centre analysis of incidental findings on low-resolution CT attenuation correction images. Br J Radiol. 2014;87(1042):20130701.
13. Coward J, Lawson R, Kane T, et al. Multicentre analysis of incidental findings on low-resolution CT attenuation correction images: an extended study. Br J Radiol. 2015;88(1056):20150555.
14. Haller S, Kaiser C, Buser P, Bongartz G, Bremerich J. Coronary artery imaging with contrast-enhanced MDCT: extracardiac findings. AJR Am J Roentgenol. 2006;187(1):105-110.
15. Flor N, Di Leo G, Squarza SA, et al. Malignant incidental extracardiac findings on cardiac CT: systematic review and meta-analysis. AJR Am J Roentgenol. 2013;201(3):555-564.
16. Budoff MJ, Gopal A. Incidental findings on cardiac computed tomography. Should we look? J Cardiovasc Comput Tomogr. 2007;1(2):97-105.
17. Earls JP. The pros and cons of searching for extracardiac findings at cardiac CT: studies should be reconstructed in the maximum field of view and adequately reviewed to detect pathologic findings. Radiology. 2011;261(2):342-346.
18. Douglas PS, Cerqueria M, Rubin GD, Chin AS. Extracardiac findings: what is a cardiologist to do? JACC Cardiovasc Imaging. 2008;1(5):682-687.
19. Holly TA, Abbott BG, Al-Mallah M, et al. Single photon-emission computed tomography. J Nucl Cardiol. 2010;17(5):941-973.
20. Dorbala S, Ananthasubramaniam K, Armstrong IS, et al. Single photon emission computed tomography (SPECT) myocardial perfusion imaging guidelines: instrumentation, acquisition, processing, and interpretation. J Nucl Cardiol. 2018;25(5):1784-1846.
21. Tilkemeier PL, Bourque J, Doukky R, Sanghani R, Weinberg RL. ASNC imaging guidelines for nuclear cardiology procedures : Standardized reporting of nuclear cardiology procedures. J Nucl Cardiol. 2017;24(6):2064-2128.
22. Dorbala S, Di Carli MF, Delbeke D, et al. SNMMI/ASNC/SCCT guideline for cardiac SPECT/CT and PET/CT 1.0. J Nucl Med. 2013;54(8):1485-1507.
23. Dilsizian V, Bacharach SL, Beanlands RS, et al. ASNC imaging guidelines/SNMMI procedure standard for positron emission tomography (PET) nuclear cardiology procedures. J Nucl Cardiol. 2016;23(5):1187-1226.
24. Jemal A, Ward EM, Johnson CJ, et al. Annual report to the nation on the status of cancer, 1975-2014, Featuring Survival. J Natl Cancer Inst. 2017;109(9):djx030.
25. US Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014. Printed with corrections, January 2014.
26. Odani S, Agaku IT, Graffunder CM, Tynan MA, Armour BS. Tobacco Product Use Among Military Veterans - United States, 2010-2015. MMWR Morb Mortal Wkly Rep. 2018;67(1):7-12.
Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for the evaluation of coronary artery disease (CAD).1 To improve image quality, low-resolution computed tomography (CT) is used commonly for anatomical correct and artifact attenuation during SPECT MPI.2 The low resolution, unenhanced CT images are considered low quality and are, therefore, labeled by the manufacturer as nondiagnostic. The CT portion of the MPI in many centers is used only for image fusion and attenuation correction, and these images are not routinely reviewed or reported by cardiologists.
Incidental findings by these low-resolution CT were frequent. However, clinically significant findings, including lung cancer, although relatively infrequent, were serious enough for major clinical management.3-5 Currently, there are no consensus recommendations for reviewing low-resolution CT images or the interpretation of such incidental findings during cardiac MPI.6 Clinically, low-dose CT were used for early detection and screening of lung cancer and were associated with reduced lung-cancer and any cause mortality in National Lung Screening Trial (NLST).7,8 Therefore, low-dose CT is recommended for lung cancer screening of high-risk patients by the US Preventive Service Task Force (USPSTF).9 In the veteran population, current and past smoking history are more common when compared with the general population; therefore, veterans are potentially at increased risk of lung cancer.10 In this study, we did not intend to use low-resolution CT for lung cancer screening or detection but rather to identify and report incidental findings of pulmonary/hilar malignancy detected during cardiac MPI.
Methods
The Siemens’ (Munich, Germany) Symbia Intevo Excel SPECT/CT MPI cameras with dedicated cardiac collimators were used at both the Dwight D. Eisenhower VA Medical Center (VAMC) in Leavenworth, Kansas and Colmery-O'Neil VAMC in Topeka, Kansas. The integrated CT scanner (x-ray tube current 30 to 240 mA; voltage 110 Kv with a 40 kW power generator) has the capability to image up to a 2-slice/rotation, each of 5.0 mm per slice with a scan time of about 30 seconds. The SPECT/CT gamma camera has a low energy (140 KeV), high resolution, parallel hole collimator with IQ SPECT capabilities.
The radiation dose received by the patients were expressed in dose length product (DLP), which reflects the total energy absorbed by the patient and represents integrated dose in terms of the total scan length. Additionally, each patients received 2 injections of Technetium Tc 99m sestamibi (1-day Protocol: 10 mCi rest injection, 30 mCi stress injection: 2-day Protocol for patients weighing > 350 pounds: 30 mCi at rest injection and 30 mCi at stress injection) for myocardial perfusion imaging.
All CT images and cardiac MPI findings were reviewed and reported contemporaneously by 1 of 2 experienced, board-certified radiologists who were blinded to patients’ clinical information except the indication for the cardiac stress testing. When suspicious pulmonary/hilar nodules or masses were detected, these findings and recommendations for further evaluation were conveyed to primary care provider or ordering physician via the electronic health record system.
All CT images were reviewed with cardiac MPI from September 1, 2017 to August 31, 2018. When pulmonary/hilar malignancies were identified, the health records were reviewed. Patients with known history of prior pulmonary malignancy were excluded from the study.
Results
A total of 1,098 patients underwent cardiac MPI during the study period. When the CT imaging and cardiac MPI were reviewed, incidental findings led to the diagnosis of lung cancer in 5 patients and hilar mantle cell lymphoma in 1 patient. Their clinical characteristics, CT findings, and types of malignancies for these 6 patients are summarized in the Table and Figure. Only 0.55% (6 of 1,098) patients were found to have incidental pulmonary/hilar malignancy with the cardiac evaluation low-resolution CT. Four patients with prior, known history of lung cancer were excluded from the study.
For the 6 patients found to have cancer, the average CT radiation dose during the cardiac MPI was 100 mGy-cm (range, 77 -133 mCy-cm). The subsequent chest CT with or without contrast delivered a radiation dose of 726.4 mGy-cm (range, 279.4 - 1,075 mGy-cm).
A total of 79 (7.2%) patients were found to have significant pulmonary nodules that required further evaluation; after CT examination, 32 patients had findings of benign nature and required no further follow-up; the other 47 patients are being followed according to the Fleischner Society 2017 guidelines for pulmonary nodules.11 The follow-up findings on these patients are not within the scope of this report.
Discussion
Although incidental findings on low-resolution CT during cardiac MPI are frequent, clinically significant findings are less common. However, some incidental findings may be of important clinical significance.3-5 A multicenter analysis by Coward and colleagues reported that 2.4% findings on low-resolution CT were significant enough to warrant follow-up tests, but only 0.2% were deemed potentially detrimental to patient outcomes (ie, pathology confirmed malignancies).12 Thus, the authors suggested that routine reporting of incidental findings on low-dose CT images was not beneficial.12,13
Currently, the majority of cardiac MPIs are reviewed and interpreted by nuclear cardiologists, the use of hybrid SPECT/CT for attenuation correction give rise of issue of reviewing and interpreting these CT images during cardiac MPI. Since low-dose, low-resolution CT are considered nondiagnostic, these images are not routinely and readily reviewed by cardiologists who are not trained or skilled in CT interpretations.
Studies of high-resolution cardiac CT (including multidetector CT with contrast) suggest that incidental extracardiac findings should always be reported as there was a 0.7% incidence of previously unknown malignancies, while others have argued against “performing large field reconstructionsfor the explicit purpose of screening as it will lead to additional cost, liability and anxiety without proven benefits.”14-16 A review of incidental findings of cardiac CT by Earls suggested that all cardiac CT should be reconstructed in the maximal field of view available and images should be adequately reviewed to detect pathological findings.17 This led to an interesting discussion by Douglas and colleagues regarding the role of cardiologists and radiologists in this issue.18 Currently there is no uniform or consensus recommendations regarding incidental findings during cardiac CT imaging. Guidances range from no recommendations to optional reporting or mandatory reporting.19-23
Risk Factors for Veterans
Lung cancer is the second most common cancer and the leading cause of cancer-related death in the US.24 Smoking is the most important risk factor for lung cancer and CAD.25 Current or past smoking are more common among the veterans.10 According to a report for the US Centers for Disease Control and Prevention report, about 29.2% US veterans use tobacco products between 2010-2015, which is similar to the rate reported in 1997.26
When low-dose CT was used for lung cancer screening, it was associated with a 20.0% reduction in lung cancer mortality and a 6.7% reduction in any cause mortality.7 Currently, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT screening for lung cancer in high-risk adults that includes patients aged 55 to 80 years who have a 30-pack-year smoking history and currently smoke or have quit within the past 15 years.8
It is likely that the cardiac patients in this study might have pulmonary malignancy mortality similar to those reported in the NLST. While other studies have shown a low incidence (0.2%) of detection of malignancy by low-resolution CT during cardiac MPI,12,13 in this study we found pulmonary or hilar malignancy in 0.55% of patients.The higher incidence of malignancy in our study might be due in part to differences in the patient population studied (ie, our veterans patients have a higher proportion of current or past smoking history).10
The CT used in this study is part of the cardiac imaging process. Therefore, there was no additional radiation exposure besides that of the cardiac MPI for patients. Despite the limitations of low-resolution CT, which may miss small lesions, this study showed 0.55% incidence of incidental detection of pulmonary/hilar malignancy. This is comparable with 0.65%/year of diagnosing lung cancer using low-dose CT for lung cancer screening in NLST.8
Two of the 5 study patients who were found to have lung cancer, had quit smoking > 15 years previously and thus would not be considered as high-risk for lung cancer screening according to USPSTF guideline. These patients would not have been candidates for annual low-dose CT lung cancer screening. This study suggests that it is appropriate and necessary to review the low-resolution CT images for incidental findings during cardiac MPI.
Limitations
The study was retrospective in nature and limited by its small number of patients. The CT modality used in the study also has limitations, including low resolution, respiratory motion artifacts, and scans that did not include the entire chest area. Therefore, small and apical lesions may have been missed. However, both sets of CT at rest and after stress were reviewed to reduce or minimize the effects of respiratory motion artifacts. The true prevalence or incidence of pulmonary/hilar malignancies may have been higher than reported here. Our study population of veterans may not be representative of the general population with regards to gender (as most of our veteran patient population are of male gender, vs general population), smoking history, or lung cancer risk, thus the results should be interpreted with caution.
Conclusion
Low-resolution CTs used for attenuation correction during cardiac MPI should be routinely reviewed and interpreted by a physician or radiologist skilled in CT interpretation in order to identify incidental findings of pulmonary/hilar malignancy. This would require close collaboration between cardiologists and radiologists in the field to ensure unfragmented and high-quality patient care.
Acknowledgements
We want to thank all the staffs in cardiology and radiology department on both campuses for their dedication for our patients. Special thanks to Laura Knox, Radiation Safety Officer, Nuclear Medicine Supervisor for her technical assistance.
Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for the evaluation of coronary artery disease (CAD).1 To improve image quality, low-resolution computed tomography (CT) is used commonly for anatomical correct and artifact attenuation during SPECT MPI.2 The low resolution, unenhanced CT images are considered low quality and are, therefore, labeled by the manufacturer as nondiagnostic. The CT portion of the MPI in many centers is used only for image fusion and attenuation correction, and these images are not routinely reviewed or reported by cardiologists.
Incidental findings by these low-resolution CT were frequent. However, clinically significant findings, including lung cancer, although relatively infrequent, were serious enough for major clinical management.3-5 Currently, there are no consensus recommendations for reviewing low-resolution CT images or the interpretation of such incidental findings during cardiac MPI.6 Clinically, low-dose CT were used for early detection and screening of lung cancer and were associated with reduced lung-cancer and any cause mortality in National Lung Screening Trial (NLST).7,8 Therefore, low-dose CT is recommended for lung cancer screening of high-risk patients by the US Preventive Service Task Force (USPSTF).9 In the veteran population, current and past smoking history are more common when compared with the general population; therefore, veterans are potentially at increased risk of lung cancer.10 In this study, we did not intend to use low-resolution CT for lung cancer screening or detection but rather to identify and report incidental findings of pulmonary/hilar malignancy detected during cardiac MPI.
Methods
The Siemens’ (Munich, Germany) Symbia Intevo Excel SPECT/CT MPI cameras with dedicated cardiac collimators were used at both the Dwight D. Eisenhower VA Medical Center (VAMC) in Leavenworth, Kansas and Colmery-O'Neil VAMC in Topeka, Kansas. The integrated CT scanner (x-ray tube current 30 to 240 mA; voltage 110 Kv with a 40 kW power generator) has the capability to image up to a 2-slice/rotation, each of 5.0 mm per slice with a scan time of about 30 seconds. The SPECT/CT gamma camera has a low energy (140 KeV), high resolution, parallel hole collimator with IQ SPECT capabilities.
The radiation dose received by the patients were expressed in dose length product (DLP), which reflects the total energy absorbed by the patient and represents integrated dose in terms of the total scan length. Additionally, each patients received 2 injections of Technetium Tc 99m sestamibi (1-day Protocol: 10 mCi rest injection, 30 mCi stress injection: 2-day Protocol for patients weighing > 350 pounds: 30 mCi at rest injection and 30 mCi at stress injection) for myocardial perfusion imaging.
All CT images and cardiac MPI findings were reviewed and reported contemporaneously by 1 of 2 experienced, board-certified radiologists who were blinded to patients’ clinical information except the indication for the cardiac stress testing. When suspicious pulmonary/hilar nodules or masses were detected, these findings and recommendations for further evaluation were conveyed to primary care provider or ordering physician via the electronic health record system.
All CT images were reviewed with cardiac MPI from September 1, 2017 to August 31, 2018. When pulmonary/hilar malignancies were identified, the health records were reviewed. Patients with known history of prior pulmonary malignancy were excluded from the study.
Results
A total of 1,098 patients underwent cardiac MPI during the study period. When the CT imaging and cardiac MPI were reviewed, incidental findings led to the diagnosis of lung cancer in 5 patients and hilar mantle cell lymphoma in 1 patient. Their clinical characteristics, CT findings, and types of malignancies for these 6 patients are summarized in the Table and Figure. Only 0.55% (6 of 1,098) patients were found to have incidental pulmonary/hilar malignancy with the cardiac evaluation low-resolution CT. Four patients with prior, known history of lung cancer were excluded from the study.
For the 6 patients found to have cancer, the average CT radiation dose during the cardiac MPI was 100 mGy-cm (range, 77 -133 mCy-cm). The subsequent chest CT with or without contrast delivered a radiation dose of 726.4 mGy-cm (range, 279.4 - 1,075 mGy-cm).
A total of 79 (7.2%) patients were found to have significant pulmonary nodules that required further evaluation; after CT examination, 32 patients had findings of benign nature and required no further follow-up; the other 47 patients are being followed according to the Fleischner Society 2017 guidelines for pulmonary nodules.11 The follow-up findings on these patients are not within the scope of this report.
Discussion
Although incidental findings on low-resolution CT during cardiac MPI are frequent, clinically significant findings are less common. However, some incidental findings may be of important clinical significance.3-5 A multicenter analysis by Coward and colleagues reported that 2.4% findings on low-resolution CT were significant enough to warrant follow-up tests, but only 0.2% were deemed potentially detrimental to patient outcomes (ie, pathology confirmed malignancies).12 Thus, the authors suggested that routine reporting of incidental findings on low-dose CT images was not beneficial.12,13
Currently, the majority of cardiac MPIs are reviewed and interpreted by nuclear cardiologists, the use of hybrid SPECT/CT for attenuation correction give rise of issue of reviewing and interpreting these CT images during cardiac MPI. Since low-dose, low-resolution CT are considered nondiagnostic, these images are not routinely and readily reviewed by cardiologists who are not trained or skilled in CT interpretations.
Studies of high-resolution cardiac CT (including multidetector CT with contrast) suggest that incidental extracardiac findings should always be reported as there was a 0.7% incidence of previously unknown malignancies, while others have argued against “performing large field reconstructionsfor the explicit purpose of screening as it will lead to additional cost, liability and anxiety without proven benefits.”14-16 A review of incidental findings of cardiac CT by Earls suggested that all cardiac CT should be reconstructed in the maximal field of view available and images should be adequately reviewed to detect pathological findings.17 This led to an interesting discussion by Douglas and colleagues regarding the role of cardiologists and radiologists in this issue.18 Currently there is no uniform or consensus recommendations regarding incidental findings during cardiac CT imaging. Guidances range from no recommendations to optional reporting or mandatory reporting.19-23
Risk Factors for Veterans
Lung cancer is the second most common cancer and the leading cause of cancer-related death in the US.24 Smoking is the most important risk factor for lung cancer and CAD.25 Current or past smoking are more common among the veterans.10 According to a report for the US Centers for Disease Control and Prevention report, about 29.2% US veterans use tobacco products between 2010-2015, which is similar to the rate reported in 1997.26
When low-dose CT was used for lung cancer screening, it was associated with a 20.0% reduction in lung cancer mortality and a 6.7% reduction in any cause mortality.7 Currently, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT screening for lung cancer in high-risk adults that includes patients aged 55 to 80 years who have a 30-pack-year smoking history and currently smoke or have quit within the past 15 years.8
It is likely that the cardiac patients in this study might have pulmonary malignancy mortality similar to those reported in the NLST. While other studies have shown a low incidence (0.2%) of detection of malignancy by low-resolution CT during cardiac MPI,12,13 in this study we found pulmonary or hilar malignancy in 0.55% of patients.The higher incidence of malignancy in our study might be due in part to differences in the patient population studied (ie, our veterans patients have a higher proportion of current or past smoking history).10
The CT used in this study is part of the cardiac imaging process. Therefore, there was no additional radiation exposure besides that of the cardiac MPI for patients. Despite the limitations of low-resolution CT, which may miss small lesions, this study showed 0.55% incidence of incidental detection of pulmonary/hilar malignancy. This is comparable with 0.65%/year of diagnosing lung cancer using low-dose CT for lung cancer screening in NLST.8
Two of the 5 study patients who were found to have lung cancer, had quit smoking > 15 years previously and thus would not be considered as high-risk for lung cancer screening according to USPSTF guideline. These patients would not have been candidates for annual low-dose CT lung cancer screening. This study suggests that it is appropriate and necessary to review the low-resolution CT images for incidental findings during cardiac MPI.
Limitations
The study was retrospective in nature and limited by its small number of patients. The CT modality used in the study also has limitations, including low resolution, respiratory motion artifacts, and scans that did not include the entire chest area. Therefore, small and apical lesions may have been missed. However, both sets of CT at rest and after stress were reviewed to reduce or minimize the effects of respiratory motion artifacts. The true prevalence or incidence of pulmonary/hilar malignancies may have been higher than reported here. Our study population of veterans may not be representative of the general population with regards to gender (as most of our veteran patient population are of male gender, vs general population), smoking history, or lung cancer risk, thus the results should be interpreted with caution.
Conclusion
Low-resolution CTs used for attenuation correction during cardiac MPI should be routinely reviewed and interpreted by a physician or radiologist skilled in CT interpretation in order to identify incidental findings of pulmonary/hilar malignancy. This would require close collaboration between cardiologists and radiologists in the field to ensure unfragmented and high-quality patient care.
Acknowledgements
We want to thank all the staffs in cardiology and radiology department on both campuses for their dedication for our patients. Special thanks to Laura Knox, Radiation Safety Officer, Nuclear Medicine Supervisor for her technical assistance.
1. Hendel RC, Berman DS, Di Carli MF, et al. ACCF/ASNC/ACR/AHA/ASE/SCCT/SCMR/SNM 2009 appropriate use criteria for cardiac radionuclide imaging: a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the American Society of Nuclear Cardiology, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the Society of Cardiovascular Computed Tomography, the Society for Cardiovascular Magnetic Resonance, and the Society of Nuclear Medicine. Circulation. 2009;119(22):e561-e587.
2. Hendel RC, Corbett JR, Cullom SJ, DePuey EG, Garcia EV, Bateman TM. The value and practice of attenuation correction for myocardial perfusion SPECT imaging: a joint position statement from the American Society of Nuclear Cardiology and the Society of Nuclear Medicine. J Nucl Cardiol. 2002;9(1):135–143.
3. Coward J, Nightingale J, Hogg P. The clinical dilemma of incidental findings on the low-resolution CT images from SPECT/CT MPI studies. J Nucl Med Technol. 2016;44(3):167-172.
4. Osman MM, Cohade C, Fishman E, Wahl RL. Clinically significant incidental findings on the unenhanced CT portion of PET/CT studies: frequency in 250 patients. J Nucl Med. 2005;46(8):1352-1355.
5. Goetze S, Pannu HK, Wahl RL. Clinically significant abnormal findings on the “nondiagnostic” CT portion of low-amperage-CT attenuation-corrected myocardial perfusion SPECT/CT studies. J Nucl Med. 2006;47(8):1312-1318.
6. American College of Cardiology Foundation Task Force on Expert Consensus Documents, Mark DB, Berman DS, et al. ACCF/ACR/AHA/NASCI/SAIP/SCAI/SCCT 2010 expert consensus document on coronary computed tomographic angiography: a report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents. J Am Coll Cardiol. 2010;55(23):2663-2699.
7. Diederich S, Wormanns D, Semik M, et al. Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology. 2002;222(3):773-781.
8. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409.
9. Moyer VA; U.S. Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338.
10. McKinney WP, McIntire DD, Carmody TJ, Joseph A. Comparing the smoking behavior of veterans and nonveterans. Public Health Rep. 1997;112(3):212-218.
11. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
12. Coward J, Lawson R, Kane T, et al. Multi-centre analysis of incidental findings on low-resolution CT attenuation correction images. Br J Radiol. 2014;87(1042):20130701.
13. Coward J, Lawson R, Kane T, et al. Multicentre analysis of incidental findings on low-resolution CT attenuation correction images: an extended study. Br J Radiol. 2015;88(1056):20150555.
14. Haller S, Kaiser C, Buser P, Bongartz G, Bremerich J. Coronary artery imaging with contrast-enhanced MDCT: extracardiac findings. AJR Am J Roentgenol. 2006;187(1):105-110.
15. Flor N, Di Leo G, Squarza SA, et al. Malignant incidental extracardiac findings on cardiac CT: systematic review and meta-analysis. AJR Am J Roentgenol. 2013;201(3):555-564.
16. Budoff MJ, Gopal A. Incidental findings on cardiac computed tomography. Should we look? J Cardiovasc Comput Tomogr. 2007;1(2):97-105.
17. Earls JP. The pros and cons of searching for extracardiac findings at cardiac CT: studies should be reconstructed in the maximum field of view and adequately reviewed to detect pathologic findings. Radiology. 2011;261(2):342-346.
18. Douglas PS, Cerqueria M, Rubin GD, Chin AS. Extracardiac findings: what is a cardiologist to do? JACC Cardiovasc Imaging. 2008;1(5):682-687.
19. Holly TA, Abbott BG, Al-Mallah M, et al. Single photon-emission computed tomography. J Nucl Cardiol. 2010;17(5):941-973.
20. Dorbala S, Ananthasubramaniam K, Armstrong IS, et al. Single photon emission computed tomography (SPECT) myocardial perfusion imaging guidelines: instrumentation, acquisition, processing, and interpretation. J Nucl Cardiol. 2018;25(5):1784-1846.
21. Tilkemeier PL, Bourque J, Doukky R, Sanghani R, Weinberg RL. ASNC imaging guidelines for nuclear cardiology procedures : Standardized reporting of nuclear cardiology procedures. J Nucl Cardiol. 2017;24(6):2064-2128.
22. Dorbala S, Di Carli MF, Delbeke D, et al. SNMMI/ASNC/SCCT guideline for cardiac SPECT/CT and PET/CT 1.0. J Nucl Med. 2013;54(8):1485-1507.
23. Dilsizian V, Bacharach SL, Beanlands RS, et al. ASNC imaging guidelines/SNMMI procedure standard for positron emission tomography (PET) nuclear cardiology procedures. J Nucl Cardiol. 2016;23(5):1187-1226.
24. Jemal A, Ward EM, Johnson CJ, et al. Annual report to the nation on the status of cancer, 1975-2014, Featuring Survival. J Natl Cancer Inst. 2017;109(9):djx030.
25. US Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014. Printed with corrections, January 2014.
26. Odani S, Agaku IT, Graffunder CM, Tynan MA, Armour BS. Tobacco Product Use Among Military Veterans - United States, 2010-2015. MMWR Morb Mortal Wkly Rep. 2018;67(1):7-12.
1. Hendel RC, Berman DS, Di Carli MF, et al. ACCF/ASNC/ACR/AHA/ASE/SCCT/SCMR/SNM 2009 appropriate use criteria for cardiac radionuclide imaging: a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the American Society of Nuclear Cardiology, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the Society of Cardiovascular Computed Tomography, the Society for Cardiovascular Magnetic Resonance, and the Society of Nuclear Medicine. Circulation. 2009;119(22):e561-e587.
2. Hendel RC, Corbett JR, Cullom SJ, DePuey EG, Garcia EV, Bateman TM. The value and practice of attenuation correction for myocardial perfusion SPECT imaging: a joint position statement from the American Society of Nuclear Cardiology and the Society of Nuclear Medicine. J Nucl Cardiol. 2002;9(1):135–143.
3. Coward J, Nightingale J, Hogg P. The clinical dilemma of incidental findings on the low-resolution CT images from SPECT/CT MPI studies. J Nucl Med Technol. 2016;44(3):167-172.
4. Osman MM, Cohade C, Fishman E, Wahl RL. Clinically significant incidental findings on the unenhanced CT portion of PET/CT studies: frequency in 250 patients. J Nucl Med. 2005;46(8):1352-1355.
5. Goetze S, Pannu HK, Wahl RL. Clinically significant abnormal findings on the “nondiagnostic” CT portion of low-amperage-CT attenuation-corrected myocardial perfusion SPECT/CT studies. J Nucl Med. 2006;47(8):1312-1318.
6. American College of Cardiology Foundation Task Force on Expert Consensus Documents, Mark DB, Berman DS, et al. ACCF/ACR/AHA/NASCI/SAIP/SCAI/SCCT 2010 expert consensus document on coronary computed tomographic angiography: a report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents. J Am Coll Cardiol. 2010;55(23):2663-2699.
7. Diederich S, Wormanns D, Semik M, et al. Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology. 2002;222(3):773-781.
8. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409.
9. Moyer VA; U.S. Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338.
10. McKinney WP, McIntire DD, Carmody TJ, Joseph A. Comparing the smoking behavior of veterans and nonveterans. Public Health Rep. 1997;112(3):212-218.
11. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
12. Coward J, Lawson R, Kane T, et al. Multi-centre analysis of incidental findings on low-resolution CT attenuation correction images. Br J Radiol. 2014;87(1042):20130701.
13. Coward J, Lawson R, Kane T, et al. Multicentre analysis of incidental findings on low-resolution CT attenuation correction images: an extended study. Br J Radiol. 2015;88(1056):20150555.
14. Haller S, Kaiser C, Buser P, Bongartz G, Bremerich J. Coronary artery imaging with contrast-enhanced MDCT: extracardiac findings. AJR Am J Roentgenol. 2006;187(1):105-110.
15. Flor N, Di Leo G, Squarza SA, et al. Malignant incidental extracardiac findings on cardiac CT: systematic review and meta-analysis. AJR Am J Roentgenol. 2013;201(3):555-564.
16. Budoff MJ, Gopal A. Incidental findings on cardiac computed tomography. Should we look? J Cardiovasc Comput Tomogr. 2007;1(2):97-105.
17. Earls JP. The pros and cons of searching for extracardiac findings at cardiac CT: studies should be reconstructed in the maximum field of view and adequately reviewed to detect pathologic findings. Radiology. 2011;261(2):342-346.
18. Douglas PS, Cerqueria M, Rubin GD, Chin AS. Extracardiac findings: what is a cardiologist to do? JACC Cardiovasc Imaging. 2008;1(5):682-687.
19. Holly TA, Abbott BG, Al-Mallah M, et al. Single photon-emission computed tomography. J Nucl Cardiol. 2010;17(5):941-973.
20. Dorbala S, Ananthasubramaniam K, Armstrong IS, et al. Single photon emission computed tomography (SPECT) myocardial perfusion imaging guidelines: instrumentation, acquisition, processing, and interpretation. J Nucl Cardiol. 2018;25(5):1784-1846.
21. Tilkemeier PL, Bourque J, Doukky R, Sanghani R, Weinberg RL. ASNC imaging guidelines for nuclear cardiology procedures : Standardized reporting of nuclear cardiology procedures. J Nucl Cardiol. 2017;24(6):2064-2128.
22. Dorbala S, Di Carli MF, Delbeke D, et al. SNMMI/ASNC/SCCT guideline for cardiac SPECT/CT and PET/CT 1.0. J Nucl Med. 2013;54(8):1485-1507.
23. Dilsizian V, Bacharach SL, Beanlands RS, et al. ASNC imaging guidelines/SNMMI procedure standard for positron emission tomography (PET) nuclear cardiology procedures. J Nucl Cardiol. 2016;23(5):1187-1226.
24. Jemal A, Ward EM, Johnson CJ, et al. Annual report to the nation on the status of cancer, 1975-2014, Featuring Survival. J Natl Cancer Inst. 2017;109(9):djx030.
25. US Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014. Printed with corrections, January 2014.
26. Odani S, Agaku IT, Graffunder CM, Tynan MA, Armour BS. Tobacco Product Use Among Military Veterans - United States, 2010-2015. MMWR Morb Mortal Wkly Rep. 2018;67(1):7-12.
Distress and Factors Associated with Suicidal Ideation in Veterans Living with Cancer (FULL)
It was estimated that physicians would diagnose a form of invasive cancer > 1.7 million times in 2019. As the second most common cause of death in the US, > 600,000 people were projected to die from cancer in 2019.1 Many individuals with cancer endure distress, which the National Comprehensive Cancer Network (NCCN) defines as a “multifactorial unpleasant experience of a psychological (ie, cognitive, behavioral, emotional), social, spiritual, and/or physical nature that may interfere with the ability to cope effectively with cancer, its physical symptoms, and its treatment.”2,3 Distress in people living with cancer has been attributed to various psychosocial concerns, such as family problems, whichinclude dealing with partners and children; emotional problems, such as depression and anxiety; and physical symptoms, such as pain and fatigue.4-9 Certain factors associated with distress may increase a patient’s risk for suicide.4
Veterans are at particularly high risk for suicide.10 In 2014, veterans accounted for 18% of completed suicides in the US but only were 8.5% of the total population that same year.10 Yet, little research has been done on the relationship between distress and suicide in veterans living with cancer. Aboumrad and colleagues found that 45% of veterans with cancer who completed suicide reported family issues and 41% endorsed chronic pain.11 This study recommended continued efforts to assess and treat distress to lessen risk of suicide in veterans living with cancer; however, to date, only 1 study has specifically evaluated distress and problems endorsed among veterans living with cancer.7
Suicide prevention is of the highest priority to the US Department of Veterans Affairs (VA).12 Consistent with the VA mission to end veteran suicide, the current study aimed to better understand the relationship between distress and suicide within a sample of veterans living with cancer. Findings would additionally be used to tailor clinical assessments and interventions for veterans living with cancer.
This study had 3 primary goals. First, we sought to understand demographic and clinical factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. Second, the study investigated the most commonly endorsed problems by veterans living with cancer. Finally, we examined which problems were related to suicidal ideation (SI). It was hypothesized that veterans who reported severe distress would be significantly more likely to endorse SI when compared with veterans who reported mild or moderate distress. Based on existing literature, it was further hypothesized that family, emotional, and physical problems would be significantly associated with SI.7,11
Methods
The current study was conducted at James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida. Inclusion criteria included veterans who were diagnosed with cancer, attended an outpatient psychology-oncology evaluation, and completed mental health screening measures provided during their evaluation. Exclusion criteria included veterans who: were seen in response to an inpatient consult, were seen solely for a stem cell transplant evaluation, or did not complete the screening measures.
Measures
A veteran’s demographic (eg, age, sex, ethnicity) and clinical (eg, cancer type, stage of disease, recurrence, cancer treatments received) information was abstracted from their VA medical records. Marital status was assessed during a clinical interview and documented as part of the standardized suicide risk assessment.
The Distress Thermometer (DT) is a subjective measure developed by the NCCN.2 The DT provides a visual representation of a thermometer and asks patients to rate their level of distress over the past week with 0 indicating no distress and 10 indicating extreme distress.
