Original Research

Cervical lymphadenitis is a common superficial neck infection in childhood. While most children with cervical lymphadenitis recover with antibiotic therapy, a subset can develop an abscess that may require surgical drainage. Radiologic imaging, most commonly ultrasound or computed tomography (CT), is often performed to identify such an abscess.^1-3 However, no national standards exist to guide clinician decision making around imaging in this population. In the absence of evidence-based guidelines, variability in frequency, timing, and modality of imaging likely exists in children hospitalized with cervical lymphadenitis.

As demonstrated for several other common pediatric conditions,^4,5 variability in imaging practices may contribute to overutilization of resources in children with cervical lymphadenitis. In particular, routinely conducting imaging on presentation may constitute overuse, as children with cervical lymphadenitis who present with less than 72 hours of neck swelling rarely undergo surgical drainage within the first 24 hours of hospitalization.^1,6,7 Imaging performed on presentation is often repeated later during hospitalization, particularly if the patient has not improved with antibiotic therapy. The net result may be unnecessary, redundant radiologic studies. Furthermore, serious complications such as bacteremia, extension of infection into the retropharyngeal space, or involvement of the airway or vasculature rarely occur in children with cervical lymphadenitis.^6,8 In this context, deferring initial imaging in this population is unlikely to lead to adverse outcomes and may reduce radiation exposure.

The overall objectives of this study are to describe hospital-level variation in imaging practices for pediatric cervical lymphadenitis and to examine the association between early imaging and outcomes in this population.

METHODS

Study Design and Data Source

We conducted a multicenter, cross-sectional study using the Pediatric Health Information Systems (PHIS) database, which contains administrative and billing data from 49 geographically diverse children’s hospitals across the United States (US) affiliated with the Children’s Hospital Association (Lenexa, Kansas). PHIS includes data on patient demographics, discharge diagnoses, and procedures using the International Classification of Diseases, 9th (ICD-9) and 10th Revision (ICD-10) diagnosis codes, as well as daily billed resource utilization for laboratory tests, imaging studies, and medications. Encrypted medical record numbers permit longitudinal identification of children across multiple visits to the same hospital. Use of de-identified PHIS data was deemed to be nonhuman subjects research; our approach to validation of ICD codes using local electronic medical record review was reviewed and approved by the Cincinnati Children’s Hospital Medical Center Institutional Review Board.

Study Population

Our study team developed an algorithm to identify children with cervical lymphadenitis and minimize misclassification using PHIS (Appendix A). All children with lymphadenitis-related ICD-9 and ICD-10 discharge diagnosis codes were eligible for inclusion. Codes were validated at a single center via electronic medical record review; clinician-documented discharge diagnosis of cervical lymphadenitis or the presence of fever and unilateral or asymmetrical neck swelling with overlying skin changes was used as the reference standard. We then excluded children who did not receive antibiotics, children who received radiologic imaging not involving the head or neck (which suggested noncervical lymphadenitis or other illness), and children who had discharge diagnosis codes for other specified conditions that are sometimes associated with enlarged cervical lymph nodes but warrant different evaluation or treatment (eg, Kawasaki disease, retropharyngeal abscess, and dental abscess; Appendix A). Our final algorithm yielded a positive predictive value of 87.5% (95% CI: 79.2%-93.4%) when ICD-9 codes were considered, and 95.1% (95% CI: 88.9%-98.4%) when ICD-10 codes were considered (Appendix A).

This algorithm was subsequently applied to the PHIS database. Children ages two months to 18 years hospitalized at participating PHIS institutions between July 2013 and December 2017 with a diagnosis of cervical lymphadenitis as per the algorithm (Appendix A) were eligible for inclusion. For children with multiple eligible admissions during the study period, we only included the first hospitalization. Children with complex chronic condition diagnosis codes⁹ were excluded as their clinical complexity could influence decisions around timing and modality of diagnostic imaging. In addition, we excluded children who did not have an emergency department (ED) visit associated with their hospitalization. This step was intended to exclude children who were transferred from another institution, as imaging performed at outside institutions prior to transfer is not available in PHIS. To avoid overinflating hospital-level variation in the setting of a small sample size, we also excluded all children admitted to the five hospitals with fewer than 50 cases of cervical lymphadenitis during the study period. Our final cohort consisted of 44 PHIS hospitals.

Measures of Interest

To examine hospital-level variation in imaging practices, we measured the proportion of children at each hospital who underwent any neck imaging study, CT or ultrasound imaging, early imaging, and multiple imaging studies within a single hospitalization. Neck imaging was defined as the presence of a billing code for ultrasound, CT, or magnetic resonance imaging (MRI) study of the neck (Appendix B). Early imaging was defined as neck imaging conducted on day 0 of hospitalization (ie, calendar day of admission and ending at midnight). Multiple imaging studies were defined as the receipt of more than one imaging study, regardless of timing or modality. We also measured the proportion of children by hospital who received surgical drainage, defined by the presence of procedure codes for incision and drainage of abscess of the neck (Appendix B).

In examining patient-level association between early imaging and clinical outcomes, our primary outcome of interest was the receipt of multiple imaging studies. Secondary outcomes included rates of surgical drainage, length of stay (in hospital days), and rates of lymphadenitis-related hospital readmission within 30 days of index discharge.

Covariates

Baseline demographic characteristics included age, gender, race/ethnicity, and insurance type. We measured ED visits associated with lymphadenitis-related diagnosis codes in the 30 days prior to admission as a proxy measure for illness duration prior to presentation. To approximate illness severity, we included the following covariates: rates of intensive care unit admission on presentation, rates of receipt of intravenous (IV) analgesia (Appendix B) on hospital days prior to surgical drainage, and rates of receipt of broad-spectrum antibiotics on day 0 or 1 of hospitalization. Broad-spectrum antibiotics (Appendix B) were defined by an independent three-person review of available antibiotic codes (SD, SSS, and JT); differences were resolved by group consensus.

 

Analysis

Categorical variables were described using frequencies and percentages, while continuous data were described using median and interquartile range. We described hospital-level variation in imaging practices by calculating and comparing the proportion of children at each hospital who underwent any neck imaging study, CT imaging, ultrasound imaging, early imaging, multiple imaging studies, and surgical drainage.

Patient-level demographics and clinical characteristics were compared across groups using chi-square test. To examine the association between early imaging and outcomes, we used generalized linear or logistic mixed effects models to control for patient demographic characteristics and clinical markers of illness duration and severity, with a random effect for hospital to account for clustering. Patient demographics in the model defined a priori included age, race/ethnicity, and insurance type; clinical characteristics included prior ED visit for lymphadenitis, initial intensive care unit (ICU) admission, use of IV analgesia, and use of broad-spectrum antibiotics on day 0 or 1 of hospitalization. To assess the potential for misclassification related to the availability of calendar day but not time of imaging in PHIS, we conducted a secondary analysis to examine the patient-level association between early imaging and outcomes using an alternative definition for early imaging (defined as imaging conducted on day 0 or day 1 of hospitalization).

All statistical analyses were performed by using SAS version 9.4 (SAS Institute, Cary, North Carolina); P < .05 was considered statistically significant.

RESULTS

We identified 19,785 PHIS hospitalizations with lymphadenitis-related discharge diagnosis codes between July 1, 2013 and December 31, 2017. Applying our algorithm and exclusion criteria, we assembled a cohort of 10,014 children hospitalized with cervical lymphadenitis (Figure 1). Two-thirds of the children in our cohort were <4 years old, 42% were non-Hispanic white, and 63% had a government payor (Table 1). Neck imaging (ultrasound, CT, or MRI) was conducted in 8,103 (81%) children. CT imaging was performed in 4,097 (41%) of children, and early imaging was conducted in 6,111 (61%) of children with cervical lymphadenitis.

We noted hospital-level variation in rates of any neck imaging (median: 82.1%, interquartile range [IQR]: 77.7%-85.5%, full range: 68.7%-93.1%), CT imaging (median: 42.3%, IQR: 26.7%-55.2%, full range: 12.0%-81.5%), early imaging (median: 64.4%, IQR: 59.8%-68.4%, full range: 13.8%-76.9%), and multiple imaging studies (median: 23.7%, IQR: 18.6%-28.9%, full range: 1.2%-40.7%; Figure 2). Rates of surgical drainage also varied by hospital (median: 35.1%, IQR: 31.3%-42.0%, full range: 17.1%-54.5%).

At the patient level, children who received early imaging were more likely to be <1 year old (21% vs 16%, P < .001), or Hispanic or Black when compared with children who did not receive early imaging (Table 1). Children who received early imaging were more likely to have had an ED visit for lymphadenitis in the preceding 30 days (8% vs 6%, P = .001). However, they were less likely to have received broad-spectrum antibiotics on admission (6% vs 8%, P < .001; Table 1). Of the 6,111 patients who received early imaging, 2,538 (41.5%) received CT imaging and 3,902 (63.9%) received ultrasound imaging on day 0. Of the 2,272 patients receiving multiple imaging studies, 116 (5.1%) received two or more CT scans.

In multivariable analysis at the patient level, early imaging was associated with higher adjusted odds of receiving multiple imaging studies (adjusted odds ratio [aOR] 3.0, 95% CI: 2.6-3.6). Similarly, early imaging was associated with higher adjusted odds of surgical drainage (aOR: 1.3, 95% CI: 1.1-1.4), increased 30-day readmission for lymphadenitis (aOR: 1.5, 95% CI: 1.2-1.9), and longer length of stay (adjusted rate ratio: 1.2, 95% CI: 1.1-1.2; Table 2). For the subset of patients who did not receive surgical drainage during the index admission, the adjusted odds ratio for the association between early imaging at index admission and 30-day readmission was 1.7 (95% CI: 1.3-2.1). About 63% of readmissions occurred within 7 days of index discharge; 89% occurred within 14 days (Appendix Figure).

In secondary analysis using an alternative definition for early imaging (ie, imaging conducted on day 0 or day 1 of hospitalization), the adjusted odds ratio for multiple imaging studies was 22.6 (95% CI: 15.8-32.4). The adjusted odds and rate ratios for the remaining outcomes were similar to our primary analysis.

 

DISCUSSION

In this large multicenter study of children with cervical lymphadenitis, we found variation in imaging practices across 44 US children’s hospitals. Children with cervical lymphadenitis who underwent early imaging were more likely to receive multiple imaging studies during a single hospitalization than those who did not receive early imaging. At the patient level, early imaging was also associated with higher rates of surgical drainage, more frequent 30-day readmission, and longer lengths of stay.

To our knowledge, imaging practices in the population of children hospitalized with cervical lymphadenitis have not been previously characterized in the US; one study from Atlanta, Georgia, describes imaging practices in all children evaluated in the ED.¹ Single-center studies of children hospitalized with cervical lymphadenitis have been previously conducted in Canada⁶ and New Zealand,⁸ in which 42%-51% of children received imaging. In our study, most (81%) children hospitalized with lymphadenitis received some form of imaging, with 61% of all children receiving early imaging. Furthermore, 41% received CT imaging, as compared with 8%-10% of children in the aforementioned studies from Canada and New Zealand.^6,8 This finding is consistent with a pattern of imaging overuse in the US, which has amongst the highest utilization rates globally for advanced imaging such as CT and MRI.^10,11 Identifying opportunities to safely reduce routine imaging, particularly CT imaging, in this population could decrease unnecessary radiation exposure without compromising outcomes.

We also noted variability in imaging practices across PHIS hospitals. Some of this variability may be partially explained by differences in the patient population or illness severity across hospitals. However, given the absence of evidence-based best practices for children with cervical lymphadenitis, clinicians may rely on anecdotal experience or local practice culture to guide their decision making,¹² leading to variability in frequency, timing, and modality of imaging.

At the patient level, we found that children who received early imaging were more likely to receive multiple imaging studies. This finding supports our hypothesis that clinicians often order a second imaging study when the initial imaging study does not clearly demonstrate an abscess, and the child subsequently fails to demonstrate clear improvement after 24-48 hours of antibiotics.

Furthermore, early imaging was associated with overall increased utilization in our cohort, including increased likelihood of surgical drainage, 30-day readmission for lymphadenitis, as well as longer lengths of stay. Confounding may be one explanation for this finding. For instance, clinicians may pursue early imaging in children who present with longer duration of symptoms or more severe illness on presentation, as these factors may be associated with abscess formation.^1,6,7 These clinical covariates are not available in PHIS. Thus, we used prior ED visits for lymphadenitis to approximate illness duration, and initial admission to ICU, receipt of IV analgesia, and receipt of broad-spectrum antibiotics to approximate illness severity in an attempt to mitigate confounding. However, our proxy measures may not appropriately estimate illness duration and severity. For instance, children who had urgent care or outpatient visits for lymphadenitis would not be captured using the proxy of prior ED visit for lymphadenitis. Similarly, use of broad-spectrum antibiotics and IV analgesia may be influenced by provider or institutional preference rather than illness severity. Thus, residual confounding may exist despite adjusting for these measures.

On the other hand, it is also possible that a proportion of children with a small fluid collection on imaging may have improved with antibiotics alone. There is a growing body of evidence in children with other head and neck infections (eg, retropharyngeal abscess and orbital cellulitis with periosteal abscess)^13-15 that suggests that children with small abscesses often improve with antibiotic therapy alone. In children with cervical lymphadenitis who have small or developing abscesses identified via routine imaging on presentation, clinicians may be driven to pursue a surgical intervention with uncertain benefit. Deferring routine imaging in this population may provide an opportunity to improve the value of care in children with lymphadenitis without adversely affecting outcomes.

Upon closer examination of readmissions, children who received early imaging during index hospitalization were more likely to have a 30-day readmission when only evaluating the subset of patients who did not receive surgical drainage during the index admission. This suggests that readmissions are less likely attributable to surgical complications and more likely a reflection of the natural history of lymphadenitis in which a subset of patients eventually develop an abscess. Further supporting this, 61% of children who had a 30-day readmission for lymphadenitis underwent surgical drainage during readmission. Given that lymphadenitis is a slow-brewing infection in which serious complications are rare, patients who demonstrate gradual clinical improvement do not need to remain hospitalized and serially imaged to identify a possible abscess. Outpatient expectant management and readmission as needed for drainage may be an acceptable approach.

This study has several limitations given our use of an administrative database. Children with lymphadenitis may have been misclassified as these patients were identified using discharge diagnosis codes. To mitigate this potential misclassification, we conducted a structured validation process and found that the included codes had high positive predictive values (Appendix A). This validation process was conducted at a single hospital, and coding may vary across hospitals. To approximate sensitivity, we also sampled children without our included codes but with neck imaging and antibiotic use, and found that rates of cervical lymphadenitis were very low among children without our included diagnosis codes.

Furthermore, we were unable to measure the exact time of imaging study in PHIS; we used imaging conducted on hospital day 0 as a proxy measure for imaging conducted within the first 24 hours of presentation. With this definition, some children who had early imaging were likely misclassified as not having received early imaging. For example, a patient who arrived in the ED at 9 pm on day 0 of admission and had a neck ultrasound performed at 1 am would be classified as having had an imaging study on day 1 of hospitalization even though the imaging study was conducted within 4 hours of presentation. Using an alternative definition of early imaging as imaging conducted on hospital day 0 and day 1, we found a much higher adjusted OR for multiple imaging studies, with similar associations for secondary outcomes. As such, our definition of early imaging as day 0 likely biases the results toward the null; the true increase in likelihood of multiple imaging for those who receive early imaging is probably greater than our conservative estimation.

Additionally, there may be a subset of children who underwent imaging prior to presentation at the PHIS hospital ED for further workup and admission. Imaging conducted outside a PHIS hospital was not captured in this database. Similarly, children who had a readmission at a different hospital than their index admission would not be captured using PHIS. Finally, PHIS captures data from children’s hospitals; practices at these hospitals may not be generalizable to practices in the community hospital setting.

 

CONCLUSION

In conclusion, we found that imaging practices in children hospitalized with cervical lymphadenitis were widely variable across hospitals. Children receiving early imaging had more resource utilization and intervention when compared with children who did not receive early imaging. Our findings may represent a cascade effect, in which routinely conducted early imaging prompts clinicians to pursue more testing and interventions in this population. Future studies should obtain more detailed patient level covariates to further characterize clinical factors that may impact decisions around imaging and clinical outcomes for children with cervical lymphadenitis.

Acknowledgments

The authors would like to acknowledge the following investigators for their contributions to data interpretation and review of the final manuscript: Angela Choe MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Margaret Rush MD, Children’s National Medical Center, Washington, DC; Ryosuke Takei MD, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania; Wallis Molchen DO, Texas Children’s Hospital, Houston, Texas; Stephanie Royer Moss MD, Cleveland Clinic, Cleveland, Ohio; Rebecca Dang, MD, Lucile Packard Children’s Hospital Stanford, Palo Alto, California; Joy Solano MD, Children’s Mercy Hospital Kansas, Overland Park, Kansas; Nathaniel P. Goodrich MD, Children’s Hospital & Medical Center, Omaha, Nebraska; Ngozi Eboh MD, Texas Tech University Health Sciences Center, Dallas, Texas; Ashley Jenkins MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Rebecca Steuart MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Sonya Tang Girdwood MD, PhD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Alissa McInerney MD, Maria Fareri Children’s Hospital at Westchester Medical Center, Valhalla, New York; Sumeet Banker MD, MPH, New York Presbyterian Morgan Stanley Children’s Hospital, New York, New York; Corrie McDaniel DO, Seattle Children’s Hospital, Seattle, Washington; Christiane Lenzen MD, Rady Children’s Hospital, San Diego, California; Aleisha Nabower MD, Children’s Hospital & Medical Center, Omaha, Nebraska; Waheeda Samady MD, Ann & Robert H. Lurie Children’s Hospital, Chicago, Illinois; Jennifer Chen MD, Rady Children’s Hospital, San Diego, California; Marquita Genies MD, MPH, John’s Hopkins Children’s Center, Baltimore, Maryland; Justin Lockwood MD, Children’s Hospital Colorado, Aurora, Colorado; David Synhorst MD, Children’s Mercy Hospital Kansas, Overland Park, Kansas.

Cervical lymphadenitis is a common superficial neck infection in childhood. While most children with cervical lymphadenitis recover with antibiotic therapy, a subset can develop an abscess that may require surgical drainage. Radiologic imaging, most commonly ultrasound or computed tomography (CT), is often performed to identify such an abscess.^1-3 However, no national standards exist to guide clinician decision making around imaging in this population. In the absence of evidence-based guidelines, variability in frequency, timing, and modality of imaging likely exists in children hospitalized with cervical lymphadenitis.

As demonstrated for several other common pediatric conditions,^4,5 variability in imaging practices may contribute to overutilization of resources in children with cervical lymphadenitis. In particular, routinely conducting imaging on presentation may constitute overuse, as children with cervical lymphadenitis who present with less than 72 hours of neck swelling rarely undergo surgical drainage within the first 24 hours of hospitalization.^1,6,7 Imaging performed on presentation is often repeated later during hospitalization, particularly if the patient has not improved with antibiotic therapy. The net result may be unnecessary, redundant radiologic studies. Furthermore, serious complications such as bacteremia, extension of infection into the retropharyngeal space, or involvement of the airway or vasculature rarely occur in children with cervical lymphadenitis.^6,8 In this context, deferring initial imaging in this population is unlikely to lead to adverse outcomes and may reduce radiation exposure.

The overall objectives of this study are to describe hospital-level variation in imaging practices for pediatric cervical lymphadenitis and to examine the association between early imaging and outcomes in this population.

METHODS

Study Design and Data Source

We conducted a multicenter, cross-sectional study using the Pediatric Health Information Systems (PHIS) database, which contains administrative and billing data from 49 geographically diverse children’s hospitals across the United States (US) affiliated with the Children’s Hospital Association (Lenexa, Kansas). PHIS includes data on patient demographics, discharge diagnoses, and procedures using the International Classification of Diseases, 9th (ICD-9) and 10th Revision (ICD-10) diagnosis codes, as well as daily billed resource utilization for laboratory tests, imaging studies, and medications. Encrypted medical record numbers permit longitudinal identification of children across multiple visits to the same hospital. Use of de-identified PHIS data was deemed to be nonhuman subjects research; our approach to validation of ICD codes using local electronic medical record review was reviewed and approved by the Cincinnati Children’s Hospital Medical Center Institutional Review Board.

Study Population

Our study team developed an algorithm to identify children with cervical lymphadenitis and minimize misclassification using PHIS (Appendix A). All children with lymphadenitis-related ICD-9 and ICD-10 discharge diagnosis codes were eligible for inclusion. Codes were validated at a single center via electronic medical record review; clinician-documented discharge diagnosis of cervical lymphadenitis or the presence of fever and unilateral or asymmetrical neck swelling with overlying skin changes was used as the reference standard. We then excluded children who did not receive antibiotics, children who received radiologic imaging not involving the head or neck (which suggested noncervical lymphadenitis or other illness), and children who had discharge diagnosis codes for other specified conditions that are sometimes associated with enlarged cervical lymph nodes but warrant different evaluation or treatment (eg, Kawasaki disease, retropharyngeal abscess, and dental abscess; Appendix A). Our final algorithm yielded a positive predictive value of 87.5% (95% CI: 79.2%-93.4%) when ICD-9 codes were considered, and 95.1% (95% CI: 88.9%-98.4%) when ICD-10 codes were considered (Appendix A).

This algorithm was subsequently applied to the PHIS database. Children ages two months to 18 years hospitalized at participating PHIS institutions between July 2013 and December 2017 with a diagnosis of cervical lymphadenitis as per the algorithm (Appendix A) were eligible for inclusion. For children with multiple eligible admissions during the study period, we only included the first hospitalization. Children with complex chronic condition diagnosis codes⁹ were excluded as their clinical complexity could influence decisions around timing and modality of diagnostic imaging. In addition, we excluded children who did not have an emergency department (ED) visit associated with their hospitalization. This step was intended to exclude children who were transferred from another institution, as imaging performed at outside institutions prior to transfer is not available in PHIS. To avoid overinflating hospital-level variation in the setting of a small sample size, we also excluded all children admitted to the five hospitals with fewer than 50 cases of cervical lymphadenitis during the study period. Our final cohort consisted of 44 PHIS hospitals.

Measures of Interest

To examine hospital-level variation in imaging practices, we measured the proportion of children at each hospital who underwent any neck imaging study, CT or ultrasound imaging, early imaging, and multiple imaging studies within a single hospitalization. Neck imaging was defined as the presence of a billing code for ultrasound, CT, or magnetic resonance imaging (MRI) study of the neck (Appendix B). Early imaging was defined as neck imaging conducted on day 0 of hospitalization (ie, calendar day of admission and ending at midnight). Multiple imaging studies were defined as the receipt of more than one imaging study, regardless of timing or modality. We also measured the proportion of children by hospital who received surgical drainage, defined by the presence of procedure codes for incision and drainage of abscess of the neck (Appendix B).

In examining patient-level association between early imaging and clinical outcomes, our primary outcome of interest was the receipt of multiple imaging studies. Secondary outcomes included rates of surgical drainage, length of stay (in hospital days), and rates of lymphadenitis-related hospital readmission within 30 days of index discharge.

Covariates

Baseline demographic characteristics included age, gender, race/ethnicity, and insurance type. We measured ED visits associated with lymphadenitis-related diagnosis codes in the 30 days prior to admission as a proxy measure for illness duration prior to presentation. To approximate illness severity, we included the following covariates: rates of intensive care unit admission on presentation, rates of receipt of intravenous (IV) analgesia (Appendix B) on hospital days prior to surgical drainage, and rates of receipt of broad-spectrum antibiotics on day 0 or 1 of hospitalization. Broad-spectrum antibiotics (Appendix B) were defined by an independent three-person review of available antibiotic codes (SD, SSS, and JT); differences were resolved by group consensus.

 

Analysis

Categorical variables were described using frequencies and percentages, while continuous data were described using median and interquartile range. We described hospital-level variation in imaging practices by calculating and comparing the proportion of children at each hospital who underwent any neck imaging study, CT imaging, ultrasound imaging, early imaging, multiple imaging studies, and surgical drainage.

Patient-level demographics and clinical characteristics were compared across groups using chi-square test. To examine the association between early imaging and outcomes, we used generalized linear or logistic mixed effects models to control for patient demographic characteristics and clinical markers of illness duration and severity, with a random effect for hospital to account for clustering. Patient demographics in the model defined a priori included age, race/ethnicity, and insurance type; clinical characteristics included prior ED visit for lymphadenitis, initial intensive care unit (ICU) admission, use of IV analgesia, and use of broad-spectrum antibiotics on day 0 or 1 of hospitalization. To assess the potential for misclassification related to the availability of calendar day but not time of imaging in PHIS, we conducted a secondary analysis to examine the patient-level association between early imaging and outcomes using an alternative definition for early imaging (defined as imaging conducted on day 0 or day 1 of hospitalization).

All statistical analyses were performed by using SAS version 9.4 (SAS Institute, Cary, North Carolina); P < .05 was considered statistically significant.

RESULTS

We identified 19,785 PHIS hospitalizations with lymphadenitis-related discharge diagnosis codes between July 1, 2013 and December 31, 2017. Applying our algorithm and exclusion criteria, we assembled a cohort of 10,014 children hospitalized with cervical lymphadenitis (Figure 1). Two-thirds of the children in our cohort were <4 years old, 42% were non-Hispanic white, and 63% had a government payor (Table 1). Neck imaging (ultrasound, CT, or MRI) was conducted in 8,103 (81%) children. CT imaging was performed in 4,097 (41%) of children, and early imaging was conducted in 6,111 (61%) of children with cervical lymphadenitis.

We noted hospital-level variation in rates of any neck imaging (median: 82.1%, interquartile range [IQR]: 77.7%-85.5%, full range: 68.7%-93.1%), CT imaging (median: 42.3%, IQR: 26.7%-55.2%, full range: 12.0%-81.5%), early imaging (median: 64.4%, IQR: 59.8%-68.4%, full range: 13.8%-76.9%), and multiple imaging studies (median: 23.7%, IQR: 18.6%-28.9%, full range: 1.2%-40.7%; Figure 2). Rates of surgical drainage also varied by hospital (median: 35.1%, IQR: 31.3%-42.0%, full range: 17.1%-54.5%).

At the patient level, children who received early imaging were more likely to be <1 year old (21% vs 16%, P < .001), or Hispanic or Black when compared with children who did not receive early imaging (Table 1). Children who received early imaging were more likely to have had an ED visit for lymphadenitis in the preceding 30 days (8% vs 6%, P = .001). However, they were less likely to have received broad-spectrum antibiotics on admission (6% vs 8%, P < .001; Table 1). Of the 6,111 patients who received early imaging, 2,538 (41.5%) received CT imaging and 3,902 (63.9%) received ultrasound imaging on day 0. Of the 2,272 patients receiving multiple imaging studies, 116 (5.1%) received two or more CT scans.

In multivariable analysis at the patient level, early imaging was associated with higher adjusted odds of receiving multiple imaging studies (adjusted odds ratio [aOR] 3.0, 95% CI: 2.6-3.6). Similarly, early imaging was associated with higher adjusted odds of surgical drainage (aOR: 1.3, 95% CI: 1.1-1.4), increased 30-day readmission for lymphadenitis (aOR: 1.5, 95% CI: 1.2-1.9), and longer length of stay (adjusted rate ratio: 1.2, 95% CI: 1.1-1.2; Table 2). For the subset of patients who did not receive surgical drainage during the index admission, the adjusted odds ratio for the association between early imaging at index admission and 30-day readmission was 1.7 (95% CI: 1.3-2.1). About 63% of readmissions occurred within 7 days of index discharge; 89% occurred within 14 days (Appendix Figure).

In secondary analysis using an alternative definition for early imaging (ie, imaging conducted on day 0 or day 1 of hospitalization), the adjusted odds ratio for multiple imaging studies was 22.6 (95% CI: 15.8-32.4). The adjusted odds and rate ratios for the remaining outcomes were similar to our primary analysis.

 

DISCUSSION

In this large multicenter study of children with cervical lymphadenitis, we found variation in imaging practices across 44 US children’s hospitals. Children with cervical lymphadenitis who underwent early imaging were more likely to receive multiple imaging studies during a single hospitalization than those who did not receive early imaging. At the patient level, early imaging was also associated with higher rates of surgical drainage, more frequent 30-day readmission, and longer lengths of stay.

To our knowledge, imaging practices in the population of children hospitalized with cervical lymphadenitis have not been previously characterized in the US; one study from Atlanta, Georgia, describes imaging practices in all children evaluated in the ED.¹ Single-center studies of children hospitalized with cervical lymphadenitis have been previously conducted in Canada⁶ and New Zealand,⁸ in which 42%-51% of children received imaging. In our study, most (81%) children hospitalized with lymphadenitis received some form of imaging, with 61% of all children receiving early imaging. Furthermore, 41% received CT imaging, as compared with 8%-10% of children in the aforementioned studies from Canada and New Zealand.^6,8 This finding is consistent with a pattern of imaging overuse in the US, which has amongst the highest utilization rates globally for advanced imaging such as CT and MRI.^10,11 Identifying opportunities to safely reduce routine imaging, particularly CT imaging, in this population could decrease unnecessary radiation exposure without compromising outcomes.

We also noted variability in imaging practices across PHIS hospitals. Some of this variability may be partially explained by differences in the patient population or illness severity across hospitals. However, given the absence of evidence-based best practices for children with cervical lymphadenitis, clinicians may rely on anecdotal experience or local practice culture to guide their decision making,¹² leading to variability in frequency, timing, and modality of imaging.

At the patient level, we found that children who received early imaging were more likely to receive multiple imaging studies. This finding supports our hypothesis that clinicians often order a second imaging study when the initial imaging study does not clearly demonstrate an abscess, and the child subsequently fails to demonstrate clear improvement after 24-48 hours of antibiotics.

Furthermore, early imaging was associated with overall increased utilization in our cohort, including increased likelihood of surgical drainage, 30-day readmission for lymphadenitis, as well as longer lengths of stay. Confounding may be one explanation for this finding. For instance, clinicians may pursue early imaging in children who present with longer duration of symptoms or more severe illness on presentation, as these factors may be associated with abscess formation.^1,6,7 These clinical covariates are not available in PHIS. Thus, we used prior ED visits for lymphadenitis to approximate illness duration, and initial admission to ICU, receipt of IV analgesia, and receipt of broad-spectrum antibiotics to approximate illness severity in an attempt to mitigate confounding. However, our proxy measures may not appropriately estimate illness duration and severity. For instance, children who had urgent care or outpatient visits for lymphadenitis would not be captured using the proxy of prior ED visit for lymphadenitis. Similarly, use of broad-spectrum antibiotics and IV analgesia may be influenced by provider or institutional preference rather than illness severity. Thus, residual confounding may exist despite adjusting for these measures.

On the other hand, it is also possible that a proportion of children with a small fluid collection on imaging may have improved with antibiotics alone. There is a growing body of evidence in children with other head and neck infections (eg, retropharyngeal abscess and orbital cellulitis with periosteal abscess)^13-15 that suggests that children with small abscesses often improve with antibiotic therapy alone. In children with cervical lymphadenitis who have small or developing abscesses identified via routine imaging on presentation, clinicians may be driven to pursue a surgical intervention with uncertain benefit. Deferring routine imaging in this population may provide an opportunity to improve the value of care in children with lymphadenitis without adversely affecting outcomes.

Upon closer examination of readmissions, children who received early imaging during index hospitalization were more likely to have a 30-day readmission when only evaluating the subset of patients who did not receive surgical drainage during the index admission. This suggests that readmissions are less likely attributable to surgical complications and more likely a reflection of the natural history of lymphadenitis in which a subset of patients eventually develop an abscess. Further supporting this, 61% of children who had a 30-day readmission for lymphadenitis underwent surgical drainage during readmission. Given that lymphadenitis is a slow-brewing infection in which serious complications are rare, patients who demonstrate gradual clinical improvement do not need to remain hospitalized and serially imaged to identify a possible abscess. Outpatient expectant management and readmission as needed for drainage may be an acceptable approach.

This study has several limitations given our use of an administrative database. Children with lymphadenitis may have been misclassified as these patients were identified using discharge diagnosis codes. To mitigate this potential misclassification, we conducted a structured validation process and found that the included codes had high positive predictive values (Appendix A). This validation process was conducted at a single hospital, and coding may vary across hospitals. To approximate sensitivity, we also sampled children without our included codes but with neck imaging and antibiotic use, and found that rates of cervical lymphadenitis were very low among children without our included diagnosis codes.

Furthermore, we were unable to measure the exact time of imaging study in PHIS; we used imaging conducted on hospital day 0 as a proxy measure for imaging conducted within the first 24 hours of presentation. With this definition, some children who had early imaging were likely misclassified as not having received early imaging. For example, a patient who arrived in the ED at 9 pm on day 0 of admission and had a neck ultrasound performed at 1 am would be classified as having had an imaging study on day 1 of hospitalization even though the imaging study was conducted within 4 hours of presentation. Using an alternative definition of early imaging as imaging conducted on hospital day 0 and day 1, we found a much higher adjusted OR for multiple imaging studies, with similar associations for secondary outcomes. As such, our definition of early imaging as day 0 likely biases the results toward the null; the true increase in likelihood of multiple imaging for those who receive early imaging is probably greater than our conservative estimation.

Additionally, there may be a subset of children who underwent imaging prior to presentation at the PHIS hospital ED for further workup and admission. Imaging conducted outside a PHIS hospital was not captured in this database. Similarly, children who had a readmission at a different hospital than their index admission would not be captured using PHIS. Finally, PHIS captures data from children’s hospitals; practices at these hospitals may not be generalizable to practices in the community hospital setting.

 

CONCLUSION

In conclusion, we found that imaging practices in children hospitalized with cervical lymphadenitis were widely variable across hospitals. Children receiving early imaging had more resource utilization and intervention when compared with children who did not receive early imaging. Our findings may represent a cascade effect, in which routinely conducted early imaging prompts clinicians to pursue more testing and interventions in this population. Future studies should obtain more detailed patient level covariates to further characterize clinical factors that may impact decisions around imaging and clinical outcomes for children with cervical lymphadenitis.

Acknowledgments

The authors would like to acknowledge the following investigators for their contributions to data interpretation and review of the final manuscript: Angela Choe MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Margaret Rush MD, Children’s National Medical Center, Washington, DC; Ryosuke Takei MD, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania; Wallis Molchen DO, Texas Children’s Hospital, Houston, Texas; Stephanie Royer Moss MD, Cleveland Clinic, Cleveland, Ohio; Rebecca Dang, MD, Lucile Packard Children’s Hospital Stanford, Palo Alto, California; Joy Solano MD, Children’s Mercy Hospital Kansas, Overland Park, Kansas; Nathaniel P. Goodrich MD, Children’s Hospital & Medical Center, Omaha, Nebraska; Ngozi Eboh MD, Texas Tech University Health Sciences Center, Dallas, Texas; Ashley Jenkins MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Rebecca Steuart MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Sonya Tang Girdwood MD, PhD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Alissa McInerney MD, Maria Fareri Children’s Hospital at Westchester Medical Center, Valhalla, New York; Sumeet Banker MD, MPH, New York Presbyterian Morgan Stanley Children’s Hospital, New York, New York; Corrie McDaniel DO, Seattle Children’s Hospital, Seattle, Washington; Christiane Lenzen MD, Rady Children’s Hospital, San Diego, California; Aleisha Nabower MD, Children’s Hospital & Medical Center, Omaha, Nebraska; Waheeda Samady MD, Ann & Robert H. Lurie Children’s Hospital, Chicago, Illinois; Jennifer Chen MD, Rady Children’s Hospital, San Diego, California; Marquita Genies MD, MPH, John’s Hopkins Children’s Center, Baltimore, Maryland; Justin Lockwood MD, Children’s Hospital Colorado, Aurora, Colorado; David Synhorst MD, Children’s Mercy Hospital Kansas, Overland Park, Kansas.

Cervical lymphadenitis is a common superficial neck infection in childhood. While most children with cervical lymphadenitis recover with antibiotic therapy, a subset can develop an abscess that may require surgical drainage. Radiologic imaging, most commonly ultrasound or computed tomography (CT), is often performed to identify such an abscess.^1-3 However, no national standards exist to guide clinician decision making around imaging in this population. In the absence of evidence-based guidelines, variability in frequency, timing, and modality of imaging likely exists in children hospitalized with cervical lymphadenitis.

As demonstrated for several other common pediatric conditions,^4,5 variability in imaging practices may contribute to overutilization of resources in children with cervical lymphadenitis. In particular, routinely conducting imaging on presentation may constitute overuse, as children with cervical lymphadenitis who present with less than 72 hours of neck swelling rarely undergo surgical drainage within the first 24 hours of hospitalization.^1,6,7 Imaging performed on presentation is often repeated later during hospitalization, particularly if the patient has not improved with antibiotic therapy. The net result may be unnecessary, redundant radiologic studies. Furthermore, serious complications such as bacteremia, extension of infection into the retropharyngeal space, or involvement of the airway or vasculature rarely occur in children with cervical lymphadenitis.^6,8 In this context, deferring initial imaging in this population is unlikely to lead to adverse outcomes and may reduce radiation exposure.

The overall objectives of this study are to describe hospital-level variation in imaging practices for pediatric cervical lymphadenitis and to examine the association between early imaging and outcomes in this population.

METHODS

Study Design and Data Source

We conducted a multicenter, cross-sectional study using the Pediatric Health Information Systems (PHIS) database, which contains administrative and billing data from 49 geographically diverse children’s hospitals across the United States (US) affiliated with the Children’s Hospital Association (Lenexa, Kansas). PHIS includes data on patient demographics, discharge diagnoses, and procedures using the International Classification of Diseases, 9th (ICD-9) and 10th Revision (ICD-10) diagnosis codes, as well as daily billed resource utilization for laboratory tests, imaging studies, and medications. Encrypted medical record numbers permit longitudinal identification of children across multiple visits to the same hospital. Use of de-identified PHIS data was deemed to be nonhuman subjects research; our approach to validation of ICD codes using local electronic medical record review was reviewed and approved by the Cincinnati Children’s Hospital Medical Center Institutional Review Board.

Study Population

Our study team developed an algorithm to identify children with cervical lymphadenitis and minimize misclassification using PHIS (Appendix A). All children with lymphadenitis-related ICD-9 and ICD-10 discharge diagnosis codes were eligible for inclusion. Codes were validated at a single center via electronic medical record review; clinician-documented discharge diagnosis of cervical lymphadenitis or the presence of fever and unilateral or asymmetrical neck swelling with overlying skin changes was used as the reference standard. We then excluded children who did not receive antibiotics, children who received radiologic imaging not involving the head or neck (which suggested noncervical lymphadenitis or other illness), and children who had discharge diagnosis codes for other specified conditions that are sometimes associated with enlarged cervical lymph nodes but warrant different evaluation or treatment (eg, Kawasaki disease, retropharyngeal abscess, and dental abscess; Appendix A). Our final algorithm yielded a positive predictive value of 87.5% (95% CI: 79.2%-93.4%) when ICD-9 codes were considered, and 95.1% (95% CI: 88.9%-98.4%) when ICD-10 codes were considered (Appendix A).

This algorithm was subsequently applied to the PHIS database. Children ages two months to 18 years hospitalized at participating PHIS institutions between July 2013 and December 2017 with a diagnosis of cervical lymphadenitis as per the algorithm (Appendix A) were eligible for inclusion. For children with multiple eligible admissions during the study period, we only included the first hospitalization. Children with complex chronic condition diagnosis codes⁹ were excluded as their clinical complexity could influence decisions around timing and modality of diagnostic imaging. In addition, we excluded children who did not have an emergency department (ED) visit associated with their hospitalization. This step was intended to exclude children who were transferred from another institution, as imaging performed at outside institutions prior to transfer is not available in PHIS. To avoid overinflating hospital-level variation in the setting of a small sample size, we also excluded all children admitted to the five hospitals with fewer than 50 cases of cervical lymphadenitis during the study period. Our final cohort consisted of 44 PHIS hospitals.

Measures of Interest

To examine hospital-level variation in imaging practices, we measured the proportion of children at each hospital who underwent any neck imaging study, CT or ultrasound imaging, early imaging, and multiple imaging studies within a single hospitalization. Neck imaging was defined as the presence of a billing code for ultrasound, CT, or magnetic resonance imaging (MRI) study of the neck (Appendix B). Early imaging was defined as neck imaging conducted on day 0 of hospitalization (ie, calendar day of admission and ending at midnight). Multiple imaging studies were defined as the receipt of more than one imaging study, regardless of timing or modality. We also measured the proportion of children by hospital who received surgical drainage, defined by the presence of procedure codes for incision and drainage of abscess of the neck (Appendix B).

In examining patient-level association between early imaging and clinical outcomes, our primary outcome of interest was the receipt of multiple imaging studies. Secondary outcomes included rates of surgical drainage, length of stay (in hospital days), and rates of lymphadenitis-related hospital readmission within 30 days of index discharge.

Covariates

Baseline demographic characteristics included age, gender, race/ethnicity, and insurance type. We measured ED visits associated with lymphadenitis-related diagnosis codes in the 30 days prior to admission as a proxy measure for illness duration prior to presentation. To approximate illness severity, we included the following covariates: rates of intensive care unit admission on presentation, rates of receipt of intravenous (IV) analgesia (Appendix B) on hospital days prior to surgical drainage, and rates of receipt of broad-spectrum antibiotics on day 0 or 1 of hospitalization. Broad-spectrum antibiotics (Appendix B) were defined by an independent three-person review of available antibiotic codes (SD, SSS, and JT); differences were resolved by group consensus.

 

Analysis

Categorical variables were described using frequencies and percentages, while continuous data were described using median and interquartile range. We described hospital-level variation in imaging practices by calculating and comparing the proportion of children at each hospital who underwent any neck imaging study, CT imaging, ultrasound imaging, early imaging, multiple imaging studies, and surgical drainage.

Patient-level demographics and clinical characteristics were compared across groups using chi-square test. To examine the association between early imaging and outcomes, we used generalized linear or logistic mixed effects models to control for patient demographic characteristics and clinical markers of illness duration and severity, with a random effect for hospital to account for clustering. Patient demographics in the model defined a priori included age, race/ethnicity, and insurance type; clinical characteristics included prior ED visit for lymphadenitis, initial intensive care unit (ICU) admission, use of IV analgesia, and use of broad-spectrum antibiotics on day 0 or 1 of hospitalization. To assess the potential for misclassification related to the availability of calendar day but not time of imaging in PHIS, we conducted a secondary analysis to examine the patient-level association between early imaging and outcomes using an alternative definition for early imaging (defined as imaging conducted on day 0 or day 1 of hospitalization).

All statistical analyses were performed by using SAS version 9.4 (SAS Institute, Cary, North Carolina); P < .05 was considered statistically significant.

RESULTS

We identified 19,785 PHIS hospitalizations with lymphadenitis-related discharge diagnosis codes between July 1, 2013 and December 31, 2017. Applying our algorithm and exclusion criteria, we assembled a cohort of 10,014 children hospitalized with cervical lymphadenitis (Figure 1). Two-thirds of the children in our cohort were <4 years old, 42% were non-Hispanic white, and 63% had a government payor (Table 1). Neck imaging (ultrasound, CT, or MRI) was conducted in 8,103 (81%) children. CT imaging was performed in 4,097 (41%) of children, and early imaging was conducted in 6,111 (61%) of children with cervical lymphadenitis.

We noted hospital-level variation in rates of any neck imaging (median: 82.1%, interquartile range [IQR]: 77.7%-85.5%, full range: 68.7%-93.1%), CT imaging (median: 42.3%, IQR: 26.7%-55.2%, full range: 12.0%-81.5%), early imaging (median: 64.4%, IQR: 59.8%-68.4%, full range: 13.8%-76.9%), and multiple imaging studies (median: 23.7%, IQR: 18.6%-28.9%, full range: 1.2%-40.7%; Figure 2). Rates of surgical drainage also varied by hospital (median: 35.1%, IQR: 31.3%-42.0%, full range: 17.1%-54.5%).

At the patient level, children who received early imaging were more likely to be <1 year old (21% vs 16%, P < .001), or Hispanic or Black when compared with children who did not receive early imaging (Table 1). Children who received early imaging were more likely to have had an ED visit for lymphadenitis in the preceding 30 days (8% vs 6%, P = .001). However, they were less likely to have received broad-spectrum antibiotics on admission (6% vs 8%, P < .001; Table 1). Of the 6,111 patients who received early imaging, 2,538 (41.5%) received CT imaging and 3,902 (63.9%) received ultrasound imaging on day 0. Of the 2,272 patients receiving multiple imaging studies, 116 (5.1%) received two or more CT scans.

In multivariable analysis at the patient level, early imaging was associated with higher adjusted odds of receiving multiple imaging studies (adjusted odds ratio [aOR] 3.0, 95% CI: 2.6-3.6). Similarly, early imaging was associated with higher adjusted odds of surgical drainage (aOR: 1.3, 95% CI: 1.1-1.4), increased 30-day readmission for lymphadenitis (aOR: 1.5, 95% CI: 1.2-1.9), and longer length of stay (adjusted rate ratio: 1.2, 95% CI: 1.1-1.2; Table 2). For the subset of patients who did not receive surgical drainage during the index admission, the adjusted odds ratio for the association between early imaging at index admission and 30-day readmission was 1.7 (95% CI: 1.3-2.1). About 63% of readmissions occurred within 7 days of index discharge; 89% occurred within 14 days (Appendix Figure).

In secondary analysis using an alternative definition for early imaging (ie, imaging conducted on day 0 or day 1 of hospitalization), the adjusted odds ratio for multiple imaging studies was 22.6 (95% CI: 15.8-32.4). The adjusted odds and rate ratios for the remaining outcomes were similar to our primary analysis.

 

DISCUSSION

In this large multicenter study of children with cervical lymphadenitis, we found variation in imaging practices across 44 US children’s hospitals. Children with cervical lymphadenitis who underwent early imaging were more likely to receive multiple imaging studies during a single hospitalization than those who did not receive early imaging. At the patient level, early imaging was also associated with higher rates of surgical drainage, more frequent 30-day readmission, and longer lengths of stay.

To our knowledge, imaging practices in the population of children hospitalized with cervical lymphadenitis have not been previously characterized in the US; one study from Atlanta, Georgia, describes imaging practices in all children evaluated in the ED.¹ Single-center studies of children hospitalized with cervical lymphadenitis have been previously conducted in Canada⁶ and New Zealand,⁸ in which 42%-51% of children received imaging. In our study, most (81%) children hospitalized with lymphadenitis received some form of imaging, with 61% of all children receiving early imaging. Furthermore, 41% received CT imaging, as compared with 8%-10% of children in the aforementioned studies from Canada and New Zealand.^6,8 This finding is consistent with a pattern of imaging overuse in the US, which has amongst the highest utilization rates globally for advanced imaging such as CT and MRI.^10,11 Identifying opportunities to safely reduce routine imaging, particularly CT imaging, in this population could decrease unnecessary radiation exposure without compromising outcomes.

We also noted variability in imaging practices across PHIS hospitals. Some of this variability may be partially explained by differences in the patient population or illness severity across hospitals. However, given the absence of evidence-based best practices for children with cervical lymphadenitis, clinicians may rely on anecdotal experience or local practice culture to guide their decision making,¹² leading to variability in frequency, timing, and modality of imaging.

At the patient level, we found that children who received early imaging were more likely to receive multiple imaging studies. This finding supports our hypothesis that clinicians often order a second imaging study when the initial imaging study does not clearly demonstrate an abscess, and the child subsequently fails to demonstrate clear improvement after 24-48 hours of antibiotics.

Furthermore, early imaging was associated with overall increased utilization in our cohort, including increased likelihood of surgical drainage, 30-day readmission for lymphadenitis, as well as longer lengths of stay. Confounding may be one explanation for this finding. For instance, clinicians may pursue early imaging in children who present with longer duration of symptoms or more severe illness on presentation, as these factors may be associated with abscess formation.^1,6,7 These clinical covariates are not available in PHIS. Thus, we used prior ED visits for lymphadenitis to approximate illness duration, and initial admission to ICU, receipt of IV analgesia, and receipt of broad-spectrum antibiotics to approximate illness severity in an attempt to mitigate confounding. However, our proxy measures may not appropriately estimate illness duration and severity. For instance, children who had urgent care or outpatient visits for lymphadenitis would not be captured using the proxy of prior ED visit for lymphadenitis. Similarly, use of broad-spectrum antibiotics and IV analgesia may be influenced by provider or institutional preference rather than illness severity. Thus, residual confounding may exist despite adjusting for these measures.