The measurement additionally lists 39 problems nested within 5 domains: practical, family, emotional, spiritual/religious, and physical. Patients may endorse listed items under each problem domain by indicating yes or no. Endorsement of various items are intended to provide more detailed information about sources of distress. Due to the predominantly male and mostly older population included in this study the ability to have children measure was removed from the family problem domain.
SI was assessed in 2 ways. First, by patients’ self-report through item-9 of the Patient Health Questionnaire-9 (PHQ-9).14 Item-9 asks “over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?” Responses range from 0 (not at all) to 3 (nearly every day).14 Responses > 0 were considered a positive screen for SI.
Procedure
Participants were a sample of veterans who were referred for psychology-oncology services. The NCCN DT and Problems List were administered prior to the start of clinical interviews, which followed a checklist and included standardized assessments of SI and history of a suicide attempt(s). A licensed clinical psychologist or a postdoctoral resident conducted these assessments under the supervision of a licensed psychologist. Data gathered during the clinical interview and from the DT and problems list were documented in health records, which were retrospectively reviewed for relevant information (eg, cancer diagnosis, SI). Therefore, informed consent was waived. This study was approved by the JAHVH Institutional Review Board.
Analysis
Data were analyzed using SPSS Version 25. Data analysis proceeded in 3 steps. First, descriptive statistics included the demographic and clinical factors present in the current sample. Difference between those with and without suicidal ideation were compared using F-statistic for continuous variables and χ2 analyses for categorical variables. Second, to examine relationships between each DT problem domain and SI, χ2 analyses were conducted. Third, DT problem domains that had a significant relationship with SI were entered in a logistic regression. This analysis determined which items, if any, from a DT problem domain predicted SI. In the logistic regression model, history of suicide attempts was entered into the first block, as history of suicide attempts is a well-established risk factor for subsequent suicidal ideation. In the second block, other variables that were significantly related to suicidal ideation in the second step of analyses were included. Before interpreting the results of the logistic regression, model fit was tested using the Hosmer-Lemeshow test. Significance of each individual predictor variable in the model is reported using the Wald χ2 statistic; each Wald statistic is compared with a χ2 distribution with 1 degree of freedom (df). Results of logistic regression models also provide information about the effect of each predictor variable in the regression equation (beta weight), odds a veteran who endorsed each predictor variable in the model would also endorse SI (as indicated by the odds ratio), and an estimate of the amount of variance accounted for by each predictor variable (using Nagelkerke’s pseudo R2, which ranges in value from 0 to 1 with higher values indicating more variance explained). For all analyses, P value of .05 (2-tailed) was used for statistical significance.
Results
The sample consisted of 174 veterans (Table 1). The majority (77.6%) were male with a mean age of nearly 62 years (range, 29-87). Most identified as white (74.1%) with half reporting they were either married or living with a partner.
Prostate cancer (19.0%) was the most common type of cancer among study participants followed by head and neck (18.4%), lymphoma/leukemia (11.5%), lung (11.5%), and breast (10.9%); 31.6% had metastatic disease and 14.9% had recurrent disease. Chemotherapy (42.5%) was the most common treatment modality, followed by surgery (38.5%) and radiation (31.6%). The sample was distributed among the 3 distress DT categories: mild (18.4%), moderate (42.5%), and severe (39.1%).
Problems Endorsed
Treatment decisions (44.3%) and insurance/financial concerns (35.1%) were the most frequently endorsed practical problems (Figure 1). Family health issues (33.9%) and dealing with partner (23.0%) were the most frequently endorsed family problems (Figure 2). Worry (73.0%) and depression (69.5%) were the most frequent emotional problems; of note, all emotional problems were endorsed by at least 50% of veterans (Figure 3). Fatigue (71.3%), sleep (70.7%), and pain (69%), were the most frequently endorsed physical problems (Figure 4). Spiritual/religious problems were endorsed by 15% of veterans.
Suicidal Ideation
Overall, 25.3% of veterans endorsed SI. About 20% of veterans reported a history of ≥ 1 suicide attempts in their lifetime. A significant relationship among distress categories and SI was found (χ2 = 18.36, P < .001). Veterans with severe distress were more likely to endorse SI (42.7%) when compared with veterans with mild (9.4%) or moderate (16.2%) distress.
Similarly, a significant relationship among distress categories and a history of a suicide attempt(s) was found (χ2 = 6.08, P = .048). Veterans with severe distress were more likely to have attempted suicide (29.4%) when compared with veterans with mild (12.5%) or moderate (14.9%) distress.
χ2 analyses were conducted to examine the relationships between DT problem domains and SI. A significant relationship was found between family problems and SI (
Logistic regression analyses determined whether items representative of the family problems domain were predictive of SI. Suicide attempt(s) were entered in the first step of the model to evaluate risk factors for SI over this already established risk factor. The assumptions of logistic regression were met.
The Hosmer-Lemeshow test (χ2 = 3.66, df = 5, P = .56) demonstrated that the model fit was good. The group of predictors used in the model differentiate between people who were experiencing SI and those who were not experiencing SI at the time of evaluation. A history of a suicide attempt(s) predicted SI, as expected (Wald = 6.821, df = 1, P = .01). The odds that a veteran with a history of a suicide attempt(s) would endorse SI at the time of the evaluation was nearly 3 times greater than that of veterans without a history of a suicide attempt(s). Over and above suicide attempts, problems dealing with partner (Wald = 15.142; df = 1, P < .001) was a second significant predictor of current SI. The odds that a veteran who endorsed problems dealing with partner would also endorse SI was > 5 times higher than that of veterans who did not endorse problems dealing with partner. This finding represents a significant risk factor for SI, over and above a history of a suicide attempt(s). The other items from the family problems domains were not significant (P > .05) (Table 3).
Discussion
This study aimed to understand factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. As hypothesized, veterans who endorsed severe distress were significantly more likely to endorse SI. They also were more likely to have a history of a suicide attempt(s) when compared with those with mild or moderate distress.
A second aim of this study was to understand the most commonly endorsed problems. Consistent with prior literature, treatment decisions were the most commonly endorsed practical problem; worry and depression were the most common emotional problems; and fatigue, sleep, and pain were the most common physical problems.7
A finding unique to the current study is that family health issues and dealing with partner were specified as the most common family problems. However, a study by Smith and colleagues did not provide information about the rank of most frequently reported problems within this domain.7
The third aim was to understand which problems were related to SI. It was hypothesized that family, emotional, and physical problems would be related to SI. However, results indicated that only family problems (specifically, problems dealing with a partner) were significantly associated with SI among veterans living with cancer.
Contrary to expectations, emotional and physical problems were not found to have a significant relationship with SI. This is likely because veterans endorsed items nested within these problem domains with similar frequency. The lack of significant findings does not suggest that emotional and physical problems are not significant predictors of SI for veterans living with cancer, but that no specific emotional or physical symptom stood out as a predictor of suicidal ideation above the others.
The finding of a significant relationship between family problems (specifically, problems dealing with a partner) and SI in this study is consistent with findings of Aboumrad and colleagues in a study that examined root-cause analyses of completed suicides by veterans living with cancer.11 They found that nearly half the sample endorsed family problems prior to their death, and a small but notable percentage of veterans who completed suicide reported divorce as a stressor prior to their death.
This finding may be explained by Thomas Joiner's interpersonal-psychological theory of suicidal behavior (IPT), which suggests that completed suicide may result from a thwarted sense of belonging, perceived burdensomeness, and acquired capability for suicide.16 Problems dealing with a partner may impact a veteran’s sense of belonging or social connectedness. Problems dealing with a partner also may be attributed to perceived burdens due to limitations imposed by living with cancer and/or undergoing treatment. In both circumstances, the veteran’s social support system may be negatively impacted, and perceived social support is a well-established protective factor against suicide.17
Partner distress is a second consideration. It is likely that veterans’ partners experienced their own distress in response to the veteran’s cancer diagnosis and/or treatment. The partner’s cause, severity, and expression of distress may contribute to problems for the couple.
Finally, the latter point of the IPT refers to acquired capability, or the ability to inflict deadly harm to oneself.18 A military sample was found to have more acquired capability for suicide when compared with a college undergraduate sample.19 A history of a suicide attempt(s) and male gender have been found to significantly predict acquired capability to complete suicide.18 Furthermore, because veterans living with cancer often are in pain, fear of pain associated with suicide may be reduced and, therefore, acquired capability increased. This suggests that male veterans living with cancer who are in pain, have a history of a suicide attempt(s), and current problems with their partner may be an extremely vulnerable population at-risk for suicide. Results from the current study emphasize the importance of veterans having access to mental health and crisis resources for problems dealing with their partner. Partner problems may foreshadow a potentially lethal type of distress.
Strengths
This study’s aims are consistent with the VA’s mission to end veteran suicide and contributes to literature in several important ways.12 First, veterans living with cancer are an understudied population. The current study addresses a gap in existing literature by researching veterans living with cancer and aims to better understand the relationship between cancer-related distress and SI. Second, to the best of the authors’ knowledge, this study is the first to find that problems dealing with a partner significantly increases a veteran’s risk for SI above a history of a suicide attempt(s). Risk assessments now may be more comprehensive through inclusion of this distress factor.
It is recommended that future research use IPT to further investigate the relationship between problems dealing with a partner and SI.16 Future research may do so by including specific measures to assess for the tenants of the theory, including measurements of burdensomeness and belongingness. An expanded knowledge base about what makes problems dealing with a partner a significant suicide risk factor (eg, increased conflict, lack of support, etc.) would better enable clinicians to intervene effectively. Effective intervention may lessen suicidal behaviors or deaths from suicides within the Veteran population.
Limitations
One limitation is the focus on patients who accepted a mental health referral. This study design may limit the generalizability of results to veterans who would not accept mental health treatment. The homogenous sample of veterans is a second limitation. Most participants were male, white, and had a mean age of 62 years. These demographics are representative of the veterans that most typically utilize VA services; however, more research is needed on veterans living with cancer who are female and of diverse racial and ethnic backgrounds. There are likely differences in problems endorsed and factors associated with SI based on age, race, sex, and other socioeconomic factors. A third limitation is the cross-sectional, retrospective nature of this study. Future studies are advised to assess for distress at multiple time points. This is consistent with NCCN Standards of Care for Distress Management.2 Longitudinal data would enable more findings about distress and SI throughout the course of cancer diagnosis and treatment, therefore enhancing clinical implications and informing future research.
Conclusion
This is among the first of studies to investigate distress and factors associated with SI in veterans living with cancer who were referred for psychology services. The prevalence of distress caused by psychosocial factors (including treatment decisions, worry, and depression) highlights the importance of including mental health services as part of comprehensive cancer treatment.
Distress due to treatment decisions may be attributed to a litany of factors such as a veteran’s consideration of adverse effects, effectiveness of treatments, changes to quality of life or functioning, and inclusion of alternative or complimentary treatments. These types of decisions often are reported to be difficult conversations to have with family members or loved ones, who are likely experiencing distress of their own. The role of a mental health provider to assist veterans in exploring their treatment decisions and the implications of such decisions appears important to lessening distress.
Early intervention for emotional symptoms would likely benefit veterans’ management of distress and may lessen suicide risk as depression is known to place veterans at-risk for SI.20 This underscores the importance of timely distress assessment to prevent mild emotional distress from progressing to potentially severe or life-threatening emotional distress. For veterans with a psychiatric history, timely assessment and intervention is essential because psychiatric history is an established suicide risk factor that may be exacerbated by cancer-related distress.12
Furthermore, management of intolerable physical symptoms may lessen risk for suicide.4 Under medical guidance, fatigue may be improved using exercise.21 Behavioral intervention is commonly used as first-line treatment for sleep problems.22 While pain may be lessened through medication or nonpharmacological interventions.23
Considering the numerous ways that distress may present itself (eg, practical, emotional, or physical) and increase risk for SI, it is essential that all veterans living with cancer are assessed for distress and SI, regardless of their presentation. Although veterans may not outwardly express distress, this does not indicate the absence of either distress or risk for suicide. For example, a veteran may be distressed due to financial concerns, transportation issues, and the health of his/her partner or spouse. This veteran may not exhibit visible symptoms of distress, as would be expected when the source of distress is emotional (eg, depression, anxiety). However, this veteran is equally vulnerable to impairing distress and SI as someone who exhibits emotional distress. Distress assessments should be further developed to capture both the visible and less apparent sources of distress, while also serving the imperative function of screening for suicide. Other researchers also have noted the necessity of this development.24 Currently, the NCCN DT and Problems List does not include any assessment of SI or behavior.
Finally, this study identified a potentially critical factor to include in distress assessment: problems dealing with a partner. Problems dealing with a partner have been noted as a source of distress in existing literature, but this is the first study to find problems dealing with a partner to be a predictor of SI in veterans living with cancer.4-6
Because partners often attend appointments with veterans, it is not surprising that problems dealing with their partner are not disclosed more readily. It is recommended that clinicians ask veterans about potential problems with their partner when they are alone. Directly gathering information about such problems while assessing for distress may assist health care workers in providing the most effective, accurate type of intervention in a timely manner, and potentially mitigate risk for suicide.
As recommended by the NCCN and numerous researchers, findings from the current study underscore the importance of accurate, timely assessment of distress.2,4,8 This study makes several important recommendations about how distress assessment may be strengthened and further developed, specifically for the veteran population. This study also expands the current knowledge base of what is known about veterans living with cancer, and has begun to fill a gap in the existing literature. Consistent with the VA mission to end veteran suicide, results suggest that veterans living with cancer should be regularly screened for distress, asked about distress related to their partner, and assessed for SI. Continued efforts to enhance assessment of and response to distress may lessen suicide risk in veterans with cancer.11
Acknowledgements
This study is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7-34.
2. Riba MB, Donovan, KA, Andersen, B. National Comprehensive Cancer Network clinical practice guidelines in oncology. Distress management (Version 3.2019). J Natl Compr Can Net, 2019;17(10):1229-1249.
3. Zabora J, BrintzenhofeSzoc K, Curbow B, Hooker C, Pianta dosi S. The prevalence of psychological distress by cancer site. Psychooncology. 2001;10(1):19–28.
4. Holland JC, Alici Y. Management of distress in cancer patients. J Support Oncol. 2010;8(1):4-12.
5. Bulli F, Miccinesi G, Maruelli A, Katz M, Paci E. The measure of psychological distress in cancer patients: the use of distress thermometer in the oncological rehabilitation center of Florence. Support Care Cancer. 2009;17(7):771–779.
6. Jacobsen PB, Donovan KA, Trask PC, et al. Screening for psychologic distress in ambulatory cancer patients. Cancer. 2005;103(7):1494-1502.
7. Smith J, Berman S, Dimick J, et al. Distress Screening and Management in an Outpatient VA Cancer Clinic: A Pilot Project Involving Ambulatory Patients Across the Disease Trajectory. Fed Pract. 2017;34(Suppl 1):43S–50S.
8. Carlson LE, Waller A, Groff SL, Bultz BD. Screening for distress, the sixth vital sign, in lung cancer patients: effects on pain, fatigue, and common problems--secondary outcomes of a randomized controlled trial. Psychooncology. 2013;22(8):1880-1888.
9. Cooley ME, Short TH, Moriarty HJ. Symptom prevalence, distress, and change over time in adults receiving treatment for lung cancer. Psychooncology. 2003;12(7):694-708.
10. US Department of Veterans Affairs Office of Suicide Prevention. Suicide among veterans and other Americans 2001-2014. https://www.mentalhealth.va.gov/docs/2016suicidedatareport.pdf. Published August 3, 2016. Accessed April 13, 2020.
11. Aboumrad M, Shiner B, Riblet N, Mills, PD, Watts BV. Factors contributing to cancer-related suicide: a study of root-cause-analysis reports. Psychooncology. 2018;27(9):2237-2244.
12. US Department of Veterans Affairs, Office of Mental Health and Suicide Prevention. National Strategy for Preventing Veteran Suicide 2018–2028. https://www.mentalhealth.va.gov/suicide_prevention/docs/Office-of-Mental-Health-and-Suicide-Prevention-National-Strategy-for-Preventing-Veterans-Suicide.pdf Published 2018. Accessed April 13, 2020.
13. Carlson LE, Waller A, Mitchell AJ. Screening for distress and unmet needs in patients with cancer: review and recommendations. J Clin Oncol. 2012;30(11):1160-1177.
14. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613.
15. Martin A, Rief W, Klaiberg A, Braehler E. Validity of the brief patient health questionnaire mood scale (PHQ-9) in the general population. Gen Hosp Psychiatry. 2006;28(1):71-77.
16. Joiner TE. Why People Die by Suicide. Cambridge, MA: Harvard University Press, 2005.
17. Kleiman EM, Riskind JH, Schaefer KE. Social support and positive events as suicide resiliency factors: examination of synergistic buffering effects. Arch Suicide Res. 2014;18(2):144-155.
18. Van Orden KA, Witte TK, Gordon KH, Bender TW, Joiner TE Jr. Suicidal desire and the capability for suicide: tests of the interpersonal-psychological theory of suicidal behavior among adults. J Consult Clin Psychol. 2008;76(1):72–83.
19. Bryan CJ, Morrow CE, Anestis MD, Joiner TE. A preliminary test of the interpersonal -psychological theory of suicidal behavior in a military sample. Personal Individual Differ. 2010;48(3):347-350.
20. Miller SN, Monahan CJ, Phillips KM, Agliata D, Gironda RJ. Mental health utilization among veterans at risk for suicide: Data from a post-deployment clinic [published online ahead of print, 2018 Oct 8]. Psychol Serv. 2018;10.1037/ser0000311.
21. Galvão DA, Newton RU. Review of exercise intervention studies in cancer patients. J Clin Oncol. 2005;23(4):899-909.
22. Qaseem A, Kansagara D, Forciea MA, Cooke M, Denberg TD; Clinical Guidelines Committee of the American College of Physicians. Management of chronic insomnia disorder in adults: A clinical practice guideline from the American College of Physicians. Ann Intern Med. 2016;165(2):125-133.
23. Ngamkham S, Holden JE, Smith EL. A systematic review: Mindfulness intervention for cancer-related pain. Asia Pac J Oncol Nurs. 2019;6(2):161-169.
24. Granek L, Nakash O, Ben-David M, Shapira S, Ariad S. Oncologists’, nurses’, and social workers’ strategies and barriers to identifying suicide risk in cancer patients. Psychooncology. 2018;27(1):148-154.
It was estimated that physicians would diagnose a form of invasive cancer > 1.7 million times in 2019. As the second most common cause of death in the US, > 600,000 people were projected to die from cancer in 2019.1 Many individuals with cancer endure distress, which the National Comprehensive Cancer Network (NCCN) defines as a “multifactorial unpleasant experience of a psychological (ie, cognitive, behavioral, emotional), social, spiritual, and/or physical nature that may interfere with the ability to cope effectively with cancer, its physical symptoms, and its treatment.”2,3 Distress in people living with cancer has been attributed to various psychosocial concerns, such as family problems, whichinclude dealing with partners and children; emotional problems, such as depression and anxiety; and physical symptoms, such as pain and fatigue.4-9 Certain factors associated with distress may increase a patient’s risk for suicide.4
Veterans are at particularly high risk for suicide.10 In 2014, veterans accounted for 18% of completed suicides in the US but only were 8.5% of the total population that same year.10 Yet, little research has been done on the relationship between distress and suicide in veterans living with cancer. Aboumrad and colleagues found that 45% of veterans with cancer who completed suicide reported family issues and 41% endorsed chronic pain.11 This study recommended continued efforts to assess and treat distress to lessen risk of suicide in veterans living with cancer; however, to date, only 1 study has specifically evaluated distress and problems endorsed among veterans living with cancer.7
Suicide prevention is of the highest priority to the US Department of Veterans Affairs (VA).12 Consistent with the VA mission to end veteran suicide, the current study aimed to better understand the relationship between distress and suicide within a sample of veterans living with cancer. Findings would additionally be used to tailor clinical assessments and interventions for veterans living with cancer.
This study had 3 primary goals. First, we sought to understand demographic and clinical factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. Second, the study investigated the most commonly endorsed problems by veterans living with cancer. Finally, we examined which problems were related to suicidal ideation (SI). It was hypothesized that veterans who reported severe distress would be significantly more likely to endorse SI when compared with veterans who reported mild or moderate distress. Based on existing literature, it was further hypothesized that family, emotional, and physical problems would be significantly associated with SI.7,11
Methods
The current study was conducted at James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida. Inclusion criteria included veterans who were diagnosed with cancer, attended an outpatient psychology-oncology evaluation, and completed mental health screening measures provided during their evaluation. Exclusion criteria included veterans who: were seen in response to an inpatient consult, were seen solely for a stem cell transplant evaluation, or did not complete the screening measures.
Measures
A veteran’s demographic (eg, age, sex, ethnicity) and clinical (eg, cancer type, stage of disease, recurrence, cancer treatments received) information was abstracted from their VA medical records. Marital status was assessed during a clinical interview and documented as part of the standardized suicide risk assessment.
The Distress Thermometer (DT) is a subjective measure developed by the NCCN.2 The DT provides a visual representation of a thermometer and asks patients to rate their level of distress over the past week with 0 indicating no distress and 10 indicating extreme distress.
The measurement additionally lists 39 problems nested within 5 domains: practical, family, emotional, spiritual/religious, and physical. Patients may endorse listed items under each problem domain by indicating yes or no. Endorsement of various items are intended to provide more detailed information about sources of distress. Due to the predominantly male and mostly older population included in this study the ability to have children measure was removed from the family problem domain.
SI was assessed in 2 ways. First, by patients’ self-report through item-9 of the Patient Health Questionnaire-9 (PHQ-9).14 Item-9 asks “over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?” Responses range from 0 (not at all) to 3 (nearly every day).14 Responses > 0 were considered a positive screen for SI.
Procedure
Participants were a sample of veterans who were referred for psychology-oncology services. The NCCN DT and Problems List were administered prior to the start of clinical interviews, which followed a checklist and included standardized assessments of SI and history of a suicide attempt(s). A licensed clinical psychologist or a postdoctoral resident conducted these assessments under the supervision of a licensed psychologist. Data gathered during the clinical interview and from the DT and problems list were documented in health records, which were retrospectively reviewed for relevant information (eg, cancer diagnosis, SI). Therefore, informed consent was waived. This study was approved by the JAHVH Institutional Review Board.
Analysis
Data were analyzed using SPSS Version 25. Data analysis proceeded in 3 steps. First, descriptive statistics included the demographic and clinical factors present in the current sample. Difference between those with and without suicidal ideation were compared using F-statistic for continuous variables and χ2 analyses for categorical variables. Second, to examine relationships between each DT problem domain and SI, χ2 analyses were conducted. Third, DT problem domains that had a significant relationship with SI were entered in a logistic regression. This analysis determined which items, if any, from a DT problem domain predicted SI. In the logistic regression model, history of suicide attempts was entered into the first block, as history of suicide attempts is a well-established risk factor for subsequent suicidal ideation. In the second block, other variables that were significantly related to suicidal ideation in the second step of analyses were included. Before interpreting the results of the logistic regression, model fit was tested using the Hosmer-Lemeshow test. Significance of each individual predictor variable in the model is reported using the Wald χ2 statistic; each Wald statistic is compared with a χ2 distribution with 1 degree of freedom (df). Results of logistic regression models also provide information about the effect of each predictor variable in the regression equation (beta weight), odds a veteran who endorsed each predictor variable in the model would also endorse SI (as indicated by the odds ratio), and an estimate of the amount of variance accounted for by each predictor variable (using Nagelkerke’s pseudo R2, which ranges in value from 0 to 1 with higher values indicating more variance explained). For all analyses, P value of .05 (2-tailed) was used for statistical significance.
Results
The sample consisted of 174 veterans (Table 1). The majority (77.6%) were male with a mean age of nearly 62 years (range, 29-87). Most identified as white (74.1%) with half reporting they were either married or living with a partner.
Prostate cancer (19.0%) was the most common type of cancer among study participants followed by head and neck (18.4%), lymphoma/leukemia (11.5%), lung (11.5%), and breast (10.9%); 31.6% had metastatic disease and 14.9% had recurrent disease. Chemotherapy (42.5%) was the most common treatment modality, followed by surgery (38.5%) and radiation (31.6%). The sample was distributed among the 3 distress DT categories: mild (18.4%), moderate (42.5%), and severe (39.1%).
Problems Endorsed
Treatment decisions (44.3%) and insurance/financial concerns (35.1%) were the most frequently endorsed practical problems (Figure 1). Family health issues (33.9%) and dealing with partner (23.0%) were the most frequently endorsed family problems (Figure 2). Worry (73.0%) and depression (69.5%) were the most frequent emotional problems; of note, all emotional problems were endorsed by at least 50% of veterans (Figure 3). Fatigue (71.3%), sleep (70.7%), and pain (69%), were the most frequently endorsed physical problems (Figure 4). Spiritual/religious problems were endorsed by 15% of veterans.
Suicidal Ideation
Overall, 25.3% of veterans endorsed SI. About 20% of veterans reported a history of ≥ 1 suicide attempts in their lifetime. A significant relationship among distress categories and SI was found (χ2 = 18.36, P < .001). Veterans with severe distress were more likely to endorse SI (42.7%) when compared with veterans with mild (9.4%) or moderate (16.2%) distress.
Similarly, a significant relationship among distress categories and a history of a suicide attempt(s) was found (χ2 = 6.08, P = .048). Veterans with severe distress were more likely to have attempted suicide (29.4%) when compared with veterans with mild (12.5%) or moderate (14.9%) distress.
χ2 analyses were conducted to examine the relationships between DT problem domains and SI. A significant relationship was found between family problems and SI (
Logistic regression analyses determined whether items representative of the family problems domain were predictive of SI. Suicide attempt(s) were entered in the first step of the model to evaluate risk factors for SI over this already established risk factor. The assumptions of logistic regression were met.
The Hosmer-Lemeshow test (χ2 = 3.66, df = 5, P = .56) demonstrated that the model fit was good. The group of predictors used in the model differentiate between people who were experiencing SI and those who were not experiencing SI at the time of evaluation. A history of a suicide attempt(s) predicted SI, as expected (Wald = 6.821, df = 1, P = .01). The odds that a veteran with a history of a suicide attempt(s) would endorse SI at the time of the evaluation was nearly 3 times greater than that of veterans without a history of a suicide attempt(s). Over and above suicide attempts, problems dealing with partner (Wald = 15.142; df = 1, P < .001) was a second significant predictor of current SI. The odds that a veteran who endorsed problems dealing with partner would also endorse SI was > 5 times higher than that of veterans who did not endorse problems dealing with partner. This finding represents a significant risk factor for SI, over and above a history of a suicide attempt(s). The other items from the family problems domains were not significant (P > .05) (Table 3).
Discussion
This study aimed to understand factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. As hypothesized, veterans who endorsed severe distress were significantly more likely to endorse SI. They also were more likely to have a history of a suicide attempt(s) when compared with those with mild or moderate distress.
A second aim of this study was to understand the most commonly endorsed problems. Consistent with prior literature, treatment decisions were the most commonly endorsed practical problem; worry and depression were the most common emotional problems; and fatigue, sleep, and pain were the most common physical problems.7
A finding unique to the current study is that family health issues and dealing with partner were specified as the most common family problems. However, a study by Smith and colleagues did not provide information about the rank of most frequently reported problems within this domain.7
The third aim was to understand which problems were related to SI. It was hypothesized that family, emotional, and physical problems would be related to SI. However, results indicated that only family problems (specifically, problems dealing with a partner) were significantly associated with SI among veterans living with cancer.
Contrary to expectations, emotional and physical problems were not found to have a significant relationship with SI. This is likely because veterans endorsed items nested within these problem domains with similar frequency. The lack of significant findings does not suggest that emotional and physical problems are not significant predictors of SI for veterans living with cancer, but that no specific emotional or physical symptom stood out as a predictor of suicidal ideation above the others.
The finding of a significant relationship between family problems (specifically, problems dealing with a partner) and SI in this study is consistent with findings of Aboumrad and colleagues in a study that examined root-cause analyses of completed suicides by veterans living with cancer.11 They found that nearly half the sample endorsed family problems prior to their death, and a small but notable percentage of veterans who completed suicide reported divorce as a stressor prior to their death.
This finding may be explained by Thomas Joiner's interpersonal-psychological theory of suicidal behavior (IPT), which suggests that completed suicide may result from a thwarted sense of belonging, perceived burdensomeness, and acquired capability for suicide.16 Problems dealing with a partner may impact a veteran’s sense of belonging or social connectedness. Problems dealing with a partner also may be attributed to perceived burdens due to limitations imposed by living with cancer and/or undergoing treatment. In both circumstances, the veteran’s social support system may be negatively impacted, and perceived social support is a well-established protective factor against suicide.17
Partner distress is a second consideration. It is likely that veterans’ partners experienced their own distress in response to the veteran’s cancer diagnosis and/or treatment. The partner’s cause, severity, and expression of distress may contribute to problems for the couple.
Finally, the latter point of the IPT refers to acquired capability, or the ability to inflict deadly harm to oneself.18 A military sample was found to have more acquired capability for suicide when compared with a college undergraduate sample.19 A history of a suicide attempt(s) and male gender have been found to significantly predict acquired capability to complete suicide.18 Furthermore, because veterans living with cancer often are in pain, fear of pain associated with suicide may be reduced and, therefore, acquired capability increased. This suggests that male veterans living with cancer who are in pain, have a history of a suicide attempt(s), and current problems with their partner may be an extremely vulnerable population at-risk for suicide. Results from the current study emphasize the importance of veterans having access to mental health and crisis resources for problems dealing with their partner. Partner problems may foreshadow a potentially lethal type of distress.
Strengths
This study’s aims are consistent with the VA’s mission to end veteran suicide and contributes to literature in several important ways.12 First, veterans living with cancer are an understudied population. The current study addresses a gap in existing literature by researching veterans living with cancer and aims to better understand the relationship between cancer-related distress and SI. Second, to the best of the authors’ knowledge, this study is the first to find that problems dealing with a partner significantly increases a veteran’s risk for SI above a history of a suicide attempt(s). Risk assessments now may be more comprehensive through inclusion of this distress factor.
It is recommended that future research use IPT to further investigate the relationship between problems dealing with a partner and SI.16 Future research may do so by including specific measures to assess for the tenants of the theory, including measurements of burdensomeness and belongingness. An expanded knowledge base about what makes problems dealing with a partner a significant suicide risk factor (eg, increased conflict, lack of support, etc.) would better enable clinicians to intervene effectively. Effective intervention may lessen suicidal behaviors or deaths from suicides within the Veteran population.
Limitations
One limitation is the focus on patients who accepted a mental health referral. This study design may limit the generalizability of results to veterans who would not accept mental health treatment. The homogenous sample of veterans is a second limitation. Most participants were male, white, and had a mean age of 62 years. These demographics are representative of the veterans that most typically utilize VA services; however, more research is needed on veterans living with cancer who are female and of diverse racial and ethnic backgrounds. There are likely differences in problems endorsed and factors associated with SI based on age, race, sex, and other socioeconomic factors. A third limitation is the cross-sectional, retrospective nature of this study. Future studies are advised to assess for distress at multiple time points. This is consistent with NCCN Standards of Care for Distress Management.2 Longitudinal data would enable more findings about distress and SI throughout the course of cancer diagnosis and treatment, therefore enhancing clinical implications and informing future research.
Conclusion
This is among the first of studies to investigate distress and factors associated with SI in veterans living with cancer who were referred for psychology services. The prevalence of distress caused by psychosocial factors (including treatment decisions, worry, and depression) highlights the importance of including mental health services as part of comprehensive cancer treatment.
Distress due to treatment decisions may be attributed to a litany of factors such as a veteran’s consideration of adverse effects, effectiveness of treatments, changes to quality of life or functioning, and inclusion of alternative or complimentary treatments. These types of decisions often are reported to be difficult conversations to have with family members or loved ones, who are likely experiencing distress of their own. The role of a mental health provider to assist veterans in exploring their treatment decisions and the implications of such decisions appears important to lessening distress.
Early intervention for emotional symptoms would likely benefit veterans’ management of distress and may lessen suicide risk as depression is known to place veterans at-risk for SI.20 This underscores the importance of timely distress assessment to prevent mild emotional distress from progressing to potentially severe or life-threatening emotional distress. For veterans with a psychiatric history, timely assessment and intervention is essential because psychiatric history is an established suicide risk factor that may be exacerbated by cancer-related distress.12
Furthermore, management of intolerable physical symptoms may lessen risk for suicide.4 Under medical guidance, fatigue may be improved using exercise.21 Behavioral intervention is commonly used as first-line treatment for sleep problems.22 While pain may be lessened through medication or nonpharmacological interventions.23
Considering the numerous ways that distress may present itself (eg, practical, emotional, or physical) and increase risk for SI, it is essential that all veterans living with cancer are assessed for distress and SI, regardless of their presentation. Although veterans may not outwardly express distress, this does not indicate the absence of either distress or risk for suicide. For example, a veteran may be distressed due to financial concerns, transportation issues, and the health of his/her partner or spouse. This veteran may not exhibit visible symptoms of distress, as would be expected when the source of distress is emotional (eg, depression, anxiety). However, this veteran is equally vulnerable to impairing distress and SI as someone who exhibits emotional distress. Distress assessments should be further developed to capture both the visible and less apparent sources of distress, while also serving the imperative function of screening for suicide. Other researchers also have noted the necessity of this development.24 Currently, the NCCN DT and Problems List does not include any assessment of SI or behavior.