On the other hand, it is also possible that a proportion of children with a small fluid collection on imaging may have improved with antibiotics alone. There is a growing body of evidence in children with other head and neck infections (eg, retropharyngeal abscess and orbital cellulitis with periosteal abscess)^13-15 that suggests that children with small abscesses often improve with antibiotic therapy alone. In children with cervical lymphadenitis who have small or developing abscesses identified via routine imaging on presentation, clinicians may be driven to pursue a surgical intervention with uncertain benefit. Deferring routine imaging in this population may provide an opportunity to improve the value of care in children with lymphadenitis without adversely affecting outcomes.

Upon closer examination of readmissions, children who received early imaging during index hospitalization were more likely to have a 30-day readmission when only evaluating the subset of patients who did not receive surgical drainage during the index admission. This suggests that readmissions are less likely attributable to surgical complications and more likely a reflection of the natural history of lymphadenitis in which a subset of patients eventually develop an abscess. Further supporting this, 61% of children who had a 30-day readmission for lymphadenitis underwent surgical drainage during readmission. Given that lymphadenitis is a slow-brewing infection in which serious complications are rare, patients who demonstrate gradual clinical improvement do not need to remain hospitalized and serially imaged to identify a possible abscess. Outpatient expectant management and readmission as needed for drainage may be an acceptable approach.

This study has several limitations given our use of an administrative database. Children with lymphadenitis may have been misclassified as these patients were identified using discharge diagnosis codes. To mitigate this potential misclassification, we conducted a structured validation process and found that the included codes had high positive predictive values (Appendix A). This validation process was conducted at a single hospital, and coding may vary across hospitals. To approximate sensitivity, we also sampled children without our included codes but with neck imaging and antibiotic use, and found that rates of cervical lymphadenitis were very low among children without our included diagnosis codes.

Furthermore, we were unable to measure the exact time of imaging study in PHIS; we used imaging conducted on hospital day 0 as a proxy measure for imaging conducted within the first 24 hours of presentation. With this definition, some children who had early imaging were likely misclassified as not having received early imaging. For example, a patient who arrived in the ED at 9 pm on day 0 of admission and had a neck ultrasound performed at 1 am would be classified as having had an imaging study on day 1 of hospitalization even though the imaging study was conducted within 4 hours of presentation. Using an alternative definition of early imaging as imaging conducted on hospital day 0 and day 1, we found a much higher adjusted OR for multiple imaging studies, with similar associations for secondary outcomes. As such, our definition of early imaging as day 0 likely biases the results toward the null; the true increase in likelihood of multiple imaging for those who receive early imaging is probably greater than our conservative estimation.

Additionally, there may be a subset of children who underwent imaging prior to presentation at the PHIS hospital ED for further workup and admission. Imaging conducted outside a PHIS hospital was not captured in this database. Similarly, children who had a readmission at a different hospital than their index admission would not be captured using PHIS. Finally, PHIS captures data from children’s hospitals; practices at these hospitals may not be generalizable to practices in the community hospital setting.

 

CONCLUSION

In conclusion, we found that imaging practices in children hospitalized with cervical lymphadenitis were widely variable across hospitals. Children receiving early imaging had more resource utilization and intervention when compared with children who did not receive early imaging. Our findings may represent a cascade effect, in which routinely conducted early imaging prompts clinicians to pursue more testing and interventions in this population. Future studies should obtain more detailed patient level covariates to further characterize clinical factors that may impact decisions around imaging and clinical outcomes for children with cervical lymphadenitis.

Acknowledgments

The authors would like to acknowledge the following investigators for their contributions to data interpretation and review of the final manuscript: Angela Choe MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Margaret Rush MD, Children’s National Medical Center, Washington, DC; Ryosuke Takei MD, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania; Wallis Molchen DO, Texas Children’s Hospital, Houston, Texas; Stephanie Royer Moss MD, Cleveland Clinic, Cleveland, Ohio; Rebecca Dang, MD, Lucile Packard Children’s Hospital Stanford, Palo Alto, California; Joy Solano MD, Children’s Mercy Hospital Kansas, Overland Park, Kansas; Nathaniel P. Goodrich MD, Children’s Hospital & Medical Center, Omaha, Nebraska; Ngozi Eboh MD, Texas Tech University Health Sciences Center, Dallas, Texas; Ashley Jenkins MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Rebecca Steuart MD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Sonya Tang Girdwood MD, PhD, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; Alissa McInerney MD, Maria Fareri Children’s Hospital at Westchester Medical Center, Valhalla, New York; Sumeet Banker MD, MPH, New York Presbyterian Morgan Stanley Children’s Hospital, New York, New York; Corrie McDaniel DO, Seattle Children’s Hospital, Seattle, Washington; Christiane Lenzen MD, Rady Children’s Hospital, San Diego, California; Aleisha Nabower MD, Children’s Hospital & Medical Center, Omaha, Nebraska; Waheeda Samady MD, Ann & Robert H. Lurie Children’s Hospital, Chicago, Illinois; Jennifer Chen MD, Rady Children’s Hospital, San Diego, California; Marquita Genies MD, MPH, John’s Hopkins Children’s Center, Baltimore, Maryland; Justin Lockwood MD, Children’s Hospital Colorado, Aurora, Colorado; David Synhorst MD, Children’s Mercy Hospital Kansas, Overland Park, Kansas.

Sepsis is defined as a life-threatening organ dysfunction that occurs in response to systemic infection.^1,2 It is frequently fatal, common in hospital medicine, and a leading contributor to critical illness, morbidity, and healthcare expenditures.^2-5 While sepsis care and outcomes have improved in the past decade,^6,7 inpatient mortality remains high.⁸

A number of studies have sought to determine whether race plays a role in sepsis mortality. While Black patients with sepsis have frequently been identified as having the highest rates of death,^9-14 similar observations have been made for most non-White races/ethnicities.^13-15 Studies have also demonstrated higher rates of hospital-acquired infections among Asian and Latino patients.¹⁶

There are several possible explanations for why racial minorities experience disparate outcomes in sepsis, including access to care, comorbidities, implicit biases, and biological or environmental factors,^17-20 as well as characteristics of hospitals most likely to care for racial minorities.^13,15,21 One explanation that has not been explored is that racial disparities in sepsis are mediated by language. Limited English proficiency (LEP) has previously been associated with increased rates of adverse hospital events,²² longer length of stay,²³ and greater likelihood of readmission.²⁴ LEP has also been shown to represent a significant barrier to accessing healthcare and preventive screening.²⁵ The role of LEP in sepsis mortality, however, has yet to be examined.

The diverse patient population at the University of California, San Francisco (UCSF) provides a unique opportunity to build upon existing literature by further exploring racial differences in sepsis, specifically by investigating the role of LEP. The objective of this study was to determine the association between LEP and inpatient mortality among adults hospitalized with sepsis.

METHODS

Setting

The study was conducted at the University of California, San Francisco, California (UCSF), an 800-bed tertiary care, academic medical center. It was approved by the UCSF Institutional Review Board with waiver of informed consent. UCSF cares for a population of patients who are racially and linguistically diverse, with high proportions of patients of East Asian descent and with LEP. According to recent United States census estimates, more than half of San Francisco County residents identify as non-White (35% Asians, 15% Hispanic/Latino, 6% Black), and 44% report speaking a language other than English at home.²⁶

Study Population and Data Collection

The UCSF Medical Center uses the electronic health record (EHR) Epic (Epic 2017, Epic Systems Corporation, Verona, Wisconsin). We obtained computerized EHR data from Clarity, the relational database that stores Epic’s inpatient data in thousands of tables. We identified all patients ≥18 years of age presenting to the emergency department (ED) between June 1, 2012 and December 31, 2016 with suspected serious infection, defined as having blood cultures ordered within 72 hours of ED presentation (N = 25,441). Patients who did not receive at least two doses of intravenous (IV) antibiotics within 48 hours were excluded, as they were unlikely to have serious infections.

 

We defined sepsis based on Sepsis-3 consensus guidelines² as a change in sequential [sepsis-related] organ failure assessment (SOFA) score ≥2 within the first 48 hours of ED presentation. The SOFA score is comprised of six variables representing different organ systems, each rated 0-4 based on the degree of dysfunction.² Patient vital signs, laboratory data, vasopressor medication doses, and ventilator settings were used to determine the exact timestamp at which each patient attained a change in SOFA score ≥2. Missing values were considered to be normal. To adjust for baseline organ dysfunction, SOFA elements associated with elevated bilirubin and/or creatinine were excluded for patients with chronic liver/kidney disease based on Elixhauser comorbidities.²⁷ We chose to focus on the first 48 hours in an attempt to capture patients with the most severe illnesses and the highest probability of true sepsis.

All primary and secondary International Classification of Diseases (ICD)-9/10 diagnosis codes were extracted from Clarity coding tables at the time of hospital discharge. Diagnosis codes signifying bacterial infection were grouped into the following categories based on type/location: pneumonia; bacteremia; urinary tract infection; and skin and soft tissue infection. All remaining diagnostic codes indicating bacterial infections at other sites were categorized as “Other”. If no codes indicating infection were present, patients were categorized as “None coded”. Patients with discharge diagnosis codes of “sepsis” were also identified. Dates and times of antibiotic administrations were obtained from the medications table. Time to first antibiotic was defined as the time in minutes from ED presentation to initiation of the first IV antibacterial medication. This variable was transformed using a natural log transformation based on best fit for normal distribution.

We limited our analyses to 8,974 patients who were diagnosed with sepsis as defined above and had either (1) ≥4 qualifying antibiotic days (QADs) or (2) an ICD-9/10 discharge diagnosis code of “sepsis” (Figure). QADs were defined based on the recent publication by Rhee et al. as having received four or more consecutive days of antibiotics, with the first dose given IV within 48 hours of presentation.²⁸ Patients who died or were discharged to hospice prior to the 4th QAD were also included. These additional parameters were added to increase specificity of the study sample for patients with true sepsis. Patients admitted to all levels of care (acute care, transitional care unit [TCU], intensive care unit [ICU]) and under all hospital services were included. There were no missing data for mortality, race, or language. We chose to focus on patients with sepsis in this initial study as this is a common diagnosis in hospital medicine that is enriched for high mortality.

Primary Outcome

The primary outcome of the study was inpatient mortality, which was obtained from the hospital encounters table in Clarity.

Primary Predictors

The primary predictor of interest was LEP. The encounter numbers from the dataset were used to link to self-reported demographic data, including “preferred language” and need for interpreter services. A manual chart review of 60 patients speaking the top six languages was conducted to verify the accuracy of the data on language and interpreter use (KNK). Defining the gold standard for LEP as having any chart note indicating non-English language and/or that an interpreter was used, the “interpreter needed” variable in Epic was found to have a positive predictive value for LEP of 100%. Therefore, patients in the study cohort were defined as having LEP if they met both of the following criteria: (1) a self-reported “preferred language” other than English and (2) having the “interpreter needed” variable indicating “yes”.

 

Covariate Data Collection

Additional data were obtained from the demographics tables, including age, race, sex, and insurance status. Race and ethnicity were combined into a single five-category variable including White, Asian, Black, Latino, and Other. This approach has been suggested as the best way to operationalize these variables²⁹ and has been utilized by similar studies in the literature.^9,14,15 We considered the Asian race to include all people of East Asian, Southeast Asian, or South Asian descent, which is consistent with the United States Census Bureau definition.³⁰ Patients identifying as Native Hawaiians/Pacific Islanders, Native Americans/Alaskan Natives, as well as those with unspecified race or ethnicity, were categorized as Other. Insurance status was categorized as Commercial, Medicare, Medicaid, or Other.

We estimated illness severity in several ways. First, the total qualifying SOFA score was calculated for each patient, which was defined as the total score achieved at the time that SOFA criteria were first met (≥2, within 48 hours). Second, we dichotomized patients based on whether they had received mechanical ventilation at any point during hospitalization. Finally, we used admission location as a surrogate marker for severity at the time of initial hospitalization.

To estimate the burden of baseline comorbidities, we calculated the van Walraven score (VWS),³¹ a validated modification of the Elixhauser Comorbidity Index.²⁷ This score conveys an estimated risk of in hospital death based on ICD-9/10 diagnosis codes for preexisting conditions, which ranges from <1% for the minimum score of –19 to >99% for the maximum score of 89.

Statistical Analyses

All statistical analyses were performed using Stata software version 15 (StataCorp LLC, College Station, Texas). Baseline demographics and patient characteristics were stratified by LEP. These were compared using two-sample t-tests or chi-squared tests of significance. Wilcoxon rank-sum tests were used for non-normally distributed variables. Inpatient mortality was compared across all races stratified by LEP using chi-squared tests of significance.

We fit a series of multivariable logistic regression models to examine the association between race and inpatient mortality adjusting for LEP and other patient/clinical characteristics. We first examined the unadjusted association between mortality and race; then adjusted for LEP alone; and finally adjusted for all covariates of interest, including LEP, age, sex, insurance status, year, admission level of care, VWS, total qualifying SOFA score, need for mechanical ventilation, site of infection, and time to first IV antibiotic. A subgroup analysis was also performed using the fully adjusted model restricted to patients who were mechanically ventilated. This population was selected because the patients (1) have among the highest severity of illness and (2) share a common barrier to communication, regardless of English proficiency.

Several potential interactions between LEP with other covariates were explored, including age, race, ICU admission level of care, and need for mechanical ventilation. Lastly, a mediation analysis was performed based on Baron & Kenny’s four-step model³² in order to calculate the proportion of the association between race and mortality explained by the proposed mediator (LEP).

To evaluate for the likelihood of residual confounding, we calculated an E-value, which is defined as the minimum strength of association that an unmeasured confounder would need to have with both the predictor and outcome variables, above and beyond the measured covariates, in order to fully explain away an observed predictor-outcome association.^33,34

 

RESULTS

We identified 8,974 patients hospitalized with sepsis based on the above inclusion criteria. This represented a medically complex, racially and linguistically diverse population (Table 1). The cohort was comprised of 24% Asian, 12% Black, and 11% Latino patients. Among those categorized as Other race, Native Americans/Alaskan Natives and Native Hawaiians/Pacific Islanders accounted for 4% (n = 31) and 21% (n = 159), respectively. A fifth of all patients had LEP (n = 1,716), 62% of whom were Asian (n = 1,064). Patients with LEP tended to be older, female, and to have a greater number of comorbid conditions (Table 1). The total qualifying SOFA score was also higher among patients with LEP (median 5; interquartile range [IQR]: 4-8 vs 5; IQR: 3-7; P <.001), though there was no association between LEP and mechanical ventilation (P = .22). The prevalence of LEP differed significantly across races, with 50% LEP among Asians, 32% among Latinos, 5% among White patients (P < .001). Only eight Black patients had LEP. More than 40 unique languages were represented in the cohort, with English, Cantonese, Spanish, Russian, and Mandarin accounting for ~95% (Appendix Table 1). Among Latino patients, 63% spoke English and 36% spoke Spanish.

In-hospital mortality was significantly higher among patients who had LEP (n = 268/1,716, 16%) compared to non-LEP patients (n = 678/7,258, 9%), with 80% greater unadjusted odds of mortality (OR 1.80; 95% CI: 1.54-2.09; P < .001). Notably we also found that Asian race was associated with a 1.57 unadjusted odds of mortality compared to White race (95% CI: 1.34-1.85; P < .001). Age, VWS, total qualifying SOFA score, mechanical ventilation, and admission level of care all exhibited a positive dose-response association with mortality (Appendix Table 2). In unadjusted analyses, there was no evidence of interaction between LEP and age (P = .38), LEP and race (P = .45), LEP and ICU admission level of care (P = .31), or LEP and mechanical ventilation (P = .19). Asian patients had the highest overall mortality (14% total, 17% with LEP). LEP was associated with increased unadjusted mortality among White, Asian, and Other races compared to their non-LEP counterparts (Appendix Figure 1). There was no significant difference in mortality between Latino patients with and without LEP. The sample size for Black patients with LEP (n = 8) was too small to draw conclusions about mortality.

Following multivariable logistic regression modeling for the association between race and mortality, we found that the increased odds of death among Asian patients was partially attenuated after adjusting for LEP (odds ratio [OR] 1.23, 95% CI: 1.02-1.48; P = .03; Table 2). Meanwhile, LEP was associated with a 1.66 odds of mortality (95% CI: 1.38-1.99; P < .001) after adjustment for race. In the full multivariable model adjusting for demographics and clinical characteristics, illness severity, and comorbidities, LEP was associated with a 31% increase in the odds of mortality compared to non-LEP (95% CI: 1.06-1.63; P = .02). In this model, the association between Asian race and mortality was now fully attenuated, with a point estimate near 1.0 (OR 0.98; 95% CI: 0.79-1.22; P = .87). Markers of illness severity, including total qualifying SOFA score (OR 1.23; 95% CI: 1.20-1.27; P < .001) and need for mechanical ventilation (OR 1.88; 95% CI: 1.52-2.33; P < .001), were both associated with greater odds of death. Based on a four-step mediation analysis, LEP was found to be a partial mediator to the association between Asian race and mortality (76% proportion explained). The E-value for the association between LEP and mortality was 1.95, with an E-value for the corresponding confidence interval of 1.29.

In a subgroup analysis using the fully adjusted model restricted to patients who were mechanically ventilated during hospitalization, the association between LEP and mortality was no longer present (OR 1.15; 95% CI: 0.76-1.72; P = .51).

 

DISCUSSION

At a single US academic medical center serving a diverse population, we found that LEP was associated with sepsis mortality across all races except Black and Latino, conveying a 31% increase in the odds of death after adjusting for illness severity, comorbidities, and baseline characteristics. The higher mortality among Asian patients was largely mediated by LEP (76% proportion explained). While previous studies have variably found Black, Asian, Latino, and other non-White races/ethnicities to be at an increased risk of death from sepsis,^9-15 LEP has not been previously evaluated as a mediator of sepsis mortality. We were uniquely suited to uncover such an association due to the racial and linguistic diversity of our patient population. LEP has previously been implicated in poor health outcomes among hospitalized patients in general.^22-24 Future studies will be necessary to determine whether similar associations between LEP and mortality are observed among broader patient populations outside of sepsis.

There are a number of possible explanations for how LEP could mediate the association between race and mortality. First, LEP is known to be associated with greater difficulties in accessing medical care,²⁵ which could result in poorer baseline control of chronic comorbid conditions, fewer opportunities for preventive screening, and greater reluctance to seek medical attention when ill, theoretically leading to more severe presentations and worse outcomes. Indeed, LEP patients in our cohort had both a shorter median time to receiving their first antibiotic, as well as a higher total qualifying SOFA score, both of which may suggest more severe initial presentations. LEP is also known to contribute to, or exacerbate, the impact of low health literacy, which is itself associated with poor health.³⁵ Second, implicit biases may also have been present, as they are known to be common among healthcare providers and have been shown to negatively impact patient care.³⁶

Finally,it is possible that the association is related to the language barrier itself, which impacts providers’ ability to take an appropriate clinical history, and can lead to clinical errors or delays in care.³⁷ The fact that the association between LEP and mortality was eliminated when the analysis was restricted to mechanically ventilated patients seems to support this, since differences in language proficiency become irrelevant in this subgroup. While we are unable to comment on causality based on this observational study, we included a directed acyclic graph (DAG) in the supplemental materials, which shows one proposed model for describing these associations (Appendix Figure 2).

Assuming that the language barrier itself does, at least in part, drive the observed association, LEP represents a potentially modifiable risk factor that could be a target for quality improvement interventions. There is evidence that the use of medical interpreters among patients with LEP leads to greater satisfaction, fewer errors, and improved clinical outcomes;³⁸ however, several recent studies have documented underutilization of professional interpreter services, even when readily available.^39,40 At our institution, phone and video interpreter services are available 24/7 for approximately 150 languages. Due to limitations inherent to the EHR, we were unable to ascertain the extent to which these services were used in the present study. Heavy clinical workloads, connectivity issues, and missing or faulty equipment represent theoretical barriers to utilization of these services.

There are some limitations to our study. First, by utilizing a large database of electronic data, the quality of our analyses was reliant on the accuracy of the EHR. Demographic data such as language may have been subject to misclassification due to self-reporting. We attempted to minimize this by also including the need for interpreter services within the definition of LEP, which was validated by manual chart review. Second, generalizability is limited in this single-center study conducted at an institution with unique demographics, wherein nearly two-thirds of the LEP patients were Asian, and the Chinese-speaking population outnumbered those who speak Spanish.

Finally, the most important limitation to our study is the potential for residual confounding. While we attempted to mitigate this by adjusting for as many clinically relevant covariates as possible, there may still be unmeasured confounders to the association between LEP and mortality, such as access to outpatient care, functional status, interpreter use, and other markers of illness severity like the number and type of supportive therapies received. Based on our E-value calculations, with an observed OR of 1.31 for the association between LEP and mortality, an unmeasured confounder with an OR of 1.95 would fully explain away this association, while an OR of 1.29 would shift the confidence interval to include the null. These values suggest at least some risk of residual confounding. The fact that our fully adjusted model included multiple covariates, including several markers of illness severity, does somewhat lessen the likelihood of a confounder achieving these values, since they represent the minimum strength of an unmeasured confounder above and beyond the measured covariates. Regardless, the finding that patients with LEP are more likely to die from sepsis remains an important one, recognizing the need for further studies including multicenter investigations.

In this study, we showed that LEP was associated with sepsis mortality across nearly all races in our cohort. While Asian race was associated with a higher unadjusted odds of death compared to White race, this was attenuated after adjusting for LEP. This may suggest that some of the racial disparities in sepsis identified in prior studies were in fact mediated by language proficiency. Further studies will be required to explore the causal nature of this novel association. If modifiable factors are identified, this could represent a potential target for future quality improvement initiatives aimed at improving sepsis outcomes.

 

Disclaimer

The contents are solely the responsibility of the authors and do not necessarily represent the official views of the University of California, San Francisco or the National Institutes of Health.

Sepsis is defined as a life-threatening organ dysfunction that occurs in response to systemic infection.^1,2 It is frequently fatal, common in hospital medicine, and a leading contributor to critical illness, morbidity, and healthcare expenditures.^2-5 While sepsis care and outcomes have improved in the past decade,^6,7 inpatient mortality remains high.⁸

A number of studies have sought to determine whether race plays a role in sepsis mortality. While Black patients with sepsis have frequently been identified as having the highest rates of death,^9-14 similar observations have been made for most non-White races/ethnicities.^13-15 Studies have also demonstrated higher rates of hospital-acquired infections among Asian and Latino patients.¹⁶

There are several possible explanations for why racial minorities experience disparate outcomes in sepsis, including access to care, comorbidities, implicit biases, and biological or environmental factors,^17-20 as well as characteristics of hospitals most likely to care for racial minorities.^13,15,21 One explanation that has not been explored is that racial disparities in sepsis are mediated by language. Limited English proficiency (LEP) has previously been associated with increased rates of adverse hospital events,²² longer length of stay,²³ and greater likelihood of readmission.²⁴ LEP has also been shown to represent a significant barrier to accessing healthcare and preventive screening.²⁵ The role of LEP in sepsis mortality, however, has yet to be examined.

The diverse patient population at the University of California, San Francisco (UCSF) provides a unique opportunity to build upon existing literature by further exploring racial differences in sepsis, specifically by investigating the role of LEP. The objective of this study was to determine the association between LEP and inpatient mortality among adults hospitalized with sepsis.

METHODS

Setting

The study was conducted at the University of California, San Francisco, California (UCSF), an 800-bed tertiary care, academic medical center. It was approved by the UCSF Institutional Review Board with waiver of informed consent. UCSF cares for a population of patients who are racially and linguistically diverse, with high proportions of patients of East Asian descent and with LEP. According to recent United States census estimates, more than half of San Francisco County residents identify as non-White (35% Asians, 15% Hispanic/Latino, 6% Black), and 44% report speaking a language other than English at home.²⁶

Study Population and Data Collection

The UCSF Medical Center uses the electronic health record (EHR) Epic (Epic 2017, Epic Systems Corporation, Verona, Wisconsin). We obtained computerized EHR data from Clarity, the relational database that stores Epic’s inpatient data in thousands of tables. We identified all patients ≥18 years of age presenting to the emergency department (ED) between June 1, 2012 and December 31, 2016 with suspected serious infection, defined as having blood cultures ordered within 72 hours of ED presentation (N = 25,441). Patients who did not receive at least two doses of intravenous (IV) antibiotics within 48 hours were excluded, as they were unlikely to have serious infections.

 

We defined sepsis based on Sepsis-3 consensus guidelines² as a change in sequential [sepsis-related] organ failure assessment (SOFA) score ≥2 within the first 48 hours of ED presentation. The SOFA score is comprised of six variables representing different organ systems, each rated 0-4 based on the degree of dysfunction.² Patient vital signs, laboratory data, vasopressor medication doses, and ventilator settings were used to determine the exact timestamp at which each patient attained a change in SOFA score ≥2. Missing values were considered to be normal. To adjust for baseline organ dysfunction, SOFA elements associated with elevated bilirubin and/or creatinine were excluded for patients with chronic liver/kidney disease based on Elixhauser comorbidities.²⁷ We chose to focus on the first 48 hours in an attempt to capture patients with the most severe illnesses and the highest probability of true sepsis.

All primary and secondary International Classification of Diseases (ICD)-9/10 diagnosis codes were extracted from Clarity coding tables at the time of hospital discharge. Diagnosis codes signifying bacterial infection were grouped into the following categories based on type/location: pneumonia; bacteremia; urinary tract infection; and skin and soft tissue infection. All remaining diagnostic codes indicating bacterial infections at other sites were categorized as “Other”. If no codes indicating infection were present, patients were categorized as “None coded”. Patients with discharge diagnosis codes of “sepsis” were also identified. Dates and times of antibiotic administrations were obtained from the medications table. Time to first antibiotic was defined as the time in minutes from ED presentation to initiation of the first IV antibacterial medication. This variable was transformed using a natural log transformation based on best fit for normal distribution.

We limited our analyses to 8,974 patients who were diagnosed with sepsis as defined above and had either (1) ≥4 qualifying antibiotic days (QADs) or (2) an ICD-9/10 discharge diagnosis code of “sepsis” (Figure). QADs were defined based on the recent publication by Rhee et al. as having received four or more consecutive days of antibiotics, with the first dose given IV within 48 hours of presentation.²⁸ Patients who died or were discharged to hospice prior to the 4th QAD were also included. These additional parameters were added to increase specificity of the study sample for patients with true sepsis. Patients admitted to all levels of care (acute care, transitional care unit [TCU], intensive care unit [ICU]) and under all hospital services were included. There were no missing data for mortality, race, or language. We chose to focus on patients with sepsis in this initial study as this is a common diagnosis in hospital medicine that is enriched for high mortality.

Primary Outcome

The primary outcome of the study was inpatient mortality, which was obtained from the hospital encounters table in Clarity.

Primary Predictors

The primary predictor of interest was LEP. The encounter numbers from the dataset were used to link to self-reported demographic data, including “preferred language” and need for interpreter services. A manual chart review of 60 patients speaking the top six languages was conducted to verify the accuracy of the data on language and interpreter use (KNK). Defining the gold standard for LEP as having any chart note indicating non-English language and/or that an interpreter was used, the “interpreter needed” variable in Epic was found to have a positive predictive value for LEP of 100%. Therefore, patients in the study cohort were defined as having LEP if they met both of the following criteria: (1) a self-reported “preferred language” other than English and (2) having the “interpreter needed” variable indicating “yes”.

 

Covariate Data Collection

Additional data were obtained from the demographics tables, including age, race, sex, and insurance status. Race and ethnicity were combined into a single five-category variable including White, Asian, Black, Latino, and Other. This approach has been suggested as the best way to operationalize these variables²⁹ and has been utilized by similar studies in the literature.^9,14,15 We considered the Asian race to include all people of East Asian, Southeast Asian, or South Asian descent, which is consistent with the United States Census Bureau definition.³⁰ Patients identifying as Native Hawaiians/Pacific Islanders, Native Americans/Alaskan Natives, as well as those with unspecified race or ethnicity, were categorized as Other. Insurance status was categorized as Commercial, Medicare, Medicaid, or Other.

We estimated illness severity in several ways. First, the total qualifying SOFA score was calculated for each patient, which was defined as the total score achieved at the time that SOFA criteria were first met (≥2, within 48 hours). Second, we dichotomized patients based on whether they had received mechanical ventilation at any point during hospitalization. Finally, we used admission location as a surrogate marker for severity at the time of initial hospitalization.

To estimate the burden of baseline comorbidities, we calculated the van Walraven score (VWS),³¹ a validated modification of the Elixhauser Comorbidity Index.²⁷ This score conveys an estimated risk of in hospital death based on ICD-9/10 diagnosis codes for preexisting conditions, which ranges from <1% for the minimum score of –19 to >99% for the maximum score of 89.

Statistical Analyses

All statistical analyses were performed using Stata software version 15 (StataCorp LLC, College Station, Texas). Baseline demographics and patient characteristics were stratified by LEP. These were compared using two-sample t-tests or chi-squared tests of significance. Wilcoxon rank-sum tests were used for non-normally distributed variables. Inpatient mortality was compared across all races stratified by LEP using chi-squared tests of significance.

We fit a series of multivariable logistic regression models to examine the association between race and inpatient mortality adjusting for LEP and other patient/clinical characteristics. We first examined the unadjusted association between mortality and race; then adjusted for LEP alone; and finally adjusted for all covariates of interest, including LEP, age, sex, insurance status, year, admission level of care, VWS, total qualifying SOFA score, need for mechanical ventilation, site of infection, and time to first IV antibiotic. A subgroup analysis was also performed using the fully adjusted model restricted to patients who were mechanically ventilated. This population was selected because the patients (1) have among the highest severity of illness and (2) share a common barrier to communication, regardless of English proficiency.

Several potential interactions between LEP with other covariates were explored, including age, race, ICU admission level of care, and need for mechanical ventilation. Lastly, a mediation analysis was performed based on Baron & Kenny’s four-step model³² in order to calculate the proportion of the association between race and mortality explained by the proposed mediator (LEP).

To evaluate for the likelihood of residual confounding, we calculated an E-value, which is defined as the minimum strength of association that an unmeasured confounder would need to have with both the predictor and outcome variables, above and beyond the measured covariates, in order to fully explain away an observed predictor-outcome association.^33,34

 

RESULTS

We identified 8,974 patients hospitalized with sepsis based on the above inclusion criteria. This represented a medically complex, racially and linguistically diverse population (Table 1). The cohort was comprised of 24% Asian, 12% Black, and 11% Latino patients. Among those categorized as Other race, Native Americans/Alaskan Natives and Native Hawaiians/Pacific Islanders accounted for 4% (n = 31) and 21% (n = 159), respectively. A fifth of all patients had LEP (n = 1,716), 62% of whom were Asian (n = 1,064). Patients with LEP tended to be older, female, and to have a greater number of comorbid conditions (Table 1). The total qualifying SOFA score was also higher among patients with LEP (median 5; interquartile range [IQR]: 4-8 vs 5; IQR: 3-7; P <.001), though there was no association between LEP and mechanical ventilation (P = .22). The prevalence of LEP differed significantly across races, with 50% LEP among Asians, 32% among Latinos, 5% among White patients (P < .001). Only eight Black patients had LEP. More than 40 unique languages were represented in the cohort, with English, Cantonese, Spanish, Russian, and Mandarin accounting for ~95% (Appendix Table 1). Among Latino patients, 63% spoke English and 36% spoke Spanish.

In-hospital mortality was significantly higher among patients who had LEP (n = 268/1,716, 16%) compared to non-LEP patients (n = 678/7,258, 9%), with 80% greater unadjusted odds of mortality (OR 1.80; 95% CI: 1.54-2.09; P < .001). Notably we also found that Asian race was associated with a 1.57 unadjusted odds of mortality compared to White race (95% CI: 1.34-1.85; P < .001). Age, VWS, total qualifying SOFA score, mechanical ventilation, and admission level of care all exhibited a positive dose-response association with mortality (Appendix Table 2). In unadjusted analyses, there was no evidence of interaction between LEP and age (P = .38), LEP and race (P = .45), LEP and ICU admission level of care (P = .31), or LEP and mechanical ventilation (P = .19). Asian patients had the highest overall mortality (14% total, 17% with LEP). LEP was associated with increased unadjusted mortality among White, Asian, and Other races compared to their non-LEP counterparts (Appendix Figure 1). There was no significant difference in mortality between Latino patients with and without LEP. The sample size for Black patients with LEP (n = 8) was too small to draw conclusions about mortality.

Following multivariable logistic regression modeling for the association between race and mortality, we found that the increased odds of death among Asian patients was partially attenuated after adjusting for LEP (odds ratio [OR] 1.23, 95% CI: 1.02-1.48; P = .03; Table 2). Meanwhile, LEP was associated with a 1.66 odds of mortality (95% CI: 1.38-1.99; P < .001) after adjustment for race. In the full multivariable model adjusting for demographics and clinical characteristics, illness severity, and comorbidities, LEP was associated with a 31% increase in the odds of mortality compared to non-LEP (95% CI: 1.06-1.63; P = .02). In this model, the association between Asian race and mortality was now fully attenuated, with a point estimate near 1.0 (OR 0.98; 95% CI: 0.79-1.22; P = .87). Markers of illness severity, including total qualifying SOFA score (OR 1.23; 95% CI: 1.20-1.27; P < .001) and need for mechanical ventilation (OR 1.88; 95% CI: 1.52-2.33; P < .001), were both associated with greater odds of death. Based on a four-step mediation analysis, LEP was found to be a partial mediator to the association between Asian race and mortality (76% proportion explained). The E-value for the association between LEP and mortality was 1.95, with an E-value for the corresponding confidence interval of 1.29.

In a subgroup analysis using the fully adjusted model restricted to patients who were mechanically ventilated during hospitalization, the association between LEP and mortality was no longer present (OR 1.15; 95% CI: 0.76-1.72; P = .51).

 

DISCUSSION

At a single US academic medical center serving a diverse population, we found that LEP was associated with sepsis mortality across all races except Black and Latino, conveying a 31% increase in the odds of death after adjusting for illness severity, comorbidities, and baseline characteristics. The higher mortality among Asian patients was largely mediated by LEP (76% proportion explained). While previous studies have variably found Black, Asian, Latino, and other non-White races/ethnicities to be at an increased risk of death from sepsis,^9-15 LEP has not been previously evaluated as a mediator of sepsis mortality. We were uniquely suited to uncover such an association due to the racial and linguistic diversity of our patient population. LEP has previously been implicated in poor health outcomes among hospitalized patients in general.^22-24 Future studies will be necessary to determine whether similar associations between LEP and mortality are observed among broader patient populations outside of sepsis.

There are a number of possible explanations for how LEP could mediate the association between race and mortality. First, LEP is known to be associated with greater difficulties in accessing medical care,²⁵ which could result in poorer baseline control of chronic comorbid conditions, fewer opportunities for preventive screening, and greater reluctance to seek medical attention when ill, theoretically leading to more severe presentations and worse outcomes. Indeed, LEP patients in our cohort had both a shorter median time to receiving their first antibiotic, as well as a higher total qualifying SOFA score, both of which may suggest more severe initial presentations. LEP is also known to contribute to, or exacerbate, the impact of low health literacy, which is itself associated with poor health.³⁵ Second, implicit biases may also have been present, as they are known to be common among healthcare providers and have been shown to negatively impact patient care.³⁶

Finally,it is possible that the association is related to the language barrier itself, which impacts providers’ ability to take an appropriate clinical history, and can lead to clinical errors or delays in care.³⁷ The fact that the association between LEP and mortality was eliminated when the analysis was restricted to mechanically ventilated patients seems to support this, since differences in language proficiency become irrelevant in this subgroup. While we are unable to comment on causality based on this observational study, we included a directed acyclic graph (DAG) in the supplemental materials, which shows one proposed model for describing these associations (Appendix Figure 2).

Assuming that the language barrier itself does, at least in part, drive the observed association, LEP represents a potentially modifiable risk factor that could be a target for quality improvement interventions. There is evidence that the use of medical interpreters among patients with LEP leads to greater satisfaction, fewer errors, and improved clinical outcomes;³⁸ however, several recent studies have documented underutilization of professional interpreter services, even when readily available.^39,40 At our institution, phone and video interpreter services are available 24/7 for approximately 150 languages. Due to limitations inherent to the EHR, we were unable to ascertain the extent to which these services were used in the present study. Heavy clinical workloads, connectivity issues, and missing or faulty equipment represent theoretical barriers to utilization of these services.

There are some limitations to our study. First, by utilizing a large database of electronic data, the quality of our analyses was reliant on the accuracy of the EHR. Demographic data such as language may have been subject to misclassification due to self-reporting. We attempted to minimize this by also including the need for interpreter services within the definition of LEP, which was validated by manual chart review. Second, generalizability is limited in this single-center study conducted at an institution with unique demographics, wherein nearly two-thirds of the LEP patients were Asian, and the Chinese-speaking population outnumbered those who speak Spanish.

Finally, the most important limitation to our study is the potential for residual confounding. While we attempted to mitigate this by adjusting for as many clinically relevant covariates as possible, there may still be unmeasured confounders to the association between LEP and mortality, such as access to outpatient care, functional status, interpreter use, and other markers of illness severity like the number and type of supportive therapies received. Based on our E-value calculations, with an observed OR of 1.31 for the association between LEP and mortality, an unmeasured confounder with an OR of 1.95 would fully explain away this association, while an OR of 1.29 would shift the confidence interval to include the null. These values suggest at least some risk of residual confounding. The fact that our fully adjusted model included multiple covariates, including several markers of illness severity, does somewhat lessen the likelihood of a confounder achieving these values, since they represent the minimum strength of an unmeasured confounder above and beyond the measured covariates. Regardless, the finding that patients with LEP are more likely to die from sepsis remains an important one, recognizing the need for further studies including multicenter investigations.

In this study, we showed that LEP was associated with sepsis mortality across nearly all races in our cohort. While Asian race was associated with a higher unadjusted odds of death compared to White race, this was attenuated after adjusting for LEP. This may suggest that some of the racial disparities in sepsis identified in prior studies were in fact mediated by language proficiency. Further studies will be required to explore the causal nature of this novel association. If modifiable factors are identified, this could represent a potential target for future quality improvement initiatives aimed at improving sepsis outcomes.

 

Disclaimer

The contents are solely the responsibility of the authors and do not necessarily represent the official views of the University of California, San Francisco or the National Institutes of Health.

Sepsis is defined as a life-threatening organ dysfunction that occurs in response to systemic infection.^1,2 It is frequently fatal, common in hospital medicine, and a leading contributor to critical illness, morbidity, and healthcare expenditures.^2-5 While sepsis care and outcomes have improved in the past decade,^6,7 inpatient mortality remains high.⁸

A number of studies have sought to determine whether race plays a role in sepsis mortality. While Black patients with sepsis have frequently been identified as having the highest rates of death,^9-14 similar observations have been made for most non-White races/ethnicities.^13-15 Studies have also demonstrated higher rates of hospital-acquired infections among Asian and Latino patients.¹⁶

There are several possible explanations for why racial minorities experience disparate outcomes in sepsis, including access to care, comorbidities, implicit biases, and biological or environmental factors,^17-20 as well as characteristics of hospitals most likely to care for racial minorities.^13,15,21 One explanation that has not been explored is that racial disparities in sepsis are mediated by language. Limited English proficiency (LEP) has previously been associated with increased rates of adverse hospital events,²² longer length of stay,²³ and greater likelihood of readmission.²⁴ LEP has also been shown to represent a significant barrier to accessing healthcare and preventive screening.²⁵ The role of LEP in sepsis mortality, however, has yet to be examined.

The diverse patient population at the University of California, San Francisco (UCSF) provides a unique opportunity to build upon existing literature by further exploring racial differences in sepsis, specifically by investigating the role of LEP. The objective of this study was to determine the association between LEP and inpatient mortality among adults hospitalized with sepsis.

METHODS

Setting

The study was conducted at the University of California, San Francisco, California (UCSF), an 800-bed tertiary care, academic medical center. It was approved by the UCSF Institutional Review Board with waiver of informed consent. UCSF cares for a population of patients who are racially and linguistically diverse, with high proportions of patients of East Asian descent and with LEP. According to recent United States census estimates, more than half of San Francisco County residents identify as non-White (35% Asians, 15% Hispanic/Latino, 6% Black), and 44% report speaking a language other than English at home.²⁶

Study Population and Data Collection

The UCSF Medical Center uses the electronic health record (EHR) Epic (Epic 2017, Epic Systems Corporation, Verona, Wisconsin). We obtained computerized EHR data from Clarity, the relational database that stores Epic’s inpatient data in thousands of tables. We identified all patients ≥18 years of age presenting to the emergency department (ED) between June 1, 2012 and December 31, 2016 with suspected serious infection, defined as having blood cultures ordered within 72 hours of ED presentation (N = 25,441). Patients who did not receive at least two doses of intravenous (IV) antibiotics within 48 hours were excluded, as they were unlikely to have serious infections.

 

We defined sepsis based on Sepsis-3 consensus guidelines² as a change in sequential [sepsis-related] organ failure assessment (SOFA) score ≥2 within the first 48 hours of ED presentation. The SOFA score is comprised of six variables representing different organ systems, each rated 0-4 based on the degree of dysfunction.² Patient vital signs, laboratory data, vasopressor medication doses, and ventilator settings were used to determine the exact timestamp at which each patient attained a change in SOFA score ≥2. Missing values were considered to be normal. To adjust for baseline organ dysfunction, SOFA elements associated with elevated bilirubin and/or creatinine were excluded for patients with chronic liver/kidney disease based on Elixhauser comorbidities.²⁷ We chose to focus on the first 48 hours in an attempt to capture patients with the most severe illnesses and the highest probability of true sepsis.

All primary and secondary International Classification of Diseases (ICD)-9/10 diagnosis codes were extracted from Clarity coding tables at the time of hospital discharge. Diagnosis codes signifying bacterial infection were grouped into the following categories based on type/location: pneumonia; bacteremia; urinary tract infection; and skin and soft tissue infection. All remaining diagnostic codes indicating bacterial infections at other sites were categorized as “Other”. If no codes indicating infection were present, patients were categorized as “None coded”. Patients with discharge diagnosis codes of “sepsis” were also identified. Dates and times of antibiotic administrations were obtained from the medications table. Time to first antibiotic was defined as the time in minutes from ED presentation to initiation of the first IV antibacterial medication. This variable was transformed using a natural log transformation based on best fit for normal distribution.

We limited our analyses to 8,974 patients who were diagnosed with sepsis as defined above and had either (1) ≥4 qualifying antibiotic days (QADs) or (2) an ICD-9/10 discharge diagnosis code of “sepsis” (Figure). QADs were defined based on the recent publication by Rhee et al. as having received four or more consecutive days of antibiotics, with the first dose given IV within 48 hours of presentation.²⁸ Patients who died or were discharged to hospice prior to the 4th QAD were also included. These additional parameters were added to increase specificity of the study sample for patients with true sepsis. Patients admitted to all levels of care (acute care, transitional care unit [TCU], intensive care unit [ICU]) and under all hospital services were included. There were no missing data for mortality, race, or language. We chose to focus on patients with sepsis in this initial study as this is a common diagnosis in hospital medicine that is enriched for high mortality.

Primary Outcome

The primary outcome of the study was inpatient mortality, which was obtained from the hospital encounters table in Clarity.

Primary Predictors

The primary predictor of interest was LEP. The encounter numbers from the dataset were used to link to self-reported demographic data, including “preferred language” and need for interpreter services. A manual chart review of 60 patients speaking the top six languages was conducted to verify the accuracy of the data on language and interpreter use (KNK). Defining the gold standard for LEP as having any chart note indicating non-English language and/or that an interpreter was used, the “interpreter needed” variable in Epic was found to have a positive predictive value for LEP of 100%. Therefore, patients in the study cohort were defined as having LEP if they met both of the following criteria: (1) a self-reported “preferred language” other than English and (2) having the “interpreter needed” variable indicating “yes”.

 

Covariate Data Collection

Additional data were obtained from the demographics tables, including age, race, sex, and insurance status. Race and ethnicity were combined into a single five-category variable including White, Asian, Black, Latino, and Other. This approach has been suggested as the best way to operationalize these variables²⁹ and has been utilized by similar studies in the literature.^9,14,15 We considered the Asian race to include all people of East Asian, Southeast Asian, or South Asian descent, which is consistent with the United States Census Bureau definition.³⁰ Patients identifying as Native Hawaiians/Pacific Islanders, Native Americans/Alaskan Natives, as well as those with unspecified race or ethnicity, were categorized as Other. Insurance status was categorized as Commercial, Medicare, Medicaid, or Other.

We estimated illness severity in several ways. First, the total qualifying SOFA score was calculated for each patient, which was defined as the total score achieved at the time that SOFA criteria were first met (≥2, within 48 hours). Second, we dichotomized patients based on whether they had received mechanical ventilation at any point during hospitalization. Finally, we used admission location as a surrogate marker for severity at the time of initial hospitalization.

To estimate the burden of baseline comorbidities, we calculated the van Walraven score (VWS),³¹ a validated modification of the Elixhauser Comorbidity Index.²⁷ This score conveys an estimated risk of in hospital death based on ICD-9/10 diagnosis codes for preexisting conditions, which ranges from <1% for the minimum score of –19 to >99% for the maximum score of 89.

Statistical Analyses

All statistical analyses were performed using Stata software version 15 (StataCorp LLC, College Station, Texas). Baseline demographics and patient characteristics were stratified by LEP. These were compared using two-sample t-tests or chi-squared tests of significance. Wilcoxon rank-sum tests were used for non-normally distributed variables. Inpatient mortality was compared across all races stratified by LEP using chi-squared tests of significance.

We fit a series of multivariable logistic regression models to examine the association between race and inpatient mortality adjusting for LEP and other patient/clinical characteristics. We first examined the unadjusted association between mortality and race; then adjusted for LEP alone; and finally adjusted for all covariates of interest, including LEP, age, sex, insurance status, year, admission level of care, VWS, total qualifying SOFA score, need for mechanical ventilation, site of infection, and time to first IV antibiotic. A subgroup analysis was also performed using the fully adjusted model restricted to patients who were mechanically ventilated. This population was selected because the patients (1) have among the highest severity of illness and (2) share a common barrier to communication, regardless of English proficiency.

Several potential interactions between LEP with other covariates were explored, including age, race, ICU admission level of care, and need for mechanical ventilation. Lastly, a mediation analysis was performed based on Baron & Kenny’s four-step model³² in order to calculate the proportion of the association between race and mortality explained by the proposed mediator (LEP).

To evaluate for the likelihood of residual confounding, we calculated an E-value, which is defined as the minimum strength of association that an unmeasured confounder would need to have with both the predictor and outcome variables, above and beyond the measured covariates, in order to fully explain away an observed predictor-outcome association.^33,34

 

RESULTS

We identified 8,974 patients hospitalized with sepsis based on the above inclusion criteria. This represented a medically complex, racially and linguistically diverse population (Table 1). The cohort was comprised of 24% Asian, 12% Black, and 11% Latino patients. Among those categorized as Other race, Native Americans/Alaskan Natives and Native Hawaiians/Pacific Islanders accounted for 4% (n = 31) and 21% (n = 159), respectively. A fifth of all patients had LEP (n = 1,716), 62% of whom were Asian (n = 1,064). Patients with LEP tended to be older, female, and to have a greater number of comorbid conditions (Table 1). The total qualifying SOFA score was also higher among patients with LEP (median 5; interquartile range [IQR]: 4-8 vs 5; IQR: 3-7; P <.001), though there was no association between LEP and mechanical ventilation (P = .22). The prevalence of LEP differed significantly across races, with 50% LEP among Asians, 32% among Latinos, 5% among White patients (P < .001). Only eight Black patients had LEP. More than 40 unique languages were represented in the cohort, with English, Cantonese, Spanish, Russian, and Mandarin accounting for ~95% (Appendix Table 1). Among Latino patients, 63% spoke English and 36% spoke Spanish.