Finally, this study identified a potentially critical factor to include in distress assessment: problems dealing with a partner. Problems dealing with a partner have been noted as a source of distress in existing literature, but this is the first study to find problems dealing with a partner to be a predictor of SI in veterans living with cancer.4-6
Because partners often attend appointments with veterans, it is not surprising that problems dealing with their partner are not disclosed more readily. It is recommended that clinicians ask veterans about potential problems with their partner when they are alone. Directly gathering information about such problems while assessing for distress may assist health care workers in providing the most effective, accurate type of intervention in a timely manner, and potentially mitigate risk for suicide.
As recommended by the NCCN and numerous researchers, findings from the current study underscore the importance of accurate, timely assessment of distress.2,4,8 This study makes several important recommendations about how distress assessment may be strengthened and further developed, specifically for the veteran population. This study also expands the current knowledge base of what is known about veterans living with cancer, and has begun to fill a gap in the existing literature. Consistent with the VA mission to end veteran suicide, results suggest that veterans living with cancer should be regularly screened for distress, asked about distress related to their partner, and assessed for SI. Continued efforts to enhance assessment of and response to distress may lessen suicide risk in veterans with cancer.11
Acknowledgements
This study is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.
It was estimated that physicians would diagnose a form of invasive cancer > 1.7 million times in 2019. As the second most common cause of death in the US, > 600,000 people were projected to die from cancer in 2019.1 Many individuals with cancer endure distress, which the National Comprehensive Cancer Network (NCCN) defines as a “multifactorial unpleasant experience of a psychological (ie, cognitive, behavioral, emotional), social, spiritual, and/or physical nature that may interfere with the ability to cope effectively with cancer, its physical symptoms, and its treatment.”2,3 Distress in people living with cancer has been attributed to various psychosocial concerns, such as family problems, whichinclude dealing with partners and children; emotional problems, such as depression and anxiety; and physical symptoms, such as pain and fatigue.4-9 Certain factors associated with distress may increase a patient’s risk for suicide.4
Veterans are at particularly high risk for suicide.10 In 2014, veterans accounted for 18% of completed suicides in the US but only were 8.5% of the total population that same year.10 Yet, little research has been done on the relationship between distress and suicide in veterans living with cancer. Aboumrad and colleagues found that 45% of veterans with cancer who completed suicide reported family issues and 41% endorsed chronic pain.11 This study recommended continued efforts to assess and treat distress to lessen risk of suicide in veterans living with cancer; however, to date, only 1 study has specifically evaluated distress and problems endorsed among veterans living with cancer.7
Suicide prevention is of the highest priority to the US Department of Veterans Affairs (VA).12 Consistent with the VA mission to end veteran suicide, the current study aimed to better understand the relationship between distress and suicide within a sample of veterans living with cancer. Findings would additionally be used to tailor clinical assessments and interventions for veterans living with cancer.
This study had 3 primary goals. First, we sought to understand demographic and clinical factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. Second, the study investigated the most commonly endorsed problems by veterans living with cancer. Finally, we examined which problems were related to suicidal ideation (SI). It was hypothesized that veterans who reported severe distress would be significantly more likely to endorse SI when compared with veterans who reported mild or moderate distress. Based on existing literature, it was further hypothesized that family, emotional, and physical problems would be significantly associated with SI.7,11
Methods
The current study was conducted at James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida. Inclusion criteria included veterans who were diagnosed with cancer, attended an outpatient psychology-oncology evaluation, and completed mental health screening measures provided during their evaluation. Exclusion criteria included veterans who: were seen in response to an inpatient consult, were seen solely for a stem cell transplant evaluation, or did not complete the screening measures.
Measures
A veteran’s demographic (eg, age, sex, ethnicity) and clinical (eg, cancer type, stage of disease, recurrence, cancer treatments received) information was abstracted from their VA medical records. Marital status was assessed during a clinical interview and documented as part of the standardized suicide risk assessment.
The Distress Thermometer (DT) is a subjective measure developed by the NCCN.2 The DT provides a visual representation of a thermometer and asks patients to rate their level of distress over the past week with 0 indicating no distress and 10 indicating extreme distress.
The measurement additionally lists 39 problems nested within 5 domains: practical, family, emotional, spiritual/religious, and physical. Patients may endorse listed items under each problem domain by indicating yes or no. Endorsement of various items are intended to provide more detailed information about sources of distress. Due to the predominantly male and mostly older population included in this study the ability to have children measure was removed from the family problem domain.
SI was assessed in 2 ways. First, by patients’ self-report through item-9 of the Patient Health Questionnaire-9 (PHQ-9).14 Item-9 asks “over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?” Responses range from 0 (not at all) to 3 (nearly every day).14 Responses > 0 were considered a positive screen for SI.
Procedure
Participants were a sample of veterans who were referred for psychology-oncology services. The NCCN DT and Problems List were administered prior to the start of clinical interviews, which followed a checklist and included standardized assessments of SI and history of a suicide attempt(s). A licensed clinical psychologist or a postdoctoral resident conducted these assessments under the supervision of a licensed psychologist. Data gathered during the clinical interview and from the DT and problems list were documented in health records, which were retrospectively reviewed for relevant information (eg, cancer diagnosis, SI). Therefore, informed consent was waived. This study was approved by the JAHVH Institutional Review Board.
Analysis
Data were analyzed using SPSS Version 25. Data analysis proceeded in 3 steps. First, descriptive statistics included the demographic and clinical factors present in the current sample. Difference between those with and without suicidal ideation were compared using F-statistic for continuous variables and χ2 analyses for categorical variables. Second, to examine relationships between each DT problem domain and SI, χ2 analyses were conducted. Third, DT problem domains that had a significant relationship with SI were entered in a logistic regression. This analysis determined which items, if any, from a DT problem domain predicted SI. In the logistic regression model, history of suicide attempts was entered into the first block, as history of suicide attempts is a well-established risk factor for subsequent suicidal ideation. In the second block, other variables that were significantly related to suicidal ideation in the second step of analyses were included. Before interpreting the results of the logistic regression, model fit was tested using the Hosmer-Lemeshow test. Significance of each individual predictor variable in the model is reported using the Wald χ2 statistic; each Wald statistic is compared with a χ2 distribution with 1 degree of freedom (df). Results of logistic regression models also provide information about the effect of each predictor variable in the regression equation (beta weight), odds a veteran who endorsed each predictor variable in the model would also endorse SI (as indicated by the odds ratio), and an estimate of the amount of variance accounted for by each predictor variable (using Nagelkerke’s pseudo R2, which ranges in value from 0 to 1 with higher values indicating more variance explained). For all analyses, P value of .05 (2-tailed) was used for statistical significance.
Results
The sample consisted of 174 veterans (Table 1). The majority (77.6%) were male with a mean age of nearly 62 years (range, 29-87). Most identified as white (74.1%) with half reporting they were either married or living with a partner.
Prostate cancer (19.0%) was the most common type of cancer among study participants followed by head and neck (18.4%), lymphoma/leukemia (11.5%), lung (11.5%), and breast (10.9%); 31.6% had metastatic disease and 14.9% had recurrent disease. Chemotherapy (42.5%) was the most common treatment modality, followed by surgery (38.5%) and radiation (31.6%). The sample was distributed among the 3 distress DT categories: mild (18.4%), moderate (42.5%), and severe (39.1%).
Problems Endorsed
Treatment decisions (44.3%) and insurance/financial concerns (35.1%) were the most frequently endorsed practical problems (Figure 1). Family health issues (33.9%) and dealing with partner (23.0%) were the most frequently endorsed family problems (Figure 2). Worry (73.0%) and depression (69.5%) were the most frequent emotional problems; of note, all emotional problems were endorsed by at least 50% of veterans (Figure 3). Fatigue (71.3%), sleep (70.7%), and pain (69%), were the most frequently endorsed physical problems (Figure 4). Spiritual/religious problems were endorsed by 15% of veterans.
Suicidal Ideation
Overall, 25.3% of veterans endorsed SI. About 20% of veterans reported a history of ≥ 1 suicide attempts in their lifetime. A significant relationship among distress categories and SI was found (χ2 = 18.36, P < .001). Veterans with severe distress were more likely to endorse SI (42.7%) when compared with veterans with mild (9.4%) or moderate (16.2%) distress.
Similarly, a significant relationship among distress categories and a history of a suicide attempt(s) was found (χ2 = 6.08, P = .048). Veterans with severe distress were more likely to have attempted suicide (29.4%) when compared with veterans with mild (12.5%) or moderate (14.9%) distress.
χ2 analyses were conducted to examine the relationships between DT problem domains and SI. A significant relationship was found between family problems and SI (
Logistic regression analyses determined whether items representative of the family problems domain were predictive of SI. Suicide attempt(s) were entered in the first step of the model to evaluate risk factors for SI over this already established risk factor. The assumptions of logistic regression were met.
The Hosmer-Lemeshow test (χ2 = 3.66, df = 5, P = .56) demonstrated that the model fit was good. The group of predictors used in the model differentiate between people who were experiencing SI and those who were not experiencing SI at the time of evaluation. A history of a suicide attempt(s) predicted SI, as expected (Wald = 6.821, df = 1, P = .01). The odds that a veteran with a history of a suicide attempt(s) would endorse SI at the time of the evaluation was nearly 3 times greater than that of veterans without a history of a suicide attempt(s). Over and above suicide attempts, problems dealing with partner (Wald = 15.142; df = 1, P < .001) was a second significant predictor of current SI. The odds that a veteran who endorsed problems dealing with partner would also endorse SI was > 5 times higher than that of veterans who did not endorse problems dealing with partner. This finding represents a significant risk factor for SI, over and above a history of a suicide attempt(s). The other items from the family problems domains were not significant (P > .05) (Table 3).
Discussion
This study aimed to understand factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. As hypothesized, veterans who endorsed severe distress were significantly more likely to endorse SI. They also were more likely to have a history of a suicide attempt(s) when compared with those with mild or moderate distress.
A second aim of this study was to understand the most commonly endorsed problems. Consistent with prior literature, treatment decisions were the most commonly endorsed practical problem; worry and depression were the most common emotional problems; and fatigue, sleep, and pain were the most common physical problems.7
A finding unique to the current study is that family health issues and dealing with partner were specified as the most common family problems. However, a study by Smith and colleagues did not provide information about the rank of most frequently reported problems within this domain.7
The third aim was to understand which problems were related to SI. It was hypothesized that family, emotional, and physical problems would be related to SI. However, results indicated that only family problems (specifically, problems dealing with a partner) were significantly associated with SI among veterans living with cancer.
Contrary to expectations, emotional and physical problems were not found to have a significant relationship with SI. This is likely because veterans endorsed items nested within these problem domains with similar frequency. The lack of significant findings does not suggest that emotional and physical problems are not significant predictors of SI for veterans living with cancer, but that no specific emotional or physical symptom stood out as a predictor of suicidal ideation above the others.
The finding of a significant relationship between family problems (specifically, problems dealing with a partner) and SI in this study is consistent with findings of Aboumrad and colleagues in a study that examined root-cause analyses of completed suicides by veterans living with cancer.11 They found that nearly half the sample endorsed family problems prior to their death, and a small but notable percentage of veterans who completed suicide reported divorce as a stressor prior to their death.
This finding may be explained by Thomas Joiner's interpersonal-psychological theory of suicidal behavior (IPT), which suggests that completed suicide may result from a thwarted sense of belonging, perceived burdensomeness, and acquired capability for suicide.16 Problems dealing with a partner may impact a veteran’s sense of belonging or social connectedness. Problems dealing with a partner also may be attributed to perceived burdens due to limitations imposed by living with cancer and/or undergoing treatment. In both circumstances, the veteran’s social support system may be negatively impacted, and perceived social support is a well-established protective factor against suicide.17
Partner distress is a second consideration. It is likely that veterans’ partners experienced their own distress in response to the veteran’s cancer diagnosis and/or treatment. The partner’s cause, severity, and expression of distress may contribute to problems for the couple.
Finally, the latter point of the IPT refers to acquired capability, or the ability to inflict deadly harm to oneself.18 A military sample was found to have more acquired capability for suicide when compared with a college undergraduate sample.19 A history of a suicide attempt(s) and male gender have been found to significantly predict acquired capability to complete suicide.18 Furthermore, because veterans living with cancer often are in pain, fear of pain associated with suicide may be reduced and, therefore, acquired capability increased. This suggests that male veterans living with cancer who are in pain, have a history of a suicide attempt(s), and current problems with their partner may be an extremely vulnerable population at-risk for suicide. Results from the current study emphasize the importance of veterans having access to mental health and crisis resources for problems dealing with their partner. Partner problems may foreshadow a potentially lethal type of distress.
Strengths
This study’s aims are consistent with the VA’s mission to end veteran suicide and contributes to literature in several important ways.12 First, veterans living with cancer are an understudied population. The current study addresses a gap in existing literature by researching veterans living with cancer and aims to better understand the relationship between cancer-related distress and SI. Second, to the best of the authors’ knowledge, this study is the first to find that problems dealing with a partner significantly increases a veteran’s risk for SI above a history of a suicide attempt(s). Risk assessments now may be more comprehensive through inclusion of this distress factor.
It is recommended that future research use IPT to further investigate the relationship between problems dealing with a partner and SI.16 Future research may do so by including specific measures to assess for the tenants of the theory, including measurements of burdensomeness and belongingness. An expanded knowledge base about what makes problems dealing with a partner a significant suicide risk factor (eg, increased conflict, lack of support, etc.) would better enable clinicians to intervene effectively. Effective intervention may lessen suicidal behaviors or deaths from suicides within the Veteran population.
Limitations
One limitation is the focus on patients who accepted a mental health referral. This study design may limit the generalizability of results to veterans who would not accept mental health treatment. The homogenous sample of veterans is a second limitation. Most participants were male, white, and had a mean age of 62 years. These demographics are representative of the veterans that most typically utilize VA services; however, more research is needed on veterans living with cancer who are female and of diverse racial and ethnic backgrounds. There are likely differences in problems endorsed and factors associated with SI based on age, race, sex, and other socioeconomic factors. A third limitation is the cross-sectional, retrospective nature of this study. Future studies are advised to assess for distress at multiple time points. This is consistent with NCCN Standards of Care for Distress Management.2 Longitudinal data would enable more findings about distress and SI throughout the course of cancer diagnosis and treatment, therefore enhancing clinical implications and informing future research.
Conclusion
This is among the first of studies to investigate distress and factors associated with SI in veterans living with cancer who were referred for psychology services. The prevalence of distress caused by psychosocial factors (including treatment decisions, worry, and depression) highlights the importance of including mental health services as part of comprehensive cancer treatment.
Distress due to treatment decisions may be attributed to a litany of factors such as a veteran’s consideration of adverse effects, effectiveness of treatments, changes to quality of life or functioning, and inclusion of alternative or complimentary treatments. These types of decisions often are reported to be difficult conversations to have with family members or loved ones, who are likely experiencing distress of their own. The role of a mental health provider to assist veterans in exploring their treatment decisions and the implications of such decisions appears important to lessening distress.
Early intervention for emotional symptoms would likely benefit veterans’ management of distress and may lessen suicide risk as depression is known to place veterans at-risk for SI.20 This underscores the importance of timely distress assessment to prevent mild emotional distress from progressing to potentially severe or life-threatening emotional distress. For veterans with a psychiatric history, timely assessment and intervention is essential because psychiatric history is an established suicide risk factor that may be exacerbated by cancer-related distress.12
Furthermore, management of intolerable physical symptoms may lessen risk for suicide.4 Under medical guidance, fatigue may be improved using exercise.21 Behavioral intervention is commonly used as first-line treatment for sleep problems.22 While pain may be lessened through medication or nonpharmacological interventions.23
Considering the numerous ways that distress may present itself (eg, practical, emotional, or physical) and increase risk for SI, it is essential that all veterans living with cancer are assessed for distress and SI, regardless of their presentation. Although veterans may not outwardly express distress, this does not indicate the absence of either distress or risk for suicide. For example, a veteran may be distressed due to financial concerns, transportation issues, and the health of his/her partner or spouse. This veteran may not exhibit visible symptoms of distress, as would be expected when the source of distress is emotional (eg, depression, anxiety). However, this veteran is equally vulnerable to impairing distress and SI as someone who exhibits emotional distress. Distress assessments should be further developed to capture both the visible and less apparent sources of distress, while also serving the imperative function of screening for suicide. Other researchers also have noted the necessity of this development.24 Currently, the NCCN DT and Problems List does not include any assessment of SI or behavior.
Finally, this study identified a potentially critical factor to include in distress assessment: problems dealing with a partner. Problems dealing with a partner have been noted as a source of distress in existing literature, but this is the first study to find problems dealing with a partner to be a predictor of SI in veterans living with cancer.4-6
Because partners often attend appointments with veterans, it is not surprising that problems dealing with their partner are not disclosed more readily. It is recommended that clinicians ask veterans about potential problems with their partner when they are alone. Directly gathering information about such problems while assessing for distress may assist health care workers in providing the most effective, accurate type of intervention in a timely manner, and potentially mitigate risk for suicide.
As recommended by the NCCN and numerous researchers, findings from the current study underscore the importance of accurate, timely assessment of distress.2,4,8 This study makes several important recommendations about how distress assessment may be strengthened and further developed, specifically for the veteran population. This study also expands the current knowledge base of what is known about veterans living with cancer, and has begun to fill a gap in the existing literature. Consistent with the VA mission to end veteran suicide, results suggest that veterans living with cancer should be regularly screened for distress, asked about distress related to their partner, and assessed for SI. Continued efforts to enhance assessment of and response to distress may lessen suicide risk in veterans with cancer.11
Acknowledgements
This study is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7-34.
2. Riba MB, Donovan, KA, Andersen, B. National Comprehensive Cancer Network clinical practice guidelines in oncology. Distress management (Version 3.2019). J Natl Compr Can Net, 2019;17(10):1229-1249.
3. Zabora J, BrintzenhofeSzoc K, Curbow B, Hooker C, Pianta dosi S. The prevalence of psychological distress by cancer site. Psychooncology. 2001;10(1):19–28.
4. Holland JC, Alici Y. Management of distress in cancer patients. J Support Oncol. 2010;8(1):4-12.
5. Bulli F, Miccinesi G, Maruelli A, Katz M, Paci E. The measure of psychological distress in cancer patients: the use of distress thermometer in the oncological rehabilitation center of Florence. Support Care Cancer. 2009;17(7):771–779.
6. Jacobsen PB, Donovan KA, Trask PC, et al. Screening for psychologic distress in ambulatory cancer patients. Cancer. 2005;103(7):1494-1502.
7. Smith J, Berman S, Dimick J, et al. Distress Screening and Management in an Outpatient VA Cancer Clinic: A Pilot Project Involving Ambulatory Patients Across the Disease Trajectory. Fed Pract. 2017;34(Suppl 1):43S–50S.
8. Carlson LE, Waller A, Groff SL, Bultz BD. Screening for distress, the sixth vital sign, in lung cancer patients: effects on pain, fatigue, and common problems--secondary outcomes of a randomized controlled trial. Psychooncology. 2013;22(8):1880-1888.
9. Cooley ME, Short TH, Moriarty HJ. Symptom prevalence, distress, and change over time in adults receiving treatment for lung cancer. Psychooncology. 2003;12(7):694-708.
10. US Department of Veterans Affairs Office of Suicide Prevention. Suicide among veterans and other Americans 2001-2014. https://www.mentalhealth.va.gov/docs/2016suicidedatareport.pdf. Published August 3, 2016. Accessed April 13, 2020.
11. Aboumrad M, Shiner B, Riblet N, Mills, PD, Watts BV. Factors contributing to cancer-related suicide: a study of root-cause-analysis reports. Psychooncology. 2018;27(9):2237-2244.
12. US Department of Veterans Affairs, Office of Mental Health and Suicide Prevention. National Strategy for Preventing Veteran Suicide 2018–2028. https://www.mentalhealth.va.gov/suicide_prevention/docs/Office-of-Mental-Health-and-Suicide-Prevention-National-Strategy-for-Preventing-Veterans-Suicide.pdf Published 2018. Accessed April 13, 2020.
13. Carlson LE, Waller A, Mitchell AJ. Screening for distress and unmet needs in patients with cancer: review and recommendations. J Clin Oncol. 2012;30(11):1160-1177.
14. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613.
15. Martin A, Rief W, Klaiberg A, Braehler E. Validity of the brief patient health questionnaire mood scale (PHQ-9) in the general population. Gen Hosp Psychiatry. 2006;28(1):71-77.
16. Joiner TE. Why People Die by Suicide. Cambridge, MA: Harvard University Press, 2005.
17. Kleiman EM, Riskind JH, Schaefer KE. Social support and positive events as suicide resiliency factors: examination of synergistic buffering effects. Arch Suicide Res. 2014;18(2):144-155.
18. Van Orden KA, Witte TK, Gordon KH, Bender TW, Joiner TE Jr. Suicidal desire and the capability for suicide: tests of the interpersonal-psychological theory of suicidal behavior among adults. J Consult Clin Psychol. 2008;76(1):72–83.
19. Bryan CJ, Morrow CE, Anestis MD, Joiner TE. A preliminary test of the interpersonal -psychological theory of suicidal behavior in a military sample. Personal Individual Differ. 2010;48(3):347-350.
20. Miller SN, Monahan CJ, Phillips KM, Agliata D, Gironda RJ. Mental health utilization among veterans at risk for suicide: Data from a post-deployment clinic [published online ahead of print, 2018 Oct 8]. Psychol Serv. 2018;10.1037/ser0000311.
21. Galvão DA, Newton RU. Review of exercise intervention studies in cancer patients. J Clin Oncol. 2005;23(4):899-909.
22. Qaseem A, Kansagara D, Forciea MA, Cooke M, Denberg TD; Clinical Guidelines Committee of the American College of Physicians. Management of chronic insomnia disorder in adults: A clinical practice guideline from the American College of Physicians. Ann Intern Med. 2016;165(2):125-133.
23. Ngamkham S, Holden JE, Smith EL. A systematic review: Mindfulness intervention for cancer-related pain. Asia Pac J Oncol Nurs. 2019;6(2):161-169.
24. Granek L, Nakash O, Ben-David M, Shapira S, Ariad S. Oncologists’, nurses’, and social workers’ strategies and barriers to identifying suicide risk in cancer patients. Psychooncology. 2018;27(1):148-154.
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7-34.
2. Riba MB, Donovan, KA, Andersen, B. National Comprehensive Cancer Network clinical practice guidelines in oncology. Distress management (Version 3.2019). J Natl Compr Can Net, 2019;17(10):1229-1249.
3. Zabora J, BrintzenhofeSzoc K, Curbow B, Hooker C, Pianta dosi S. The prevalence of psychological distress by cancer site. Psychooncology. 2001;10(1):19–28.
4. Holland JC, Alici Y. Management of distress in cancer patients. J Support Oncol. 2010;8(1):4-12.
5. Bulli F, Miccinesi G, Maruelli A, Katz M, Paci E. The measure of psychological distress in cancer patients: the use of distress thermometer in the oncological rehabilitation center of Florence. Support Care Cancer. 2009;17(7):771–779.
6. Jacobsen PB, Donovan KA, Trask PC, et al. Screening for psychologic distress in ambulatory cancer patients. Cancer. 2005;103(7):1494-1502.
7. Smith J, Berman S, Dimick J, et al. Distress Screening and Management in an Outpatient VA Cancer Clinic: A Pilot Project Involving Ambulatory Patients Across the Disease Trajectory. Fed Pract. 2017;34(Suppl 1):43S–50S.
8. Carlson LE, Waller A, Groff SL, Bultz BD. Screening for distress, the sixth vital sign, in lung cancer patients: effects on pain, fatigue, and common problems--secondary outcomes of a randomized controlled trial. Psychooncology. 2013;22(8):1880-1888.
9. Cooley ME, Short TH, Moriarty HJ. Symptom prevalence, distress, and change over time in adults receiving treatment for lung cancer. Psychooncology. 2003;12(7):694-708.
10. US Department of Veterans Affairs Office of Suicide Prevention. Suicide among veterans and other Americans 2001-2014. https://www.mentalhealth.va.gov/docs/2016suicidedatareport.pdf. Published August 3, 2016. Accessed April 13, 2020.
11. Aboumrad M, Shiner B, Riblet N, Mills, PD, Watts BV. Factors contributing to cancer-related suicide: a study of root-cause-analysis reports. Psychooncology. 2018;27(9):2237-2244.
12. US Department of Veterans Affairs, Office of Mental Health and Suicide Prevention. National Strategy for Preventing Veteran Suicide 2018–2028. https://www.mentalhealth.va.gov/suicide_prevention/docs/Office-of-Mental-Health-and-Suicide-Prevention-National-Strategy-for-Preventing-Veterans-Suicide.pdf Published 2018. Accessed April 13, 2020.
13. Carlson LE, Waller A, Mitchell AJ. Screening for distress and unmet needs in patients with cancer: review and recommendations. J Clin Oncol. 2012;30(11):1160-1177.
14. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613.
15. Martin A, Rief W, Klaiberg A, Braehler E. Validity of the brief patient health questionnaire mood scale (PHQ-9) in the general population. Gen Hosp Psychiatry. 2006;28(1):71-77.
16. Joiner TE. Why People Die by Suicide. Cambridge, MA: Harvard University Press, 2005.
17. Kleiman EM, Riskind JH, Schaefer KE. Social support and positive events as suicide resiliency factors: examination of synergistic buffering effects. Arch Suicide Res. 2014;18(2):144-155.
18. Van Orden KA, Witte TK, Gordon KH, Bender TW, Joiner TE Jr. Suicidal desire and the capability for suicide: tests of the interpersonal-psychological theory of suicidal behavior among adults. J Consult Clin Psychol. 2008;76(1):72–83.
19. Bryan CJ, Morrow CE, Anestis MD, Joiner TE. A preliminary test of the interpersonal -psychological theory of suicidal behavior in a military sample. Personal Individual Differ. 2010;48(3):347-350.
20. Miller SN, Monahan CJ, Phillips KM, Agliata D, Gironda RJ. Mental health utilization among veterans at risk for suicide: Data from a post-deployment clinic [published online ahead of print, 2018 Oct 8]. Psychol Serv. 2018;10.1037/ser0000311.
21. Galvão DA, Newton RU. Review of exercise intervention studies in cancer patients. J Clin Oncol. 2005;23(4):899-909.
22. Qaseem A, Kansagara D, Forciea MA, Cooke M, Denberg TD; Clinical Guidelines Committee of the American College of Physicians. Management of chronic insomnia disorder in adults: A clinical practice guideline from the American College of Physicians. Ann Intern Med. 2016;165(2):125-133.
23. Ngamkham S, Holden JE, Smith EL. A systematic review: Mindfulness intervention for cancer-related pain. Asia Pac J Oncol Nurs. 2019;6(2):161-169.
24. Granek L, Nakash O, Ben-David M, Shapira S, Ariad S. Oncologists’, nurses’, and social workers’ strategies and barriers to identifying suicide risk in cancer patients. Psychooncology. 2018;27(1):148-154.
Atrial Fibrillation and Bleeding in Patients With Chronic Lymphocytic Leukemia Treated with Ibrutinib in the Veterans Health Administration (FULL)
Chronic lymphocytic leukemia (CLL) is the most common leukemia diagnosed in developed countries, with an estimated 21,040 new diagnoses of CLL expected in the US in 2020. 1-3 CLL is an indolent cancer characterized by the accumulation of B-lymphocytes in the blood, marrow, and lymphoid tissues. 4 It has a heterogeneous clinical course; the majority of patients are observed or receive delayed treatment following diagnosis, while a minority of patients require immediate treatment. After first-line treatment, some patients experience prolonged remissions while others require retreatment within 1 or 2 years. Fortunately, advances in cancer biology and therapeutics in the last decade have increased the number of treatment options available for patients with CLL.
Until recently, most CLL treatments relied on a chemotherapy or a chemoimmunotherapy backbone; however, the last few years have seen novel therapies introduced, such as small molecule inhibitors to target molecular pathways that promote the normal development, expansion, and survival of B-cells.5 One such therapy is ibrutinib, a targeted Bruton tyrosine kinase inhibitor that received accelerated approval by the US Food and Drug Administration (FDA) in February 2014 for patients with CLL who received at least 1 prior therapy. The FDA later expanded this approval to include use of ibrutinib in patients with CLL with relapsed or refractory disease, with or without chromosome 17p deletion. In 2016, based on data from the RESONATE-17 study, the FDA approved ibrutinib for first-line therapy in patients with CLL.6
Ibrutinib’s efficacy, ease of administration and dosing (all doses are oral and fixed, rather than based on weight or body surface area), and relatively favorable safety profile have resulted in a rapid growth in its adoption.7 Since its adverse event (AE) profile is generally more tolerable than that of a typical chemoimmunotherapy, its use in older patients with CLL and patients with significant comorbidities is particularly appealing.8
However, the results of some clinical trials suggest an association between treatment with ibrutinib and an increased risk of bleeding-related events of any grade (44%) and major bleeding events (4%).7,8 The incidence of major bleeding events was reported to be higher (9%) in one clinical trial and at 5-year follow-up, although this trial did not exclude patients receiving concomitant oral anticoagulation with warfarin.6,9
Heterogeneity in clinical trials’ definitions of major bleeding confounded the ability to calculate bleeding risk in patients treated with ibrutinib in a systematic review and meta-analysis that called for more data.10 Additionally, patients with factors that might increase the risk of major bleeding with ibrutinib treatment were likely underrepresented in clinical trials, given the carefully selected nature of clinical trial subjects. These factors include renal or hepatic disease, gastrointestinal disease, and use of a number of concomitant medications such as antiplatelets or anticoagulant medications. Accounting for use of the latter is particularly important because patients who develop atrial fibrillation (Afib), one of the recognized AEs of treatment with ibrutinib, often are treated with anticoagulant medications in order to decrease the risk of stroke or other thromboembolic complications.
A single-site observational study of patients treated with ibrutinib reported a high utilization rate of antiplatelet medications (70%), anticoagulant medications (17%), or both (13%) with a concomitant major bleeding rate of 18% of patients.11 Prevalence of bleeding events seemed to be highly affected by the presence of concomitant medications: 78% of patients treated with ibrutinib while concurrently receiving both antiplatelet and anticoagulant medications developed a major bleeding event, while none of the patients who were not receiving antiplatelets, anticoagulants, or medications that interact with cytochrome P450 (an enzyme that metabolized chemotherapeutic agents used to treat cancer) experienced a major bleeding event.11
The prevalence of major bleeding events, comorbidities, and utilization of medications that could increase the risk of major bleeding in patients with CLL on ibrutinib in the Veterans Health Administration (VHA) is not known. The VHA is the largest integrated health care system in the US. To address these knowledge gaps, a retrospective observational study was conducted using data on demographics, comorbidities that could affect bleeding, use of anticoagulant and antiplatelet medications, and bleeding events in patients with CLL who were treated in the first year of ibrutinib availability from the VHA.
The first year of ibrutinib availability was chosen for this study since we anticipated that many health care providers would be unfamiliar with ibrutinib during that time given its novelty, and therefore more likely to codispense ibrutinib with medications that could increase the risk of a bleeding event. Since Afib is both an AE associated with ibrutinib treatment and a condition that often is treated with anticoagulants, the prevalence of Afib in this population was also included. For context, the incidence of bleeding and Afib and use of anticoagulant and antiplatelet medications during treatment in a cohort of patients with CLL treated with bendamustine + rituximab (BR) also was reported.
Methods
The VHA maintains the centralized US Department of Veterans Affairs Cancer Registry System (VACRS), with electronic medical record data and other sources captured in its Corporate Data Warehouse (CDW). The VHA CDW is a national repository comprising data from several VHA clinical and administrative systems. The CDW includes patient identifiers; demographics; vital status; lab information; administrative information (such as diagnostic International Statistical Classification of Diseases and Related Health Problems [ICD-9] codes); medication dispensation tables (such as outpatient fill); IV package information; and notes from radiology, pathology, outpatient and inpatient admission, discharge, and daily progress.
Registrars abstract all cancer cases within the VHA system (or diagnosed outside the VHA, if patients subsequently receive treatment in the VHA). It is estimated that VACRS captures 3% of cancer cases in the US.12 Like most registries, VACRS captures data such as diagnosis, age, gender, race, and vital status.