In-hospital mortality was significantly higher among patients who had LEP (n = 268/1,716, 16%) compared to non-LEP patients (n = 678/7,258, 9%), with 80% greater unadjusted odds of mortality (OR 1.80; 95% CI: 1.54-2.09; P < .001). Notably we also found that Asian race was associated with a 1.57 unadjusted odds of mortality compared to White race (95% CI: 1.34-1.85; P < .001). Age, VWS, total qualifying SOFA score, mechanical ventilation, and admission level of care all exhibited a positive dose-response association with mortality (Appendix Table 2). In unadjusted analyses, there was no evidence of interaction between LEP and age (P = .38), LEP and race (P = .45), LEP and ICU admission level of care (P = .31), or LEP and mechanical ventilation (P = .19). Asian patients had the highest overall mortality (14% total, 17% with LEP). LEP was associated with increased unadjusted mortality among White, Asian, and Other races compared to their non-LEP counterparts (Appendix Figure 1). There was no significant difference in mortality between Latino patients with and without LEP. The sample size for Black patients with LEP (n = 8) was too small to draw conclusions about mortality.

Following multivariable logistic regression modeling for the association between race and mortality, we found that the increased odds of death among Asian patients was partially attenuated after adjusting for LEP (odds ratio [OR] 1.23, 95% CI: 1.02-1.48; P = .03; Table 2). Meanwhile, LEP was associated with a 1.66 odds of mortality (95% CI: 1.38-1.99; P < .001) after adjustment for race. In the full multivariable model adjusting for demographics and clinical characteristics, illness severity, and comorbidities, LEP was associated with a 31% increase in the odds of mortality compared to non-LEP (95% CI: 1.06-1.63; P = .02). In this model, the association between Asian race and mortality was now fully attenuated, with a point estimate near 1.0 (OR 0.98; 95% CI: 0.79-1.22; P = .87). Markers of illness severity, including total qualifying SOFA score (OR 1.23; 95% CI: 1.20-1.27; P < .001) and need for mechanical ventilation (OR 1.88; 95% CI: 1.52-2.33; P < .001), were both associated with greater odds of death. Based on a four-step mediation analysis, LEP was found to be a partial mediator to the association between Asian race and mortality (76% proportion explained). The E-value for the association between LEP and mortality was 1.95, with an E-value for the corresponding confidence interval of 1.29.

In a subgroup analysis using the fully adjusted model restricted to patients who were mechanically ventilated during hospitalization, the association between LEP and mortality was no longer present (OR 1.15; 95% CI: 0.76-1.72; P = .51).

 

DISCUSSION

At a single US academic medical center serving a diverse population, we found that LEP was associated with sepsis mortality across all races except Black and Latino, conveying a 31% increase in the odds of death after adjusting for illness severity, comorbidities, and baseline characteristics. The higher mortality among Asian patients was largely mediated by LEP (76% proportion explained). While previous studies have variably found Black, Asian, Latino, and other non-White races/ethnicities to be at an increased risk of death from sepsis,^9-15 LEP has not been previously evaluated as a mediator of sepsis mortality. We were uniquely suited to uncover such an association due to the racial and linguistic diversity of our patient population. LEP has previously been implicated in poor health outcomes among hospitalized patients in general.^22-24 Future studies will be necessary to determine whether similar associations between LEP and mortality are observed among broader patient populations outside of sepsis.

There are a number of possible explanations for how LEP could mediate the association between race and mortality. First, LEP is known to be associated with greater difficulties in accessing medical care,²⁵ which could result in poorer baseline control of chronic comorbid conditions, fewer opportunities for preventive screening, and greater reluctance to seek medical attention when ill, theoretically leading to more severe presentations and worse outcomes. Indeed, LEP patients in our cohort had both a shorter median time to receiving their first antibiotic, as well as a higher total qualifying SOFA score, both of which may suggest more severe initial presentations. LEP is also known to contribute to, or exacerbate, the impact of low health literacy, which is itself associated with poor health.³⁵ Second, implicit biases may also have been present, as they are known to be common among healthcare providers and have been shown to negatively impact patient care.³⁶

Finally,it is possible that the association is related to the language barrier itself, which impacts providers’ ability to take an appropriate clinical history, and can lead to clinical errors or delays in care.³⁷ The fact that the association between LEP and mortality was eliminated when the analysis was restricted to mechanically ventilated patients seems to support this, since differences in language proficiency become irrelevant in this subgroup. While we are unable to comment on causality based on this observational study, we included a directed acyclic graph (DAG) in the supplemental materials, which shows one proposed model for describing these associations (Appendix Figure 2).

Assuming that the language barrier itself does, at least in part, drive the observed association, LEP represents a potentially modifiable risk factor that could be a target for quality improvement interventions. There is evidence that the use of medical interpreters among patients with LEP leads to greater satisfaction, fewer errors, and improved clinical outcomes;³⁸ however, several recent studies have documented underutilization of professional interpreter services, even when readily available.^39,40 At our institution, phone and video interpreter services are available 24/7 for approximately 150 languages. Due to limitations inherent to the EHR, we were unable to ascertain the extent to which these services were used in the present study. Heavy clinical workloads, connectivity issues, and missing or faulty equipment represent theoretical barriers to utilization of these services.

There are some limitations to our study. First, by utilizing a large database of electronic data, the quality of our analyses was reliant on the accuracy of the EHR. Demographic data such as language may have been subject to misclassification due to self-reporting. We attempted to minimize this by also including the need for interpreter services within the definition of LEP, which was validated by manual chart review. Second, generalizability is limited in this single-center study conducted at an institution with unique demographics, wherein nearly two-thirds of the LEP patients were Asian, and the Chinese-speaking population outnumbered those who speak Spanish.

Finally, the most important limitation to our study is the potential for residual confounding. While we attempted to mitigate this by adjusting for as many clinically relevant covariates as possible, there may still be unmeasured confounders to the association between LEP and mortality, such as access to outpatient care, functional status, interpreter use, and other markers of illness severity like the number and type of supportive therapies received. Based on our E-value calculations, with an observed OR of 1.31 for the association between LEP and mortality, an unmeasured confounder with an OR of 1.95 would fully explain away this association, while an OR of 1.29 would shift the confidence interval to include the null. These values suggest at least some risk of residual confounding. The fact that our fully adjusted model included multiple covariates, including several markers of illness severity, does somewhat lessen the likelihood of a confounder achieving these values, since they represent the minimum strength of an unmeasured confounder above and beyond the measured covariates. Regardless, the finding that patients with LEP are more likely to die from sepsis remains an important one, recognizing the need for further studies including multicenter investigations.

In this study, we showed that LEP was associated with sepsis mortality across nearly all races in our cohort. While Asian race was associated with a higher unadjusted odds of death compared to White race, this was attenuated after adjusting for LEP. This may suggest that some of the racial disparities in sepsis identified in prior studies were in fact mediated by language proficiency. Further studies will be required to explore the causal nature of this novel association. If modifiable factors are identified, this could represent a potential target for future quality improvement initiatives aimed at improving sepsis outcomes.

 

Disclaimer

The contents are solely the responsibility of the authors and do not necessarily represent the official views of the University of California, San Francisco or the National Institutes of Health.

Emergency department (ED) crowding and boarding of patients awaiting admission to the hospital (ED boarding) are growing problems with important clinical care and public safety implications.^1-4 Increased ED boarding times have been associated with lower patient satisfaction, inadequate care of critically ill patients, adverse events, and increased mortality.^3,5-7 Furthermore, ED boarding can diminish the ED’s ability to evaluate new patients.^5,8,9 ED boarding is more severe in hospitals with high inpatient occupancy with resultant disproportionate burden on large urban institutions.^2,4,5,10

Earlier studies suggest, but have not consistently shown, an association between longer ED length of stay (LOS) and longer overall hospital LOS.⁵ This association implies that the additional time spent in the ED waiting for a bed does not meaningfully contribute to advancing the required inpatient care. Thus, this waiting time is “dead time” that is added to the overall hospital duration.

The complexity and the volume of medical patients boarding in the ED can challenge the resources of an already overtaxed ED staff. Potential solutions to mitigate ED boarding of medicine patients generally focus on reducing barriers to timely movement of patients from the ED to an inpatient unit.^1,3,11-13 Ultimately, these barriers are a function of inadequate hospital capacity (eg, hospital beds, staffing) and are difficult to overcome. Two primary strategies have been used to reduce these barriers. One strategy focuses on shifting inpatient discharge times earlier to better match inpatient bed supply with ED demand.^14-19 Another common strategy is utilizing inpatient attendings to triage and better match bed needs to bed availability.^20-22

A separate area of interest, and the focus of this study, is the deployment of inpatient teams to hasten delivery of inpatient care to patients waiting in the ED.^8,23 One institution implemented an “ED hospitalist” model.²³ Another created a hospital medicine team to provide inpatient medical care to ED boarder patients and to lend clinical input to bed management.⁸

At our large, urban academic medical center, the Department of Medicine in collaboration with the Department of Emergency Medicine created a full-time hospital medicine team dedicated to providing care in the ED for patients awaiting admission to a general medicine unit. We present our multiyear experience with this ED-based hospital medicine team. We hypothesized that this new team would expedite inpatient care delivery to medical boarder patients, thereby reducing the overall hospital LOS.

METHODS

Study Setting and Design

This retrospective cross-sectional study, approved by the Institutional Review Board, was conducted at a 1,011-bed academic medical center in the northeast United States. The study period was July 1, 2016 through June 30, 2018, which was divided into Academic Year 16 (AY) (July 1, 2016 to June 30, 2017) and AY17 (July 1, 2017 to June 30, 2018).

 

The Hospital Medicine Unit (HMU) was a 60 full-time equivalent hospital medicine group consisting of 80 physicians and 25 advanced practice providers (APPs). During the study, the general medical services cared for an average of 260 patients per day on inpatient units with a wide variety of diagnoses and comorbidities. The ED had 48 monitored bed spaces for adult patients, as well as two dedicated ED observation units with 32 beds. The observation units are separate units within the hospital, staffed by ED clinicians, and were not included in this study. In 2016, the ED had a total of 110,741 patient visits and 13,908 patients were admitted to a medical service.

In 2010, the Department of Public Health for the state in which the medical center resides defined an ED boarder (EDB) patient as “a patient who remains in the ED two hours after the decision to admit.”²⁴ According to this definition, any patient waiting for an inpatient bed for more than two hours after a bed request was considered as an EDB. Operationally, further distinctions were made between patients who were “eligible” for care by an internal medicine team in the ED versus those who were actually “covered”. Before the intervention outlined in the current study, some care was provided by resident and hospitalist teams to eligible EDB patients from 2010 to 2015, although this was limited in scope. From July 1, 2015 to June 30, 2016, there was no coverage of medicine EDB patients.

Intervention

ED Boarder Service Staffing

On July 1, 2016, the HMU deployed a dedicated full-time team of clinicians to care for boarding patients, which was known as the EDB service. The service was created with the goal of seeing a maximum of 25 patients over 24 hours.

Inpatient medicine attending physicians (hospitalists) and APPs worked on the EDB service. During the day (7 am-7 pm), coverage was provided by three clinicians (generally an attending physician with two APPs). At times of increased census and demand, additional hospitalists were recruited to increase staffing on the service. During the night (7 pm-7 am), one physician was assigned to the EDB service. When the nighttime EDB census was high, other hospitalists providing care on inpatient units were expected to help care for boarding patients in the ED. Starting July 1, 2017, the dedicated nighttime staffing for the EDB service increased to two physicians during weeknights.

There was a dedicated nursing team for the EDB service. For AY16, there were two daytime EDB nurses and one night nurse, all with a coverage ratio of three to four patients per nurse. For AY17, there were four to five daytime nurses and two to three nighttime nurses with the same coverage ratio as that for AY16. EDB nurses received special training on caring for boarder patients and followed the usual inpatient nursing protocols and assessments. During each shift, an EDB charge nurse worked in conjunction with the hospitalist, bed management, and inpatient units to determine patients requiring coverage by the EDB team.

 

Patient Eligibility

Similar to the workflow before the intervention, the ED team was responsible for determining a patient’s need for admission to a medical service. Patients were eligible for EDB service coverage if they waited in the ED for more than two hours after the request for an inpatient bed was made. The EDB charge nurse was responsible for identifying all eligible boarder patients based on time elapsed since bed request. Patients were not eligible for the hospital medicine EDB service if they were in the ED observation units or were being admitted to the intensive care unit, cardiology service, oncology service, or any service outside of the Department of Medicine.

The EDB service did not automatically assume care of all eligible patients. Instead, eligible patients were accepted based on several factors including EDB clinician census, anticipated availability of an inpatient bed, and clinical appropriateness as deemed by the physician. If the EDB physician census was fewer than 10 patients and an eligible patient was not expected to move to an inpatient unit within the next hour, the patient was accepted by the EDB service. Patients who were not accepted by the EDB service remained under the care of the ED team until either the patient received an inpatient bed or space became available on the EDB service census. Eligible EDB patients who received an inpatient bed before being picked up by the EDB service were considered as noncovered EDB patients. Alternatively, an eligible patient may initially be declined from EDB service coverage due to, for example, a high census but later accepted when capacity allowed—this patient would be considered a covered EDB patient.

Handoff and Coordination

When an eligible patient was accepted onto the EDB service, clinical handoff between the ED and EDB teams occurred. The EDB physician wrote admission orders, including the inpatient admission order. Once on the EDB service, when space allowed, the patient was physically moved to a dedicated geographic space (8 beds) within the ED designed for the EDB service. When the dedicated EDB area was full, new patients would remain in their original patient bay and receive care from the EDB service. Multidisciplinary rounds with nursing, inpatient clinicians, and case management that normally occur every weekday on inpatient units were adapted to occur on the EDB service to discuss patient care needs. The duration of the patient’s stay in the ED, including the time on the EDB service, was dictated by bed availability rather than by clinical discretion of the EDB clinician. When an EDB patient was assigned a ready inpatient bed, the EDB clinician immediately passed off clinical care to the inpatient medical team. There was no change in the process of assigning patients to inpatient beds during the intervention period.

Study Population

This study included patients who were admitted to the general medical services through the ED during the defined period. We excluded medicine patients who did not pass through the ED (eg, direct admissions or outside transfer) as well as patients admitted to a specialty service (cardiology, oncology) or the intensive care unit. Patients admitted to a nonmedical service were also excluded.

 

Two hours following a bed request, an ED patient was designated as an eligible EDB patient. Operationally, and for the purposes of this study, patients were separated into three groups: (1) an eligible EDB patient for whom the EDB service assumed care for any portion of their ED stay was considered as a “covered ED boarder,” (2) an eligible EDB patient who did not have any coverage by the EDB service at any point during their ED stay was considered as a “noncovered boarder,” and (3) a patient who received an inpatient bed within two hours of bed request was considered as a “nonboarder”. Patients admitted to a specialty service, intensive care unit, or nonmedical services were not included in any of the abovementioned three groups.

We defined metrics to quantify the extent of EDB team coverage. First, the number of covered EDB patients was divided by all medicine boarders (covered + noncovered) to determine the percentage of medicine EDBs covered. Second, the total patient hours spent under the care of the EDB service was divided by the total boarding hours for all medicine boarders to determine the percentage of boarder hours covered.

Data Sources and Collection

The Electronic Health Record (EHR; Epic Systems Corporation, Verona, Wisconsin) captured whether patients were eligible EDBs. For covered EDB patients, the time when care was assumed by the EDB service was captured electronically. Patient demographics, admitting diagnoses, time stamps throughout the hospitalization, admission volumes, LOS, and discharge disposition were extracted from the EHR.

Primary and Secondary Outcome Measures

The primary outcome of this study was hospital LOS defined as the time from ED arrival to hospital departure (Figure). Secondary outcomes included ED LOS (time from ED arrival to ED departure) and the rate of 30-day ED readmission to the study institution.

Statistical Analysis

SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all statistical analyses. Continuous outcomes were compared using the Mann–Whitney test and dichotomized outcomes were compared using chi-square tests. We further analyzed the differences in the primary and secondary outcomes between covered and noncovered EDB groups using a multivariable regression analysis adjusting for age, gender, race, academic year, hour of the day, and day of the week at the time of becoming an EDB. We used quantile regression and linear regression with log-transformed continuous outcomes and logistic regression for the dichotomized outcome. A P value of .05 was used as a threshold for statistical significance.

RESULTS

Study Population and Demographics

There were a total of 16,668 patients admitted from the ED to the general medical services during the study period (Table 1). There were 8,776 (53%) patients in the covered EDB group, 5,866 (35%) patients in the noncovered EDB group, and 2,026 (12%) patients in the nonboarder group. There were more patients admitted during AY17 compared with AY16 (8,934 vs 7,734 patients, respectively, Appendix 1). Patient demographics, including age, gender, race, insurance coverage, admitting diagnoses, and discharge disposition, were similar among all three patient groups (Table 1). A majority of patients in the covered EDB and nonboarder groups presented to the ED in the afternoon, whereas noncovered EDB patients presented more in the morning (Table 1). Consistent with this pattern, inpatient bed requests for covered EDB and nonboarder patients were more frequent between 7 pm and 7 am, whereas bed requests for noncovered EDB patients were more frequent between 7 am and 7 pm. Median ED volume varied by hour with a peak in volume in the afternoon hours; however, the volume of eligible and covered EDB patients had a different peak in volume around noon that was consistent across the two years (Appendix 2). Overall, 59.9% of eligible patients (excluding nonboarders) were covered by the EDB service and 62.9% of the total boarding hours were covered by the EDB service.

 

Hospital Length of Stay

Nonboarders had the shortest median hospital LOS (4.76; interquartile range [IQR] 2.90-7.22 days). Covered EDB patients had a median hospital LOS that was 4.6 hours (0.19 day) shorter compared with noncovered EDB patients (4.92 [IQR 3.00-8.03] days vs 5.11 [IQR 3.16-8.34 days]; Table 2). The differences among the three groups were all significant in the univariate comparison (P < .001). Multivariable regression controlling for patient age, gender, race, academic year, and hour and day of the week at the time of becoming an EDB demonstrated that the difference in hospital LOS between covered and noncovered EDB patients remained significant (P < .001).

ED Length of Stay and 30-Day ED Readmission

Covered EDB patients had a longer median ED LOS compared with noncovered EDB patients and nonboarder patients (20.7 [IQR 15.8-24.9] hours vs 10.1 [IQR 7.9-13.8] hours vs 5.6 [IQR 4.2-7.5] hours, respectively, Table 2). These differences remained significant in the multivariable regression models (P < .001). Finally, the 30-day same-institution ED readmission rate was similar between covered and noncovered EDB patients.

DISCUSSION

We present two years of data describing a hospital medicine-led team designed to enhance the care of medical patients boarding in the ED. The period spent boarding in the ED is a vulnerable time for patients, and we created the EDB service with the goal of delivering inpatient medicine-led care to ED patients awaiting their inpatient bed.

When a bed request is made in an efficient ideal world, patients could be immediately transferred to an open inpatient bed to initiate care. In our study, patients who were not EDBs (ie, waited for less than two hours for their inpatient bed) had the most time-efficient care as they had the shortest ED and hospital LOS. However, nonboarders represented only 12% of patients and the majority of patients admitted to medicine were boarders. Patients covered by the EDB service had an overall hospital LOS that was 4.6 hours shorter compared with noncovered EDB patients despite having an ED LOS that was 15.1 hours longer. These LOS differences were observed without any difference to 30-day ED readmission rates.

Given that not all boarding patients were cared by the EDB service, the role of selection bias in our study warrants discussion. Similar to other studies, ED LOS for our patient cohort is heavily influenced by the availability of inpatient beds.^10-12 The EDB service handed off patients they were covering as soon as an inpatient bed became available. Although there was discretion from the EDB charge nurse and the EDB physician about which patient to accept, this was primarily focused on choosing patients who did not have a pending inpatient bed (eg, a patient who was assigned a bed but was awaiting room cleaning). Importantly, there was no change in the bed assignment process as a part of the intervention. Our intervention’s design did not allow for elucidation of causation; however, we believe that the longer ED LOS for covered EDB patients compared with noncovered EDB patients reflects the fact that the team chose patients with a higher expected ED LOS rather than that the patients had a longer LOS due to being cared by the service. Consistent with this, patients covered by the EDB service tended to have bed requests placed during the night shift compared with noncovered EDB patients; patients with bed requests at night are more likely to wait longer for their inpatient bed given that inpatient beds are generally freed up in the afternoon. We acknowledge that it is impossible to completely rule out the possibility that patient factors (eg, infectious precautions) influence inpatient bed wait time and could be another factor influencing the probability of EDB service coverage.

The current study adds to the expanding literature on EDB care models. Briones et al. demonstrated that an “ED hospitalist” led to increased care delivery as measured by an increased follow-up on laboratory results and medication orders.²³ However, their study was not structured to demonstrate LOS changes.²³ In another study, Chadaga et al. reported about their experience with a hospital medicine team providing care for EDB patients, similar to our study.⁸ Their hospital medicine team consisted of a hospitalist and APP deployed in the ED during the day, with night coverage provided by existing ED clinicians. They demonstrated less ED diversion, more ED discharges, and positive perceptions among the ED team.⁸ However, there was no impact on ED or hospital LOS, although their results may have been limited by the short duration of postintervention data and the lack of nighttime coverage.⁸ Finally, a modeling study demonstrated a reduction in ED LOS by adding ED clinicians only for patients being discharged from the ED and not for those being admitted, although there was no explicit adjustment for LOS accounting for initiation of inpatient care in the ED.¹⁵ Extending the current literature, our study suggests that a hospitalist team providing continuous coverage to a large portion of EDB patients could shorten the overall hospital LOS for boarding patients, but even this was not enough to reduce LOS to the same level as that of patients who did not board.

Practically, there were challenges to creating the EDB service described in our study. Additional clinical staff (physician, APP, and nursing) were hired for the team, requiring a financial commitment from the institution. The new team required space within the ED footprint incurring construction costs. Before the existence of the EDB service, other ancillary services (eg, physical therapy) were unaccustomed to seeing ED patients, and thus new workflows were created. Another challenge was that internal medicine clinicians were not used to caring for patients for short durations of time before passing off clinical care to another team. This required a different approach, focusing on acute issues rather than conducting an exhaustive evaluation. Finally, the EDB service workflow introduced an additional handoff, increasing discontinuity of care. These challenges are factors to consider for institutions considering a similar EDB team and should be weighed against other interventions to alleviate ED boarding or improve throughput such as expanding inpatient capacity.

Ideal metrics to track the coverage and performance of an EDB service such as the one described in this study are undefined. It was difficult to know whether the goal should be complete coverage given the increase in handoffs, particularly for patients with short boarding times. This EDB service covered 59.9% of boarding patients and 62.9% of total boarding hours. Factors that contributed to covering less than 100% included physician staffing that was insufficient to meet demand and discretion to not accept patients expected to quickly get an inpatient bed. Therefore, the percentage of patients and boarding hours covered are crude metrics and further investigation is needed to develop optimal metrics for an EDB team.

Future studies on care models for EDB patients are warranted. Recognizing that EDB teams require additional resources, studies to define which patients receive the most benefit from EDB coverage will be helpful. Moreover, the EDB team composition may need to adapt to different environments (eg, academic, urban, nonacademic, rural). Diving deeper to study whether specific patient populations benefit more than others from care by the EDB service, as measured by hospital LOS or other outcomes, would be important. Clinical outcomes, in addition to throughput metrics such as LOS, must be analyzed to understand whether factors such as increased handoffs outweigh any benefits in throughput.

There were several limitations to this study. First, it was performed at a single academic institution, potentially limiting its generalizability. However, although some workflows and team coverage structures may be institution-specific, the concept of a hospital medicine-led EDB team providing earlier inpatient care can be adapted locally and may probably achieve similar benefits. Our study population included only patients destined for general medical admission; thus, it is uncertain whether the gains demonstrated in our study would be realized for patients boarding for nonmedical services. In addition, considering the observational nature of this study, it is difficult to prove the causation that a hospitalist EDB service solely led to reductions in hospital LOS. Finally, we did not adjust for nor measure whether ED clinicians provided different care to patients whom they felt were destined for the EDB service.

In summary, nonboarder patients had the shortest overall LOS; however, among those patients who boarded, coverage by a hospitalist-led team was associated with a shorter LOS. Given the limited inpatient capacity, eliminating ED boarding is often not possible. We present a model to expedite inpatient care and allow ED clinicians to focus on newly arriving ED patients. Additional studies are required to better understand how to optimally care for patients boarding in the ED.

 

Emergency department (ED) crowding and boarding of patients awaiting admission to the hospital (ED boarding) are growing problems with important clinical care and public safety implications.^1-4 Increased ED boarding times have been associated with lower patient satisfaction, inadequate care of critically ill patients, adverse events, and increased mortality.^3,5-7 Furthermore, ED boarding can diminish the ED’s ability to evaluate new patients.^5,8,9 ED boarding is more severe in hospitals with high inpatient occupancy with resultant disproportionate burden on large urban institutions.^2,4,5,10

Earlier studies suggest, but have not consistently shown, an association between longer ED length of stay (LOS) and longer overall hospital LOS.⁵ This association implies that the additional time spent in the ED waiting for a bed does not meaningfully contribute to advancing the required inpatient care. Thus, this waiting time is “dead time” that is added to the overall hospital duration.

The complexity and the volume of medical patients boarding in the ED can challenge the resources of an already overtaxed ED staff. Potential solutions to mitigate ED boarding of medicine patients generally focus on reducing barriers to timely movement of patients from the ED to an inpatient unit.^1,3,11-13 Ultimately, these barriers are a function of inadequate hospital capacity (eg, hospital beds, staffing) and are difficult to overcome. Two primary strategies have been used to reduce these barriers. One strategy focuses on shifting inpatient discharge times earlier to better match inpatient bed supply with ED demand.^14-19 Another common strategy is utilizing inpatient attendings to triage and better match bed needs to bed availability.^20-22

A separate area of interest, and the focus of this study, is the deployment of inpatient teams to hasten delivery of inpatient care to patients waiting in the ED.^8,23 One institution implemented an “ED hospitalist” model.²³ Another created a hospital medicine team to provide inpatient medical care to ED boarder patients and to lend clinical input to bed management.⁸

At our large, urban academic medical center, the Department of Medicine in collaboration with the Department of Emergency Medicine created a full-time hospital medicine team dedicated to providing care in the ED for patients awaiting admission to a general medicine unit. We present our multiyear experience with this ED-based hospital medicine team. We hypothesized that this new team would expedite inpatient care delivery to medical boarder patients, thereby reducing the overall hospital LOS.

METHODS

Study Setting and Design

This retrospective cross-sectional study, approved by the Institutional Review Board, was conducted at a 1,011-bed academic medical center in the northeast United States. The study period was July 1, 2016 through June 30, 2018, which was divided into Academic Year 16 (AY) (July 1, 2016 to June 30, 2017) and AY17 (July 1, 2017 to June 30, 2018).

 

The Hospital Medicine Unit (HMU) was a 60 full-time equivalent hospital medicine group consisting of 80 physicians and 25 advanced practice providers (APPs). During the study, the general medical services cared for an average of 260 patients per day on inpatient units with a wide variety of diagnoses and comorbidities. The ED had 48 monitored bed spaces for adult patients, as well as two dedicated ED observation units with 32 beds. The observation units are separate units within the hospital, staffed by ED clinicians, and were not included in this study. In 2016, the ED had a total of 110,741 patient visits and 13,908 patients were admitted to a medical service.

In 2010, the Department of Public Health for the state in which the medical center resides defined an ED boarder (EDB) patient as “a patient who remains in the ED two hours after the decision to admit.”²⁴ According to this definition, any patient waiting for an inpatient bed for more than two hours after a bed request was considered as an EDB. Operationally, further distinctions were made between patients who were “eligible” for care by an internal medicine team in the ED versus those who were actually “covered”. Before the intervention outlined in the current study, some care was provided by resident and hospitalist teams to eligible EDB patients from 2010 to 2015, although this was limited in scope. From July 1, 2015 to June 30, 2016, there was no coverage of medicine EDB patients.

Intervention

ED Boarder Service Staffing

On July 1, 2016, the HMU deployed a dedicated full-time team of clinicians to care for boarding patients, which was known as the EDB service. The service was created with the goal of seeing a maximum of 25 patients over 24 hours.

Inpatient medicine attending physicians (hospitalists) and APPs worked on the EDB service. During the day (7 am-7 pm), coverage was provided by three clinicians (generally an attending physician with two APPs). At times of increased census and demand, additional hospitalists were recruited to increase staffing on the service. During the night (7 pm-7 am), one physician was assigned to the EDB service. When the nighttime EDB census was high, other hospitalists providing care on inpatient units were expected to help care for boarding patients in the ED. Starting July 1, 2017, the dedicated nighttime staffing for the EDB service increased to two physicians during weeknights.

There was a dedicated nursing team for the EDB service. For AY16, there were two daytime EDB nurses and one night nurse, all with a coverage ratio of three to four patients per nurse. For AY17, there were four to five daytime nurses and two to three nighttime nurses with the same coverage ratio as that for AY16. EDB nurses received special training on caring for boarder patients and followed the usual inpatient nursing protocols and assessments. During each shift, an EDB charge nurse worked in conjunction with the hospitalist, bed management, and inpatient units to determine patients requiring coverage by the EDB team.

 

Patient Eligibility

Similar to the workflow before the intervention, the ED team was responsible for determining a patient’s need for admission to a medical service. Patients were eligible for EDB service coverage if they waited in the ED for more than two hours after the request for an inpatient bed was made. The EDB charge nurse was responsible for identifying all eligible boarder patients based on time elapsed since bed request. Patients were not eligible for the hospital medicine EDB service if they were in the ED observation units or were being admitted to the intensive care unit, cardiology service, oncology service, or any service outside of the Department of Medicine.

The EDB service did not automatically assume care of all eligible patients. Instead, eligible patients were accepted based on several factors including EDB clinician census, anticipated availability of an inpatient bed, and clinical appropriateness as deemed by the physician. If the EDB physician census was fewer than 10 patients and an eligible patient was not expected to move to an inpatient unit within the next hour, the patient was accepted by the EDB service. Patients who were not accepted by the EDB service remained under the care of the ED team until either the patient received an inpatient bed or space became available on the EDB service census. Eligible EDB patients who received an inpatient bed before being picked up by the EDB service were considered as noncovered EDB patients. Alternatively, an eligible patient may initially be declined from EDB service coverage due to, for example, a high census but later accepted when capacity allowed—this patient would be considered a covered EDB patient.

Handoff and Coordination

When an eligible patient was accepted onto the EDB service, clinical handoff between the ED and EDB teams occurred. The EDB physician wrote admission orders, including the inpatient admission order. Once on the EDB service, when space allowed, the patient was physically moved to a dedicated geographic space (8 beds) within the ED designed for the EDB service. When the dedicated EDB area was full, new patients would remain in their original patient bay and receive care from the EDB service. Multidisciplinary rounds with nursing, inpatient clinicians, and case management that normally occur every weekday on inpatient units were adapted to occur on the EDB service to discuss patient care needs. The duration of the patient’s stay in the ED, including the time on the EDB service, was dictated by bed availability rather than by clinical discretion of the EDB clinician. When an EDB patient was assigned a ready inpatient bed, the EDB clinician immediately passed off clinical care to the inpatient medical team. There was no change in the process of assigning patients to inpatient beds during the intervention period.

Study Population

This study included patients who were admitted to the general medical services through the ED during the defined period. We excluded medicine patients who did not pass through the ED (eg, direct admissions or outside transfer) as well as patients admitted to a specialty service (cardiology, oncology) or the intensive care unit. Patients admitted to a nonmedical service were also excluded.

 

Two hours following a bed request, an ED patient was designated as an eligible EDB patient. Operationally, and for the purposes of this study, patients were separated into three groups: (1) an eligible EDB patient for whom the EDB service assumed care for any portion of their ED stay was considered as a “covered ED boarder,” (2) an eligible EDB patient who did not have any coverage by the EDB service at any point during their ED stay was considered as a “noncovered boarder,” and (3) a patient who received an inpatient bed within two hours of bed request was considered as a “nonboarder”. Patients admitted to a specialty service, intensive care unit, or nonmedical services were not included in any of the abovementioned three groups.

We defined metrics to quantify the extent of EDB team coverage. First, the number of covered EDB patients was divided by all medicine boarders (covered + noncovered) to determine the percentage of medicine EDBs covered. Second, the total patient hours spent under the care of the EDB service was divided by the total boarding hours for all medicine boarders to determine the percentage of boarder hours covered.

Data Sources and Collection

The Electronic Health Record (EHR; Epic Systems Corporation, Verona, Wisconsin) captured whether patients were eligible EDBs. For covered EDB patients, the time when care was assumed by the EDB service was captured electronically. Patient demographics, admitting diagnoses, time stamps throughout the hospitalization, admission volumes, LOS, and discharge disposition were extracted from the EHR.

Primary and Secondary Outcome Measures

The primary outcome of this study was hospital LOS defined as the time from ED arrival to hospital departure (Figure). Secondary outcomes included ED LOS (time from ED arrival to ED departure) and the rate of 30-day ED readmission to the study institution.

Statistical Analysis

SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all statistical analyses. Continuous outcomes were compared using the Mann–Whitney test and dichotomized outcomes were compared using chi-square tests. We further analyzed the differences in the primary and secondary outcomes between covered and noncovered EDB groups using a multivariable regression analysis adjusting for age, gender, race, academic year, hour of the day, and day of the week at the time of becoming an EDB. We used quantile regression and linear regression with log-transformed continuous outcomes and logistic regression for the dichotomized outcome. A P value of .05 was used as a threshold for statistical significance.

RESULTS

Study Population and Demographics

There were a total of 16,668 patients admitted from the ED to the general medical services during the study period (Table 1). There were 8,776 (53%) patients in the covered EDB group, 5,866 (35%) patients in the noncovered EDB group, and 2,026 (12%) patients in the nonboarder group. There were more patients admitted during AY17 compared with AY16 (8,934 vs 7,734 patients, respectively, Appendix 1). Patient demographics, including age, gender, race, insurance coverage, admitting diagnoses, and discharge disposition, were similar among all three patient groups (Table 1). A majority of patients in the covered EDB and nonboarder groups presented to the ED in the afternoon, whereas noncovered EDB patients presented more in the morning (Table 1). Consistent with this pattern, inpatient bed requests for covered EDB and nonboarder patients were more frequent between 7 pm and 7 am, whereas bed requests for noncovered EDB patients were more frequent between 7 am and 7 pm. Median ED volume varied by hour with a peak in volume in the afternoon hours; however, the volume of eligible and covered EDB patients had a different peak in volume around noon that was consistent across the two years (Appendix 2). Overall, 59.9% of eligible patients (excluding nonboarders) were covered by the EDB service and 62.9% of the total boarding hours were covered by the EDB service.

 

Hospital Length of Stay

Nonboarders had the shortest median hospital LOS (4.76; interquartile range [IQR] 2.90-7.22 days). Covered EDB patients had a median hospital LOS that was 4.6 hours (0.19 day) shorter compared with noncovered EDB patients (4.92 [IQR 3.00-8.03] days vs 5.11 [IQR 3.16-8.34 days]; Table 2). The differences among the three groups were all significant in the univariate comparison (P < .001). Multivariable regression controlling for patient age, gender, race, academic year, and hour and day of the week at the time of becoming an EDB demonstrated that the difference in hospital LOS between covered and noncovered EDB patients remained significant (P < .001).

ED Length of Stay and 30-Day ED Readmission

Covered EDB patients had a longer median ED LOS compared with noncovered EDB patients and nonboarder patients (20.7 [IQR 15.8-24.9] hours vs 10.1 [IQR 7.9-13.8] hours vs 5.6 [IQR 4.2-7.5] hours, respectively, Table 2). These differences remained significant in the multivariable regression models (P < .001). Finally, the 30-day same-institution ED readmission rate was similar between covered and noncovered EDB patients.

DISCUSSION

We present two years of data describing a hospital medicine-led team designed to enhance the care of medical patients boarding in the ED. The period spent boarding in the ED is a vulnerable time for patients, and we created the EDB service with the goal of delivering inpatient medicine-led care to ED patients awaiting their inpatient bed.

When a bed request is made in an efficient ideal world, patients could be immediately transferred to an open inpatient bed to initiate care. In our study, patients who were not EDBs (ie, waited for less than two hours for their inpatient bed) had the most time-efficient care as they had the shortest ED and hospital LOS. However, nonboarders represented only 12% of patients and the majority of patients admitted to medicine were boarders. Patients covered by the EDB service had an overall hospital LOS that was 4.6 hours shorter compared with noncovered EDB patients despite having an ED LOS that was 15.1 hours longer. These LOS differences were observed without any difference to 30-day ED readmission rates.

Given that not all boarding patients were cared by the EDB service, the role of selection bias in our study warrants discussion. Similar to other studies, ED LOS for our patient cohort is heavily influenced by the availability of inpatient beds.^10-12 The EDB service handed off patients they were covering as soon as an inpatient bed became available. Although there was discretion from the EDB charge nurse and the EDB physician about which patient to accept, this was primarily focused on choosing patients who did not have a pending inpatient bed (eg, a patient who was assigned a bed but was awaiting room cleaning). Importantly, there was no change in the bed assignment process as a part of the intervention. Our intervention’s design did not allow for elucidation of causation; however, we believe that the longer ED LOS for covered EDB patients compared with noncovered EDB patients reflects the fact that the team chose patients with a higher expected ED LOS rather than that the patients had a longer LOS due to being cared by the service. Consistent with this, patients covered by the EDB service tended to have bed requests placed during the night shift compared with noncovered EDB patients; patients with bed requests at night are more likely to wait longer for their inpatient bed given that inpatient beds are generally freed up in the afternoon. We acknowledge that it is impossible to completely rule out the possibility that patient factors (eg, infectious precautions) influence inpatient bed wait time and could be another factor influencing the probability of EDB service coverage.

The current study adds to the expanding literature on EDB care models. Briones et al. demonstrated that an “ED hospitalist” led to increased care delivery as measured by an increased follow-up on laboratory results and medication orders.²³ However, their study was not structured to demonstrate LOS changes.²³ In another study, Chadaga et al. reported about their experience with a hospital medicine team providing care for EDB patients, similar to our study.⁸ Their hospital medicine team consisted of a hospitalist and APP deployed in the ED during the day, with night coverage provided by existing ED clinicians. They demonstrated less ED diversion, more ED discharges, and positive perceptions among the ED team.⁸ However, there was no impact on ED or hospital LOS, although their results may have been limited by the short duration of postintervention data and the lack of nighttime coverage.⁸ Finally, a modeling study demonstrated a reduction in ED LOS by adding ED clinicians only for patients being discharged from the ED and not for those being admitted, although there was no explicit adjustment for LOS accounting for initiation of inpatient care in the ED.¹⁵ Extending the current literature, our study suggests that a hospitalist team providing continuous coverage to a large portion of EDB patients could shorten the overall hospital LOS for boarding patients, but even this was not enough to reduce LOS to the same level as that of patients who did not board.

Practically, there were challenges to creating the EDB service described in our study. Additional clinical staff (physician, APP, and nursing) were hired for the team, requiring a financial commitment from the institution. The new team required space within the ED footprint incurring construction costs. Before the existence of the EDB service, other ancillary services (eg, physical therapy) were unaccustomed to seeing ED patients, and thus new workflows were created. Another challenge was that internal medicine clinicians were not used to caring for patients for short durations of time before passing off clinical care to another team. This required a different approach, focusing on acute issues rather than conducting an exhaustive evaluation. Finally, the EDB service workflow introduced an additional handoff, increasing discontinuity of care. These challenges are factors to consider for institutions considering a similar EDB team and should be weighed against other interventions to alleviate ED boarding or improve throughput such as expanding inpatient capacity.

Ideal metrics to track the coverage and performance of an EDB service such as the one described in this study are undefined. It was difficult to know whether the goal should be complete coverage given the increase in handoffs, particularly for patients with short boarding times. This EDB service covered 59.9% of boarding patients and 62.9% of total boarding hours. Factors that contributed to covering less than 100% included physician staffing that was insufficient to meet demand and discretion to not accept patients expected to quickly get an inpatient bed. Therefore, the percentage of patients and boarding hours covered are crude metrics and further investigation is needed to develop optimal metrics for an EDB team.

Future studies on care models for EDB patients are warranted. Recognizing that EDB teams require additional resources, studies to define which patients receive the most benefit from EDB coverage will be helpful. Moreover, the EDB team composition may need to adapt to different environments (eg, academic, urban, nonacademic, rural). Diving deeper to study whether specific patient populations benefit more than others from care by the EDB service, as measured by hospital LOS or other outcomes, would be important. Clinical outcomes, in addition to throughput metrics such as LOS, must be analyzed to understand whether factors such as increased handoffs outweigh any benefits in throughput.

There were several limitations to this study. First, it was performed at a single academic institution, potentially limiting its generalizability. However, although some workflows and team coverage structures may be institution-specific, the concept of a hospital medicine-led EDB team providing earlier inpatient care can be adapted locally and may probably achieve similar benefits. Our study population included only patients destined for general medical admission; thus, it is uncertain whether the gains demonstrated in our study would be realized for patients boarding for nonmedical services. In addition, considering the observational nature of this study, it is difficult to prove the causation that a hospitalist EDB service solely led to reductions in hospital LOS. Finally, we did not adjust for nor measure whether ED clinicians provided different care to patients whom they felt were destined for the EDB service.

In summary, nonboarder patients had the shortest overall LOS; however, among those patients who boarded, coverage by a hospitalist-led team was associated with a shorter LOS. Given the limited inpatient capacity, eliminating ED boarding is often not possible. We present a model to expedite inpatient care and allow ED clinicians to focus on newly arriving ED patients. Additional studies are required to better understand how to optimally care for patients boarding in the ED.

 

Emergency department (ED) crowding and boarding of patients awaiting admission to the hospital (ED boarding) are growing problems with important clinical care and public safety implications.^1-4 Increased ED boarding times have been associated with lower patient satisfaction, inadequate care of critically ill patients, adverse events, and increased mortality.^3,5-7 Furthermore, ED boarding can diminish the ED’s ability to evaluate new patients.^5,8,9 ED boarding is more severe in hospitals with high inpatient occupancy with resultant disproportionate burden on large urban institutions.^2,4,5,10

Earlier studies suggest, but have not consistently shown, an association between longer ED length of stay (LOS) and longer overall hospital LOS.⁵ This association implies that the additional time spent in the ED waiting for a bed does not meaningfully contribute to advancing the required inpatient care. Thus, this waiting time is “dead time” that is added to the overall hospital duration.

The complexity and the volume of medical patients boarding in the ED can challenge the resources of an already overtaxed ED staff. Potential solutions to mitigate ED boarding of medicine patients generally focus on reducing barriers to timely movement of patients from the ED to an inpatient unit.^1,3,11-13 Ultimately, these barriers are a function of inadequate hospital capacity (eg, hospital beds, staffing) and are difficult to overcome. Two primary strategies have been used to reduce these barriers. One strategy focuses on shifting inpatient discharge times earlier to better match inpatient bed supply with ED demand.^14-19 Another common strategy is utilizing inpatient attendings to triage and better match bed needs to bed availability.^20-22

A separate area of interest, and the focus of this study, is the deployment of inpatient teams to hasten delivery of inpatient care to patients waiting in the ED.^8,23 One institution implemented an “ED hospitalist” model.²³ Another created a hospital medicine team to provide inpatient medical care to ED boarder patients and to lend clinical input to bed management.⁸

At our large, urban academic medical center, the Department of Medicine in collaboration with the Department of Emergency Medicine created a full-time hospital medicine team dedicated to providing care in the ED for patients awaiting admission to a general medicine unit. We present our multiyear experience with this ED-based hospital medicine team. We hypothesized that this new team would expedite inpatient care delivery to medical boarder patients, thereby reducing the overall hospital LOS.

METHODS

Study Setting and Design

This retrospective cross-sectional study, approved by the Institutional Review Board, was conducted at a 1,011-bed academic medical center in the northeast United States. The study period was July 1, 2016 through June 30, 2018, which was divided into Academic Year 16 (AY) (July 1, 2016 to June 30, 2017) and AY17 (July 1, 2017 to June 30, 2018).

 

The Hospital Medicine Unit (HMU) was a 60 full-time equivalent hospital medicine group consisting of 80 physicians and 25 advanced practice providers (APPs). During the study, the general medical services cared for an average of 260 patients per day on inpatient units with a wide variety of diagnoses and comorbidities. The ED had 48 monitored bed spaces for adult patients, as well as two dedicated ED observation units with 32 beds. The observation units are separate units within the hospital, staffed by ED clinicians, and were not included in this study. In 2016, the ED had a total of 110,741 patient visits and 13,908 patients were admitted to a medical service.

In 2010, the Department of Public Health for the state in which the medical center resides defined an ED boarder (EDB) patient as “a patient who remains in the ED two hours after the decision to admit.”²⁴ According to this definition, any patient waiting for an inpatient bed for more than two hours after a bed request was considered as an EDB. Operationally, further distinctions were made between patients who were “eligible” for care by an internal medicine team in the ED versus those who were actually “covered”. Before the intervention outlined in the current study, some care was provided by resident and hospitalist teams to eligible EDB patients from 2010 to 2015, although this was limited in scope. From July 1, 2015 to June 30, 2016, there was no coverage of medicine EDB patients.

Intervention

ED Boarder Service Staffing

On July 1, 2016, the HMU deployed a dedicated full-time team of clinicians to care for boarding patients, which was known as the EDB service. The service was created with the goal of seeing a maximum of 25 patients over 24 hours.

Inpatient medicine attending physicians (hospitalists) and APPs worked on the EDB service. During the day (7 am-7 pm), coverage was provided by three clinicians (generally an attending physician with two APPs). At times of increased census and demand, additional hospitalists were recruited to increase staffing on the service. During the night (7 pm-7 am), one physician was assigned to the EDB service. When the nighttime EDB census was high, other hospitalists providing care on inpatient units were expected to help care for boarding patients in the ED. Starting July 1, 2017, the dedicated nighttime staffing for the EDB service increased to two physicians during weeknights.

There was a dedicated nursing team for the EDB service. For AY16, there were two daytime EDB nurses and one night nurse, all with a coverage ratio of three to four patients per nurse. For AY17, there were four to five daytime nurses and two to three nighttime nurses with the same coverage ratio as that for AY16. EDB nurses received special training on caring for boarder patients and followed the usual inpatient nursing protocols and assessments. During each shift, an EDB charge nurse worked in conjunction with the hospitalist, bed management, and inpatient units to determine patients requiring coverage by the EDB team.

 

Patient Eligibility

Similar to the workflow before the intervention, the ED team was responsible for determining a patient’s need for admission to a medical service. Patients were eligible for EDB service coverage if they waited in the ED for more than two hours after the request for an inpatient bed was made. The EDB charge nurse was responsible for identifying all eligible boarder patients based on time elapsed since bed request. Patients were not eligible for the hospital medicine EDB service if they were in the ED observation units or were being admitted to the intensive care unit, cardiology service, oncology service, or any service outside of the Department of Medicine.

The EDB service did not automatically assume care of all eligible patients. Instead, eligible patients were accepted based on several factors including EDB clinician census, anticipated availability of an inpatient bed, and clinical appropriateness as deemed by the physician. If the EDB physician census was fewer than 10 patients and an eligible patient was not expected to move to an inpatient unit within the next hour, the patient was accepted by the EDB service. Patients who were not accepted by the EDB service remained under the care of the ED team until either the patient received an inpatient bed or space became available on the EDB service census. Eligible EDB patients who received an inpatient bed before being picked up by the EDB service were considered as noncovered EDB patients. Alternatively, an eligible patient may initially be declined from EDB service coverage due to, for example, a high census but later accepted when capacity allowed—this patient would be considered a covered EDB patient.