The study received approval from the University of Utah Institutional Review Board and used individual patient-level historical administrative, cancer registry, and electronic health care record data. Patients diagnosed and treated for CLL at the VHA from 2010 to 2014 were identified through the VACRS and CDW; patients with a prior malignancy were excluded. Patients who received ibrutinib or BR based on pharmacy dispensation information were selected. Patients were followed until December 31, 2016 or death; patients with documentation of another cancer or lack of utilization of the VHA hematology or oncology services (defined as absence of any hematology and/or oncology clinic visits for ≥ 18 months) were omitted from the final analysis (Figure).
Previous and concomitant utilization of antiplatelet (aspirin, clopidogrel) or anticoagulant (dalteparin, enoxaparin, fondaparinux, heparin, rivaroxaban, and warfarin) medications was extracted 6 months before and after the first dispensation of ibrutinib or BR using pharmacy dispensation records.
Study Definitions
Prevalence of comorbidities that could increase bleeding risk was determined using administrative ICD-9-CM codes. Liver disease was identified by presence of cirrhosis, hepatitis C virus, or alcoholic liver disease using administrative codes validated by Kramer and colleagues, who reported positive and negative predictive values of 90% and 87% for cirrhosis, 93% and 92% for hepatitis C virus, and 71% and 98% for alcoholic liver disease.13 Similarly, end-stage liver disease was identified using a validated coding algorithm developed by Goldberg and colleagues, with a positive predictive value of 89.3%.14 The presence of controlled or uncontrolled diabetes mellitus (DM) was identified using the procedure described by Guzman and colleagues.15 Quan’s algorithm was used to calculate Charlson Comorbidity Index (CCI) based on ICD-9-CM codes for inpatient and outpatient visits within a 6-month lookback period prior to treatment initiation.16
A major bleeding event was defined as a hospitalization with an ICD-9-CM code suggestive of major bleeding as the primary reason, as defined by Lane and colleagues in their study of major bleeding related to warfarin in a cohort of patients treated within the VHA.17 Incidence rates of major bleeding events were identified during the first 6 months of treatment. Incidence of Afib—defined as an inpatient or outpatient encounter with the 427.31 ICD-9-CM code—also was examined within the first 6 months after starting treatment. The period of 6 months was chosen because bendamustine must be discontinued after 6 months.
Study Analysis
Descriptive statistics were used to examine patient demographics, disease characteristics, and treatment history from initial CLL diagnosis through end of study observation period. Categorical variables were summarized using frequencies and accompanying proportions, while a mean and standard deviation were used to summarize continuous variables. For the means of continuous variables and of categorical data, 95% CIs were used. Proportions and accompanying 95% CIs characterized treatment patterns, including line of therapy, comorbidities, and bleeding events. Treatment duration was described using mean and accompanying 95% CI. Statistical tests were not conducted for comparisons among treatment groups. Patients were censored at the end of follow-up, defined as the earliest of the following scenarios: (1) end of study observation period (December 31, 2016); (2) development of a secondary cancer; or (3) last day of contact given absence of care within the VHA for ≥ 18 months (with care defined as oncology and/or oncology/hematology visit with an associated note). Analysis was performed using R 3.4.0.
Results
Between 2010 and 2014, 2,796 patients were diagnosed and received care for CLL within the VHA. Overall, all 172 patients who were treated with ibrutinib during our inclusion period were selected. These patients were treated between January 1, 2014 and December 31, 2016, following ibrutinib’s approval in early 2014. An additional 291 patients were selected who received BR (Table). Reflecting the predominantly male population of the VHA, 282 (97%) BR patients and 167 (97%) ibrutinib patients were male. The median age at diagnosis was 67 years for BR patients and 69 years for ibrutinib patients. About 76% of patients who received ibrutinib and 82% of patients who received BR were non-Hispanic white; 17% and 14% were African American, respectively.
Less than 10% of patients receiving either ibrutinib or BR had liver disease per criteria used by Kramer and colleagues, or end-stage liver disease using criteria developed by Goldberg and colleagues.12,13 About 5% of patients had a history of previous bleeding in the 6-month period prior to initiating either therapy. Mean CCI (excluding malignancy) score was 1.5 (range, 0-11) for the ibrutinib group, and 2.1 (range, 0-9) for the BR group. About 16% of the ibrutinib group had controlled DM and fewer than 10% had uncontrolled DM, while 4% of patients in the BR group met the criteria for controlled DM and another 4% met the criteria for uncontrolled DM.
There was very low utilization of anticoagulant or antiplatelet medication prior to initiation of ibrutinib (2.9% and 2.3%, respectively) or BR (< 1% each). In the first 6 months after treatment initiation, about 8% of patients in both ibrutinib and BR cohorts received anticoagulant medication while antiplatelet utilization was < 5% in either group.
In the BR group, 8 patients (2.7%) experienced a major bleeding event, while 14 patients (8.1%) in the ibrutinib group experienced a bleeding event (P = .008). While these numbers were too low to perform a formal statistical analysis of the association between clinical covariates and bleeding in either group, there did not seem to be an association between bleeding and liver disease or DM. Of patients who experienced a bleeding event, about 1 in 4 patients had had a prior bleeding event in both the ibrutinib and the BR groups. Interestingly, while none of the patients who experienced a bleeding event while receiving BR were taking concomitant anticoagulant medication, 3 of the 14 patients who experienced a bleeding event in the ibrutinib group showed evidence of anticoagulant utilization. Finally, the incidence of Afib (defined as patients with no evidence of Afib in the 6 months prior to treatment but with evidence of Afib in the 6 months following treatment initiation) was 4% in the BR group, and about 8% in the ibrutinib group (P = .003).
Discussion
To the authors’ knowledge, this study is the first to examine the real-world incidence of bleeding and Afib in veterans who received ibrutinib for CLL in the first year of its availability. The study found minimal use of anticoagulants and/or antiplatelet agents prior to receiving first-line ibrutinib or BR, and very low use of these agents in the first 6 months following the initiation of first-line treatment. This finding suggests a high awareness among VA providers of potential adverse effects (AEs) of ibrutinib and chemotherapy, and a careful selection of patients that lack risk factors for AEs.
In patients treated with first-line ibrutinib when compared with patients treated with first-line BR, moderate increases in bleeding (2.7% vs 8.1%, P = .008) and Afib (10.5% vs 3%, P = .003) also were observed. These results are concordant with previous findings examining the use of ibrutinib in patients with CLL.18-20
Limitations
The results of this study should be interpreted with caution, as some limitations must be considered. The study was conducted in the early days of ibrutinib adoption. Since then, more patients have been treated with ibrutinib and for longer durations. As clinicians gain more familiarity and with ibrutinib, and as additional novel therapeutics emerge, it is possible that the initial awareness about risks for possible AEs may diminish; patients with high comorbidity burdens and concomitant medications would be especially vulnerable in cases of reduced physician vigilance.
Another limitation of this study stems from the potential for dual system use among patients treated in the VHA. Concurrent or alternating use of multiple health care systems (use of VHA and private-sector facilities) may present gaps in the reconstruction of patient histories, resulting in missing data as patients transition between commercial, the Centers for Medicare and Medicaid Services, and VHA care. As a result, the results presented here do not reflect instances where a patient experienced a bleeding event treated outside the VA.
Problems with missing data also may occur due to incomplete extraction from the electronic health record; these issues were addressed by leveraging an understanding of the multiple data marts within the CDW environment to harmonize missing and/or erroneous information through use of other data marts when possible. Lastly, this research represents a population-level study of the VHA, thus all findings are directly relevant to the VHA. The generalizability of the findings outside the VHA would depend on the characteristics of the external population.
Conclusion
Real-world evidence from a nationwide cohort of veteran patients with CLL treated with ibrutinib suggest that, while there is an association of increased bleeding-related events and Afib, the risk is comparable to those reported in previous studies.18-20 These findings suggest that patients in real-world clinical care settings with higher levels of comorbidities may be at a slight increased risk for bleeding events and Afib.
1. Scarfò L, Ferreri AJ, Ghia P. Chronic lymphocytic leukaemia. Crit Rev Oncol Hematol. 2016;104:169-182.
2. Devereux S, Cuthill K. Chronic lymphocytic leukaemia. Medicine (Baltimore). 2017;45(5):292-296.
3. American Cancer Society. Cancer facts & figures 2020. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2020/cancer-facts-and-figures-2020.pdf. Accessed April 24, 2020.
4. Kipps TJ, Stevenson FK, Wu CJ, et al. Chronic lymphocytic leukaemia. Nat Rev Dis Primers. 2017;3:16096.
5. Owen C, Assouline S, Kuruvilla J, Uchida C, Bellingham C, Sehn L. Novel therapies for chronic lymphocytic leukemia: a Canadian perspective. Clin Lymphoma Myeloma Leuk. 2015;15(11):627-634.e5.
6. O’Brien S, Jones JA, Coutre SE, et al. Ibrutinib for patients with relapsed or refractory chronic lymphocytic leukaemia with 17p deletion (RESONATE-17): a phase 2, open-label, multicentre study. Lancet Oncol. 2016;17(10):1409–1418.
7. Burger JA, Tedeschi A, Barr PM, et al; RESONATE-2 Investigators. Ibrutinib as initial therapy for patients with chronic lymphocytic leukemia. N Engl J Med. 2015;373(25):2425-2437.
8. Byrd JC, Furman RR, Coutre SE, et al. Targeting BTK with ibrutinib in relapsed chronic lymphocytic leukemia. N Engl J Med. 2013;369(1):32-42.
9. O’Brien S, Furman R, Coutre S, et al. Single-agent ibrutinib in treatment-naive and relapsed/refractory chronic lymphocytic leukemia: a 5-year experience. Blood. 2018;131(17):1910-1919.
10. Caron F, Leong DP, Hillis C, Fraser G, Siegal D. Current understanding of bleeding with ibrutinib use: a systematic review and meta-analysis. Blood Adv. 2017;1(12):772-778.
11. Kunk PR, Mock J, Devitt ME, Palkimas S, et al. Major bleeding with ibrutinib: more than expected. Blood. 2016;128(22):3229.
12. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701.
13. Kramer JR, Davila JA, Miller ED, Richardson P, Giordano TP, El-Serag HB. The validity of viral hepatitis and chronic liver disease diagnoses in Veterans Affairs administrative databases. Aliment Pharmacol Ther. 2008;27(3):274-282.
14. Goldberg D, Lewis JD, Halpern SD, Weiner M, Lo Re V 3rd. Validation of three coding algorithms to identify patients with end-stage liver disease in an administrative database. Pharmacoepidemiol Drug Saf. 2012;21(7):765-769.
15. Guzman JZ, Iatridis JC, Skovrlj B, et al. Outcomes and complications of diabetes mellitus on patients undergoing degenerative lumbar spine surgery. Spine (Phila Pa 1976). 2014;39(19):1596-1604.
16. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.
17. Lane MA, Zeringue A, McDonald JR. Serious bleeding events due to warfarin and antibiotic co-prescription in a cohort of veterans. Am J Med. 2014;127(7):657–663.e2.
18. Leong DP, Caron F, Hillis C, et al. The risk of atrial fibrillation with ibrutinib use: a systematic review and meta-analysis. Blood. 2016;128(1):138-140.
19. Lipsky AH, Farooqui MZ, Tian X, et al. Incidence and risk factors of bleeding-related adverse events in patients with chronic lymphocytic leukemia treated with ibrutinib. Haematologica. 2015;100(12):1571-1578.
20. Brown JR, Moslehi J, O’Brien S, et al. Characterization of atrial fibrillation adverse events reported in ibrutinib randomized controlled registration trials. Haematologica. 2017;102(10):1796-1805.
Chronic lymphocytic leukemia (CLL) is the most common leukemia diagnosed in developed countries, with an estimated 21,040 new diagnoses of CLL expected in the US in 2020. 1-3 CLL is an indolent cancer characterized by the accumulation of B-lymphocytes in the blood, marrow, and lymphoid tissues. 4 It has a heterogeneous clinical course; the majority of patients are observed or receive delayed treatment following diagnosis, while a minority of patients require immediate treatment. After first-line treatment, some patients experience prolonged remissions while others require retreatment within 1 or 2 years. Fortunately, advances in cancer biology and therapeutics in the last decade have increased the number of treatment options available for patients with CLL.
Until recently, most CLL treatments relied on a chemotherapy or a chemoimmunotherapy backbone; however, the last few years have seen novel therapies introduced, such as small molecule inhibitors to target molecular pathways that promote the normal development, expansion, and survival of B-cells.5 One such therapy is ibrutinib, a targeted Bruton tyrosine kinase inhibitor that received accelerated approval by the US Food and Drug Administration (FDA) in February 2014 for patients with CLL who received at least 1 prior therapy. The FDA later expanded this approval to include use of ibrutinib in patients with CLL with relapsed or refractory disease, with or without chromosome 17p deletion. In 2016, based on data from the RESONATE-17 study, the FDA approved ibrutinib for first-line therapy in patients with CLL.6
Ibrutinib’s efficacy, ease of administration and dosing (all doses are oral and fixed, rather than based on weight or body surface area), and relatively favorable safety profile have resulted in a rapid growth in its adoption.7 Since its adverse event (AE) profile is generally more tolerable than that of a typical chemoimmunotherapy, its use in older patients with CLL and patients with significant comorbidities is particularly appealing.8
However, the results of some clinical trials suggest an association between treatment with ibrutinib and an increased risk of bleeding-related events of any grade (44%) and major bleeding events (4%).7,8 The incidence of major bleeding events was reported to be higher (9%) in one clinical trial and at 5-year follow-up, although this trial did not exclude patients receiving concomitant oral anticoagulation with warfarin.6,9
Heterogeneity in clinical trials’ definitions of major bleeding confounded the ability to calculate bleeding risk in patients treated with ibrutinib in a systematic review and meta-analysis that called for more data.10 Additionally, patients with factors that might increase the risk of major bleeding with ibrutinib treatment were likely underrepresented in clinical trials, given the carefully selected nature of clinical trial subjects. These factors include renal or hepatic disease, gastrointestinal disease, and use of a number of concomitant medications such as antiplatelets or anticoagulant medications. Accounting for use of the latter is particularly important because patients who develop atrial fibrillation (Afib), one of the recognized AEs of treatment with ibrutinib, often are treated with anticoagulant medications in order to decrease the risk of stroke or other thromboembolic complications.
A single-site observational study of patients treated with ibrutinib reported a high utilization rate of antiplatelet medications (70%), anticoagulant medications (17%), or both (13%) with a concomitant major bleeding rate of 18% of patients.11 Prevalence of bleeding events seemed to be highly affected by the presence of concomitant medications: 78% of patients treated with ibrutinib while concurrently receiving both antiplatelet and anticoagulant medications developed a major bleeding event, while none of the patients who were not receiving antiplatelets, anticoagulants, or medications that interact with cytochrome P450 (an enzyme that metabolized chemotherapeutic agents used to treat cancer) experienced a major bleeding event.11
The prevalence of major bleeding events, comorbidities, and utilization of medications that could increase the risk of major bleeding in patients with CLL on ibrutinib in the Veterans Health Administration (VHA) is not known. The VHA is the largest integrated health care system in the US. To address these knowledge gaps, a retrospective observational study was conducted using data on demographics, comorbidities that could affect bleeding, use of anticoagulant and antiplatelet medications, and bleeding events in patients with CLL who were treated in the first year of ibrutinib availability from the VHA.
The first year of ibrutinib availability was chosen for this study since we anticipated that many health care providers would be unfamiliar with ibrutinib during that time given its novelty, and therefore more likely to codispense ibrutinib with medications that could increase the risk of a bleeding event. Since Afib is both an AE associated with ibrutinib treatment and a condition that often is treated with anticoagulants, the prevalence of Afib in this population was also included. For context, the incidence of bleeding and Afib and use of anticoagulant and antiplatelet medications during treatment in a cohort of patients with CLL treated with bendamustine + rituximab (BR) also was reported.
Methods
The VHA maintains the centralized US Department of Veterans Affairs Cancer Registry System (VACRS), with electronic medical record data and other sources captured in its Corporate Data Warehouse (CDW). The VHA CDW is a national repository comprising data from several VHA clinical and administrative systems. The CDW includes patient identifiers; demographics; vital status; lab information; administrative information (such as diagnostic International Statistical Classification of Diseases and Related Health Problems [ICD-9] codes); medication dispensation tables (such as outpatient fill); IV package information; and notes from radiology, pathology, outpatient and inpatient admission, discharge, and daily progress.
Registrars abstract all cancer cases within the VHA system (or diagnosed outside the VHA, if patients subsequently receive treatment in the VHA). It is estimated that VACRS captures 3% of cancer cases in the US.12 Like most registries, VACRS captures data such as diagnosis, age, gender, race, and vital status.
The study received approval from the University of Utah Institutional Review Board and used individual patient-level historical administrative, cancer registry, and electronic health care record data. Patients diagnosed and treated for CLL at the VHA from 2010 to 2014 were identified through the VACRS and CDW; patients with a prior malignancy were excluded. Patients who received ibrutinib or BR based on pharmacy dispensation information were selected. Patients were followed until December 31, 2016 or death; patients with documentation of another cancer or lack of utilization of the VHA hematology or oncology services (defined as absence of any hematology and/or oncology clinic visits for ≥ 18 months) were omitted from the final analysis (Figure).
Previous and concomitant utilization of antiplatelet (aspirin, clopidogrel) or anticoagulant (dalteparin, enoxaparin, fondaparinux, heparin, rivaroxaban, and warfarin) medications was extracted 6 months before and after the first dispensation of ibrutinib or BR using pharmacy dispensation records.
Study Definitions
Prevalence of comorbidities that could increase bleeding risk was determined using administrative ICD-9-CM codes. Liver disease was identified by presence of cirrhosis, hepatitis C virus, or alcoholic liver disease using administrative codes validated by Kramer and colleagues, who reported positive and negative predictive values of 90% and 87% for cirrhosis, 93% and 92% for hepatitis C virus, and 71% and 98% for alcoholic liver disease.13 Similarly, end-stage liver disease was identified using a validated coding algorithm developed by Goldberg and colleagues, with a positive predictive value of 89.3%.14 The presence of controlled or uncontrolled diabetes mellitus (DM) was identified using the procedure described by Guzman and colleagues.15 Quan’s algorithm was used to calculate Charlson Comorbidity Index (CCI) based on ICD-9-CM codes for inpatient and outpatient visits within a 6-month lookback period prior to treatment initiation.16
A major bleeding event was defined as a hospitalization with an ICD-9-CM code suggestive of major bleeding as the primary reason, as defined by Lane and colleagues in their study of major bleeding related to warfarin in a cohort of patients treated within the VHA.17 Incidence rates of major bleeding events were identified during the first 6 months of treatment. Incidence of Afib—defined as an inpatient or outpatient encounter with the 427.31 ICD-9-CM code—also was examined within the first 6 months after starting treatment. The period of 6 months was chosen because bendamustine must be discontinued after 6 months.
Study Analysis
Descriptive statistics were used to examine patient demographics, disease characteristics, and treatment history from initial CLL diagnosis through end of study observation period. Categorical variables were summarized using frequencies and accompanying proportions, while a mean and standard deviation were used to summarize continuous variables. For the means of continuous variables and of categorical data, 95% CIs were used. Proportions and accompanying 95% CIs characterized treatment patterns, including line of therapy, comorbidities, and bleeding events. Treatment duration was described using mean and accompanying 95% CI. Statistical tests were not conducted for comparisons among treatment groups. Patients were censored at the end of follow-up, defined as the earliest of the following scenarios: (1) end of study observation period (December 31, 2016); (2) development of a secondary cancer; or (3) last day of contact given absence of care within the VHA for ≥ 18 months (with care defined as oncology and/or oncology/hematology visit with an associated note). Analysis was performed using R 3.4.0.
Results
Between 2010 and 2014, 2,796 patients were diagnosed and received care for CLL within the VHA. Overall, all 172 patients who were treated with ibrutinib during our inclusion period were selected. These patients were treated between January 1, 2014 and December 31, 2016, following ibrutinib’s approval in early 2014. An additional 291 patients were selected who received BR (Table). Reflecting the predominantly male population of the VHA, 282 (97%) BR patients and 167 (97%) ibrutinib patients were male. The median age at diagnosis was 67 years for BR patients and 69 years for ibrutinib patients. About 76% of patients who received ibrutinib and 82% of patients who received BR were non-Hispanic white; 17% and 14% were African American, respectively.
Less than 10% of patients receiving either ibrutinib or BR had liver disease per criteria used by Kramer and colleagues, or end-stage liver disease using criteria developed by Goldberg and colleagues.12,13 About 5% of patients had a history of previous bleeding in the 6-month period prior to initiating either therapy. Mean CCI (excluding malignancy) score was 1.5 (range, 0-11) for the ibrutinib group, and 2.1 (range, 0-9) for the BR group. About 16% of the ibrutinib group had controlled DM and fewer than 10% had uncontrolled DM, while 4% of patients in the BR group met the criteria for controlled DM and another 4% met the criteria for uncontrolled DM.
There was very low utilization of anticoagulant or antiplatelet medication prior to initiation of ibrutinib (2.9% and 2.3%, respectively) or BR (< 1% each). In the first 6 months after treatment initiation, about 8% of patients in both ibrutinib and BR cohorts received anticoagulant medication while antiplatelet utilization was < 5% in either group.
In the BR group, 8 patients (2.7%) experienced a major bleeding event, while 14 patients (8.1%) in the ibrutinib group experienced a bleeding event (P = .008). While these numbers were too low to perform a formal statistical analysis of the association between clinical covariates and bleeding in either group, there did not seem to be an association between bleeding and liver disease or DM. Of patients who experienced a bleeding event, about 1 in 4 patients had had a prior bleeding event in both the ibrutinib and the BR groups. Interestingly, while none of the patients who experienced a bleeding event while receiving BR were taking concomitant anticoagulant medication, 3 of the 14 patients who experienced a bleeding event in the ibrutinib group showed evidence of anticoagulant utilization. Finally, the incidence of Afib (defined as patients with no evidence of Afib in the 6 months prior to treatment but with evidence of Afib in the 6 months following treatment initiation) was 4% in the BR group, and about 8% in the ibrutinib group (P = .003).
Discussion
To the authors’ knowledge, this study is the first to examine the real-world incidence of bleeding and Afib in veterans who received ibrutinib for CLL in the first year of its availability. The study found minimal use of anticoagulants and/or antiplatelet agents prior to receiving first-line ibrutinib or BR, and very low use of these agents in the first 6 months following the initiation of first-line treatment. This finding suggests a high awareness among VA providers of potential adverse effects (AEs) of ibrutinib and chemotherapy, and a careful selection of patients that lack risk factors for AEs.
In patients treated with first-line ibrutinib when compared with patients treated with first-line BR, moderate increases in bleeding (2.7% vs 8.1%, P = .008) and Afib (10.5% vs 3%, P = .003) also were observed. These results are concordant with previous findings examining the use of ibrutinib in patients with CLL.18-20
Limitations
The results of this study should be interpreted with caution, as some limitations must be considered. The study was conducted in the early days of ibrutinib adoption. Since then, more patients have been treated with ibrutinib and for longer durations. As clinicians gain more familiarity and with ibrutinib, and as additional novel therapeutics emerge, it is possible that the initial awareness about risks for possible AEs may diminish; patients with high comorbidity burdens and concomitant medications would be especially vulnerable in cases of reduced physician vigilance.
Another limitation of this study stems from the potential for dual system use among patients treated in the VHA. Concurrent or alternating use of multiple health care systems (use of VHA and private-sector facilities) may present gaps in the reconstruction of patient histories, resulting in missing data as patients transition between commercial, the Centers for Medicare and Medicaid Services, and VHA care. As a result, the results presented here do not reflect instances where a patient experienced a bleeding event treated outside the VA.
Problems with missing data also may occur due to incomplete extraction from the electronic health record; these issues were addressed by leveraging an understanding of the multiple data marts within the CDW environment to harmonize missing and/or erroneous information through use of other data marts when possible. Lastly, this research represents a population-level study of the VHA, thus all findings are directly relevant to the VHA. The generalizability of the findings outside the VHA would depend on the characteristics of the external population.
Conclusion
Real-world evidence from a nationwide cohort of veteran patients with CLL treated with ibrutinib suggest that, while there is an association of increased bleeding-related events and Afib, the risk is comparable to those reported in previous studies.18-20 These findings suggest that patients in real-world clinical care settings with higher levels of comorbidities may be at a slight increased risk for bleeding events and Afib.
Chronic lymphocytic leukemia (CLL) is the most common leukemia diagnosed in developed countries, with an estimated 21,040 new diagnoses of CLL expected in the US in 2020. 1-3 CLL is an indolent cancer characterized by the accumulation of B-lymphocytes in the blood, marrow, and lymphoid tissues. 4 It has a heterogeneous clinical course; the majority of patients are observed or receive delayed treatment following diagnosis, while a minority of patients require immediate treatment. After first-line treatment, some patients experience prolonged remissions while others require retreatment within 1 or 2 years. Fortunately, advances in cancer biology and therapeutics in the last decade have increased the number of treatment options available for patients with CLL.
Until recently, most CLL treatments relied on a chemotherapy or a chemoimmunotherapy backbone; however, the last few years have seen novel therapies introduced, such as small molecule inhibitors to target molecular pathways that promote the normal development, expansion, and survival of B-cells.5 One such therapy is ibrutinib, a targeted Bruton tyrosine kinase inhibitor that received accelerated approval by the US Food and Drug Administration (FDA) in February 2014 for patients with CLL who received at least 1 prior therapy. The FDA later expanded this approval to include use of ibrutinib in patients with CLL with relapsed or refractory disease, with or without chromosome 17p deletion. In 2016, based on data from the RESONATE-17 study, the FDA approved ibrutinib for first-line therapy in patients with CLL.6
Ibrutinib’s efficacy, ease of administration and dosing (all doses are oral and fixed, rather than based on weight or body surface area), and relatively favorable safety profile have resulted in a rapid growth in its adoption.7 Since its adverse event (AE) profile is generally more tolerable than that of a typical chemoimmunotherapy, its use in older patients with CLL and patients with significant comorbidities is particularly appealing.8
However, the results of some clinical trials suggest an association between treatment with ibrutinib and an increased risk of bleeding-related events of any grade (44%) and major bleeding events (4%).7,8 The incidence of major bleeding events was reported to be higher (9%) in one clinical trial and at 5-year follow-up, although this trial did not exclude patients receiving concomitant oral anticoagulation with warfarin.6,9
Heterogeneity in clinical trials’ definitions of major bleeding confounded the ability to calculate bleeding risk in patients treated with ibrutinib in a systematic review and meta-analysis that called for more data.10 Additionally, patients with factors that might increase the risk of major bleeding with ibrutinib treatment were likely underrepresented in clinical trials, given the carefully selected nature of clinical trial subjects. These factors include renal or hepatic disease, gastrointestinal disease, and use of a number of concomitant medications such as antiplatelets or anticoagulant medications. Accounting for use of the latter is particularly important because patients who develop atrial fibrillation (Afib), one of the recognized AEs of treatment with ibrutinib, often are treated with anticoagulant medications in order to decrease the risk of stroke or other thromboembolic complications.
A single-site observational study of patients treated with ibrutinib reported a high utilization rate of antiplatelet medications (70%), anticoagulant medications (17%), or both (13%) with a concomitant major bleeding rate of 18% of patients.11 Prevalence of bleeding events seemed to be highly affected by the presence of concomitant medications: 78% of patients treated with ibrutinib while concurrently receiving both antiplatelet and anticoagulant medications developed a major bleeding event, while none of the patients who were not receiving antiplatelets, anticoagulants, or medications that interact with cytochrome P450 (an enzyme that metabolized chemotherapeutic agents used to treat cancer) experienced a major bleeding event.11
The prevalence of major bleeding events, comorbidities, and utilization of medications that could increase the risk of major bleeding in patients with CLL on ibrutinib in the Veterans Health Administration (VHA) is not known. The VHA is the largest integrated health care system in the US. To address these knowledge gaps, a retrospective observational study was conducted using data on demographics, comorbidities that could affect bleeding, use of anticoagulant and antiplatelet medications, and bleeding events in patients with CLL who were treated in the first year of ibrutinib availability from the VHA.
The first year of ibrutinib availability was chosen for this study since we anticipated that many health care providers would be unfamiliar with ibrutinib during that time given its novelty, and therefore more likely to codispense ibrutinib with medications that could increase the risk of a bleeding event. Since Afib is both an AE associated with ibrutinib treatment and a condition that often is treated with anticoagulants, the prevalence of Afib in this population was also included. For context, the incidence of bleeding and Afib and use of anticoagulant and antiplatelet medications during treatment in a cohort of patients with CLL treated with bendamustine + rituximab (BR) also was reported.
Methods
The VHA maintains the centralized US Department of Veterans Affairs Cancer Registry System (VACRS), with electronic medical record data and other sources captured in its Corporate Data Warehouse (CDW). The VHA CDW is a national repository comprising data from several VHA clinical and administrative systems. The CDW includes patient identifiers; demographics; vital status; lab information; administrative information (such as diagnostic International Statistical Classification of Diseases and Related Health Problems [ICD-9] codes); medication dispensation tables (such as outpatient fill); IV package information; and notes from radiology, pathology, outpatient and inpatient admission, discharge, and daily progress.
Registrars abstract all cancer cases within the VHA system (or diagnosed outside the VHA, if patients subsequently receive treatment in the VHA). It is estimated that VACRS captures 3% of cancer cases in the US.12 Like most registries, VACRS captures data such as diagnosis, age, gender, race, and vital status.
The study received approval from the University of Utah Institutional Review Board and used individual patient-level historical administrative, cancer registry, and electronic health care record data. Patients diagnosed and treated for CLL at the VHA from 2010 to 2014 were identified through the VACRS and CDW; patients with a prior malignancy were excluded. Patients who received ibrutinib or BR based on pharmacy dispensation information were selected. Patients were followed until December 31, 2016 or death; patients with documentation of another cancer or lack of utilization of the VHA hematology or oncology services (defined as absence of any hematology and/or oncology clinic visits for ≥ 18 months) were omitted from the final analysis (Figure).
Previous and concomitant utilization of antiplatelet (aspirin, clopidogrel) or anticoagulant (dalteparin, enoxaparin, fondaparinux, heparin, rivaroxaban, and warfarin) medications was extracted 6 months before and after the first dispensation of ibrutinib or BR using pharmacy dispensation records.
Study Definitions
Prevalence of comorbidities that could increase bleeding risk was determined using administrative ICD-9-CM codes. Liver disease was identified by presence of cirrhosis, hepatitis C virus, or alcoholic liver disease using administrative codes validated by Kramer and colleagues, who reported positive and negative predictive values of 90% and 87% for cirrhosis, 93% and 92% for hepatitis C virus, and 71% and 98% for alcoholic liver disease.13 Similarly, end-stage liver disease was identified using a validated coding algorithm developed by Goldberg and colleagues, with a positive predictive value of 89.3%.14 The presence of controlled or uncontrolled diabetes mellitus (DM) was identified using the procedure described by Guzman and colleagues.15 Quan’s algorithm was used to calculate Charlson Comorbidity Index (CCI) based on ICD-9-CM codes for inpatient and outpatient visits within a 6-month lookback period prior to treatment initiation.16
A major bleeding event was defined as a hospitalization with an ICD-9-CM code suggestive of major bleeding as the primary reason, as defined by Lane and colleagues in their study of major bleeding related to warfarin in a cohort of patients treated within the VHA.17 Incidence rates of major bleeding events were identified during the first 6 months of treatment. Incidence of Afib—defined as an inpatient or outpatient encounter with the 427.31 ICD-9-CM code—also was examined within the first 6 months after starting treatment. The period of 6 months was chosen because bendamustine must be discontinued after 6 months.
Study Analysis
Descriptive statistics were used to examine patient demographics, disease characteristics, and treatment history from initial CLL diagnosis through end of study observation period. Categorical variables were summarized using frequencies and accompanying proportions, while a mean and standard deviation were used to summarize continuous variables. For the means of continuous variables and of categorical data, 95% CIs were used. Proportions and accompanying 95% CIs characterized treatment patterns, including line of therapy, comorbidities, and bleeding events. Treatment duration was described using mean and accompanying 95% CI. Statistical tests were not conducted for comparisons among treatment groups. Patients were censored at the end of follow-up, defined as the earliest of the following scenarios: (1) end of study observation period (December 31, 2016); (2) development of a secondary cancer; or (3) last day of contact given absence of care within the VHA for ≥ 18 months (with care defined as oncology and/or oncology/hematology visit with an associated note). Analysis was performed using R 3.4.0.
Results
Between 2010 and 2014, 2,796 patients were diagnosed and received care for CLL within the VHA. Overall, all 172 patients who were treated with ibrutinib during our inclusion period were selected. These patients were treated between January 1, 2014 and December 31, 2016, following ibrutinib’s approval in early 2014. An additional 291 patients were selected who received BR (Table). Reflecting the predominantly male population of the VHA, 282 (97%) BR patients and 167 (97%) ibrutinib patients were male. The median age at diagnosis was 67 years for BR patients and 69 years for ibrutinib patients. About 76% of patients who received ibrutinib and 82% of patients who received BR were non-Hispanic white; 17% and 14% were African American, respectively.