Handoff and Coordination

When an eligible patient was accepted onto the EDB service, clinical handoff between the ED and EDB teams occurred. The EDB physician wrote admission orders, including the inpatient admission order. Once on the EDB service, when space allowed, the patient was physically moved to a dedicated geographic space (8 beds) within the ED designed for the EDB service. When the dedicated EDB area was full, new patients would remain in their original patient bay and receive care from the EDB service. Multidisciplinary rounds with nursing, inpatient clinicians, and case management that normally occur every weekday on inpatient units were adapted to occur on the EDB service to discuss patient care needs. The duration of the patient’s stay in the ED, including the time on the EDB service, was dictated by bed availability rather than by clinical discretion of the EDB clinician. When an EDB patient was assigned a ready inpatient bed, the EDB clinician immediately passed off clinical care to the inpatient medical team. There was no change in the process of assigning patients to inpatient beds during the intervention period.

Study Population

This study included patients who were admitted to the general medical services through the ED during the defined period. We excluded medicine patients who did not pass through the ED (eg, direct admissions or outside transfer) as well as patients admitted to a specialty service (cardiology, oncology) or the intensive care unit. Patients admitted to a nonmedical service were also excluded.

 

Two hours following a bed request, an ED patient was designated as an eligible EDB patient. Operationally, and for the purposes of this study, patients were separated into three groups: (1) an eligible EDB patient for whom the EDB service assumed care for any portion of their ED stay was considered as a “covered ED boarder,” (2) an eligible EDB patient who did not have any coverage by the EDB service at any point during their ED stay was considered as a “noncovered boarder,” and (3) a patient who received an inpatient bed within two hours of bed request was considered as a “nonboarder”. Patients admitted to a specialty service, intensive care unit, or nonmedical services were not included in any of the abovementioned three groups.

We defined metrics to quantify the extent of EDB team coverage. First, the number of covered EDB patients was divided by all medicine boarders (covered + noncovered) to determine the percentage of medicine EDBs covered. Second, the total patient hours spent under the care of the EDB service was divided by the total boarding hours for all medicine boarders to determine the percentage of boarder hours covered.

Data Sources and Collection

The Electronic Health Record (EHR; Epic Systems Corporation, Verona, Wisconsin) captured whether patients were eligible EDBs. For covered EDB patients, the time when care was assumed by the EDB service was captured electronically. Patient demographics, admitting diagnoses, time stamps throughout the hospitalization, admission volumes, LOS, and discharge disposition were extracted from the EHR.

Primary and Secondary Outcome Measures

The primary outcome of this study was hospital LOS defined as the time from ED arrival to hospital departure (Figure). Secondary outcomes included ED LOS (time from ED arrival to ED departure) and the rate of 30-day ED readmission to the study institution.

Statistical Analysis

SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all statistical analyses. Continuous outcomes were compared using the Mann–Whitney test and dichotomized outcomes were compared using chi-square tests. We further analyzed the differences in the primary and secondary outcomes between covered and noncovered EDB groups using a multivariable regression analysis adjusting for age, gender, race, academic year, hour of the day, and day of the week at the time of becoming an EDB. We used quantile regression and linear regression with log-transformed continuous outcomes and logistic regression for the dichotomized outcome. A P value of .05 was used as a threshold for statistical significance.

RESULTS

Study Population and Demographics

There were a total of 16,668 patients admitted from the ED to the general medical services during the study period (Table 1). There were 8,776 (53%) patients in the covered EDB group, 5,866 (35%) patients in the noncovered EDB group, and 2,026 (12%) patients in the nonboarder group. There were more patients admitted during AY17 compared with AY16 (8,934 vs 7,734 patients, respectively, Appendix 1). Patient demographics, including age, gender, race, insurance coverage, admitting diagnoses, and discharge disposition, were similar among all three patient groups (Table 1). A majority of patients in the covered EDB and nonboarder groups presented to the ED in the afternoon, whereas noncovered EDB patients presented more in the morning (Table 1). Consistent with this pattern, inpatient bed requests for covered EDB and nonboarder patients were more frequent between 7 pm and 7 am, whereas bed requests for noncovered EDB patients were more frequent between 7 am and 7 pm. Median ED volume varied by hour with a peak in volume in the afternoon hours; however, the volume of eligible and covered EDB patients had a different peak in volume around noon that was consistent across the two years (Appendix 2). Overall, 59.9% of eligible patients (excluding nonboarders) were covered by the EDB service and 62.9% of the total boarding hours were covered by the EDB service.

 

Hospital Length of Stay

Nonboarders had the shortest median hospital LOS (4.76; interquartile range [IQR] 2.90-7.22 days). Covered EDB patients had a median hospital LOS that was 4.6 hours (0.19 day) shorter compared with noncovered EDB patients (4.92 [IQR 3.00-8.03] days vs 5.11 [IQR 3.16-8.34 days]; Table 2). The differences among the three groups were all significant in the univariate comparison (P < .001). Multivariable regression controlling for patient age, gender, race, academic year, and hour and day of the week at the time of becoming an EDB demonstrated that the difference in hospital LOS between covered and noncovered EDB patients remained significant (P < .001).

ED Length of Stay and 30-Day ED Readmission

Covered EDB patients had a longer median ED LOS compared with noncovered EDB patients and nonboarder patients (20.7 [IQR 15.8-24.9] hours vs 10.1 [IQR 7.9-13.8] hours vs 5.6 [IQR 4.2-7.5] hours, respectively, Table 2). These differences remained significant in the multivariable regression models (P < .001). Finally, the 30-day same-institution ED readmission rate was similar between covered and noncovered EDB patients.

DISCUSSION

We present two years of data describing a hospital medicine-led team designed to enhance the care of medical patients boarding in the ED. The period spent boarding in the ED is a vulnerable time for patients, and we created the EDB service with the goal of delivering inpatient medicine-led care to ED patients awaiting their inpatient bed.

When a bed request is made in an efficient ideal world, patients could be immediately transferred to an open inpatient bed to initiate care. In our study, patients who were not EDBs (ie, waited for less than two hours for their inpatient bed) had the most time-efficient care as they had the shortest ED and hospital LOS. However, nonboarders represented only 12% of patients and the majority of patients admitted to medicine were boarders. Patients covered by the EDB service had an overall hospital LOS that was 4.6 hours shorter compared with noncovered EDB patients despite having an ED LOS that was 15.1 hours longer. These LOS differences were observed without any difference to 30-day ED readmission rates.

Given that not all boarding patients were cared by the EDB service, the role of selection bias in our study warrants discussion. Similar to other studies, ED LOS for our patient cohort is heavily influenced by the availability of inpatient beds.^10-12 The EDB service handed off patients they were covering as soon as an inpatient bed became available. Although there was discretion from the EDB charge nurse and the EDB physician about which patient to accept, this was primarily focused on choosing patients who did not have a pending inpatient bed (eg, a patient who was assigned a bed but was awaiting room cleaning). Importantly, there was no change in the bed assignment process as a part of the intervention. Our intervention’s design did not allow for elucidation of causation; however, we believe that the longer ED LOS for covered EDB patients compared with noncovered EDB patients reflects the fact that the team chose patients with a higher expected ED LOS rather than that the patients had a longer LOS due to being cared by the service. Consistent with this, patients covered by the EDB service tended to have bed requests placed during the night shift compared with noncovered EDB patients; patients with bed requests at night are more likely to wait longer for their inpatient bed given that inpatient beds are generally freed up in the afternoon. We acknowledge that it is impossible to completely rule out the possibility that patient factors (eg, infectious precautions) influence inpatient bed wait time and could be another factor influencing the probability of EDB service coverage.

The current study adds to the expanding literature on EDB care models. Briones et al. demonstrated that an “ED hospitalist” led to increased care delivery as measured by an increased follow-up on laboratory results and medication orders.²³ However, their study was not structured to demonstrate LOS changes.²³ In another study, Chadaga et al. reported about their experience with a hospital medicine team providing care for EDB patients, similar to our study.⁸ Their hospital medicine team consisted of a hospitalist and APP deployed in the ED during the day, with night coverage provided by existing ED clinicians. They demonstrated less ED diversion, more ED discharges, and positive perceptions among the ED team.⁸ However, there was no impact on ED or hospital LOS, although their results may have been limited by the short duration of postintervention data and the lack of nighttime coverage.⁸ Finally, a modeling study demonstrated a reduction in ED LOS by adding ED clinicians only for patients being discharged from the ED and not for those being admitted, although there was no explicit adjustment for LOS accounting for initiation of inpatient care in the ED.¹⁵ Extending the current literature, our study suggests that a hospitalist team providing continuous coverage to a large portion of EDB patients could shorten the overall hospital LOS for boarding patients, but even this was not enough to reduce LOS to the same level as that of patients who did not board.

Practically, there were challenges to creating the EDB service described in our study. Additional clinical staff (physician, APP, and nursing) were hired for the team, requiring a financial commitment from the institution. The new team required space within the ED footprint incurring construction costs. Before the existence of the EDB service, other ancillary services (eg, physical therapy) were unaccustomed to seeing ED patients, and thus new workflows were created. Another challenge was that internal medicine clinicians were not used to caring for patients for short durations of time before passing off clinical care to another team. This required a different approach, focusing on acute issues rather than conducting an exhaustive evaluation. Finally, the EDB service workflow introduced an additional handoff, increasing discontinuity of care. These challenges are factors to consider for institutions considering a similar EDB team and should be weighed against other interventions to alleviate ED boarding or improve throughput such as expanding inpatient capacity.

Ideal metrics to track the coverage and performance of an EDB service such as the one described in this study are undefined. It was difficult to know whether the goal should be complete coverage given the increase in handoffs, particularly for patients with short boarding times. This EDB service covered 59.9% of boarding patients and 62.9% of total boarding hours. Factors that contributed to covering less than 100% included physician staffing that was insufficient to meet demand and discretion to not accept patients expected to quickly get an inpatient bed. Therefore, the percentage of patients and boarding hours covered are crude metrics and further investigation is needed to develop optimal metrics for an EDB team.

Future studies on care models for EDB patients are warranted. Recognizing that EDB teams require additional resources, studies to define which patients receive the most benefit from EDB coverage will be helpful. Moreover, the EDB team composition may need to adapt to different environments (eg, academic, urban, nonacademic, rural). Diving deeper to study whether specific patient populations benefit more than others from care by the EDB service, as measured by hospital LOS or other outcomes, would be important. Clinical outcomes, in addition to throughput metrics such as LOS, must be analyzed to understand whether factors such as increased handoffs outweigh any benefits in throughput.

There were several limitations to this study. First, it was performed at a single academic institution, potentially limiting its generalizability. However, although some workflows and team coverage structures may be institution-specific, the concept of a hospital medicine-led EDB team providing earlier inpatient care can be adapted locally and may probably achieve similar benefits. Our study population included only patients destined for general medical admission; thus, it is uncertain whether the gains demonstrated in our study would be realized for patients boarding for nonmedical services. In addition, considering the observational nature of this study, it is difficult to prove the causation that a hospitalist EDB service solely led to reductions in hospital LOS. Finally, we did not adjust for nor measure whether ED clinicians provided different care to patients whom they felt were destined for the EDB service.

In summary, nonboarder patients had the shortest overall LOS; however, among those patients who boarded, coverage by a hospitalist-led team was associated with a shorter LOS. Given the limited inpatient capacity, eliminating ED boarding is often not possible. We present a model to expedite inpatient care and allow ED clinicians to focus on newly arriving ED patients. Additional studies are required to better understand how to optimally care for patients boarding in the ED.

 

Neurologic impairment (NI) encompasses static and progressive diseases of the central and/or peripheral nervous systems that result in functional and intellectual impairments.¹ While a variety of neurologic diseases are responsible for NI (eg, hypoxic-ischemic encephalopathy, muscular dystrophy), consequences of these diseases extend beyond neurologic manifestations.¹ These children are at an increased risk for aspiration of oral and gastric contents given their common comorbidities of dysphagia, gastroesophageal reflux, impaired cough, and respiratory muscle weakness.² While aspiration may manifest as a self-resolving pneumonitis, the presence of oral or enteric bacteria in aspirated material may result in the development of bacterial pneumonia. Children with NI hospitalized with aspiration pneumonia have higher complication rates, longer and costlier hospitalizations, and higher readmission rates when compared with children with nonaspiration pneumonia.³

While pediatric aspiration pneumonia is commonly attributed to anaerobic bacteria, this is largely based on extrapolation from epidemiologic studies that were conducted in past decades.^4-8 A single randomized controlled trial found that penicillin and clindamycin, antimicrobials with similar antimicrobial activity against anaerobes, to be equally effective.⁹ However, the recent literature emphasizes the polymicrobial nature of aspiration pneumonia in adults, with the common isolation of Gram-negative enteric bacteria.¹⁰ Further, while Pseudomonas aeruginosa is often identified in respiratory cultures from children with NI and chronic respiratory insufficiency,^11,12 the significance of P. aeruginosa in lower airways remains unclear.

We designed this study to compare hospital outcomes associated with the most commonly prescribed empiric antimicrobial therapies for aspiration pneumonia in children with NI.

MATERIALS AND METHODS

Study Design and Data Source

This multicenter, retrospective cohort study used the Pediatric Health Information System (PHIS) database. PHIS, an administrative database of 50 not-for-profit tertiary care pediatric hospitals, contains data regarding patient demographics, diagnoses and procedures, and daily billed resource utilization, including laboratory and imaging studies. Data quality and reliability are assured through the Children’s Hospital Association (CHA; Lenexa, Kansas) and participating hospitals. Due to incomplete data through the study period and data quality issues, six hospitals were excluded.

STUDY POPULATION

Inclusion Criteria

Children 1-18 years of age who were discharged between July 1, 2007 and June 30, 2015 were included if they had a NI diagnosis,¹ a principal diagnosis indicative of aspiration pneumonia (507.x),^3,13,14 and received antibiotics in the first two calendar days of admission. NI was determined using previously defined International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) diagnosis codes.¹ We only included children who received antibiotics in the first two calendar days of admission to minimize the likelihood of including children admitted for other reasons who acquired aspiration pneumonia after hospitalization. For children with multiple hospitalizations, one admission was randomly selected for inclusion to minimize weighting results toward repeat visits.

 

Exclusion Criteria

Children transferred from another hospital were excluded as records from their initial presentation, including treatment and outcomes, were not available. We also excluded children with tracheostomy^15,16 or chronic ventilator dependence,¹⁷ those with a diagnosis of human immunodeficiency virus or tuberculosis, and children who received chemotherapy during hospitalization given expected differences in etiology, treatment, and outcomes.¹⁸

Exposure

The primary exposure was antibiotic therapy received in the first two days of admission. Antibiotics were classified by their antimicrobial spectra of activity as defined by The Sanford Guide to Antimicrobial Therapy¹⁹ against the most commonly recognized pathogens of aspiration pneumonia: anaerobes, Gram-negatives, and P. aeruginosa (Appendix Table 1).^10,20 For example, penicillin G and clindamycin were among the antibiotics classified as providing anaerobic coverage alone, whereas ceftriaxone was classified as providing Gram-negative coverage alone and ampicillin-sulbactam or as combination therapy with clindamycin and ceftriaxone were classified as providing anaerobic and Gram-negative coverage. Piperacillin-tazobactam and meropenem were classified as providing anaerobic, Gram-negative, and P. aeruginosa coverage. We excluded antibiotics that do not provide coverage against anaerobes, Gram-negative, or P. aeruginosa (eg, ampicillin, azithromycin) or that provide coverage against Gram-negative and P. aeruginosa, but not anaerobes (eg, cefepime, tobramycin), as these therapies were prescribed for <5% of the cohort. We chose not to examine the coverage for Streptococcus pneumonia or Staphylococcus aureus as antibiotics included in this analysis covered these bacteria for 99.9% of our cohort.

OUTCOMES

Outcomes included acute respiratory failure during hospitalization, intensive care unit (ICU) transfer, and hospital length of stay (LOS). Acute respiratory failure during hospitalization was defined as the presence of Clinical Transaction Classification (CTC) or ICD-9 procedure code for noninvasive or invasive mechanical ventilation on day two or later of hospitalization, with or without the need for respiratory support on day 0 or day 1 (Appendix Table 2). Given the variability in hospital policies that may drive ICU admission criteria for complex patients, our outcome of ICU transfer was defined as the requirement for ICU level care on day two or later of hospitalization without ICU admission. Acute respiratory failure and ICU care occurring within the first two hospital days were not classified as outcomes because these early events likely reflect illness severity at presentation rather than outcomes attributable to treatment failure; these were included as markers of severity in the models.

Patient Demographics and Clinical Characteristics

Demographic and clinical characteristics that might influence antibiotic choice and/or hospital outcomes were assessed. Clinical characteristics included complex chronic conditions,^21-23 medical technology assistance,²⁴ performance of diagnostic testing, and markers of severe illness on presentation. Diagnostic testing included bacterial cultures (blood, respiratory, urine) and chest radiograph performance in the first two days of hospitalization. Results of diagnostic testing are not available in the PHIS. Illness severity on presentation included acute respiratory failure, pleural drainage, receipt of vasoactive agents, and transfusion of blood products in the first two days of hospitalization (Appendix Table 2).^17,25,26

STASTICAL ANALYSIS

Continuous data were described with median and interquartile ranges (IQR) due to nonnormal distribution. Categorical data were described with frequencies and percentages. Patient demographics, clinical characteristics, and hospital outcomes were stratified by empiric antimicrobial coverage and compared using chi-square and Kruskal–Wallis tests as appropriate.

 

Generalized linear mixed-effects models with random hospital intercepts were derived to assess the independent effect of antimicrobial spectra of activity on outcomes of acute respiratory failure, ICU transfer, and LOS while adjusting for important differences in demographic and clinical characteristics. LOS had a nonnormal distribution. Thus, we used an exponential distribution. Covariates were chosen a priori given the clinical and biological relevance to exposure and outcomes—age, presence of complex chronic condition diagnoses, the number of complex chronic conditions, technology dependence, the performance of diagnostic tests on presentation, and illness severity on presentation. ICU admission was included as a covariate in acute respiratory failure and LOS outcome models. The results of the model for acute respiratory failure and ICU transfer are presented as adjusted odds ratios (OR) with a 95% CI. LOS results are presented as adjusted rate ratios (RR) with 95% CI.

All analyses were performed with SAS 9.3 (SAS Institute, Cary, North Carolina). P values <.05 were considered statistically significant. Cincinnati Children’s Hospital Medical Center Institutional Review Board considered this deidentified dataset study as not human subjects research.

RESULTS

Study Cohort

At the 44 hospitals included, 4,812 children with NI hospitalized with the diagnosis of aspiration pneumonia met the eligibility criteria. However, 79 received antibiotics with the spectra of activity not examined, leaving 4,733 children in our final analysis (Appendix Figure). Demographic and clinical characteristics of the study cohort are shown in Table 1. Median age was five years (interquartile range [IQR]: 2-11 years). Most subjects were male (53.9%), non-Hispanic white (47.9%), and publicly insured (63.6%). There was a slight variation in the distribution of admissions across seasons (spring 31.6%, summer 19.2%, fall 21.3%, and winter 27.9%). One-third of children had four or more comorbid CCCs (complex chronic conditions; 34.2%). The three most common nonneurologic CCC diagnosis categories were gastrointestinal (63.1%), congenital and/or genetic defects (36.9%), and respiratory (8.9%). Assistance with medical technologies was also common (82%)—particularly gastrointestinal (63.1%) and neurologic/neuromuscular (9.8%) technologies. The vast majority of children (92.5%) had either a chest radiograph (90.5%), respiratory viral study (33.7%), or respiratory culture (10.0%) obtained on presentation. A minority required noninvasive or invasive respiratory support (25.4%), vasoactive agents (8.9%), blood products (1.2%), or pleural drainage (0.3%) in the first two hospital days.

Spectrum of Antimicrobial Coverage

Most children (57.9%) received anaerobic and Gram-negative coverage; 16.2% received anaerobic, Gram-negative and P. aeruginosa coverage; 15.3% received anaerobic coverage alone; and 10.6% received Gram-negative coverage alone. Empiric antimicrobial coverage varied substantially across hospitals: anaerobic coverage was prescribed for 0%-44% of patients; Gram-negative coverage was prescribed for 3%-26% of patients; anaerobic and Gram-negative coverage was prescribed for 25%-90% of patients; and anaerobic, Gram-negative, and P. aeruginosa coverage was prescribed for 0%-65% of patients (Figure 1).

There were several important differences between treatment groups (Table 1). Children receiving anaerobic, Gram-negative, and P. aeruginosa coverage were older, more likely to have certain CCCs (respiratory, gastrointestinal, and malignancy), have ≥4 CCCs, and require assistance with medical technologies (respiratory, gastrointestinal) compared with all other treatment groups. They were also more likely to have respiratory viral testing and bacterial cultures obtained and to have markers of severe illness on presentation.

 

Outcomes

Acute Respiratory Failure

One-quarter (25.4%) of patients had acute respiratory failure on presentation; 22.5% required respiratory support (continued from presentation or were new) on day two or later of hospitalization (Table 2). In the adjusted analysis, children receiving Gram-negative coverage alone had two-fold greater odds (OR 2.15, 95% CI: 1.41-3.27) and children receiving anaerobic and Gram-negative coverage had 1.6-fold greater odds (OR 1.65, 95% CI: 1.19-2.28), of respiratory failure during hospitalization compared with those receiving anaerobic coverage alone (Figure 2). Odds of respiratory failure during hospitalization did not significantly differ for children receiving anaerobic, Gram-negative, and P. aeruginosa coverage compared with those receiving anaerobic coverage alone.

ICU Transfer

Nearly thirty percent (29.0%) of children required ICU admission, with an additional 3.8% requiring ICU transfer following admission (Table 2). In the multivariable analysis, the odds of an ICU transfer were greater for children receiving Gram-negative coverage alone (OR 1.80, 95% CI: 1.03-3.14) compared with those receiving anaerobic coverage alone. There was no statistical difference in ICU transfer for those receiving anaerobic and Gram-negative coverage (with or without P. aeruginosa coverage) compared with those receiving anaerobic coverage alone (Figure 2).

Length of Stay

Median hospital LOS for the total cohort was five days (IQR: 3-9 days; Table 2). In the multivariable analysis, children receiving Gram-negative coverage alone had a longer LOS (RR 1.28; 95% CI: 1.16-1.41) compared with those receiving anaerobic coverage alone, whereas children receiving anaerobic, Gram-negative, and P. aeruginosa coverage had a shorter LOS (RR 0.83; 95% CI: 0.76-0.90) than those receiving anaerobic coverage alone (Figure 2). There was no statistical difference in the LOS between children receiving anaerobic and Gram-negative coverage and those receiving anaerobic coverage alone.

DISCUSSION

In this multicenter study of children with NI hospitalized with aspiration pneumonia, we found substantial variation in empiric antimicrobial coverage for children with aspiration pneumonia. When comparing outcomes across groups, children who received anaerobic and Gram-negative coverage had outcomes similar to children who received anaerobic therapy alone. However, children who did not receive anaerobic coverage (ie, Gram-negative coverage alone) had worse outcomes, most notably a greater than two-fold increase in the odds of experiencing acute respiratory failure during hospitalization when compared with children receiving anaerobic therapy. These findings support prior literature that has highlighted the importance of anaerobic therapy in the treatment of aspiration pneumonia. The benefit of antibiotics targeting Gram-negative organisms, in addition to anaerobes, remains uncertain.

The variability in empiric antimicrobial coverage likely reflects the paucity of available information on oral and/or enteric bacteria required to identify them as causative organisms in aspiration pneumonia. In part, this problem is due to the difficulty in obtaining adequate sputum for culture from pediatric patients.²⁷ While it may be more feasible to obtain tracheal aspirates for respiratory culture in children with a tracheostomy, interpretation of culture results remains challenging because the lower airways of children with tracheostomy are commonly colonized with bacterial pathogens.²⁸ Thus, physicians are often left to choose empiric antimicrobial coverage with inadequate supporting evidence.²⁹ Although the polymicrobial nature of aspiration pneumonia is well recognized in adult and pediatric literature,^10,30 it is less clear which organisms are of pathological significance and require treatment.