Less than 10% of patients receiving either ibrutinib or BR had liver disease per criteria used by Kramer and colleagues, or end-stage liver disease using criteria developed by Goldberg and colleagues.12,13 About 5% of patients had a history of previous bleeding in the 6-month period prior to initiating either therapy. Mean CCI (excluding malignancy) score was 1.5 (range, 0-11) for the ibrutinib group, and 2.1 (range, 0-9) for the BR group. About 16% of the ibrutinib group had controlled DM and fewer than 10% had uncontrolled DM, while 4% of patients in the BR group met the criteria for controlled DM and another 4% met the criteria for uncontrolled DM.
There was very low utilization of anticoagulant or antiplatelet medication prior to initiation of ibrutinib (2.9% and 2.3%, respectively) or BR (< 1% each). In the first 6 months after treatment initiation, about 8% of patients in both ibrutinib and BR cohorts received anticoagulant medication while antiplatelet utilization was < 5% in either group.
In the BR group, 8 patients (2.7%) experienced a major bleeding event, while 14 patients (8.1%) in the ibrutinib group experienced a bleeding event (P = .008). While these numbers were too low to perform a formal statistical analysis of the association between clinical covariates and bleeding in either group, there did not seem to be an association between bleeding and liver disease or DM. Of patients who experienced a bleeding event, about 1 in 4 patients had had a prior bleeding event in both the ibrutinib and the BR groups. Interestingly, while none of the patients who experienced a bleeding event while receiving BR were taking concomitant anticoagulant medication, 3 of the 14 patients who experienced a bleeding event in the ibrutinib group showed evidence of anticoagulant utilization. Finally, the incidence of Afib (defined as patients with no evidence of Afib in the 6 months prior to treatment but with evidence of Afib in the 6 months following treatment initiation) was 4% in the BR group, and about 8% in the ibrutinib group (P = .003).
Discussion
To the authors’ knowledge, this study is the first to examine the real-world incidence of bleeding and Afib in veterans who received ibrutinib for CLL in the first year of its availability. The study found minimal use of anticoagulants and/or antiplatelet agents prior to receiving first-line ibrutinib or BR, and very low use of these agents in the first 6 months following the initiation of first-line treatment. This finding suggests a high awareness among VA providers of potential adverse effects (AEs) of ibrutinib and chemotherapy, and a careful selection of patients that lack risk factors for AEs.
In patients treated with first-line ibrutinib when compared with patients treated with first-line BR, moderate increases in bleeding (2.7% vs 8.1%, P = .008) and Afib (10.5% vs 3%, P = .003) also were observed. These results are concordant with previous findings examining the use of ibrutinib in patients with CLL.18-20
Limitations
The results of this study should be interpreted with caution, as some limitations must be considered. The study was conducted in the early days of ibrutinib adoption. Since then, more patients have been treated with ibrutinib and for longer durations. As clinicians gain more familiarity and with ibrutinib, and as additional novel therapeutics emerge, it is possible that the initial awareness about risks for possible AEs may diminish; patients with high comorbidity burdens and concomitant medications would be especially vulnerable in cases of reduced physician vigilance.
Another limitation of this study stems from the potential for dual system use among patients treated in the VHA. Concurrent or alternating use of multiple health care systems (use of VHA and private-sector facilities) may present gaps in the reconstruction of patient histories, resulting in missing data as patients transition between commercial, the Centers for Medicare and Medicaid Services, and VHA care. As a result, the results presented here do not reflect instances where a patient experienced a bleeding event treated outside the VA.
Problems with missing data also may occur due to incomplete extraction from the electronic health record; these issues were addressed by leveraging an understanding of the multiple data marts within the CDW environment to harmonize missing and/or erroneous information through use of other data marts when possible. Lastly, this research represents a population-level study of the VHA, thus all findings are directly relevant to the VHA. The generalizability of the findings outside the VHA would depend on the characteristics of the external population.
Conclusion
Real-world evidence from a nationwide cohort of veteran patients with CLL treated with ibrutinib suggest that, while there is an association of increased bleeding-related events and Afib, the risk is comparable to those reported in previous studies.18-20 These findings suggest that patients in real-world clinical care settings with higher levels of comorbidities may be at a slight increased risk for bleeding events and Afib.
1. Scarfò L, Ferreri AJ, Ghia P. Chronic lymphocytic leukaemia. Crit Rev Oncol Hematol. 2016;104:169-182.
2. Devereux S, Cuthill K. Chronic lymphocytic leukaemia. Medicine (Baltimore). 2017;45(5):292-296.
3. American Cancer Society. Cancer facts & figures 2020. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2020/cancer-facts-and-figures-2020.pdf. Accessed April 24, 2020.
4. Kipps TJ, Stevenson FK, Wu CJ, et al. Chronic lymphocytic leukaemia. Nat Rev Dis Primers. 2017;3:16096.
5. Owen C, Assouline S, Kuruvilla J, Uchida C, Bellingham C, Sehn L. Novel therapies for chronic lymphocytic leukemia: a Canadian perspective. Clin Lymphoma Myeloma Leuk. 2015;15(11):627-634.e5.
6. O’Brien S, Jones JA, Coutre SE, et al. Ibrutinib for patients with relapsed or refractory chronic lymphocytic leukaemia with 17p deletion (RESONATE-17): a phase 2, open-label, multicentre study. Lancet Oncol. 2016;17(10):1409–1418.
7. Burger JA, Tedeschi A, Barr PM, et al; RESONATE-2 Investigators. Ibrutinib as initial therapy for patients with chronic lymphocytic leukemia. N Engl J Med. 2015;373(25):2425-2437.
8. Byrd JC, Furman RR, Coutre SE, et al. Targeting BTK with ibrutinib in relapsed chronic lymphocytic leukemia. N Engl J Med. 2013;369(1):32-42.
9. O’Brien S, Furman R, Coutre S, et al. Single-agent ibrutinib in treatment-naive and relapsed/refractory chronic lymphocytic leukemia: a 5-year experience. Blood. 2018;131(17):1910-1919.
10. Caron F, Leong DP, Hillis C, Fraser G, Siegal D. Current understanding of bleeding with ibrutinib use: a systematic review and meta-analysis. Blood Adv. 2017;1(12):772-778.
11. Kunk PR, Mock J, Devitt ME, Palkimas S, et al. Major bleeding with ibrutinib: more than expected. Blood. 2016;128(22):3229.
12. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701.
13. Kramer JR, Davila JA, Miller ED, Richardson P, Giordano TP, El-Serag HB. The validity of viral hepatitis and chronic liver disease diagnoses in Veterans Affairs administrative databases. Aliment Pharmacol Ther. 2008;27(3):274-282.
14. Goldberg D, Lewis JD, Halpern SD, Weiner M, Lo Re V 3rd. Validation of three coding algorithms to identify patients with end-stage liver disease in an administrative database. Pharmacoepidemiol Drug Saf. 2012;21(7):765-769.
15. Guzman JZ, Iatridis JC, Skovrlj B, et al. Outcomes and complications of diabetes mellitus on patients undergoing degenerative lumbar spine surgery. Spine (Phila Pa 1976). 2014;39(19):1596-1604.
16. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.
17. Lane MA, Zeringue A, McDonald JR. Serious bleeding events due to warfarin and antibiotic co-prescription in a cohort of veterans. Am J Med. 2014;127(7):657–663.e2.
18. Leong DP, Caron F, Hillis C, et al. The risk of atrial fibrillation with ibrutinib use: a systematic review and meta-analysis. Blood. 2016;128(1):138-140.
19. Lipsky AH, Farooqui MZ, Tian X, et al. Incidence and risk factors of bleeding-related adverse events in patients with chronic lymphocytic leukemia treated with ibrutinib. Haematologica. 2015;100(12):1571-1578.
20. Brown JR, Moslehi J, O’Brien S, et al. Characterization of atrial fibrillation adverse events reported in ibrutinib randomized controlled registration trials. Haematologica. 2017;102(10):1796-1805.
1. Scarfò L, Ferreri AJ, Ghia P. Chronic lymphocytic leukaemia. Crit Rev Oncol Hematol. 2016;104:169-182.
2. Devereux S, Cuthill K. Chronic lymphocytic leukaemia. Medicine (Baltimore). 2017;45(5):292-296.
3. American Cancer Society. Cancer facts & figures 2020. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2020/cancer-facts-and-figures-2020.pdf. Accessed April 24, 2020.
4. Kipps TJ, Stevenson FK, Wu CJ, et al. Chronic lymphocytic leukaemia. Nat Rev Dis Primers. 2017;3:16096.
5. Owen C, Assouline S, Kuruvilla J, Uchida C, Bellingham C, Sehn L. Novel therapies for chronic lymphocytic leukemia: a Canadian perspective. Clin Lymphoma Myeloma Leuk. 2015;15(11):627-634.e5.
6. O’Brien S, Jones JA, Coutre SE, et al. Ibrutinib for patients with relapsed or refractory chronic lymphocytic leukaemia with 17p deletion (RESONATE-17): a phase 2, open-label, multicentre study. Lancet Oncol. 2016;17(10):1409–1418.
7. Burger JA, Tedeschi A, Barr PM, et al; RESONATE-2 Investigators. Ibrutinib as initial therapy for patients with chronic lymphocytic leukemia. N Engl J Med. 2015;373(25):2425-2437.
8. Byrd JC, Furman RR, Coutre SE, et al. Targeting BTK with ibrutinib in relapsed chronic lymphocytic leukemia. N Engl J Med. 2013;369(1):32-42.
9. O’Brien S, Furman R, Coutre S, et al. Single-agent ibrutinib in treatment-naive and relapsed/refractory chronic lymphocytic leukemia: a 5-year experience. Blood. 2018;131(17):1910-1919.
10. Caron F, Leong DP, Hillis C, Fraser G, Siegal D. Current understanding of bleeding with ibrutinib use: a systematic review and meta-analysis. Blood Adv. 2017;1(12):772-778.
11. Kunk PR, Mock J, Devitt ME, Palkimas S, et al. Major bleeding with ibrutinib: more than expected. Blood. 2016;128(22):3229.
12. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701.
13. Kramer JR, Davila JA, Miller ED, Richardson P, Giordano TP, El-Serag HB. The validity of viral hepatitis and chronic liver disease diagnoses in Veterans Affairs administrative databases. Aliment Pharmacol Ther. 2008;27(3):274-282.
14. Goldberg D, Lewis JD, Halpern SD, Weiner M, Lo Re V 3rd. Validation of three coding algorithms to identify patients with end-stage liver disease in an administrative database. Pharmacoepidemiol Drug Saf. 2012;21(7):765-769.
15. Guzman JZ, Iatridis JC, Skovrlj B, et al. Outcomes and complications of diabetes mellitus on patients undergoing degenerative lumbar spine surgery. Spine (Phila Pa 1976). 2014;39(19):1596-1604.
16. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.
17. Lane MA, Zeringue A, McDonald JR. Serious bleeding events due to warfarin and antibiotic co-prescription in a cohort of veterans. Am J Med. 2014;127(7):657–663.e2.
18. Leong DP, Caron F, Hillis C, et al. The risk of atrial fibrillation with ibrutinib use: a systematic review and meta-analysis. Blood. 2016;128(1):138-140.
19. Lipsky AH, Farooqui MZ, Tian X, et al. Incidence and risk factors of bleeding-related adverse events in patients with chronic lymphocytic leukemia treated with ibrutinib. Haematologica. 2015;100(12):1571-1578.
20. Brown JR, Moslehi J, O’Brien S, et al. Characterization of atrial fibrillation adverse events reported in ibrutinib randomized controlled registration trials. Haematologica. 2017;102(10):1796-1805.
Radiotherapeutic Care of Patients With Stage IV Lung Cancer with Thoracic Symptoms in the Veterans Health Administration (FULL)
Lung cancer is the leading cause of cancer mortality both in the US and worldwide.1 Many patients diagnosed with lung cancer present with advanced disease with thoracic symptoms such as cough, hemoptysis, dyspnea, and chest pain.2-4 Palliative radiotherapy is routinely used in patients with locally advanced and metastatic lung cancer with the goal of relieving these symptoms and improving quality of life. Guidelines published by the American Society for Radiation Oncology (ASTRO) in 2011, and updated in 2018, provide recommendations on palliation of lung cancer with external beam radiotherapy (EBRT) and clarify the roles of concurrent chemotherapy and endobronchial brachytherapy (EBB) for palliation.5,6
After prostate cancer, lung cancer is the second most frequently diagnosed cancer in the Veterans Health Administration (VHA).7 The VHA consists of 172 medical centers and is the largest integrated health care system in the US. At the time of this study, 40 of these centers had onsite radiation facilities. The VHA Palliative Radiation Taskforce has conducted a series of surveys to evaluate use of palliative radiotherapy in the VHA, determine VHA practice concordance with ASTRO and American College of Radiology (ACR) guidelines, and direct educational efforts towards addressing gaps in knowledge. These efforts are directed at ensuring best practices throughout this large and heterogeneous healthcare system. In 2016 a survey was conducted to evaluate concordance of VHA radiation oncologist (RO) practice with the 2011 ASTRO guidelines on palliative thoracic radiotherapy for non-small cell lung cancer (NSCLC).
Methods
A survey instrument was generated by VHA National Palliative Radiotherapy Taskforce members. It was reviewed and approved for use by the VHA Patient Care Services office. In May of 2016, the online survey was sent to the 88 VHA ROs practicing at the 40 sites with onsite radiation facilities. The survey aimed to determine patterns of practice for palliation of thoracic symptoms secondary to lung cancer.
Demographic information obtained included years in practice, employment status, academic appointment, board certification, and familiarity with ASTRO lung cancer guidelines. Two clinical scenarios were presented to glean opinions on dose/fractionation schemes preferred, use of concurrent chemotherapy, and use of EBB and/or yttrium aluminum garnet (YAG) laser technology. Survey questions also assessed use of EBRT for palliation of hemoptysis, chest wall pain, and/or stridor as well as use of stereotactic body radiotherapy (SBRT) for palliation.
Survey results were assessed for concordance with published ASTRO guidelines. χ2 tests were run to test for associations between demographic factors such as academic appointment, years of practice, full time vs part time employment, and familiarity with ASTRO palliative lung cancer guidelines, with use of EBRT for palliation, dose and fractionation preference, use of concurrent chemotherapy, and strategy for management of endobronchial lesions.
Results
Of the 88 physicians surveyed, 54 responded for a response rate of 61%. Respondents represented 37 of the 40 (93%) VHA radiation oncology departments (Table 1). Among respondents, most were board certified (96%), held academic appointments (91%), and were full-time employees (85%). Forty-four percent of respondents were in practice for > 20 years, 19% for 11 to 20 years, 20% for 6 to 10 years, and 17% for < 6 years. A majority reported familiarity with the ASTRO guidelines (64%), while just 11% reported no familiarity with the guidelines.
When asked about use of SBRT for palliation of hemoptysis, stridor, and/or chest pain, the majority (87%) preferred conventional EBRT. Of the 13% who reported use of SBRT, most (11%) performed it onsite, with 2% of respondents referring offsite to non-VHA centers for the service. When asked about use of EBB for palliation, only 2% reported use of that procedure at their facilities, while 26% reported referral to non-VHA facilities for EBB. The remaining 72% of respondents favor use of conventional EBRT.
Respondents were presented with a case of a male patient aged 70 years who smoked and had widely metastatic NSCLC, a life expectancy of about 3 months, and 10/10 chest wall pain from direct tumor invasion. All respondents recommended palliative radiotherapy. The preferred fractionation was 20 Gray (Gy) in 5 fractions, which was recommended by 69% of respondents. The remainder recommended 30 Gy in 10 fractions (22%) or a single fraction of 10 Gy (9%). No respondent recommended the longer fractionation options of 60 Gy in 30 fractions, 45 Gy in 15 fractions, or 40 Gy in 20 fractions. The majority (98%) did not recommend concurrent chemotherapy.
When the above case was modified for an endobronchial lesion requiring palliation with associated lung collapse, rather than chest wall invasion, 20 respondents (38%) reported they would refer for EBB, and 20 respondents reported they would refer for YAG laser. As > 1 answer could be selected for this question, there were 12 respondents who selected both EBB and YAG laser; 8 selected only EBB, and 8 selected only YAG laser. Many respondents added comments about treating with EBRT, which had not been presented as an answer choice. Nearly half of respondents (49%) were amenable to referral for the use of EBB or YAG laser for lung reexpansion prior to radiotherapy. Three respondents mentioned referral for an endobronchial stent prior to palliative radiotherapy to address this question.
χ2 tests were used to evaluate for significant associations between demographic factors, such as number of years in practice, academic appointment, full-time vs part-time status, and familiarity with ASTRO guidelines with clinical management choices (Table 2). The χ2 analysis revealed that these demographic factors were not significantly associated with familiarity with ASTRO guidelines, offering SBRT for palliation, EBRT fractionation scheme preferred, use of concurrent chemotherapy, or use of EBB or YAG laser.
Discussion
This survey was conducted to evaluate concordance of management of metastatic lung cancer in the VHA with ASTRO guidelines. The relationship between respondents’ familiarity with the guidelines and responses also was evaluated to determine the impact such guidelines have on decision-making. The ASTRO guidelines for palliative thoracic radiation make recommendations regarding 3 issues: (1) radiation doses and fractionations for palliation; (2) the role of EBB; and (3) the use of concurrent chemotherapy.5,6
Radiation Dose and Fractionation for Palliation
A variety of dose/fractionation schemes are considered appropriate in the ASTRO guideline statement, including more prolonged courses such as 30 Gy/10 fractions as well as more hypofractionated regimens (ie, 20 Gy/5 fractions, 17 Gy/2 fractions, and a single fraction of 10 Gy). Higher dose regimens, such as 30 Gy/10 fractions, have been associated with prolonged survival, as well as increased toxicities such as radiation esophagitis.8 Therefore, the guidelines support use of 30 Gy/10 fractions for patients with good performance status while encouraging use of more hypofractionated regimens for patients with poor performance status. In considering more hypofractionated regimens, one must consider the possibility of adverse effects that can be associated with higher dose per fraction. For instance, 17 Gy/2 fractions has been associated with myelopathy; therefore it should be used with caution and careful treatment planning.9
For the survey case example (a male aged 70 years with a 3-month life expectancy who required palliation for chest wall pain), all respondents selected hypofractionated regimens; with no respondent selected the more prolonged fractionations of 60 Gy/30 fractions, 45 Gy/15 fractions, or 40 Gy/20 fractions. These more prolonged fractionations are not endorsed by the guidelines in general, and particularly not for a patient with poor life expectancy. All responses for this case selected by survey respondents are considered appropriate per the consensus guideline statement.
Role of Concurrent Chemotherapy
The ASTRO guidelines do not support use of concurrent chemotherapy for palliation of stage IV NSCLC.5,6 The 2018 updated guidelines established a role for concurrent chemotherapy for patients with stage III NSCLC with good performance status and life expectancy of > 3 months. This updated recommendation is based on data from 2 randomized trials demonstrating improvement in overall survival with the addition of chemotherapy for patients with stage III NSCLC undergoing palliative radiotherapy.10-12
These newer studies are in contrast to an older randomized study by Ball and colleagues that demonstrated greater toxicity from concurrent chemotherapy, with no improvement in outcomes such as palliation of symptoms, overall survival, or progression free survival.13 In contrast to the newer studies that included only patients with stage III NSCLC, about half of the patients in the Ball and colleagues study had known metastatic disease.10-13 Of note, staging for metastatic disease was not carried out routinely, so it is possible that a greater proportion of patients had metastatic disease that would have been seen on imaging. In concordance with the guidelines, 98% of the survey respondents did not recommend concurrent chemotherapy for palliation of intrathoracic symptom; only 1 respondent recommended use of chemotherapy for palliation.
Role of Endobronchial Brachytherapy
EBB involves implantation of radioactive sources for treatment of endobronchial lesions causing obstructive symptoms.14 Given the lack of randomized data that demonstrate a benefit of EBB over EBRT, the ASTRO guidelines do not endorse routine use of EBB for initial palliative management.15,16 The ASTRO guidelines reference a Cochrane Review of 13 trials that concluded that EBRT alone is superior to EBB alone for initial palliation of symptoms from endobronchial NSCLC.17
Of respondents surveyed, only 1 facility offered onsite EBB. The majority of respondents (72%) preferred the use of conventional EBRT techniques, while 26% refer to non-VHA centers for EBB. Lack of incorporation of EBB into routine VHA practice likely is a reflection of the unclear role of this technology based on the available literature and ASTRO guidelines. In the setting of a right lower lung collapse, more respondents (49%) would consider use of EBB or YAG laser technology for lung reexpansion prior to EBRT.
The ASTRO guidelines recommend that initial EBB in conjunction with EBRT be considered based on randomized data demonstrating significant improvement in lung reexpansion and in patient reported dyspnea with addition of EBB to EBRT over EBRT alone.18 However, the guidelines do not mandate the use of EBB in this situation. It is possible that targeted education regarding the role of EBB would improve knowledge of the potential benefit in the setting of lung collapse and increase the percentage of VHA ROs who would recommend this procedure.
Limitations
The study is limited by lack of generalizability of these findings to all ROs in the country. It is also possible that physician responses do not represent practice patterns with complete accuracy. The use of EBB varied among practitioners. Further study of this technology is necessary to clarify its role in the management of endobronchial obstructive symptoms and to determine whether efforts should be made to increase access to EBB within the VHA.
Conclusions
Most of the ROs who responded to our survey were cognizant and compliant with current ASTRO guidelines on management of lung cancer. Furthermore, familiarity with ASTRO guidelines and management choices were not associated with the respondents’ years in practice, academic appointment, full-time vs part-time status, or familiarity with ASTRO guidelines. This study is a nationwide survey of ROs in the VHA system that reflects the radiation-related care received by veterans with metastatic lung cancer. Responses were obtained from 93% of the 40 radiation oncology centers, so it is likely that the survey accurately represents the decision-making process at the majority of centers. It is possible that those who did not respond to the survey do not treat thoracic cases.
1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015 65(2):87-108.
2. Kocher F, Hilbe W, Seeber A, et al. Longitudinal analysis of 2293 NSCLC patients: a comprehensive study from the TYROL registry. Lung Cancer. 2015;87(2):193-200.
3. Chute CG, Greenberg ER, Baron J, Korson R, Baker J, Yates J. Presenting conditions of 1539 population-based lung cancer patients by cell type and stage in New Hampshire and Vermont. Cancer. 1985;56(8):2107-2111.
4. Hyde L, Hyde Cl. Clinical manifestations of lung cancer. Chest. 1974;65(3):299-306.
5. Rodrigues G, Videtic GM, Sur R, et al. Palliative thoracic radiotherapy in lung cancer: An American Society for Radiation Oncology evidence-based clinical practice guideline. Pract Radiat Oncol. 2011;1(2):60-71.
6. Moeller B, Balagamwala EH, Chen A, et al. Palliative thoracic radiation therapy for non-small cell lung cancer: 2018 Update of an American Society for Radiation Oncology (ASTRO) Evidence-Based Guideline. Pract Radiat Oncol. 2018;8(4):245-250.
7. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the United States Veterans Affairs (VA) healthcare system. Mil Med. 2012;177(6):693-701.
8. Fairchild A, Harris K, Barnes E, et al. Palliative thoracic radiotherapy for lung cancer: a systematic review. J Clin Oncol. 2008;26(24):4001-4011.
9. A Medical Research Council (MRC) randomised trial of palliative radiotherapy with two fractions or a single fraction in patients with inoperable non-small-cell lung cancer (NSCLC) and poor performance status. Medical Research Council Lung Cancer Working Party. Br J Cancer. 1992;65(6):934-941.
10. Nawrocki S, Krzakowski M, Wasilewska-Tesluk E, et al. Concurrent chemotherapy and short course radiotherapy in patients with stage IIIA to IIIB non-small cell lung cancer not eligible for radical treatment: results of a randomized phase II study. J Thorac Oncol. 2010;5(8):1255-1262.
11. Strøm HH, Bremnes RM, Sundstrøm SH, Helbekkmo N, Fløtten O, Aasebø U. Concurrent palliative chemoradiation leads to survival and quality of life benefits in poor prognosis stage III non-small-cell lung cancer: a randomised trial by the Norwegian Lung Cancer Study Group. Br J Cancer. 2013;109(6):1467-1475.
12. Strøm HH, Bremnes RM, Sundstrøm SH, Helbekkmo N, Aasebø U. Poor prognosis patients with inoperable locally advanced NSCLC and large tumors benefit from palliative chemoradiotherapy: a subset analysis from a randomized clinical phase III trial. J Thorac Oncol. 2014;9(6):825-833.
13. Ball D, Smith J, Bishop J, et al. A phase III study of radiotherapy with and without continuous-infusion fluorouracil as palliation for non-small-cell lung cancer. Br J Cancer. 1997;75(5):690-697.
14. Stewart A, Parashar B, Patel M, et al. American Brachytherapy Society consensus guidelines for thoracic brachytherapy for lung cancer. Brachytherapy. 2016;15(1):1-11.
15. Sur R, Ahmed SN, Donde B, Morar R, Mohamed G, Sur M, Pacella JA, Van der Merwe E, Feldman C. Brachytherapy boost vs teletherapy boost in palliation of symptomatic, locally advanced non-small cell lung cancer: preliminary analysis of a randomized prospective study. J Brachytherapy Int. 2001;17(4):309-315.
16. Sur R, Donde B, Mohuiddin M, et al. Randomized prospective study on the role of high dose rate intraluminal brachytherapy (HDRILBT) in palliation of symptoms in advanced non-small cell lung cancer (NSCLC) treated with radiation alone. Int J Radiat Oncol Biol Phys. 2004;60(1):S205.
17. Ung YC, Yu E, Falkson C, et al. The role of high-dose-rate brachytherapy in the palliation of symptoms in patients with non-small cell lung cancer: a systematic review. Brachytherapy. 2006;5:189-202.
18. Langendijk H, de Jong J, Tjwa M, et al. External irradiation versus external irradiation plus endobronchial brachytherapy in inoperable non-small cell lung cancer: a prospective randomized study. Radiother Oncol. 2001;58(3):257-268.
Lung cancer is the leading cause of cancer mortality both in the US and worldwide.1 Many patients diagnosed with lung cancer present with advanced disease with thoracic symptoms such as cough, hemoptysis, dyspnea, and chest pain.2-4 Palliative radiotherapy is routinely used in patients with locally advanced and metastatic lung cancer with the goal of relieving these symptoms and improving quality of life. Guidelines published by the American Society for Radiation Oncology (ASTRO) in 2011, and updated in 2018, provide recommendations on palliation of lung cancer with external beam radiotherapy (EBRT) and clarify the roles of concurrent chemotherapy and endobronchial brachytherapy (EBB) for palliation.5,6
After prostate cancer, lung cancer is the second most frequently diagnosed cancer in the Veterans Health Administration (VHA).7 The VHA consists of 172 medical centers and is the largest integrated health care system in the US. At the time of this study, 40 of these centers had onsite radiation facilities. The VHA Palliative Radiation Taskforce has conducted a series of surveys to evaluate use of palliative radiotherapy in the VHA, determine VHA practice concordance with ASTRO and American College of Radiology (ACR) guidelines, and direct educational efforts towards addressing gaps in knowledge. These efforts are directed at ensuring best practices throughout this large and heterogeneous healthcare system. In 2016 a survey was conducted to evaluate concordance of VHA radiation oncologist (RO) practice with the 2011 ASTRO guidelines on palliative thoracic radiotherapy for non-small cell lung cancer (NSCLC).
Methods
A survey instrument was generated by VHA National Palliative Radiotherapy Taskforce members. It was reviewed and approved for use by the VHA Patient Care Services office. In May of 2016, the online survey was sent to the 88 VHA ROs practicing at the 40 sites with onsite radiation facilities. The survey aimed to determine patterns of practice for palliation of thoracic symptoms secondary to lung cancer.
Demographic information obtained included years in practice, employment status, academic appointment, board certification, and familiarity with ASTRO lung cancer guidelines. Two clinical scenarios were presented to glean opinions on dose/fractionation schemes preferred, use of concurrent chemotherapy, and use of EBB and/or yttrium aluminum garnet (YAG) laser technology. Survey questions also assessed use of EBRT for palliation of hemoptysis, chest wall pain, and/or stridor as well as use of stereotactic body radiotherapy (SBRT) for palliation.
Survey results were assessed for concordance with published ASTRO guidelines. χ2 tests were run to test for associations between demographic factors such as academic appointment, years of practice, full time vs part time employment, and familiarity with ASTRO palliative lung cancer guidelines, with use of EBRT for palliation, dose and fractionation preference, use of concurrent chemotherapy, and strategy for management of endobronchial lesions.
Results
Of the 88 physicians surveyed, 54 responded for a response rate of 61%. Respondents represented 37 of the 40 (93%) VHA radiation oncology departments (Table 1). Among respondents, most were board certified (96%), held academic appointments (91%), and were full-time employees (85%). Forty-four percent of respondents were in practice for > 20 years, 19% for 11 to 20 years, 20% for 6 to 10 years, and 17% for < 6 years. A majority reported familiarity with the ASTRO guidelines (64%), while just 11% reported no familiarity with the guidelines.
When asked about use of SBRT for palliation of hemoptysis, stridor, and/or chest pain, the majority (87%) preferred conventional EBRT. Of the 13% who reported use of SBRT, most (11%) performed it onsite, with 2% of respondents referring offsite to non-VHA centers for the service. When asked about use of EBB for palliation, only 2% reported use of that procedure at their facilities, while 26% reported referral to non-VHA facilities for EBB. The remaining 72% of respondents favor use of conventional EBRT.
Respondents were presented with a case of a male patient aged 70 years who smoked and had widely metastatic NSCLC, a life expectancy of about 3 months, and 10/10 chest wall pain from direct tumor invasion. All respondents recommended palliative radiotherapy. The preferred fractionation was 20 Gray (Gy) in 5 fractions, which was recommended by 69% of respondents. The remainder recommended 30 Gy in 10 fractions (22%) or a single fraction of 10 Gy (9%). No respondent recommended the longer fractionation options of 60 Gy in 30 fractions, 45 Gy in 15 fractions, or 40 Gy in 20 fractions. The majority (98%) did not recommend concurrent chemotherapy.
When the above case was modified for an endobronchial lesion requiring palliation with associated lung collapse, rather than chest wall invasion, 20 respondents (38%) reported they would refer for EBB, and 20 respondents reported they would refer for YAG laser. As > 1 answer could be selected for this question, there were 12 respondents who selected both EBB and YAG laser; 8 selected only EBB, and 8 selected only YAG laser. Many respondents added comments about treating with EBRT, which had not been presented as an answer choice. Nearly half of respondents (49%) were amenable to referral for the use of EBB or YAG laser for lung reexpansion prior to radiotherapy. Three respondents mentioned referral for an endobronchial stent prior to palliative radiotherapy to address this question.
χ2 tests were used to evaluate for significant associations between demographic factors, such as number of years in practice, academic appointment, full-time vs part-time status, and familiarity with ASTRO guidelines with clinical management choices (Table 2). The χ2 analysis revealed that these demographic factors were not significantly associated with familiarity with ASTRO guidelines, offering SBRT for palliation, EBRT fractionation scheme preferred, use of concurrent chemotherapy, or use of EBB or YAG laser.
Discussion
This survey was conducted to evaluate concordance of management of metastatic lung cancer in the VHA with ASTRO guidelines. The relationship between respondents’ familiarity with the guidelines and responses also was evaluated to determine the impact such guidelines have on decision-making. The ASTRO guidelines for palliative thoracic radiation make recommendations regarding 3 issues: (1) radiation doses and fractionations for palliation; (2) the role of EBB; and (3) the use of concurrent chemotherapy.5,6
Radiation Dose and Fractionation for Palliation
A variety of dose/fractionation schemes are considered appropriate in the ASTRO guideline statement, including more prolonged courses such as 30 Gy/10 fractions as well as more hypofractionated regimens (ie, 20 Gy/5 fractions, 17 Gy/2 fractions, and a single fraction of 10 Gy). Higher dose regimens, such as 30 Gy/10 fractions, have been associated with prolonged survival, as well as increased toxicities such as radiation esophagitis.8 Therefore, the guidelines support use of 30 Gy/10 fractions for patients with good performance status while encouraging use of more hypofractionated regimens for patients with poor performance status. In considering more hypofractionated regimens, one must consider the possibility of adverse effects that can be associated with higher dose per fraction. For instance, 17 Gy/2 fractions has been associated with myelopathy; therefore it should be used with caution and careful treatment planning.9
For the survey case example (a male aged 70 years with a 3-month life expectancy who required palliation for chest wall pain), all respondents selected hypofractionated regimens; with no respondent selected the more prolonged fractionations of 60 Gy/30 fractions, 45 Gy/15 fractions, or 40 Gy/20 fractions. These more prolonged fractionations are not endorsed by the guidelines in general, and particularly not for a patient with poor life expectancy. All responses for this case selected by survey respondents are considered appropriate per the consensus guideline statement.
Role of Concurrent Chemotherapy
The ASTRO guidelines do not support use of concurrent chemotherapy for palliation of stage IV NSCLC.5,6 The 2018 updated guidelines established a role for concurrent chemotherapy for patients with stage III NSCLC with good performance status and life expectancy of > 3 months. This updated recommendation is based on data from 2 randomized trials demonstrating improvement in overall survival with the addition of chemotherapy for patients with stage III NSCLC undergoing palliative radiotherapy.10-12
These newer studies are in contrast to an older randomized study by Ball and colleagues that demonstrated greater toxicity from concurrent chemotherapy, with no improvement in outcomes such as palliation of symptoms, overall survival, or progression free survival.13 In contrast to the newer studies that included only patients with stage III NSCLC, about half of the patients in the Ball and colleagues study had known metastatic disease.10-13 Of note, staging for metastatic disease was not carried out routinely, so it is possible that a greater proportion of patients had metastatic disease that would have been seen on imaging. In concordance with the guidelines, 98% of the survey respondents did not recommend concurrent chemotherapy for palliation of intrathoracic symptom; only 1 respondent recommended use of chemotherapy for palliation.
Role of Endobronchial Brachytherapy
EBB involves implantation of radioactive sources for treatment of endobronchial lesions causing obstructive symptoms.14 Given the lack of randomized data that demonstrate a benefit of EBB over EBRT, the ASTRO guidelines do not endorse routine use of EBB for initial palliative management.15,16 The ASTRO guidelines reference a Cochrane Review of 13 trials that concluded that EBRT alone is superior to EBB alone for initial palliation of symptoms from endobronchial NSCLC.17
Of respondents surveyed, only 1 facility offered onsite EBB. The majority of respondents (72%) preferred the use of conventional EBRT techniques, while 26% refer to non-VHA centers for EBB. Lack of incorporation of EBB into routine VHA practice likely is a reflection of the unclear role of this technology based on the available literature and ASTRO guidelines. In the setting of a right lower lung collapse, more respondents (49%) would consider use of EBB or YAG laser technology for lung reexpansion prior to EBRT.
The ASTRO guidelines recommend that initial EBB in conjunction with EBRT be considered based on randomized data demonstrating significant improvement in lung reexpansion and in patient reported dyspnea with addition of EBB to EBRT over EBRT alone.18 However, the guidelines do not mandate the use of EBB in this situation. It is possible that targeted education regarding the role of EBB would improve knowledge of the potential benefit in the setting of lung collapse and increase the percentage of VHA ROs who would recommend this procedure.
Limitations
The study is limited by lack of generalizability of these findings to all ROs in the country. It is also possible that physician responses do not represent practice patterns with complete accuracy. The use of EBB varied among practitioners. Further study of this technology is necessary to clarify its role in the management of endobronchial obstructive symptoms and to determine whether efforts should be made to increase access to EBB within the VHA.
Conclusions
Most of the ROs who responded to our survey were cognizant and compliant with current ASTRO guidelines on management of lung cancer. Furthermore, familiarity with ASTRO guidelines and management choices were not associated with the respondents’ years in practice, academic appointment, full-time vs part-time status, or familiarity with ASTRO guidelines. This study is a nationwide survey of ROs in the VHA system that reflects the radiation-related care received by veterans with metastatic lung cancer. Responses were obtained from 93% of the 40 radiation oncology centers, so it is likely that the survey accurately represents the decision-making process at the majority of centers. It is possible that those who did not respond to the survey do not treat thoracic cases.
Lung cancer is the leading cause of cancer mortality both in the US and worldwide.1 Many patients diagnosed with lung cancer present with advanced disease with thoracic symptoms such as cough, hemoptysis, dyspnea, and chest pain.2-4 Palliative radiotherapy is routinely used in patients with locally advanced and metastatic lung cancer with the goal of relieving these symptoms and improving quality of life. Guidelines published by the American Society for Radiation Oncology (ASTRO) in 2011, and updated in 2018, provide recommendations on palliation of lung cancer with external beam radiotherapy (EBRT) and clarify the roles of concurrent chemotherapy and endobronchial brachytherapy (EBB) for palliation.5,6
After prostate cancer, lung cancer is the second most frequently diagnosed cancer in the Veterans Health Administration (VHA).7 The VHA consists of 172 medical centers and is the largest integrated health care system in the US. At the time of this study, 40 of these centers had onsite radiation facilities. The VHA Palliative Radiation Taskforce has conducted a series of surveys to evaluate use of palliative radiotherapy in the VHA, determine VHA practice concordance with ASTRO and American College of Radiology (ACR) guidelines, and direct educational efforts towards addressing gaps in knowledge. These efforts are directed at ensuring best practices throughout this large and heterogeneous healthcare system. In 2016 a survey was conducted to evaluate concordance of VHA radiation oncologist (RO) practice with the 2011 ASTRO guidelines on palliative thoracic radiotherapy for non-small cell lung cancer (NSCLC).
Methods
A survey instrument was generated by VHA National Palliative Radiotherapy Taskforce members. It was reviewed and approved for use by the VHA Patient Care Services office. In May of 2016, the online survey was sent to the 88 VHA ROs practicing at the 40 sites with onsite radiation facilities. The survey aimed to determine patterns of practice for palliation of thoracic symptoms secondary to lung cancer.
Demographic information obtained included years in practice, employment status, academic appointment, board certification, and familiarity with ASTRO lung cancer guidelines. Two clinical scenarios were presented to glean opinions on dose/fractionation schemes preferred, use of concurrent chemotherapy, and use of EBB and/or yttrium aluminum garnet (YAG) laser technology. Survey questions also assessed use of EBRT for palliation of hemoptysis, chest wall pain, and/or stridor as well as use of stereotactic body radiotherapy (SBRT) for palliation.
Survey results were assessed for concordance with published ASTRO guidelines. χ2 tests were run to test for associations between demographic factors such as academic appointment, years of practice, full time vs part time employment, and familiarity with ASTRO palliative lung cancer guidelines, with use of EBRT for palliation, dose and fractionation preference, use of concurrent chemotherapy, and strategy for management of endobronchial lesions.
Results
Of the 88 physicians surveyed, 54 responded for a response rate of 61%. Respondents represented 37 of the 40 (93%) VHA radiation oncology departments (Table 1). Among respondents, most were board certified (96%), held academic appointments (91%), and were full-time employees (85%). Forty-four percent of respondents were in practice for > 20 years, 19% for 11 to 20 years, 20% for 6 to 10 years, and 17% for < 6 years. A majority reported familiarity with the ASTRO guidelines (64%), while just 11% reported no familiarity with the guidelines.
When asked about use of SBRT for palliation of hemoptysis, stridor, and/or chest pain, the majority (87%) preferred conventional EBRT. Of the 13% who reported use of SBRT, most (11%) performed it onsite, with 2% of respondents referring offsite to non-VHA centers for the service. When asked about use of EBB for palliation, only 2% reported use of that procedure at their facilities, while 26% reported referral to non-VHA facilities for EBB. The remaining 72% of respondents favor use of conventional EBRT.
Respondents were presented with a case of a male patient aged 70 years who smoked and had widely metastatic NSCLC, a life expectancy of about 3 months, and 10/10 chest wall pain from direct tumor invasion. All respondents recommended palliative radiotherapy. The preferred fractionation was 20 Gray (Gy) in 5 fractions, which was recommended by 69% of respondents. The remainder recommended 30 Gy in 10 fractions (22%) or a single fraction of 10 Gy (9%). No respondent recommended the longer fractionation options of 60 Gy in 30 fractions, 45 Gy in 15 fractions, or 40 Gy in 20 fractions. The majority (98%) did not recommend concurrent chemotherapy.
When the above case was modified for an endobronchial lesion requiring palliation with associated lung collapse, rather than chest wall invasion, 20 respondents (38%) reported they would refer for EBB, and 20 respondents reported they would refer for YAG laser. As > 1 answer could be selected for this question, there were 12 respondents who selected both EBB and YAG laser; 8 selected only EBB, and 8 selected only YAG laser. Many respondents added comments about treating with EBRT, which had not been presented as an answer choice. Nearly half of respondents (49%) were amenable to referral for the use of EBB or YAG laser for lung reexpansion prior to radiotherapy. Three respondents mentioned referral for an endobronchial stent prior to palliative radiotherapy to address this question.
χ2 tests were used to evaluate for significant associations between demographic factors, such as number of years in practice, academic appointment, full-time vs part-time status, and familiarity with ASTRO guidelines with clinical management choices (Table 2). The χ2 analysis revealed that these demographic factors were not significantly associated with familiarity with ASTRO guidelines, offering SBRT for palliation, EBRT fractionation scheme preferred, use of concurrent chemotherapy, or use of EBB or YAG laser.
Discussion
This survey was conducted to evaluate concordance of management of metastatic lung cancer in the VHA with ASTRO guidelines. The relationship between respondents’ familiarity with the guidelines and responses also was evaluated to determine the impact such guidelines have on decision-making. The ASTRO guidelines for palliative thoracic radiation make recommendations regarding 3 issues: (1) radiation doses and fractionations for palliation; (2) the role of EBB; and (3) the use of concurrent chemotherapy.5,6
Radiation Dose and Fractionation for Palliation
A variety of dose/fractionation schemes are considered appropriate in the ASTRO guideline statement, including more prolonged courses such as 30 Gy/10 fractions as well as more hypofractionated regimens (ie, 20 Gy/5 fractions, 17 Gy/2 fractions, and a single fraction of 10 Gy). Higher dose regimens, such as 30 Gy/10 fractions, have been associated with prolonged survival, as well as increased toxicities such as radiation esophagitis.8 Therefore, the guidelines support use of 30 Gy/10 fractions for patients with good performance status while encouraging use of more hypofractionated regimens for patients with poor performance status. In considering more hypofractionated regimens, one must consider the possibility of adverse effects that can be associated with higher dose per fraction. For instance, 17 Gy/2 fractions has been associated with myelopathy; therefore it should be used with caution and careful treatment planning.9
For the survey case example (a male aged 70 years with a 3-month life expectancy who required palliation for chest wall pain), all respondents selected hypofractionated regimens; with no respondent selected the more prolonged fractionations of 60 Gy/30 fractions, 45 Gy/15 fractions, or 40 Gy/20 fractions. These more prolonged fractionations are not endorsed by the guidelines in general, and particularly not for a patient with poor life expectancy. All responses for this case selected by survey respondents are considered appropriate per the consensus guideline statement.
Role of Concurrent Chemotherapy
The ASTRO guidelines do not support use of concurrent chemotherapy for palliation of stage IV NSCLC.5,6 The 2018 updated guidelines established a role for concurrent chemotherapy for patients with stage III NSCLC with good performance status and life expectancy of > 3 months. This updated recommendation is based on data from 2 randomized trials demonstrating improvement in overall survival with the addition of chemotherapy for patients with stage III NSCLC undergoing palliative radiotherapy.10-12
These newer studies are in contrast to an older randomized study by Ball and colleagues that demonstrated greater toxicity from concurrent chemotherapy, with no improvement in outcomes such as palliation of symptoms, overall survival, or progression free survival.13 In contrast to the newer studies that included only patients with stage III NSCLC, about half of the patients in the Ball and colleagues study had known metastatic disease.10-13 Of note, staging for metastatic disease was not carried out routinely, so it is possible that a greater proportion of patients had metastatic disease that would have been seen on imaging. In concordance with the guidelines, 98% of the survey respondents did not recommend concurrent chemotherapy for palliation of intrathoracic symptom; only 1 respondent recommended use of chemotherapy for palliation.
Role of Endobronchial Brachytherapy
EBB involves implantation of radioactive sources for treatment of endobronchial lesions causing obstructive symptoms.14 Given the lack of randomized data that demonstrate a benefit of EBB over EBRT, the ASTRO guidelines do not endorse routine use of EBB for initial palliative management.15,16 The ASTRO guidelines reference a Cochrane Review of 13 trials that concluded that EBRT alone is superior to EBB alone for initial palliation of symptoms from endobronchial NSCLC.17
Of respondents surveyed, only 1 facility offered onsite EBB. The majority of respondents (72%) preferred the use of conventional EBRT techniques, while 26% refer to non-VHA centers for EBB. Lack of incorporation of EBB into routine VHA practice likely is a reflection of the unclear role of this technology based on the available literature and ASTRO guidelines. In the setting of a right lower lung collapse, more respondents (49%) would consider use of EBB or YAG laser technology for lung reexpansion prior to EBRT.
The ASTRO guidelines recommend that initial EBB in conjunction with EBRT be considered based on randomized data demonstrating significant improvement in lung reexpansion and in patient reported dyspnea with addition of EBB to EBRT over EBRT alone.18 However, the guidelines do not mandate the use of EBB in this situation. It is possible that targeted education regarding the role of EBB would improve knowledge of the potential benefit in the setting of lung collapse and increase the percentage of VHA ROs who would recommend this procedure.
Limitations
The study is limited by lack of generalizability of these findings to all ROs in the country. It is also possible that physician responses do not represent practice patterns with complete accuracy. The use of EBB varied among practitioners. Further study of this technology is necessary to clarify its role in the management of endobronchial obstructive symptoms and to determine whether efforts should be made to increase access to EBB within the VHA.
Conclusions
Most of the ROs who responded to our survey were cognizant and compliant with current ASTRO guidelines on management of lung cancer. Furthermore, familiarity with ASTRO guidelines and management choices were not associated with the respondents’ years in practice, academic appointment, full-time vs part-time status, or familiarity with ASTRO guidelines. This study is a nationwide survey of ROs in the VHA system that reflects the radiation-related care received by veterans with metastatic lung cancer. Responses were obtained from 93% of the 40 radiation oncology centers, so it is likely that the survey accurately represents the decision-making process at the majority of centers. It is possible that those who did not respond to the survey do not treat thoracic cases.
1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015 65(2):87-108.
2. Kocher F, Hilbe W, Seeber A, et al. Longitudinal analysis of 2293 NSCLC patients: a comprehensive study from the TYROL registry. Lung Cancer. 2015;87(2):193-200.
3. Chute CG, Greenberg ER, Baron J, Korson R, Baker J, Yates J. Presenting conditions of 1539 population-based lung cancer patients by cell type and stage in New Hampshire and Vermont. Cancer. 1985;56(8):2107-2111.
4. Hyde L, Hyde Cl. Clinical manifestations of lung cancer. Chest. 1974;65(3):299-306.
5. Rodrigues G, Videtic GM, Sur R, et al. Palliative thoracic radiotherapy in lung cancer: An American Society for Radiation Oncology evidence-based clinical practice guideline. Pract Radiat Oncol. 2011;1(2):60-71.
6. Moeller B, Balagamwala EH, Chen A, et al. Palliative thoracic radiation therapy for non-small cell lung cancer: 2018 Update of an American Society for Radiation Oncology (ASTRO) Evidence-Based Guideline. Pract Radiat Oncol. 2018;8(4):245-250.
7. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the United States Veterans Affairs (VA) healthcare system. Mil Med. 2012;177(6):693-701.
8. Fairchild A, Harris K, Barnes E, et al. Palliative thoracic radiotherapy for lung cancer: a systematic review. J Clin Oncol. 2008;26(24):4001-4011.
9. A Medical Research Council (MRC) randomised trial of palliative radiotherapy with two fractions or a single fraction in patients with inoperable non-small-cell lung cancer (NSCLC) and poor performance status. Medical Research Council Lung Cancer Working Party. Br J Cancer. 1992;65(6):934-941.
10. Nawrocki S, Krzakowski M, Wasilewska-Tesluk E, et al. Concurrent chemotherapy and short course radiotherapy in patients with stage IIIA to IIIB non-small cell lung cancer not eligible for radical treatment: results of a randomized phase II study. J Thorac Oncol. 2010;5(8):1255-1262.
11. Strøm HH, Bremnes RM, Sundstrøm SH, Helbekkmo N, Fløtten O, Aasebø U. Concurrent palliative chemoradiation leads to survival and quality of life benefits in poor prognosis stage III non-small-cell lung cancer: a randomised trial by the Norwegian Lung Cancer Study Group. Br J Cancer. 2013;109(6):1467-1475.
12. Strøm HH, Bremnes RM, Sundstrøm SH, Helbekkmo N, Aasebø U. Poor prognosis patients with inoperable locally advanced NSCLC and large tumors benefit from palliative chemoradiotherapy: a subset analysis from a randomized clinical phase III trial. J Thorac Oncol. 2014;9(6):825-833.
13. Ball D, Smith J, Bishop J, et al. A phase III study of radiotherapy with and without continuous-infusion fluorouracil as palliation for non-small-cell lung cancer. Br J Cancer. 1997;75(5):690-697.
14. Stewart A, Parashar B, Patel M, et al. American Brachytherapy Society consensus guidelines for thoracic brachytherapy for lung cancer. Brachytherapy. 2016;15(1):1-11.
15. Sur R, Ahmed SN, Donde B, Morar R, Mohamed G, Sur M, Pacella JA, Van der Merwe E, Feldman C. Brachytherapy boost vs teletherapy boost in palliation of symptomatic, locally advanced non-small cell lung cancer: preliminary analysis of a randomized prospective study. J Brachytherapy Int. 2001;17(4):309-315.
16. Sur R, Donde B, Mohuiddin M, et al. Randomized prospective study on the role of high dose rate intraluminal brachytherapy (HDRILBT) in palliation of symptoms in advanced non-small cell lung cancer (NSCLC) treated with radiation alone. Int J Radiat Oncol Biol Phys. 2004;60(1):S205.
17. Ung YC, Yu E, Falkson C, et al. The role of high-dose-rate brachytherapy in the palliation of symptoms in patients with non-small cell lung cancer: a systematic review. Brachytherapy. 2006;5:189-202.
18. Langendijk H, de Jong J, Tjwa M, et al. External irradiation versus external irradiation plus endobronchial brachytherapy in inoperable non-small cell lung cancer: a prospective randomized study. Radiother Oncol. 2001;58(3):257-268.
1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015 65(2):87-108.
2. Kocher F, Hilbe W, Seeber A, et al. Longitudinal analysis of 2293 NSCLC patients: a comprehensive study from the TYROL registry. Lung Cancer. 2015;87(2):193-200.
3. Chute CG, Greenberg ER, Baron J, Korson R, Baker J, Yates J. Presenting conditions of 1539 population-based lung cancer patients by cell type and stage in New Hampshire and Vermont. Cancer. 1985;56(8):2107-2111.
4. Hyde L, Hyde Cl. Clinical manifestations of lung cancer. Chest. 1974;65(3):299-306.
5. Rodrigues G, Videtic GM, Sur R, et al. Palliative thoracic radiotherapy in lung cancer: An American Society for Radiation Oncology evidence-based clinical practice guideline. Pract Radiat Oncol. 2011;1(2):60-71.
6. Moeller B, Balagamwala EH, Chen A, et al. Palliative thoracic radiation therapy for non-small cell lung cancer: 2018 Update of an American Society for Radiation Oncology (ASTRO) Evidence-Based Guideline. Pract Radiat Oncol. 2018;8(4):245-250.
7. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the United States Veterans Affairs (VA) healthcare system. Mil Med. 2012;177(6):693-701.
8. Fairchild A, Harris K, Barnes E, et al. Palliative thoracic radiotherapy for lung cancer: a systematic review. J Clin Oncol. 2008;26(24):4001-4011.
9. A Medical Research Council (MRC) randomised trial of palliative radiotherapy with two fractions or a single fraction in patients with inoperable non-small-cell lung cancer (NSCLC) and poor performance status. Medical Research Council Lung Cancer Working Party. Br J Cancer. 1992;65(6):934-941.
10. Nawrocki S, Krzakowski M, Wasilewska-Tesluk E, et al. Concurrent chemotherapy and short course radiotherapy in patients with stage IIIA to IIIB non-small cell lung cancer not eligible for radical treatment: results of a randomized phase II study. J Thorac Oncol. 2010;5(8):1255-1262.
11. Strøm HH, Bremnes RM, Sundstrøm SH, Helbekkmo N, Fløtten O, Aasebø U. Concurrent palliative chemoradiation leads to survival and quality of life benefits in poor prognosis stage III non-small-cell lung cancer: a randomised trial by the Norwegian Lung Cancer Study Group. Br J Cancer. 2013;109(6):1467-1475.
12. Strøm HH, Bremnes RM, Sundstrøm SH, Helbekkmo N, Aasebø U. Poor prognosis patients with inoperable locally advanced NSCLC and large tumors benefit from palliative chemoradiotherapy: a subset analysis from a randomized clinical phase III trial. J Thorac Oncol. 2014;9(6):825-833.
13. Ball D, Smith J, Bishop J, et al. A phase III study of radiotherapy with and without continuous-infusion fluorouracil as palliation for non-small-cell lung cancer. Br J Cancer. 1997;75(5):690-697.
14. Stewart A, Parashar B, Patel M, et al. American Brachytherapy Society consensus guidelines for thoracic brachytherapy for lung cancer. Brachytherapy. 2016;15(1):1-11.
15. Sur R, Ahmed SN, Donde B, Morar R, Mohamed G, Sur M, Pacella JA, Van der Merwe E, Feldman C. Brachytherapy boost vs teletherapy boost in palliation of symptomatic, locally advanced non-small cell lung cancer: preliminary analysis of a randomized prospective study. J Brachytherapy Int. 2001;17(4):309-315.
16. Sur R, Donde B, Mohuiddin M, et al. Randomized prospective study on the role of high dose rate intraluminal brachytherapy (HDRILBT) in palliation of symptoms in advanced non-small cell lung cancer (NSCLC) treated with radiation alone. Int J Radiat Oncol Biol Phys. 2004;60(1):S205.
17. Ung YC, Yu E, Falkson C, et al. The role of high-dose-rate brachytherapy in the palliation of symptoms in patients with non-small cell lung cancer: a systematic review. Brachytherapy. 2006;5:189-202.
18. Langendijk H, de Jong J, Tjwa M, et al. External irradiation versus external irradiation plus endobronchial brachytherapy in inoperable non-small cell lung cancer: a prospective randomized study. Radiother Oncol. 2001;58(3):257-268.
SARS-CoV-2 Seroprevalence Among Healthcare Workers by Job Function and Work Location in a New York Inner-City Hospital
SARS-CoV-2 has infected 141 million people worldwide and 31 million people in the United States as of April 20, 2021.1,2 The influx of hospital admissions and deaths has severely strained healthcare systems worldwide and placed healthcare workers (HCWs) at increased risk for acquiring COVID-19.3-5
Several studies have described the impact of COVID-19 on this heterogeneous group of HCWs. Shields et al reported a seroprevalence of 24.4% in HCWs at University Hospitals Birmingham (UK), with the highest rate, 34.5%, in housekeeping staff.6 Steensels et al reported a lower prevalence of 6.4% at a tertiary care center in Belgium, and showed no increased risk for HCWs when directly involved in clinical care.7 The authors attributed this to adequate use of personal protective equipment (PPE). Other studies have reported seroprevalences ranging from 1.6% to 18%.8-11 In the New York City (NYC) metro area, Jeremias et al reported a seroprevalence of 9.8% in HCWs and found no difference by job title or work location,12 whereas Moscola et al reported a seroprevalence of 13.7% and demonstrated a 3% increased risk for those working in service or maintenance.13 Antibody tests were conducted between March and April 2020 in all but two of these studies; testing in these two studies was performed between April 13 and June 23, 2020, with one reporting a seroprevalence of 6%11 and the other, 13.7%.13
NYC became the earliest pandemic epicenter in the United States following untracked transmission from ongoing circulation of SARS-CoV-2 in Europe.14 As a result, the COVID-19 surge in NYC commenced in March and largely subsided by the end of May 2020. Most HCW data reported to date do not reflect the situation at the end of the surge, and may underestimate true seroprevalence. We describe SARS-CoV-2 seroprevalence in HCWs in a large inner-city hospital in NYC, with antibody testing conducted from May 18 to June 26, 2020, at the subsidence of the surge. To further our understanding of occupational risk among different groups of HCWs, we examined associations of seroprevalence with HCWs’ job function and work location.
METHODS
This was a cross-sectional seroprevalence study conducted in the BronxCare Health System located in South and Central Bronx, an area that experienced one of the highest incidences of SARS-CoV-2 infections within NYC’s five boroughs.
HCWs were offered voluntary testing for serum antibodies to SARS-CoV-2 between May 18 and June 26, 2020. Testing occurred in the institution’s auditorium, a central and easily accessible location. Weekly emails were sent to all employees and department heads during the testing period, offering antibody testing and providing location and testing time information. The Elecsys Anti-SARS-CoV-2 (Roche) assay measuring total qualitative antibodies was used; the assay has a reported sensitivity of 97.1% 14 days after a positive SARS-CoV-2 RNA polymerase chain reaction (PCR) test result and a specificity of 100%.15
Demographic and work-related information was abstracted from electronic medical records, including all comorbid conditions that affected 30 or more HCWs. Pulmonary diagnoses, including asthma and chronic obstructive pulmonary disease, were grouped as chronic lung disease, and cardiovascular diseases, including hypertension, as chronic heart disease. Personal identifiers and data were delinked upon completion of data abstraction. The study was approved by the hospital’s institutional review board.
Job Function and Work Location
HCWs were grouped by job function as follows: physicians; nurses (including physician assistants and nurse practitioners); allied HCW I (medical assistants, patient care, and electrocardiogram, radiology, and ear, nose and throat technicians); allied HCW II (social workers, dieticians and nutritionists, registration clerks and unit associates, physical and occupational therapists); nonclinical staff (patient transporters, housekeeping staff, and security staff); pharmacists; engineering; and administrative staff. Respiratory therapists were considered as a separate group as their work placed them at high risk for respiratory diseases.
Work locations were as follows: clinics (including dental, outpatient, and satellite clinics), emergency departments (ED), inpatient units (including floors and intensive care units [ICU]), radiology suite, laboratory and pharmacy, and offices.
Statistical Analysis
Descriptive statistics were calculated using χ2 analyses. All demographic variables were tested against serology status (positive/negative). A binary logistic regression analysis was used to calculate odds ratios (ORs). Eight separate univariate unadjusted ORs were calculated by running each predictor variable against serology status (dependent variable), which included the six categorical variables—race, ethnicity, age, sex, body mass index (BMI), and prior SARS-CoV-2 PCR results—and the two main predictors—job function and work location. To obtain adjusted ORs, two final separate multivariable logistic regression analyses were executed including the six covariates listed. Due to high collinearity between job function and work location (χ2 = 3030.13, df = 35 [6 levels of work location – 1]*[8 levels of job function – 1]; P < .001), we included only one of the main predictors in each model. The regressions were specified such that the reference groups for the work location and job function variables were office work and administration, respectively. This choice was made based on the fact that their nonclinical functions do not confer an exposure risk in excess of that experienced by typical community populations. Sensitivity analyses were performed on the subset of HCWs whose address zip codes indicated residence within NYC to exclude the effect of different community seroprevalences in areas outside of NYC. The 95% CI for seroprevalence of antibodies within tested HCWs was estimated using the Clopper-Pearson binomial method.
RESULTS
Among all HCWs in the institution (N = 4,807), 2,749 (57.2%) underwent voluntary testing. Of those who underwent testing, 831 were positive for antibodies to SARS-CoV-2 (Figure 1), a seroprevalence of 30.2% (95% CI, 29%-32%). Among the age groups, the 45-to-64−year group had the highest seropositivity at 33% (400/1203), and those ≥75 years of age, the lowest at 16.7% (2/12) (P < .009).
Among all tested HCWs, 70.1% (1,928/2,749) resided in NYC. SARS-CoV-2 seroprevalence in this subset was 32% (616/1,928) (Figure 1). Demographic and comorbid conditions in HCWs who lived in NYC were similar to those of the whole group (Appendix Table 1).
HCWs who underwent voluntary antibody testing (Appendix Table 2) had a higher percentage of persons in the 45-to-64−year age group (43.8% vs 40.9%) and a lower percentage of persons in the 65-to-74−year age group (3.3% vs 5.3%) compared with the group of HCWs that did not undergo testing (P < .001). Gender, race, ethnicity, comorbid conditions, SARS-CoV-2 PCR testing, and work locations were not different between groups. The tested group had higher proportions of clinicians (physicians, nurses, allied HCWs I and II) than the untested nonparticipant group (P = .014).
SARS-CoV-2 PCR Tests on HCWs
More than one-third (34.1%; 938/2,749) of HCWs had a documented nasopharyngeal PCR test between March 23 and June 26, 2020 (Table). Of all PCRs performed, 262 were positive, giving an overall PCR positivity rate of 27.9%. Positivity was 51.4% in March and 36.6% in April. The reasons for PCR testing were not available, but likely represent a combination of exposure-related testing among asymptomatic individuals and diagnostic testing of symptomatic HCWs. In contrast, serology testing was indicative of prior infection and yielded a cumulative seroprevalence at the end of the surge. Findings were similar among HCWs residing in NYC
Work Location and Job Function
Among all HCWs (Table, Figure 2), there were differences in seropositivity by work location (P = .001). The largest number of HCWs worked in inpatient units (1,348/2,749, 49%), and the second largest in offices (554/2,749, 20%). The highest seropositivity rate was in the EDs, at 36.4% (64/176), followed by radiology suites, at 32.7% (17/52); the seropositivity rate in office locations was 25.8% (143/554). Among HCWs residing in NYC (Appendix Table 1, Appendix Figure 1), the rank order according to proportion seropositive by work location was similar to that of the whole group (P = .004), except that the second highest seropositivity rate was in the inpatient units (33.9% [323/953]). In the group of HCWs residing in NYC, office locations had a seropositivity of 27.4% (102/372). The seropositivity rates for both groups working in office locations were slightly higher than the 22% community seroprevalence in NYC reported for the same period.16
Among all HCWs, there were differences in seropositivity by job function (P = .001). The greatest proportion of HCWs were allied HCW II (23% [631/2,749]), followed by nurses (22.2% [611/2,749]) and physicians (21.3% [585/2,749] ). Seropositivity was highest for nonclinical staff (44.0% [51/116]), followed by nurses (37.5% [229/611]) and allied clinical HCW I and II (34.5% [143/414] and 32.0% [202/631], respectively). It was lowest for administrative staff (20.9% [42/201]) and pharmacists (11.1% [5/45]). Among HCWs residing in NYC, the rank order according to proportion seropositive by location was similar to that of the whole group. Administrative staff seropositivity was 18.3% (20/109). Administrative staff seropositivity for both groups was marginally lower than the 22% community seroprevalence in NYC for the same period.16
Odds Ratios for SARS-CoV-2 Seropositivity
For all HCWs, in unadjusted models (Appendix Table 3), age 45 to 64 years and Black race were associated with increased odds of being seropositive (1.26; 95% CI, 1.07-1.49 and 2.26; 95% CI, 1.51-3.37, respectively). Increased odds were seen for HCWs working in the ED (1.64; 95% CI, 1.14-2.36) and inpatient units (1.35; 95% CI, 1.08-1.69), and decreased odds were seen for those working in the laboratory and pharmacy (0.47; 95% CI, 0.26-0.86). Increased odds for seropositivity were found for nurses (2.27; 95% CI, 1.56-3.31), allied HCW I (2.00; 95% CI, 1.34-2.97), allied HCW II (1.78; 95% CI, 1.22-2.60), and nonclinical staff (2.97; 95% CI,1.80-4.90).
After adjusting for all covariates, HCWs who were Black remained at increased odds for being seropositive in the two final models (adjusted OR, 2.29; 95% CI, 1.38-3.81 and adjusted OR, 2.94; 95% CI, 1.78-4.85), as did those who had a BMI >30 kg/m2, with an adjusted OR of 1.36 (95% CI, 1.05-1.77) in one of the final models (Appendix Table 3). None of the other comorbid conditions had increased ORs. Those who worked in the ED and inpatient units also remained at increased odds after adjusting for covariates (2.27; 95% CI, 1.53-3.37 and 1.48; 95% CI, 1.14-1.92, respectively; Figure 3). Other job functions that had increased odds for seropositivity were nurses (2.54; 95% CI, 1.64-3.94), allied HCW I (1.83; 95% CI, 1.15-2.89) and II (1.70; 95% CI, 1.10-2.63), and nonclinical staff (2.51; 95% CI, 1.42-4.43).
Having a positive PCR for SAR-CoV-2 on nasopharyngeal swabs was strongly associated with seropositivity (OR, 47.26; 95% CI, 29.30-76.23 and OR, 44.79; 95% CI, 27.87-72.00) in the two multivariate-adjusted models. These findings were confirmed when the analyses were performed on HCWs who resided in NYC (Appendix Table 4 and Appendix Figure 2).
DISCUSSION
In a large inner-city New York hospital, we report a cumulative SARS-CoV-2 seroprevalence of 30.2% in HCWs at the end of the first surge of SARS-CoV-2 infections in NYC. We identified the highest seropositivity rates for nonclinical staff and nurses, followed by allied HCWs, with the odds of being seropositive ranging from 1.7 to 2.5. The work locations with the highest seroprevalences were the ED and inpatient units, with 2.3-fold and 1.5-fold increased odds of seropositivity, respectively.
Serosurveillance studies have reported the trajectory of community prevalence in NYC over the first wave. A 6.3% prevalence was reported in samples collected between March 23 and April 1, 2020.17 In a study by Rosenberg et al18 with testing performed from April 9 through April 28, 2020, prevalence increased to 22.7%. Serosurveillance data from the NYC Department of Health show prevalence ranging from 20.1% to 23.3% (average 22%) during the study period.16 Compared to the estimated seroprevalence of 9.3% in the United States,19 these rates established NYC as an early epicenter for the COVID-19 pandemic, with our institution’s HCW seroprevalence considerably higher than NYC community serosurveillance rates, 2.2 times higher than reported in the earlier HCW study in the greater NYC area,13 and higher than the 27% rate during May 2020 recently reported in another NYC hospital.20
Data from studies of hospital transmission and effects of mitigation measures, such as a universal masking policy for HCWs and patients, clearly demonstrate the high effectiveness of these measures in reducing hospital transmissions.21,22 This suggests HCW seroprevalence in institutions with well-implemented infection control and universal masking policies may not be a consequence of workplace exposures, but rather may be reflective of community rates.23 Our institution’s response commenced February 3, 2020, with implementation of social distancing, a universal masking policy, transmission-based precautions, and use of fitted N95 masks. Mid-March, elective surgeries were canceled, and inpatient visitation suspended. During the surge, these measures were widely and consistently implemented for all categories of HCWs throughout the work environment, based on emerging guidelines from the Centers for Disease Control and Prevention (CDC) and NYC Department of Health. Our overall observed HCW seroprevalence, well above that of the community, with differences in categories of job function and work locations, is therefore an important finding. Our sample of 2,749 HCWs lived in NYC and its surrounding suburbs and nearby states. There is heterogeneity in community seroprevalence between areas outside of NYC and NYC (an epicenter) itself. We therefore analyzed our data in the subset with NYC zip codes, confirming a similar overall prevalence and increased odds of seropositivity in nurses, allied HCWs, and nonclinical staff.
Physicians and administrative and office staff had seropositivity rates of 18.1%, 20.9%, and 25.8%, respectively, consistent with community rates and illustrating the effectiveness of PPE in the hospital setting. Since PPE use was part of a universal policy applied to all HCWs in our institution, other possible reasons may explain the differences we found. We speculate that the close working relationship nurses have with their patients resulted in a longer duration and higher frequency of daily interactions, increasing the risk for transmission and causing breakthrough infections.24,25 This increased risk is reflected in a study in which 28% of hospitalized patients were nurses and 9% certified nursing assistants.26
The CDC recently redefined close contact with someone with COVID-19 as a cumulative total of >15 minutes over 24 hours.25 Thus, several multiple short periods of exposure can increase risk for infection with SARS-CoV-2; such exposure is characteristic of the job function of nurses, nursing staff, and nonclinical staff. Further, housekeeping, transportation, and security officers are all nonclinical staff with significant and multiple exposures to COVID-19 patients during the surge, and for security officers, to continuous public traffic in and out of the hospital. SARS-CoV-2 spreads by virus shedding in large droplets and aerosols, with droplet nuclei <5 microns in size efficiently dispersed in air, an important additional mode of transmission.27-30 Airborne transmission coupled with virus shedding in asymptomatic and presymptomatic persons, which has been shown to cause secondary attack rates of up to 32%, are other factors that likely contributed to the increased seroprevalence in this group.31 Our observation is consistent with the Birmingham study, which reported the highest rate in housekeeping staff, with a prevalence of 34.5%, compared to 44% in this study.6 Similar reasons for high seropositivity rates apply to the two groups of allied HCWs (eg, medical assistants and patient care technicians, social workers, nutritionists and therapists), whose job functions place them in intermittent but significant proximity with inpatients and outpatients.
Consistent with public health data showing that minorities are disproportionately affected by this disease, we found that Black HCWs were three times more likely to be seropositive.32 However, an unexpected observation was the association between obesity and SARS-CoV-2 seropositivity. A possible explanation for this association may be inability to achieve optimal fit testing for N95 masks, thereby increasing the risk of exposure to droplet nuclei. This is important given that obesity is associated with poorer outcomes from COVID-19.
During the height of the first wave in NYC, EDs and inpatient units handled a large volume of COVID-19 patients with high PCR positivity rates (peak of 51% in March in our hospital). It was not unexpected that we observed increased odds of seropositivity in these work locations. As ICUs were at capacity, inpatient units cared for critically ill patients they would not normally have. HCWs in these locations coped with an increased workload, increased demand on PPE supplies, and work fatigue, which contributed to increased risk for hospital-acquired SARS-CoV-2 infections.
Reporting seroprevalence at a single institution was a limitation of the study. Approximately 57% of the hospital’s total HCW population was tested for antibodies. It is possible their risk profile influenced their decision to volunteer for testing when it became available, introducing selection bias. A comparison between tested and untested HCWs showed similarity in all demographic measures, including nasopharyngeal PCR testing, except for age. We did not have information on symptoms that would prompt PCR testing. HCWs who underwent voluntary testing were younger compared to those who did not undergo testing. Current NYC serosurveillance data showed higher seropositivity in the 45-to-64–year age group (27.8%-28.6%) compared to the 65-to-74–year age group (24.3%), which suggests that the tested group may overestimate seroprevalence among HCWs relative to a randomly selected sample.33 Similarly, there were more nurses, allied HCWs, physicians, and administrative staff in the tested group, with the former two having higher SARS-CoV-2 seropositivity compared to community prevalence, which could also overestimate seroprevalence. Our large sample size provided us with the power to detect differences within several different job functions and work locations, a strength of this study. It was not possible to differentiate community- from hospital-acquired infection in our HCWs, a limitation in many observational HCW seroprevalence studies. However, when we analyzed data restricted only to HCWs in NYC, to reduce the effect of differing community prevalences outside the city, our results were unchanged. Since it is possible that nonclinical HCWs are of a lower socioeconomic status compared to others (nurses and allied HCWs), we cannot exclude the possibility that higher SARS-CoV-2 seroprevalence associated with lower status explains, partly or completely, the increased odds of seropositivity we observed.34 Due to the high proportion of missing data for race (61.3%), we advise caution in interpreting our finding that the odds of seropositivity were three times higher for Black race, even though consistent with prior literature.34 Healthcare organizations have similar job function and work location categories incorporated in their infrastructure, suggesting that our observations may be generalizable to other hospitals in the United States.
CONCLUSION
These findings show that during the first surge in NYC, with its increased burden of disease, hospitalizations, morbidity, and mortality, seroprevalences varied based on job function and work location within this institution. Nurses were at highest risk for SARS-CoV-2 infection, as were those who worked in the ED. In preparation for subsequent waves of SARS-CoV-2 and other highly contagious respiratory infections, major medical centers need to enhance efforts aimed at protecting HCWs, with particular attention to these groups. This study also strongly supports the recent CDC guideline prioritizing HCWs to receive COVID-19 mRNA and adenovirus vector vaccines that have obtained emergency use authorization by the US Food and Drug Administration.35
Acknowledgments
The authors thank all the residents, nurses, and staff of the Department of Family Medicine for their contribution to this work.
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8. Stubblefield WB, Talbot HK, Feldstein L, et al. Seroprevalence of SARS-CoV-2 Among frontline healthcare personnel during the first month of caring for COVID-19 patients - Nashville, Tennessee. Clin Infect Dis. 2020. https://doi.org/10.1093/cid/ciaa936
9. Korth J, Wilde B, Dolff S, et al. SARS-CoV-2-specific antibody detection in healthcare workers in Germany with direct contact to COVID-19 patients. J Clin Virol. 2020;128:104437. https://doi.org/10.1016/j.jcv.2020.104437
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11. Self WH, Tenforde MW, Stubblefield WB, et al. Seroprevalence of SARS-CoV-2 among frontline health care personnel in a multistate hospital network - 13 academic medical centers, April-June 2020. MMWR Morb Mortal Wkly Rep. 2020;69(35):1221-1226. https://doi.org/10.15585/mmwr.mm6935e2
12. Jeremias A, Nguyen J, Levine J, et al. Prevalence of SARS-CoV-2 infection among health care workers in a tertiary community hospital. JAMA Intern Med. 2020 Aug 11:e204214. https://doi.org/10.1001/jamainternmed.2020.4214
13. Moscola J, Sembajwe G, Jarrett M, et al. Prevalence of SARS-CoV-2 antibodies in health care personnel in the New York City area. JAMA. 2020;324(9):893-895. https://doi.org/10.1001/jama.2020.14765
14. Gonzalez-Reiche AS, Hernandez MM, Sullivan MJ, et al. Introductions and early spread of SARS-CoV-2 in the New York City area. Science. 2020;369(6501):297-301. https://doi.org/10.1126/science.abc1917
15. Lau CS, Hoo SF, Yew SF, et al. Evaluation of the Roche Elecsys Anti-SARS-CoV-2 assay. Preprint. Posted online June 29, 2020. Accessed November 8, 2020. https://www.medrxiv.org/content/10.1101/2020.06.28.20142232v1 https://doi.org/10.1101/2020.06.28.20142232
16. New York City Department of Health. Covid-19: data. long-term trends. Antibody testing. Accessed March 5, 2021. https://www1.nyc.gov/site/doh/covid/covid-19-data-trends.page#antibody
17. Havers FP, Reed C, Lim T, et al. Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23-May 12, 2020. JAMA Intern Med. Published online July 21, 2020. https://doi.org/10.1001/jamainternmed.2020.4130
18. Rosenberg ES, Tesoriero JM, Rosenthal EM, et al. Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Ann Epidemiol. Aug 2020;48:23-29 e4. https://doi.org/10.1016/j.annepidem.2020.06.004
19. Anand S, Montez-Rath M, Han J, et al. Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study. Lancet. 2020;396(10259):1335-1344. https://doi.org/10.1016/S0140-6736(20)32009-2
20. Venugopal U, Jilani N, Rabah S, et al. SARS-CoV-2 seroprevalence among health care workers in a New York City hospital: a cross-sectional analysis during the COVID-19 pandemic. Int J Infect Dis. 2020;102:63-69. https://doi.org/10.1016/j.ijid.2020.10.036
21. Samaranayake LP, Fakhruddin KS, Ngo HC, Chang JWW, Panduwawala C. The effectiveness and efficacy of respiratory protective equipment (RPE) in dentistry and other health care settings: a systematic review. Acta Odontol Scand. 2020;78(8):626-639. https://doi.org/10.1080/00016357.2020.1810769
22. Seidelman JL, Lewis SS, Advani SD, et al. Universal masking is an effective strategy to flatten the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) healthcare worker epidemiologic curve. Infect Control Hosp Epidemiol. 2020;41(12):1466-1467. https://doi.org/10.1017/ice.2020.313
23. Richterman A, Meyerowitz EA, Cevik M. Hospital-acquired SARS-CoV-2 infection: lessons for public health. JAMA. Published online November 13, 2020. https://doi.org/10.1001/jama.2020.21399
24. Degesys NF, Wang RC, Kwan E, Fahimi J, Noble JA, Raven MC. Correlation between n95 extended use and reuse and fit failure in an emergency department. JAMA. 2020;324(1):94-96. https://doi.org/10.1001/jama.2020.9843
25. Pringle JC, Leikauskas J, Ransom-Kelley S, et al. COVID-19 in a correctional facility employee following multiple brief exposures to persons with COVID-19 - Vermont, July-August 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1569-1570. https://doi.org/10.15585/mmwr.mm6943e1
26. Kambhampati AK, O’Halloran AC, Whitaker M, et al. COVID-19-associated hospitalizations among health care personnel - COVID-NET, 13 states, March 1-May 31, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1576-1583. https://doi.org/10.15585/mmwr.mm6943e3
27. Zhang R, Li Y, Zhang AL, Wang Y, Molina MJ. Identifying airborne transmission as the dominant route for the spread of COVID-19. Proc Natl Acad Sci U S A. 2020;117(26):14857-14863. https://doi.org/10.1073/pnas.2009637117
28. Setti L, Passarini F, De Gennaro G, et al. Airborne transmission route of COVID-19: why 2 meters/6 feet of inter-personal distance could not be enough. Int J Environ Res Public Health. 2020;17(8):2932. https://doi.org/doi:10.3390/ijerph17082932
29. Klompas M, Baker MA, Rhee C. Airborne transmission of SARS-CoV-2: theoretical considerations and available evidence. JAMA. 2020;324(5):441-442. https://doi.org/10.1001/jama.2020.12458
30. Bourouiba L. Turbulent gas clouds and respiratory pathogen emissions: potential implications for reducing transmission of COVID-19. JAMA. 2020;323(18):1837-1838. https://doi.org/10.1001/jama.2020.4756
31. Qiu X, Nergiz A, Maraolo A, Bogoch I, Low N, Cevik M. The role of asymptomatic and pre-symptomatic infection in SARS-CoV-2 transmission – a living systematic review. Clin Mibrobiol Infect. 2021;20:S1198-743X(21)00038-0. Published online January 20, 2021. https://doi.org/10.1016/j.cmi.2021.01.011
32. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and mortality among black patients and white patients with Covid-19. N Engl J Med. 2020;382(26):2534-2543. https://doi.org/doi:10.1056/NEJMsa2011686
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35. Polack FP, Thomas SJ, Kitchin N, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383(27):2603-2615. https://doi.org/10.1056/NEJMoa2034577
SARS-CoV-2 has infected 141 million people worldwide and 31 million people in the United States as of April 20, 2021.1,2 The influx of hospital admissions and deaths has severely strained healthcare systems worldwide and placed healthcare workers (HCWs) at increased risk for acquiring COVID-19.3-5
Several studies have described the impact of COVID-19 on this heterogeneous group of HCWs. Shields et al reported a seroprevalence of 24.4% in HCWs at University Hospitals Birmingham (UK), with the highest rate, 34.5%, in housekeeping staff.6 Steensels et al reported a lower prevalence of 6.4% at a tertiary care center in Belgium, and showed no increased risk for HCWs when directly involved in clinical care.7 The authors attributed this to adequate use of personal protective equipment (PPE). Other studies have reported seroprevalences ranging from 1.6% to 18%.8-11 In the New York City (NYC) metro area, Jeremias et al reported a seroprevalence of 9.8% in HCWs and found no difference by job title or work location,12 whereas Moscola et al reported a seroprevalence of 13.7% and demonstrated a 3% increased risk for those working in service or maintenance.13 Antibody tests were conducted between March and April 2020 in all but two of these studies; testing in these two studies was performed between April 13 and June 23, 2020, with one reporting a seroprevalence of 6%11 and the other, 13.7%.13
NYC became the earliest pandemic epicenter in the United States following untracked transmission from ongoing circulation of SARS-CoV-2 in Europe.14 As a result, the COVID-19 surge in NYC commenced in March and largely subsided by the end of May 2020. Most HCW data reported to date do not reflect the situation at the end of the surge, and may underestimate true seroprevalence. We describe SARS-CoV-2 seroprevalence in HCWs in a large inner-city hospital in NYC, with antibody testing conducted from May 18 to June 26, 2020, at the subsidence of the surge. To further our understanding of occupational risk among different groups of HCWs, we examined associations of seroprevalence with HCWs’ job function and work location.
METHODS
This was a cross-sectional seroprevalence study conducted in the BronxCare Health System located in South and Central Bronx, an area that experienced one of the highest incidences of SARS-CoV-2 infections within NYC’s five boroughs.
HCWs were offered voluntary testing for serum antibodies to SARS-CoV-2 between May 18 and June 26, 2020. Testing occurred in the institution’s auditorium, a central and easily accessible location. Weekly emails were sent to all employees and department heads during the testing period, offering antibody testing and providing location and testing time information. The Elecsys Anti-SARS-CoV-2 (Roche) assay measuring total qualitative antibodies was used; the assay has a reported sensitivity of 97.1% 14 days after a positive SARS-CoV-2 RNA polymerase chain reaction (PCR) test result and a specificity of 100%.15
Demographic and work-related information was abstracted from electronic medical records, including all comorbid conditions that affected 30 or more HCWs. Pulmonary diagnoses, including asthma and chronic obstructive pulmonary disease, were grouped as chronic lung disease, and cardiovascular diseases, including hypertension, as chronic heart disease. Personal identifiers and data were delinked upon completion of data abstraction. The study was approved by the hospital’s institutional review board.
Job Function and Work Location
HCWs were grouped by job function as follows: physicians; nurses (including physician assistants and nurse practitioners); allied HCW I (medical assistants, patient care, and electrocardiogram, radiology, and ear, nose and throat technicians); allied HCW II (social workers, dieticians and nutritionists, registration clerks and unit associates, physical and occupational therapists); nonclinical staff (patient transporters, housekeeping staff, and security staff); pharmacists; engineering; and administrative staff. Respiratory therapists were considered as a separate group as their work placed them at high risk for respiratory diseases.
Work locations were as follows: clinics (including dental, outpatient, and satellite clinics), emergency departments (ED), inpatient units (including floors and intensive care units [ICU]), radiology suite, laboratory and pharmacy, and offices.
Statistical Analysis
Descriptive statistics were calculated using χ2 analyses. All demographic variables were tested against serology status (positive/negative). A binary logistic regression analysis was used to calculate odds ratios (ORs). Eight separate univariate unadjusted ORs were calculated by running each predictor variable against serology status (dependent variable), which included the six categorical variables—race, ethnicity, age, sex, body mass index (BMI), and prior SARS-CoV-2 PCR results—and the two main predictors—job function and work location. To obtain adjusted ORs, two final separate multivariable logistic regression analyses were executed including the six covariates listed. Due to high collinearity between job function and work location (χ2 = 3030.13, df = 35 [6 levels of work location – 1]*[8 levels of job function – 1]; P < .001), we included only one of the main predictors in each model. The regressions were specified such that the reference groups for the work location and job function variables were office work and administration, respectively. This choice was made based on the fact that their nonclinical functions do not confer an exposure risk in excess of that experienced by typical community populations. Sensitivity analyses were performed on the subset of HCWs whose address zip codes indicated residence within NYC to exclude the effect of different community seroprevalences in areas outside of NYC. The 95% CI for seroprevalence of antibodies within tested HCWs was estimated using the Clopper-Pearson binomial method.
RESULTS
Among all HCWs in the institution (N = 4,807), 2,749 (57.2%) underwent voluntary testing. Of those who underwent testing, 831 were positive for antibodies to SARS-CoV-2 (Figure 1), a seroprevalence of 30.2% (95% CI, 29%-32%). Among the age groups, the 45-to-64−year group had the highest seropositivity at 33% (400/1203), and those ≥75 years of age, the lowest at 16.7% (2/12) (P < .009).
Among all tested HCWs, 70.1% (1,928/2,749) resided in NYC. SARS-CoV-2 seroprevalence in this subset was 32% (616/1,928) (Figure 1). Demographic and comorbid conditions in HCWs who lived in NYC were similar to those of the whole group (Appendix Table 1).
HCWs who underwent voluntary antibody testing (Appendix Table 2) had a higher percentage of persons in the 45-to-64−year age group (43.8% vs 40.9%) and a lower percentage of persons in the 65-to-74−year age group (3.3% vs 5.3%) compared with the group of HCWs that did not undergo testing (P < .001). Gender, race, ethnicity, comorbid conditions, SARS-CoV-2 PCR testing, and work locations were not different between groups. The tested group had higher proportions of clinicians (physicians, nurses, allied HCWs I and II) than the untested nonparticipant group (P = .014).
SARS-CoV-2 PCR Tests on HCWs
More than one-third (34.1%; 938/2,749) of HCWs had a documented nasopharyngeal PCR test between March 23 and June 26, 2020 (Table). Of all PCRs performed, 262 were positive, giving an overall PCR positivity rate of 27.9%. Positivity was 51.4% in March and 36.6% in April. The reasons for PCR testing were not available, but likely represent a combination of exposure-related testing among asymptomatic individuals and diagnostic testing of symptomatic HCWs. In contrast, serology testing was indicative of prior infection and yielded a cumulative seroprevalence at the end of the surge. Findings were similar among HCWs residing in NYC
Work Location and Job Function
Among all HCWs (Table, Figure 2), there were differences in seropositivity by work location (P = .001). The largest number of HCWs worked in inpatient units (1,348/2,749, 49%), and the second largest in offices (554/2,749, 20%). The highest seropositivity rate was in the EDs, at 36.4% (64/176), followed by radiology suites, at 32.7% (17/52); the seropositivity rate in office locations was 25.8% (143/554). Among HCWs residing in NYC (Appendix Table 1, Appendix Figure 1), the rank order according to proportion seropositive by work location was similar to that of the whole group (P = .004), except that the second highest seropositivity rate was in the inpatient units (33.9% [323/953]). In the group of HCWs residing in NYC, office locations had a seropositivity of 27.4% (102/372). The seropositivity rates for both groups working in office locations were slightly higher than the 22% community seroprevalence in NYC reported for the same period.16
Among all HCWs, there were differences in seropositivity by job function (P = .001). The greatest proportion of HCWs were allied HCW II (23% [631/2,749]), followed by nurses (22.2% [611/2,749]) and physicians (21.3% [585/2,749] ). Seropositivity was highest for nonclinical staff (44.0% [51/116]), followed by nurses (37.5% [229/611]) and allied clinical HCW I and II (34.5% [143/414] and 32.0% [202/631], respectively). It was lowest for administrative staff (20.9% [42/201]) and pharmacists (11.1% [5/45]). Among HCWs residing in NYC, the rank order according to proportion seropositive by location was similar to that of the whole group. Administrative staff seropositivity was 18.3% (20/109). Administrative staff seropositivity for both groups was marginally lower than the 22% community seroprevalence in NYC for the same period.16
Odds Ratios for SARS-CoV-2 Seropositivity
For all HCWs, in unadjusted models (Appendix Table 3), age 45 to 64 years and Black race were associated with increased odds of being seropositive (1.26; 95% CI, 1.07-1.49 and 2.26; 95% CI, 1.51-3.37, respectively). Increased odds were seen for HCWs working in the ED (1.64; 95% CI, 1.14-2.36) and inpatient units (1.35; 95% CI, 1.08-1.69), and decreased odds were seen for those working in the laboratory and pharmacy (0.47; 95% CI, 0.26-0.86). Increased odds for seropositivity were found for nurses (2.27; 95% CI, 1.56-3.31), allied HCW I (2.00; 95% CI, 1.34-2.97), allied HCW II (1.78; 95% CI, 1.22-2.60), and nonclinical staff (2.97; 95% CI,1.80-4.90).
After adjusting for all covariates, HCWs who were Black remained at increased odds for being seropositive in the two final models (adjusted OR, 2.29; 95% CI, 1.38-3.81 and adjusted OR, 2.94; 95% CI, 1.78-4.85), as did those who had a BMI >30 kg/m2, with an adjusted OR of 1.36 (95% CI, 1.05-1.77) in one of the final models (Appendix Table 3). None of the other comorbid conditions had increased ORs. Those who worked in the ED and inpatient units also remained at increased odds after adjusting for covariates (2.27; 95% CI, 1.53-3.37 and 1.48; 95% CI, 1.14-1.92, respectively; Figure 3). Other job functions that had increased odds for seropositivity were nurses (2.54; 95% CI, 1.64-3.94), allied HCW I (1.83; 95% CI, 1.15-2.89) and II (1.70; 95% CI, 1.10-2.63), and nonclinical staff (2.51; 95% CI, 1.42-4.43).
Having a positive PCR for SAR-CoV-2 on nasopharyngeal swabs was strongly associated with seropositivity (OR, 47.26; 95% CI, 29.30-76.23 and OR, 44.79; 95% CI, 27.87-72.00) in the two multivariate-adjusted models. These findings were confirmed when the analyses were performed on HCWs who resided in NYC (Appendix Table 4 and Appendix Figure 2).
DISCUSSION
In a large inner-city New York hospital, we report a cumulative SARS-CoV-2 seroprevalence of 30.2% in HCWs at the end of the first surge of SARS-CoV-2 infections in NYC. We identified the highest seropositivity rates for nonclinical staff and nurses, followed by allied HCWs, with the odds of being seropositive ranging from 1.7 to 2.5. The work locations with the highest seroprevalences were the ED and inpatient units, with 2.3-fold and 1.5-fold increased odds of seropositivity, respectively.
Serosurveillance studies have reported the trajectory of community prevalence in NYC over the first wave. A 6.3% prevalence was reported in samples collected between March 23 and April 1, 2020.17 In a study by Rosenberg et al18 with testing performed from April 9 through April 28, 2020, prevalence increased to 22.7%. Serosurveillance data from the NYC Department of Health show prevalence ranging from 20.1% to 23.3% (average 22%) during the study period.16 Compared to the estimated seroprevalence of 9.3% in the United States,19 these rates established NYC as an early epicenter for the COVID-19 pandemic, with our institution’s HCW seroprevalence considerably higher than NYC community serosurveillance rates, 2.2 times higher than reported in the earlier HCW study in the greater NYC area,13 and higher than the 27% rate during May 2020 recently reported in another NYC hospital.20
Data from studies of hospital transmission and effects of mitigation measures, such as a universal masking policy for HCWs and patients, clearly demonstrate the high effectiveness of these measures in reducing hospital transmissions.21,22 This suggests HCW seroprevalence in institutions with well-implemented infection control and universal masking policies may not be a consequence of workplace exposures, but rather may be reflective of community rates.23 Our institution’s response commenced February 3, 2020, with implementation of social distancing, a universal masking policy, transmission-based precautions, and use of fitted N95 masks. Mid-March, elective surgeries were canceled, and inpatient visitation suspended. During the surge, these measures were widely and consistently implemented for all categories of HCWs throughout the work environment, based on emerging guidelines from the Centers for Disease Control and Prevention (CDC) and NYC Department of Health. Our overall observed HCW seroprevalence, well above that of the community, with differences in categories of job function and work locations, is therefore an important finding. Our sample of 2,749 HCWs lived in NYC and its surrounding suburbs and nearby states. There is heterogeneity in community seroprevalence between areas outside of NYC and NYC (an epicenter) itself. We therefore analyzed our data in the subset with NYC zip codes, confirming a similar overall prevalence and increased odds of seropositivity in nurses, allied HCWs, and nonclinical staff.
Physicians and administrative and office staff had seropositivity rates of 18.1%, 20.9%, and 25.8%, respectively, consistent with community rates and illustrating the effectiveness of PPE in the hospital setting. Since PPE use was part of a universal policy applied to all HCWs in our institution, other possible reasons may explain the differences we found. We speculate that the close working relationship nurses have with their patients resulted in a longer duration and higher frequency of daily interactions, increasing the risk for transmission and causing breakthrough infections.24,25 This increased risk is reflected in a study in which 28% of hospitalized patients were nurses and 9% certified nursing assistants.26
The CDC recently redefined close contact with someone with COVID-19 as a cumulative total of >15 minutes over 24 hours.25 Thus, several multiple short periods of exposure can increase risk for infection with SARS-CoV-2; such exposure is characteristic of the job function of nurses, nursing staff, and nonclinical staff. Further, housekeeping, transportation, and security officers are all nonclinical staff with significant and multiple exposures to COVID-19 patients during the surge, and for security officers, to continuous public traffic in and out of the hospital. SARS-CoV-2 spreads by virus shedding in large droplets and aerosols, with droplet nuclei <5 microns in size efficiently dispersed in air, an important additional mode of transmission.27-30 Airborne transmission coupled with virus shedding in asymptomatic and presymptomatic persons, which has been shown to cause secondary attack rates of up to 32%, are other factors that likely contributed to the increased seroprevalence in this group.31 Our observation is consistent with the Birmingham study, which reported the highest rate in housekeeping staff, with a prevalence of 34.5%, compared to 44% in this study.6 Similar reasons for high seropositivity rates apply to the two groups of allied HCWs (eg, medical assistants and patient care technicians, social workers, nutritionists and therapists), whose job functions place them in intermittent but significant proximity with inpatients and outpatients.
Consistent with public health data showing that minorities are disproportionately affected by this disease, we found that Black HCWs were three times more likely to be seropositive.32 However, an unexpected observation was the association between obesity and SARS-CoV-2 seropositivity. A possible explanation for this association may be inability to achieve optimal fit testing for N95 masks, thereby increasing the risk of exposure to droplet nuclei. This is important given that obesity is associated with poorer outcomes from COVID-19.
During the height of the first wave in NYC, EDs and inpatient units handled a large volume of COVID-19 patients with high PCR positivity rates (peak of 51% in March in our hospital). It was not unexpected that we observed increased odds of seropositivity in these work locations. As ICUs were at capacity, inpatient units cared for critically ill patients they would not normally have. HCWs in these locations coped with an increased workload, increased demand on PPE supplies, and work fatigue, which contributed to increased risk for hospital-acquired SARS-CoV-2 infections.
Reporting seroprevalence at a single institution was a limitation of the study. Approximately 57% of the hospital’s total HCW population was tested for antibodies. It is possible their risk profile influenced their decision to volunteer for testing when it became available, introducing selection bias. A comparison between tested and untested HCWs showed similarity in all demographic measures, including nasopharyngeal PCR testing, except for age. We did not have information on symptoms that would prompt PCR testing. HCWs who underwent voluntary testing were younger compared to those who did not undergo testing. Current NYC serosurveillance data showed higher seropositivity in the 45-to-64–year age group (27.8%-28.6%) compared to the 65-to-74–year age group (24.3%), which suggests that the tested group may overestimate seroprevalence among HCWs relative to a randomly selected sample.33 Similarly, there were more nurses, allied HCWs, physicians, and administrative staff in the tested group, with the former two having higher SARS-CoV-2 seropositivity compared to community prevalence, which could also overestimate seroprevalence. Our large sample size provided us with the power to detect differences within several different job functions and work locations, a strength of this study. It was not possible to differentiate community- from hospital-acquired infection in our HCWs, a limitation in many observational HCW seroprevalence studies. However, when we analyzed data restricted only to HCWs in NYC, to reduce the effect of differing community prevalences outside the city, our results were unchanged. Since it is possible that nonclinical HCWs are of a lower socioeconomic status compared to others (nurses and allied HCWs), we cannot exclude the possibility that higher SARS-CoV-2 seroprevalence associated with lower status explains, partly or completely, the increased odds of seropositivity we observed.34 Due to the high proportion of missing data for race (61.3%), we advise caution in interpreting our finding that the odds of seropositivity were three times higher for Black race, even though consistent with prior literature.34 Healthcare organizations have similar job function and work location categories incorporated in their infrastructure, suggesting that our observations may be generalizable to other hospitals in the United States.
CONCLUSION
These findings show that during the first surge in NYC, with its increased burden of disease, hospitalizations, morbidity, and mortality, seroprevalences varied based on job function and work location within this institution. Nurses were at highest risk for SARS-CoV-2 infection, as were those who worked in the ED. In preparation for subsequent waves of SARS-CoV-2 and other highly contagious respiratory infections, major medical centers need to enhance efforts aimed at protecting HCWs, with particular attention to these groups. This study also strongly supports the recent CDC guideline prioritizing HCWs to receive COVID-19 mRNA and adenovirus vector vaccines that have obtained emergency use authorization by the US Food and Drug Administration.35
Acknowledgments
The authors thank all the residents, nurses, and staff of the Department of Family Medicine for their contribution to this work.
SARS-CoV-2 has infected 141 million people worldwide and 31 million people in the United States as of April 20, 2021.1,2 The influx of hospital admissions and deaths has severely strained healthcare systems worldwide and placed healthcare workers (HCWs) at increased risk for acquiring COVID-19.3-5
Several studies have described the impact of COVID-19 on this heterogeneous group of HCWs. Shields et al reported a seroprevalence of 24.4% in HCWs at University Hospitals Birmingham (UK), with the highest rate, 34.5%, in housekeeping staff.6 Steensels et al reported a lower prevalence of 6.4% at a tertiary care center in Belgium, and showed no increased risk for HCWs when directly involved in clinical care.7 The authors attributed this to adequate use of personal protective equipment (PPE). Other studies have reported seroprevalences ranging from 1.6% to 18%.8-11 In the New York City (NYC) metro area, Jeremias et al reported a seroprevalence of 9.8% in HCWs and found no difference by job title or work location,12 whereas Moscola et al reported a seroprevalence of 13.7% and demonstrated a 3% increased risk for those working in service or maintenance.13 Antibody tests were conducted between March and April 2020 in all but two of these studies; testing in these two studies was performed between April 13 and June 23, 2020, with one reporting a seroprevalence of 6%11 and the other, 13.7%.13
NYC became the earliest pandemic epicenter in the United States following untracked transmission from ongoing circulation of SARS-CoV-2 in Europe.14 As a result, the COVID-19 surge in NYC commenced in March and largely subsided by the end of May 2020. Most HCW data reported to date do not reflect the situation at the end of the surge, and may underestimate true seroprevalence. We describe SARS-CoV-2 seroprevalence in HCWs in a large inner-city hospital in NYC, with antibody testing conducted from May 18 to June 26, 2020, at the subsidence of the surge. To further our understanding of occupational risk among different groups of HCWs, we examined associations of seroprevalence with HCWs’ job function and work location.
METHODS
This was a cross-sectional seroprevalence study conducted in the BronxCare Health System located in South and Central Bronx, an area that experienced one of the highest incidences of SARS-CoV-2 infections within NYC’s five boroughs.
HCWs were offered voluntary testing for serum antibodies to SARS-CoV-2 between May 18 and June 26, 2020. Testing occurred in the institution’s auditorium, a central and easily accessible location. Weekly emails were sent to all employees and department heads during the testing period, offering antibody testing and providing location and testing time information. The Elecsys Anti-SARS-CoV-2 (Roche) assay measuring total qualitative antibodies was used; the assay has a reported sensitivity of 97.1% 14 days after a positive SARS-CoV-2 RNA polymerase chain reaction (PCR) test result and a specificity of 100%.15
Demographic and work-related information was abstracted from electronic medical records, including all comorbid conditions that affected 30 or more HCWs. Pulmonary diagnoses, including asthma and chronic obstructive pulmonary disease, were grouped as chronic lung disease, and cardiovascular diseases, including hypertension, as chronic heart disease. Personal identifiers and data were delinked upon completion of data abstraction. The study was approved by the hospital’s institutional review board.
Job Function and Work Location
HCWs were grouped by job function as follows: physicians; nurses (including physician assistants and nurse practitioners); allied HCW I (medical assistants, patient care, and electrocardiogram, radiology, and ear, nose and throat technicians); allied HCW II (social workers, dieticians and nutritionists, registration clerks and unit associates, physical and occupational therapists); nonclinical staff (patient transporters, housekeeping staff, and security staff); pharmacists; engineering; and administrative staff. Respiratory therapists were considered as a separate group as their work placed them at high risk for respiratory diseases.
Work locations were as follows: clinics (including dental, outpatient, and satellite clinics), emergency departments (ED), inpatient units (including floors and intensive care units [ICU]), radiology suite, laboratory and pharmacy, and offices.
Statistical Analysis
Descriptive statistics were calculated using χ2 analyses. All demographic variables were tested against serology status (positive/negative). A binary logistic regression analysis was used to calculate odds ratios (ORs). Eight separate univariate unadjusted ORs were calculated by running each predictor variable against serology status (dependent variable), which included the six categorical variables—race, ethnicity, age, sex, body mass index (BMI), and prior SARS-CoV-2 PCR results—and the two main predictors—job function and work location. To obtain adjusted ORs, two final separate multivariable logistic regression analyses were executed including the six covariates listed. Due to high collinearity between job function and work location (χ2 = 3030.13, df = 35 [6 levels of work location – 1]*[8 levels of job function – 1]; P < .001), we included only one of the main predictors in each model. The regressions were specified such that the reference groups for the work location and job function variables were office work and administration, respectively. This choice was made based on the fact that their nonclinical functions do not confer an exposure risk in excess of that experienced by typical community populations. Sensitivity analyses were performed on the subset of HCWs whose address zip codes indicated residence within NYC to exclude the effect of different community seroprevalences in areas outside of NYC. The 95% CI for seroprevalence of antibodies within tested HCWs was estimated using the Clopper-Pearson binomial method.
RESULTS
Among all HCWs in the institution (N = 4,807), 2,749 (57.2%) underwent voluntary testing. Of those who underwent testing, 831 were positive for antibodies to SARS-CoV-2 (Figure 1), a seroprevalence of 30.2% (95% CI, 29%-32%). Among the age groups, the 45-to-64−year group had the highest seropositivity at 33% (400/1203), and those ≥75 years of age, the lowest at 16.7% (2/12) (P < .009).
Among all tested HCWs, 70.1% (1,928/2,749) resided in NYC. SARS-CoV-2 seroprevalence in this subset was 32% (616/1,928) (Figure 1). Demographic and comorbid conditions in HCWs who lived in NYC were similar to those of the whole group (Appendix Table 1).
HCWs who underwent voluntary antibody testing (Appendix Table 2) had a higher percentage of persons in the 45-to-64−year age group (43.8% vs 40.9%) and a lower percentage of persons in the 65-to-74−year age group (3.3% vs 5.3%) compared with the group of HCWs that did not undergo testing (P < .001). Gender, race, ethnicity, comorbid conditions, SARS-CoV-2 PCR testing, and work locations were not different between groups. The tested group had higher proportions of clinicians (physicians, nurses, allied HCWs I and II) than the untested nonparticipant group (P = .014).
SARS-CoV-2 PCR Tests on HCWs
More than one-third (34.1%; 938/2,749) of HCWs had a documented nasopharyngeal PCR test between March 23 and June 26, 2020 (Table). Of all PCRs performed, 262 were positive, giving an overall PCR positivity rate of 27.9%. Positivity was 51.4% in March and 36.6% in April. The reasons for PCR testing were not available, but likely represent a combination of exposure-related testing among asymptomatic individuals and diagnostic testing of symptomatic HCWs. In contrast, serology testing was indicative of prior infection and yielded a cumulative seroprevalence at the end of the surge. Findings were similar among HCWs residing in NYC
Work Location and Job Function
Among all HCWs (Table, Figure 2), there were differences in seropositivity by work location (P = .001). The largest number of HCWs worked in inpatient units (1,348/2,749, 49%), and the second largest in offices (554/2,749, 20%). The highest seropositivity rate was in the EDs, at 36.4% (64/176), followed by radiology suites, at 32.7% (17/52); the seropositivity rate in office locations was 25.8% (143/554). Among HCWs residing in NYC (Appendix Table 1, Appendix Figure 1), the rank order according to proportion seropositive by work location was similar to that of the whole group (P = .004), except that the second highest seropositivity rate was in the inpatient units (33.9% [323/953]). In the group of HCWs residing in NYC, office locations had a seropositivity of 27.4% (102/372). The seropositivity rates for both groups working in office locations were slightly higher than the 22% community seroprevalence in NYC reported for the same period.16
Among all HCWs, there were differences in seropositivity by job function (P = .001). The greatest proportion of HCWs were allied HCW II (23% [631/2,749]), followed by nurses (22.2% [611/2,749]) and physicians (21.3% [585/2,749] ). Seropositivity was highest for nonclinical staff (44.0% [51/116]), followed by nurses (37.5% [229/611]) and allied clinical HCW I and II (34.5% [143/414] and 32.0% [202/631], respectively). It was lowest for administrative staff (20.9% [42/201]) and pharmacists (11.1% [5/45]). Among HCWs residing in NYC, the rank order according to proportion seropositive by location was similar to that of the whole group. Administrative staff seropositivity was 18.3% (20/109). Administrative staff seropositivity for both groups was marginally lower than the 22% community seroprevalence in NYC for the same period.16
Odds Ratios for SARS-CoV-2 Seropositivity
For all HCWs, in unadjusted models (Appendix Table 3), age 45 to 64 years and Black race were associated with increased odds of being seropositive (1.26; 95% CI, 1.07-1.49 and 2.26; 95% CI, 1.51-3.37, respectively). Increased odds were seen for HCWs working in the ED (1.64; 95% CI, 1.14-2.36) and inpatient units (1.35; 95% CI, 1.08-1.69), and decreased odds were seen for those working in the laboratory and pharmacy (0.47; 95% CI, 0.26-0.86). Increased odds for seropositivity were found for nurses (2.27; 95% CI, 1.56-3.31), allied HCW I (2.00; 95% CI, 1.34-2.97), allied HCW II (1.78; 95% CI, 1.22-2.60), and nonclinical staff (2.97; 95% CI,1.80-4.90).
After adjusting for all covariates, HCWs who were Black remained at increased odds for being seropositive in the two final models (adjusted OR, 2.29; 95% CI, 1.38-3.81 and adjusted OR, 2.94; 95% CI, 1.78-4.85), as did those who had a BMI >30 kg/m2, with an adjusted OR of 1.36 (95% CI, 1.05-1.77) in one of the final models (Appendix Table 3). None of the other comorbid conditions had increased ORs. Those who worked in the ED and inpatient units also remained at increased odds after adjusting for covariates (2.27; 95% CI, 1.53-3.37 and 1.48; 95% CI, 1.14-1.92, respectively; Figure 3). Other job functions that had increased odds for seropositivity were nurses (2.54; 95% CI, 1.64-3.94), allied HCW I (1.83; 95% CI, 1.15-2.89) and II (1.70; 95% CI, 1.10-2.63), and nonclinical staff (2.51; 95% CI, 1.42-4.43).
Having a positive PCR for SAR-CoV-2 on nasopharyngeal swabs was strongly associated with seropositivity (OR, 47.26; 95% CI, 29.30-76.23 and OR, 44.79; 95% CI, 27.87-72.00) in the two multivariate-adjusted models. These findings were confirmed when the analyses were performed on HCWs who resided in NYC (Appendix Table 4 and Appendix Figure 2).
DISCUSSION
In a large inner-city New York hospital, we report a cumulative SARS-CoV-2 seroprevalence of 30.2% in HCWs at the end of the first surge of SARS-CoV-2 infections in NYC. We identified the highest seropositivity rates for nonclinical staff and nurses, followed by allied HCWs, with the odds of being seropositive ranging from 1.7 to 2.5. The work locations with the highest seroprevalences were the ED and inpatient units, with 2.3-fold and 1.5-fold increased odds of seropositivity, respectively.
Serosurveillance studies have reported the trajectory of community prevalence in NYC over the first wave. A 6.3% prevalence was reported in samples collected between March 23 and April 1, 2020.17 In a study by Rosenberg et al18 with testing performed from April 9 through April 28, 2020, prevalence increased to 22.7%. Serosurveillance data from the NYC Department of Health show prevalence ranging from 20.1% to 23.3% (average 22%) during the study period.16 Compared to the estimated seroprevalence of 9.3% in the United States,19 these rates established NYC as an early epicenter for the COVID-19 pandemic, with our institution’s HCW seroprevalence considerably higher than NYC community serosurveillance rates, 2.2 times higher than reported in the earlier HCW study in the greater NYC area,13 and higher than the 27% rate during May 2020 recently reported in another NYC hospital.20
Data from studies of hospital transmission and effects of mitigation measures, such as a universal masking policy for HCWs and patients, clearly demonstrate the high effectiveness of these measures in reducing hospital transmissions.21,22 This suggests HCW seroprevalence in institutions with well-implemented infection control and universal masking policies may not be a consequence of workplace exposures, but rather may be reflective of community rates.23 Our institution’s response commenced February 3, 2020, with implementation of social distancing, a universal masking policy, transmission-based precautions, and use of fitted N95 masks. Mid-March, elective surgeries were canceled, and inpatient visitation suspended. During the surge, these measures were widely and consistently implemented for all categories of HCWs throughout the work environment, based on emerging guidelines from the Centers for Disease Control and Prevention (CDC) and NYC Department of Health. Our overall observed HCW seroprevalence, well above that of the community, with differences in categories of job function and work locations, is therefore an important finding. Our sample of 2,749 HCWs lived in NYC and its surrounding suburbs and nearby states. There is heterogeneity in community seroprevalence between areas outside of NYC and NYC (an epicenter) itself. We therefore analyzed our data in the subset with NYC zip codes, confirming a similar overall prevalence and increased odds of seropositivity in nurses, allied HCWs, and nonclinical staff.
Physicians and administrative and office staff had seropositivity rates of 18.1%, 20.9%, and 25.8%, respectively, consistent with community rates and illustrating the effectiveness of PPE in the hospital setting. Since PPE use was part of a universal policy applied to all HCWs in our institution, other possible reasons may explain the differences we found. We speculate that the close working relationship nurses have with their patients resulted in a longer duration and higher frequency of daily interactions, increasing the risk for transmission and causing breakthrough infections.24,25 This increased risk is reflected in a study in which 28% of hospitalized patients were nurses and 9% certified nursing assistants.26
The CDC recently redefined close contact with someone with COVID-19 as a cumulative total of >15 minutes over 24 hours.25 Thus, several multiple short periods of exposure can increase risk for infection with SARS-CoV-2; such exposure is characteristic of the job function of nurses, nursing staff, and nonclinical staff. Further, housekeeping, transportation, and security officers are all nonclinical staff with significant and multiple exposures to COVID-19 patients during the surge, and for security officers, to continuous public traffic in and out of the hospital. SARS-CoV-2 spreads by virus shedding in large droplets and aerosols, with droplet nuclei <5 microns in size efficiently dispersed in air, an important additional mode of transmission.27-30 Airborne transmission coupled with virus shedding in asymptomatic and presymptomatic persons, which has been shown to cause secondary attack rates of up to 32%, are other factors that likely contributed to the increased seroprevalence in this group.31 Our observation is consistent with the Birmingham study, which reported the highest rate in housekeeping staff, with a prevalence of 34.5%, compared to 44% in this study.6 Similar reasons for high seropositivity rates apply to the two groups of allied HCWs (eg, medical assistants and patient care technicians, social workers, nutritionists and therapists), whose job functions place them in intermittent but significant proximity with inpatients and outpatients.
Consistent with public health data showing that minorities are disproportionately affected by this disease, we found that Black HCWs were three times more likely to be seropositive.32 However, an unexpected observation was the association between obesity and SARS-CoV-2 seropositivity. A possible explanation for this association may be inability to achieve optimal fit testing for N95 masks, thereby increasing the risk of exposure to droplet nuclei. This is important given that obesity is associated with poorer outcomes from COVID-19.
During the height of the first wave in NYC, EDs and inpatient units handled a large volume of COVID-19 patients with high PCR positivity rates (peak of 51% in March in our hospital). It was not unexpected that we observed increased odds of seropositivity in these work locations. As ICUs were at capacity, inpatient units cared for critically ill patients they would not normally have. HCWs in these locations coped with an increased workload, increased demand on PPE supplies, and work fatigue, which contributed to increased risk for hospital-acquired SARS-CoV-2 infections.
Reporting seroprevalence at a single institution was a limitation of the study. Approximately 57% of the hospital’s total HCW population was tested for antibodies. It is possible their risk profile influenced their decision to volunteer for testing when it became available, introducing selection bias. A comparison between tested and untested HCWs showed similarity in all demographic measures, including nasopharyngeal PCR testing, except for age. We did not have information on symptoms that would prompt PCR testing. HCWs who underwent voluntary testing were younger compared to those who did not undergo testing. Current NYC serosurveillance data showed higher seropositivity in the 45-to-64–year age group (27.8%-28.6%) compared to the 65-to-74–year age group (24.3%), which suggests that the tested group may overestimate seroprevalence among HCWs relative to a randomly selected sample.33 Similarly, there were more nurses, allied HCWs, physicians, and administrative staff in the tested group, with the former two having higher SARS-CoV-2 seropositivity compared to community prevalence, which could also overestimate seroprevalence. Our large sample size provided us with the power to detect differences within several different job functions and work locations, a strength of this study. It was not possible to differentiate community- from hospital-acquired infection in our HCWs, a limitation in many observational HCW seroprevalence studies. However, when we analyzed data restricted only to HCWs in NYC, to reduce the effect of differing community prevalences outside the city, our results were unchanged. Since it is possible that nonclinical HCWs are of a lower socioeconomic status compared to others (nurses and allied HCWs), we cannot exclude the possibility that higher SARS-CoV-2 seroprevalence associated with lower status explains, partly or completely, the increased odds of seropositivity we observed.34 Due to the high proportion of missing data for race (61.3%), we advise caution in interpreting our finding that the odds of seropositivity were three times higher for Black race, even though consistent with prior literature.34 Healthcare organizations have similar job function and work location categories incorporated in their infrastructure, suggesting that our observations may be generalizable to other hospitals in the United States.
CONCLUSION
These findings show that during the first surge in NYC, with its increased burden of disease, hospitalizations, morbidity, and mortality, seroprevalences varied based on job function and work location within this institution. Nurses were at highest risk for SARS-CoV-2 infection, as were those who worked in the ED. In preparation for subsequent waves of SARS-CoV-2 and other highly contagious respiratory infections, major medical centers need to enhance efforts aimed at protecting HCWs, with particular attention to these groups. This study also strongly supports the recent CDC guideline prioritizing HCWs to receive COVID-19 mRNA and adenovirus vector vaccines that have obtained emergency use authorization by the US Food and Drug Administration.35
Acknowledgments
The authors thank all the residents, nurses, and staff of the Department of Family Medicine for their contribution to this work.
1. Liu YC, Kuo RL, Shih SR. COVID-19: The first documented coronavirus pandemic in history. Biomed J. 2020;43(4):328-333. https://doi.org/10.1016/j.bj.2020.04.007
2. World Health Organization. WHO coronavirus disease (COVID-19) dashboard. Accessed April 12, 2021. https://covid19.who.int
3. Nguyen LH, Drew DA, Graham MS, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Health. 2020;5(9):e475-e483. https://doi.org/10.1016/S2468-2667(20)30164-X
4. Gupta S, Federman DG. Hospital preparedness for COVID-19 pandemic: experience from department of medicine at Veterans Affairs Connecticut Healthcare System. Postgrad Med. 2020:1-6. https://doi.org/10.1080/00325481.2020.1761668
5. Woolley K, Smith R, Arumugam S. Personal protective equipment (PPE) guidelines, adaptations and lessons during the COVID-19 pandemic. Ethics Med Public Health. 2020;14:100546. https://doi.org/10.1016/j.jemep.2020.100546
6. Shields A, Faustini SE, Perez-Toledo M, et al. SARS-CoV-2 seroprevalence and asymptomatic viral carriage in healthcare workers: a cross-sectional study. Thorax. 2020;75(12):1089-1094. https://doi.org/10.1136/thoraxjnl-2020-215414
7. Steensels D, Oris E, Coninx L, et al. Hospital-wide SARS-CoV-2 antibody screening in 3056 staff in a tertiary center in Belgium. JAMA. 2020;324(2):195-197. https://doi.org/10.1001/jama.2020.11160
8. Stubblefield WB, Talbot HK, Feldstein L, et al. Seroprevalence of SARS-CoV-2 Among frontline healthcare personnel during the first month of caring for COVID-19 patients - Nashville, Tennessee. Clin Infect Dis. 2020. https://doi.org/10.1093/cid/ciaa936
9. Korth J, Wilde B, Dolff S, et al. SARS-CoV-2-specific antibody detection in healthcare workers in Germany with direct contact to COVID-19 patients. J Clin Virol. 2020;128:104437. https://doi.org/10.1016/j.jcv.2020.104437
10. Keeley AJ, Evans C, Colton H, et al. Roll-out of SARS-CoV-2 testing for healthcare workers at a large NHS Foundation Trust in the United Kingdom, March 2020. Euro Surveill. 2020;25(14). https://doi.org/10.2807/1560-7917.ES.2020.25.14.2000433
11. Self WH, Tenforde MW, Stubblefield WB, et al. Seroprevalence of SARS-CoV-2 among frontline health care personnel in a multistate hospital network - 13 academic medical centers, April-June 2020. MMWR Morb Mortal Wkly Rep. 2020;69(35):1221-1226. https://doi.org/10.15585/mmwr.mm6935e2
12. Jeremias A, Nguyen J, Levine J, et al. Prevalence of SARS-CoV-2 infection among health care workers in a tertiary community hospital. JAMA Intern Med. 2020 Aug 11:e204214. https://doi.org/10.1001/jamainternmed.2020.4214
13. Moscola J, Sembajwe G, Jarrett M, et al. Prevalence of SARS-CoV-2 antibodies in health care personnel in the New York City area. JAMA. 2020;324(9):893-895. https://doi.org/10.1001/jama.2020.14765
14. Gonzalez-Reiche AS, Hernandez MM, Sullivan MJ, et al. Introductions and early spread of SARS-CoV-2 in the New York City area. Science. 2020;369(6501):297-301. https://doi.org/10.1126/science.abc1917
15. Lau CS, Hoo SF, Yew SF, et al. Evaluation of the Roche Elecsys Anti-SARS-CoV-2 assay. Preprint. Posted online June 29, 2020. Accessed November 8, 2020. https://www.medrxiv.org/content/10.1101/2020.06.28.20142232v1 https://doi.org/10.1101/2020.06.28.20142232
16. New York City Department of Health. Covid-19: data. long-term trends. Antibody testing. Accessed March 5, 2021. https://www1.nyc.gov/site/doh/covid/covid-19-data-trends.page#antibody
17. Havers FP, Reed C, Lim T, et al. Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23-May 12, 2020. JAMA Intern Med. Published online July 21, 2020. https://doi.org/10.1001/jamainternmed.2020.4130
18. Rosenberg ES, Tesoriero JM, Rosenthal EM, et al. Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Ann Epidemiol. Aug 2020;48:23-29 e4. https://doi.org/10.1016/j.annepidem.2020.06.004
19. Anand S, Montez-Rath M, Han J, et al. Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study. Lancet. 2020;396(10259):1335-1344. https://doi.org/10.1016/S0140-6736(20)32009-2
20. Venugopal U, Jilani N, Rabah S, et al. SARS-CoV-2 seroprevalence among health care workers in a New York City hospital: a cross-sectional analysis during the COVID-19 pandemic. Int J Infect Dis. 2020;102:63-69. https://doi.org/10.1016/j.ijid.2020.10.036
21. Samaranayake LP, Fakhruddin KS, Ngo HC, Chang JWW, Panduwawala C. The effectiveness and efficacy of respiratory protective equipment (RPE) in dentistry and other health care settings: a systematic review. Acta Odontol Scand. 2020;78(8):626-639. https://doi.org/10.1080/00016357.2020.1810769
22. Seidelman JL, Lewis SS, Advani SD, et al. Universal masking is an effective strategy to flatten the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) healthcare worker epidemiologic curve. Infect Control Hosp Epidemiol. 2020;41(12):1466-1467. https://doi.org/10.1017/ice.2020.313
23. Richterman A, Meyerowitz EA, Cevik M. Hospital-acquired SARS-CoV-2 infection: lessons for public health. JAMA. Published online November 13, 2020. https://doi.org/10.1001/jama.2020.21399
24. Degesys NF, Wang RC, Kwan E, Fahimi J, Noble JA, Raven MC. Correlation between n95 extended use and reuse and fit failure in an emergency department. JAMA. 2020;324(1):94-96. https://doi.org/10.1001/jama.2020.9843
25. Pringle JC, Leikauskas J, Ransom-Kelley S, et al. COVID-19 in a correctional facility employee following multiple brief exposures to persons with COVID-19 - Vermont, July-August 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1569-1570. https://doi.org/10.15585/mmwr.mm6943e1
26. Kambhampati AK, O’Halloran AC, Whitaker M, et al. COVID-19-associated hospitalizations among health care personnel - COVID-NET, 13 states, March 1-May 31, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1576-1583. https://doi.org/10.15585/mmwr.mm6943e3
27. Zhang R, Li Y, Zhang AL, Wang Y, Molina MJ. Identifying airborne transmission as the dominant route for the spread of COVID-19. Proc Natl Acad Sci U S A. 2020;117(26):14857-14863. https://doi.org/10.1073/pnas.2009637117
28. Setti L, Passarini F, De Gennaro G, et al. Airborne transmission route of COVID-19: why 2 meters/6 feet of inter-personal distance could not be enough. Int J Environ Res Public Health. 2020;17(8):2932. https://doi.org/doi:10.3390/ijerph17082932
29. Klompas M, Baker MA, Rhee C. Airborne transmission of SARS-CoV-2: theoretical considerations and available evidence. JAMA. 2020;324(5):441-442. https://doi.org/10.1001/jama.2020.12458
30. Bourouiba L. Turbulent gas clouds and respiratory pathogen emissions: potential implications for reducing transmission of COVID-19. JAMA. 2020;323(18):1837-1838. https://doi.org/10.1001/jama.2020.4756
31. Qiu X, Nergiz A, Maraolo A, Bogoch I, Low N, Cevik M. The role of asymptomatic and pre-symptomatic infection in SARS-CoV-2 transmission – a living systematic review. Clin Mibrobiol Infect. 2021;20:S1198-743X(21)00038-0. Published online January 20, 2021. https://doi.org/10.1016/j.cmi.2021.01.011
32. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and mortality among black patients and white patients with Covid-19. N Engl J Med. 2020;382(26):2534-2543. https://doi.org/doi:10.1056/NEJMsa2011686
33. New York City Department of Health. Covid-19: Data. Antibody testing by group - age. Accessed March 5, 2021. https://www1.nyc.gov/site/doh/covid/covid-19-data-totals.page#antibody
34. Patel JA, Nielsen FBH, Badiani AA, et al. Poverty, inequality and COVID-19: the forgotten vulnerable. Public Health. 2020;183:110-111. https://doi.org/10.1016/j.puhe.2020.05.006
35. Polack FP, Thomas SJ, Kitchin N, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383(27):2603-2615. https://doi.org/10.1056/NEJMoa2034577
1. Liu YC, Kuo RL, Shih SR. COVID-19: The first documented coronavirus pandemic in history. Biomed J. 2020;43(4):328-333. https://doi.org/10.1016/j.bj.2020.04.007
2. World Health Organization. WHO coronavirus disease (COVID-19) dashboard. Accessed April 12, 2021. https://covid19.who.int
3. Nguyen LH, Drew DA, Graham MS, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Health. 2020;5(9):e475-e483. https://doi.org/10.1016/S2468-2667(20)30164-X
4. Gupta S, Federman DG. Hospital preparedness for COVID-19 pandemic: experience from department of medicine at Veterans Affairs Connecticut Healthcare System. Postgrad Med. 2020:1-6. https://doi.org/10.1080/00325481.2020.1761668
5. Woolley K, Smith R, Arumugam S. Personal protective equipment (PPE) guidelines, adaptations and lessons during the COVID-19 pandemic. Ethics Med Public Health. 2020;14:100546. https://doi.org/10.1016/j.jemep.2020.100546
6. Shields A, Faustini SE, Perez-Toledo M, et al. SARS-CoV-2 seroprevalence and asymptomatic viral carriage in healthcare workers: a cross-sectional study. Thorax. 2020;75(12):1089-1094. https://doi.org/10.1136/thoraxjnl-2020-215414
7. Steensels D, Oris E, Coninx L, et al. Hospital-wide SARS-CoV-2 antibody screening in 3056 staff in a tertiary center in Belgium. JAMA. 2020;324(2):195-197. https://doi.org/10.1001/jama.2020.11160
8. Stubblefield WB, Talbot HK, Feldstein L, et al. Seroprevalence of SARS-CoV-2 Among frontline healthcare personnel during the first month of caring for COVID-19 patients - Nashville, Tennessee. Clin Infect Dis. 2020. https://doi.org/10.1093/cid/ciaa936
9. Korth J, Wilde B, Dolff S, et al. SARS-CoV-2-specific antibody detection in healthcare workers in Germany with direct contact to COVID-19 patients. J Clin Virol. 2020;128:104437. https://doi.org/10.1016/j.jcv.2020.104437
10. Keeley AJ, Evans C, Colton H, et al. Roll-out of SARS-CoV-2 testing for healthcare workers at a large NHS Foundation Trust in the United Kingdom, March 2020. Euro Surveill. 2020;25(14). https://doi.org/10.2807/1560-7917.ES.2020.25.14.2000433
11. Self WH, Tenforde MW, Stubblefield WB, et al. Seroprevalence of SARS-CoV-2 among frontline health care personnel in a multistate hospital network - 13 academic medical centers, April-June 2020. MMWR Morb Mortal Wkly Rep. 2020;69(35):1221-1226. https://doi.org/10.15585/mmwr.mm6935e2
12. Jeremias A, Nguyen J, Levine J, et al. Prevalence of SARS-CoV-2 infection among health care workers in a tertiary community hospital. JAMA Intern Med. 2020 Aug 11:e204214. https://doi.org/10.1001/jamainternmed.2020.4214
13. Moscola J, Sembajwe G, Jarrett M, et al. Prevalence of SARS-CoV-2 antibodies in health care personnel in the New York City area. JAMA. 2020;324(9):893-895. https://doi.org/10.1001/jama.2020.14765
14. Gonzalez-Reiche AS, Hernandez MM, Sullivan MJ, et al. Introductions and early spread of SARS-CoV-2 in the New York City area. Science. 2020;369(6501):297-301. https://doi.org/10.1126/science.abc1917
15. Lau CS, Hoo SF, Yew SF, et al. Evaluation of the Roche Elecsys Anti-SARS-CoV-2 assay. Preprint. Posted online June 29, 2020. Accessed November 8, 2020. https://www.medrxiv.org/content/10.1101/2020.06.28.20142232v1 https://doi.org/10.1101/2020.06.28.20142232
16. New York City Department of Health. Covid-19: data. long-term trends. Antibody testing. Accessed March 5, 2021. https://www1.nyc.gov/site/doh/covid/covid-19-data-trends.page#antibody
17. Havers FP, Reed C, Lim T, et al. Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23-May 12, 2020. JAMA Intern Med. Published online July 21, 2020. https://doi.org/10.1001/jamainternmed.2020.4130
18. Rosenberg ES, Tesoriero JM, Rosenthal EM, et al. Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Ann Epidemiol. Aug 2020;48:23-29 e4. https://doi.org/10.1016/j.annepidem.2020.06.004
19. Anand S, Montez-Rath M, Han J, et al. Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study. Lancet. 2020;396(10259):1335-1344. https://doi.org/10.1016/S0140-6736(20)32009-2
20. Venugopal U, Jilani N, Rabah S, et al. SARS-CoV-2 seroprevalence among health care workers in a New York City hospital: a cross-sectional analysis during the COVID-19 pandemic. Int J Infect Dis. 2020;102:63-69. https://doi.org/10.1016/j.ijid.2020.10.036
21. Samaranayake LP, Fakhruddin KS, Ngo HC, Chang JWW, Panduwawala C. The effectiveness and efficacy of respiratory protective equipment (RPE) in dentistry and other health care settings: a systematic review. Acta Odontol Scand. 2020;78(8):626-639. https://doi.org/10.1080/00016357.2020.1810769
22. Seidelman JL, Lewis SS, Advani SD, et al. Universal masking is an effective strategy to flatten the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) healthcare worker epidemiologic curve. Infect Control Hosp Epidemiol. 2020;41(12):1466-1467. https://doi.org/10.1017/ice.2020.313
23. Richterman A, Meyerowitz EA, Cevik M. Hospital-acquired SARS-CoV-2 infection: lessons for public health. JAMA. Published online November 13, 2020. https://doi.org/10.1001/jama.2020.21399
24. Degesys NF, Wang RC, Kwan E, Fahimi J, Noble JA, Raven MC. Correlation between n95 extended use and reuse and fit failure in an emergency department. JAMA. 2020;324(1):94-96. https://doi.org/10.1001/jama.2020.9843
25. Pringle JC, Leikauskas J, Ransom-Kelley S, et al. COVID-19 in a correctional facility employee following multiple brief exposures to persons with COVID-19 - Vermont, July-August 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1569-1570. https://doi.org/10.15585/mmwr.mm6943e1
26. Kambhampati AK, O’Halloran AC, Whitaker M, et al. COVID-19-associated hospitalizations among health care personnel - COVID-NET, 13 states, March 1-May 31, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1576-1583. https://doi.org/10.15585/mmwr.mm6943e3
27. Zhang R, Li Y, Zhang AL, Wang Y, Molina MJ. Identifying airborne transmission as the dominant route for the spread of COVID-19. Proc Natl Acad Sci U S A. 2020;117(26):14857-14863. https://doi.org/10.1073/pnas.2009637117
28. Setti L, Passarini F, De Gennaro G, et al. Airborne transmission route of COVID-19: why 2 meters/6 feet of inter-personal distance could not be enough. Int J Environ Res Public Health. 2020;17(8):2932. https://doi.org/doi:10.3390/ijerph17082932
29. Klompas M, Baker MA, Rhee C. Airborne transmission of SARS-CoV-2: theoretical considerations and available evidence. JAMA. 2020;324(5):441-442. https://doi.org/10.1001/jama.2020.12458
30. Bourouiba L. Turbulent gas clouds and respiratory pathogen emissions: potential implications for reducing transmission of COVID-19. JAMA. 2020;323(18):1837-1838. https://doi.org/10.1001/jama.2020.4756
31. Qiu X, Nergiz A, Maraolo A, Bogoch I, Low N, Cevik M. The role of asymptomatic and pre-symptomatic infection in SARS-CoV-2 transmission – a living systematic review. Clin Mibrobiol Infect. 2021;20:S1198-743X(21)00038-0. Published online January 20, 2021. https://doi.org/10.1016/j.cmi.2021.01.011
32. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and mortality among black patients and white patients with Covid-19. N Engl J Med. 2020;382(26):2534-2543. https://doi.org/doi:10.1056/NEJMsa2011686
33. New York City Department of Health. Covid-19: Data. Antibody testing by group - age. Accessed March 5, 2021. https://www1.nyc.gov/site/doh/covid/covid-19-data-totals.page#antibody
34. Patel JA, Nielsen FBH, Badiani AA, et al. Poverty, inequality and COVID-19: the forgotten vulnerable. Public Health. 2020;183:110-111. https://doi.org/10.1016/j.puhe.2020.05.006
35. Polack FP, Thomas SJ, Kitchin N, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383(27):2603-2615. https://doi.org/10.1056/NEJMoa2034577
© 2021 Society of Hospital Medicine