The treatment standard for aspiration pneumonia has long included anaerobic therapy.²⁹ The worse outcomes of children not receiving anaerobic therapy (ie, Gram-negative coverage alone) compared with children who received anaerobic therapy support the continued importance of anaerobic therapy in the treatment of aspiration pneumonia for hospitalized children with NI. The role of antibiotics covering Gram-negative organisms is less clear. Recent studies suggest the role of anaerobes is overemphasized in the etiology and treatment of aspiration pneumonia.^10,29,31-38 Multiple studies on aspiration pneumonia bacteriology in hospitalized adults have demonstrated a predominance of Gram-negative organisms (ranging from 37%-71% of isolates identified on respiratory culture) and a relative scarcity of anaerobes (ranging from 0%-16% of isolates).^31-37 A prospective study of 50 children hospitalized with clinical and radiographic evidence of pneumonia with known aspiration risk (eg, neuromuscular disease or dysphagia) found that ~80% of 163 bacterial isolates were Gram-negative.³⁸ However, this study included repeat cultures from the same children, and thus, may overestimate the prevalence of Gram-negative organisms. In our study, children who received both anaerobic and Gram-negative therapy had no differences in ICU transfer or LOS but did experience higher odds of acute respiratory failure. As these results may be due to unmeasured confounding, future studies should further explore the necessity of Gram-negative coverage in addition to anaerobic coverage in this population.

While these recent studies may seem to suggest that anaerobic coverage is not necessary for aspiration pneumonia, there are important limitations worth noting. First, these studies used a variety of sampling techniques. While organisms grown from samples obtained via bronchoalveolar lavage^31-34,36 are likely pathogenic, those grown from tracheal or oral samples obtained via percutaneous transtracheal aspiration,³⁴ a protected specimen brush,^34,36,37 or expectorated sputum^35,38 may not represent lower airway organisms. Second, anaerobic cultures were not obtained in all studies.^31,34,38 Anaerobic organisms are difficult to isolate using traditional clinical specimen collection techniques and aerobic culture media.¹⁸ Furthermore, anaerobes are not easily recovered from lung infections after the receipt of antibiotic therapy.³⁹ Details regarding pretreatment, which are largely lacking from these studies, are necessary to interpret the relative scarcity of anaerobes on respiratory culture. Finally, caution should be taken when extrapolating the results of studies focused on the etiology and treatment of aspiration pneumonia in elderly adults to children. Our results, particularly in the context of the limitation of these more recent studies, suggest that the role of anaerobes has been underestimated.

Recent studies examining populations of children with cerebral palsy and/or tracheostomy have emphasized the high rates of carriage and infection rates with Gram-negative and drug-resistant bacteria; in particular, P. aeruginosa accounts for 50%-72% of pathogenic bacteria.^11,12,38,40These studies note the generally poor outcomes of children with P. aeruginosa—including multiple and longer hospitalizations, frequent readmissions, and the increased severity of pneumonia, including the need for ICU admission, pleural effusions, the need for intubation, and mortality.^{11,12,38,40,41} In our study, nearly 35% of children who received anaerobic, Gram-negative, and P. aeruginosa coverage experienced acute respiratory failure during hospitalization compared with 20% of children who received other therapies. While these results might seem to suggest that broader spectrum therapy is harmful, they must be interpreted in the context of important population differences; children who received a combination of anaerobic, Gram-negative, and P. aeruginosa coverage had greater medical complexity and greater severity of illness on presentation. Such factors may provide the reason for the appropriate prescription of antipseudomonal antibiotics (eg, history of tracheostomy colonization or infection, long-term care facility resident).⁴² When we controlled for population differences, children who received antipseudomonal therapy had a significantly shorter LOS and no differences in outcomes of acute respiratory failure or ICU transfer compared with those receiving anaerobic therapy alone. This result suggests that worse outcomes were associated with antipseudomonal therapy on unadjusted analyses resulting from underlying medical complexity and illness severity rather than from colonization or infection with P. aeruginosa.

Our multicenter observational study has several limitations. We used diagnosis codes to identify patients with aspiration pneumonia. As validated clinical criteria for the diagnosis of aspiration pneumonia do not exist, clinicians may assign a diagnosis of and treatment for aspiration pneumonia by subjective suspicion based on a child’s severe NI or illness severity on presentation leading to selection bias. Although administrative data are not able to verify pneumonia type with absolute certainty, we previously demonstrated that the differences in the outcomes of children with aspiration and nonaspiration pneumonia diagnosis codes persist after accounting for the complexity that might influence the diagnosis.³It is also possible that the diagnosis of aspiration pneumonia was not made upon admission for a subset of patients leading to misclassification of exposure. Some children may have had aspiration pneumonia on admission but were not assigned that diagnosis or treated for presumed aspiration pneumonia until later in the hospital course as they demonstrated treatment failure or clinical worsening. It is also possible that some children had an aspiration event during hospitalization that developed into aspiration pneumonia. We attempted to adjust for medical complexity and illness severity through multivariable adjustment based on the diagnosis and procedure codes, as well as the laboratory testing performed. However, unmeasured or residual confounding may remain as administrative data are not equipped to distinguish detailed functional status (eg, ability to cough, chest wall strength) or illness severity (eg, respiratory distress) that might influence antibiotic selection and/or outcomes.

Frthermore, we were unable to account for laboratory, microbiology, or radiology test results, and other management practices (eg, frequency of airway clearance, previous antimicrobial therapy) that may influence outcomes. Future studies should certainly include an examination of the concordance of the antibiotics prescribed with causative organisms, as this undoubtedly affects patient outcomes. Other outcomes are important to examine (eg, time to return to respiratory baseline), but we were unable to do so, given the lack of clinical detail in our database. We randomly selected a single hospitalization for children with multiple admissions; alternative methods could have different results. Although children with NI predominately use children’s hospitals,¹ results may not be generalizable.

 

CONCLUSION

These findings support prior literature that has highlighted the important role anaerobic therapy plays in the treatment of aspiration pneumonia in children with NI. In light of the limitations of our study design, we believe that rigorous clinical trials comparing anaerobic with anaerobic and Gram-negative therapy are an important and necessary next step to determine the optimal treatment for aspiration pneumonia in this population.

Disclosures

The authors do not have any financial relationships relevant to this article to disclose.

Funding

Dr. Thomson was supported by the Agency for Healthcare Research and Quality (AHRQ) under award number K08HS025138. Dr. Ambroggio was supported by the National Institute for Allergy and Infectious Diseases (NIAID) under award number K01AI125413. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ or NIAID.

Neurologic impairment (NI) encompasses static and progressive diseases of the central and/or peripheral nervous systems that result in functional and intellectual impairments.¹ While a variety of neurologic diseases are responsible for NI (eg, hypoxic-ischemic encephalopathy, muscular dystrophy), consequences of these diseases extend beyond neurologic manifestations.¹ These children are at an increased risk for aspiration of oral and gastric contents given their common comorbidities of dysphagia, gastroesophageal reflux, impaired cough, and respiratory muscle weakness.² While aspiration may manifest as a self-resolving pneumonitis, the presence of oral or enteric bacteria in aspirated material may result in the development of bacterial pneumonia. Children with NI hospitalized with aspiration pneumonia have higher complication rates, longer and costlier hospitalizations, and higher readmission rates when compared with children with nonaspiration pneumonia.³

While pediatric aspiration pneumonia is commonly attributed to anaerobic bacteria, this is largely based on extrapolation from epidemiologic studies that were conducted in past decades.^4-8 A single randomized controlled trial found that penicillin and clindamycin, antimicrobials with similar antimicrobial activity against anaerobes, to be equally effective.⁹ However, the recent literature emphasizes the polymicrobial nature of aspiration pneumonia in adults, with the common isolation of Gram-negative enteric bacteria.¹⁰ Further, while Pseudomonas aeruginosa is often identified in respiratory cultures from children with NI and chronic respiratory insufficiency,^11,12 the significance of P. aeruginosa in lower airways remains unclear.

We designed this study to compare hospital outcomes associated with the most commonly prescribed empiric antimicrobial therapies for aspiration pneumonia in children with NI.

MATERIALS AND METHODS

Study Design and Data Source

This multicenter, retrospective cohort study used the Pediatric Health Information System (PHIS) database. PHIS, an administrative database of 50 not-for-profit tertiary care pediatric hospitals, contains data regarding patient demographics, diagnoses and procedures, and daily billed resource utilization, including laboratory and imaging studies. Data quality and reliability are assured through the Children’s Hospital Association (CHA; Lenexa, Kansas) and participating hospitals. Due to incomplete data through the study period and data quality issues, six hospitals were excluded.

STUDY POPULATION

Inclusion Criteria

Children 1-18 years of age who were discharged between July 1, 2007 and June 30, 2015 were included if they had a NI diagnosis,¹ a principal diagnosis indicative of aspiration pneumonia (507.x),^3,13,14 and received antibiotics in the first two calendar days of admission. NI was determined using previously defined International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) diagnosis codes.¹ We only included children who received antibiotics in the first two calendar days of admission to minimize the likelihood of including children admitted for other reasons who acquired aspiration pneumonia after hospitalization. For children with multiple hospitalizations, one admission was randomly selected for inclusion to minimize weighting results toward repeat visits.

 

Exclusion Criteria

Children transferred from another hospital were excluded as records from their initial presentation, including treatment and outcomes, were not available. We also excluded children with tracheostomy^15,16 or chronic ventilator dependence,¹⁷ those with a diagnosis of human immunodeficiency virus or tuberculosis, and children who received chemotherapy during hospitalization given expected differences in etiology, treatment, and outcomes.¹⁸

Exposure

The primary exposure was antibiotic therapy received in the first two days of admission. Antibiotics were classified by their antimicrobial spectra of activity as defined by The Sanford Guide to Antimicrobial Therapy¹⁹ against the most commonly recognized pathogens of aspiration pneumonia: anaerobes, Gram-negatives, and P. aeruginosa (Appendix Table 1).^10,20 For example, penicillin G and clindamycin were among the antibiotics classified as providing anaerobic coverage alone, whereas ceftriaxone was classified as providing Gram-negative coverage alone and ampicillin-sulbactam or as combination therapy with clindamycin and ceftriaxone were classified as providing anaerobic and Gram-negative coverage. Piperacillin-tazobactam and meropenem were classified as providing anaerobic, Gram-negative, and P. aeruginosa coverage. We excluded antibiotics that do not provide coverage against anaerobes, Gram-negative, or P. aeruginosa (eg, ampicillin, azithromycin) or that provide coverage against Gram-negative and P. aeruginosa, but not anaerobes (eg, cefepime, tobramycin), as these therapies were prescribed for <5% of the cohort. We chose not to examine the coverage for Streptococcus pneumonia or Staphylococcus aureus as antibiotics included in this analysis covered these bacteria for 99.9% of our cohort.

OUTCOMES

Outcomes included acute respiratory failure during hospitalization, intensive care unit (ICU) transfer, and hospital length of stay (LOS). Acute respiratory failure during hospitalization was defined as the presence of Clinical Transaction Classification (CTC) or ICD-9 procedure code for noninvasive or invasive mechanical ventilation on day two or later of hospitalization, with or without the need for respiratory support on day 0 or day 1 (Appendix Table 2). Given the variability in hospital policies that may drive ICU admission criteria for complex patients, our outcome of ICU transfer was defined as the requirement for ICU level care on day two or later of hospitalization without ICU admission. Acute respiratory failure and ICU care occurring within the first two hospital days were not classified as outcomes because these early events likely reflect illness severity at presentation rather than outcomes attributable to treatment failure; these were included as markers of severity in the models.

Patient Demographics and Clinical Characteristics

Demographic and clinical characteristics that might influence antibiotic choice and/or hospital outcomes were assessed. Clinical characteristics included complex chronic conditions,^21-23 medical technology assistance,²⁴ performance of diagnostic testing, and markers of severe illness on presentation. Diagnostic testing included bacterial cultures (blood, respiratory, urine) and chest radiograph performance in the first two days of hospitalization. Results of diagnostic testing are not available in the PHIS. Illness severity on presentation included acute respiratory failure, pleural drainage, receipt of vasoactive agents, and transfusion of blood products in the first two days of hospitalization (Appendix Table 2).^17,25,26

STASTICAL ANALYSIS

Continuous data were described with median and interquartile ranges (IQR) due to nonnormal distribution. Categorical data were described with frequencies and percentages. Patient demographics, clinical characteristics, and hospital outcomes were stratified by empiric antimicrobial coverage and compared using chi-square and Kruskal–Wallis tests as appropriate.

 

Generalized linear mixed-effects models with random hospital intercepts were derived to assess the independent effect of antimicrobial spectra of activity on outcomes of acute respiratory failure, ICU transfer, and LOS while adjusting for important differences in demographic and clinical characteristics. LOS had a nonnormal distribution. Thus, we used an exponential distribution. Covariates were chosen a priori given the clinical and biological relevance to exposure and outcomes—age, presence of complex chronic condition diagnoses, the number of complex chronic conditions, technology dependence, the performance of diagnostic tests on presentation, and illness severity on presentation. ICU admission was included as a covariate in acute respiratory failure and LOS outcome models. The results of the model for acute respiratory failure and ICU transfer are presented as adjusted odds ratios (OR) with a 95% CI. LOS results are presented as adjusted rate ratios (RR) with 95% CI.

All analyses were performed with SAS 9.3 (SAS Institute, Cary, North Carolina). P values <.05 were considered statistically significant. Cincinnati Children’s Hospital Medical Center Institutional Review Board considered this deidentified dataset study as not human subjects research.

RESULTS

Study Cohort

At the 44 hospitals included, 4,812 children with NI hospitalized with the diagnosis of aspiration pneumonia met the eligibility criteria. However, 79 received antibiotics with the spectra of activity not examined, leaving 4,733 children in our final analysis (Appendix Figure). Demographic and clinical characteristics of the study cohort are shown in Table 1. Median age was five years (interquartile range [IQR]: 2-11 years). Most subjects were male (53.9%), non-Hispanic white (47.9%), and publicly insured (63.6%). There was a slight variation in the distribution of admissions across seasons (spring 31.6%, summer 19.2%, fall 21.3%, and winter 27.9%). One-third of children had four or more comorbid CCCs (complex chronic conditions; 34.2%). The three most common nonneurologic CCC diagnosis categories were gastrointestinal (63.1%), congenital and/or genetic defects (36.9%), and respiratory (8.9%). Assistance with medical technologies was also common (82%)—particularly gastrointestinal (63.1%) and neurologic/neuromuscular (9.8%) technologies. The vast majority of children (92.5%) had either a chest radiograph (90.5%), respiratory viral study (33.7%), or respiratory culture (10.0%) obtained on presentation. A minority required noninvasive or invasive respiratory support (25.4%), vasoactive agents (8.9%), blood products (1.2%), or pleural drainage (0.3%) in the first two hospital days.

Spectrum of Antimicrobial Coverage

Most children (57.9%) received anaerobic and Gram-negative coverage; 16.2% received anaerobic, Gram-negative and P. aeruginosa coverage; 15.3% received anaerobic coverage alone; and 10.6% received Gram-negative coverage alone. Empiric antimicrobial coverage varied substantially across hospitals: anaerobic coverage was prescribed for 0%-44% of patients; Gram-negative coverage was prescribed for 3%-26% of patients; anaerobic and Gram-negative coverage was prescribed for 25%-90% of patients; and anaerobic, Gram-negative, and P. aeruginosa coverage was prescribed for 0%-65% of patients (Figure 1).

There were several important differences between treatment groups (Table 1). Children receiving anaerobic, Gram-negative, and P. aeruginosa coverage were older, more likely to have certain CCCs (respiratory, gastrointestinal, and malignancy), have ≥4 CCCs, and require assistance with medical technologies (respiratory, gastrointestinal) compared with all other treatment groups. They were also more likely to have respiratory viral testing and bacterial cultures obtained and to have markers of severe illness on presentation.

 

Outcomes

Acute Respiratory Failure

One-quarter (25.4%) of patients had acute respiratory failure on presentation; 22.5% required respiratory support (continued from presentation or were new) on day two or later of hospitalization (Table 2). In the adjusted analysis, children receiving Gram-negative coverage alone had two-fold greater odds (OR 2.15, 95% CI: 1.41-3.27) and children receiving anaerobic and Gram-negative coverage had 1.6-fold greater odds (OR 1.65, 95% CI: 1.19-2.28), of respiratory failure during hospitalization compared with those receiving anaerobic coverage alone (Figure 2). Odds of respiratory failure during hospitalization did not significantly differ for children receiving anaerobic, Gram-negative, and P. aeruginosa coverage compared with those receiving anaerobic coverage alone.

ICU Transfer

Nearly thirty percent (29.0%) of children required ICU admission, with an additional 3.8% requiring ICU transfer following admission (Table 2). In the multivariable analysis, the odds of an ICU transfer were greater for children receiving Gram-negative coverage alone (OR 1.80, 95% CI: 1.03-3.14) compared with those receiving anaerobic coverage alone. There was no statistical difference in ICU transfer for those receiving anaerobic and Gram-negative coverage (with or without P. aeruginosa coverage) compared with those receiving anaerobic coverage alone (Figure 2).

Length of Stay

Median hospital LOS for the total cohort was five days (IQR: 3-9 days; Table 2). In the multivariable analysis, children receiving Gram-negative coverage alone had a longer LOS (RR 1.28; 95% CI: 1.16-1.41) compared with those receiving anaerobic coverage alone, whereas children receiving anaerobic, Gram-negative, and P. aeruginosa coverage had a shorter LOS (RR 0.83; 95% CI: 0.76-0.90) than those receiving anaerobic coverage alone (Figure 2). There was no statistical difference in the LOS between children receiving anaerobic and Gram-negative coverage and those receiving anaerobic coverage alone.

DISCUSSION

In this multicenter study of children with NI hospitalized with aspiration pneumonia, we found substantial variation in empiric antimicrobial coverage for children with aspiration pneumonia. When comparing outcomes across groups, children who received anaerobic and Gram-negative coverage had outcomes similar to children who received anaerobic therapy alone. However, children who did not receive anaerobic coverage (ie, Gram-negative coverage alone) had worse outcomes, most notably a greater than two-fold increase in the odds of experiencing acute respiratory failure during hospitalization when compared with children receiving anaerobic therapy. These findings support prior literature that has highlighted the importance of anaerobic therapy in the treatment of aspiration pneumonia. The benefit of antibiotics targeting Gram-negative organisms, in addition to anaerobes, remains uncertain.

The variability in empiric antimicrobial coverage likely reflects the paucity of available information on oral and/or enteric bacteria required to identify them as causative organisms in aspiration pneumonia. In part, this problem is due to the difficulty in obtaining adequate sputum for culture from pediatric patients.²⁷ While it may be more feasible to obtain tracheal aspirates for respiratory culture in children with a tracheostomy, interpretation of culture results remains challenging because the lower airways of children with tracheostomy are commonly colonized with bacterial pathogens.²⁸ Thus, physicians are often left to choose empiric antimicrobial coverage with inadequate supporting evidence.²⁹ Although the polymicrobial nature of aspiration pneumonia is well recognized in adult and pediatric literature,^10,30 it is less clear which organisms are of pathological significance and require treatment.

The treatment standard for aspiration pneumonia has long included anaerobic therapy.²⁹ The worse outcomes of children not receiving anaerobic therapy (ie, Gram-negative coverage alone) compared with children who received anaerobic therapy support the continued importance of anaerobic therapy in the treatment of aspiration pneumonia for hospitalized children with NI. The role of antibiotics covering Gram-negative organisms is less clear. Recent studies suggest the role of anaerobes is overemphasized in the etiology and treatment of aspiration pneumonia.^10,29,31-38 Multiple studies on aspiration pneumonia bacteriology in hospitalized adults have demonstrated a predominance of Gram-negative organisms (ranging from 37%-71% of isolates identified on respiratory culture) and a relative scarcity of anaerobes (ranging from 0%-16% of isolates).^31-37 A prospective study of 50 children hospitalized with clinical and radiographic evidence of pneumonia with known aspiration risk (eg, neuromuscular disease or dysphagia) found that ~80% of 163 bacterial isolates were Gram-negative.³⁸ However, this study included repeat cultures from the same children, and thus, may overestimate the prevalence of Gram-negative organisms. In our study, children who received both anaerobic and Gram-negative therapy had no differences in ICU transfer or LOS but did experience higher odds of acute respiratory failure. As these results may be due to unmeasured confounding, future studies should further explore the necessity of Gram-negative coverage in addition to anaerobic coverage in this population.

While these recent studies may seem to suggest that anaerobic coverage is not necessary for aspiration pneumonia, there are important limitations worth noting. First, these studies used a variety of sampling techniques. While organisms grown from samples obtained via bronchoalveolar lavage^31-34,36 are likely pathogenic, those grown from tracheal or oral samples obtained via percutaneous transtracheal aspiration,³⁴ a protected specimen brush,^34,36,37 or expectorated sputum^35,38 may not represent lower airway organisms. Second, anaerobic cultures were not obtained in all studies.^31,34,38 Anaerobic organisms are difficult to isolate using traditional clinical specimen collection techniques and aerobic culture media.¹⁸ Furthermore, anaerobes are not easily recovered from lung infections after the receipt of antibiotic therapy.³⁹ Details regarding pretreatment, which are largely lacking from these studies, are necessary to interpret the relative scarcity of anaerobes on respiratory culture. Finally, caution should be taken when extrapolating the results of studies focused on the etiology and treatment of aspiration pneumonia in elderly adults to children. Our results, particularly in the context of the limitation of these more recent studies, suggest that the role of anaerobes has been underestimated.

Recent studies examining populations of children with cerebral palsy and/or tracheostomy have emphasized the high rates of carriage and infection rates with Gram-negative and drug-resistant bacteria; in particular, P. aeruginosa accounts for 50%-72% of pathogenic bacteria.^11,12,38,40These studies note the generally poor outcomes of children with P. aeruginosa—including multiple and longer hospitalizations, frequent readmissions, and the increased severity of pneumonia, including the need for ICU admission, pleural effusions, the need for intubation, and mortality.^{11,12,38,40,41} In our study, nearly 35% of children who received anaerobic, Gram-negative, and P. aeruginosa coverage experienced acute respiratory failure during hospitalization compared with 20% of children who received other therapies. While these results might seem to suggest that broader spectrum therapy is harmful, they must be interpreted in the context of important population differences; children who received a combination of anaerobic, Gram-negative, and P. aeruginosa coverage had greater medical complexity and greater severity of illness on presentation. Such factors may provide the reason for the appropriate prescription of antipseudomonal antibiotics (eg, history of tracheostomy colonization or infection, long-term care facility resident).⁴² When we controlled for population differences, children who received antipseudomonal therapy had a significantly shorter LOS and no differences in outcomes of acute respiratory failure or ICU transfer compared with those receiving anaerobic therapy alone. This result suggests that worse outcomes were associated with antipseudomonal therapy on unadjusted analyses resulting from underlying medical complexity and illness severity rather than from colonization or infection with P. aeruginosa.

Our multicenter observational study has several limitations. We used diagnosis codes to identify patients with aspiration pneumonia. As validated clinical criteria for the diagnosis of aspiration pneumonia do not exist, clinicians may assign a diagnosis of and treatment for aspiration pneumonia by subjective suspicion based on a child’s severe NI or illness severity on presentation leading to selection bias. Although administrative data are not able to verify pneumonia type with absolute certainty, we previously demonstrated that the differences in the outcomes of children with aspiration and nonaspiration pneumonia diagnosis codes persist after accounting for the complexity that might influence the diagnosis.³It is also possible that the diagnosis of aspiration pneumonia was not made upon admission for a subset of patients leading to misclassification of exposure. Some children may have had aspiration pneumonia on admission but were not assigned that diagnosis or treated for presumed aspiration pneumonia until later in the hospital course as they demonstrated treatment failure or clinical worsening. It is also possible that some children had an aspiration event during hospitalization that developed into aspiration pneumonia. We attempted to adjust for medical complexity and illness severity through multivariable adjustment based on the diagnosis and procedure codes, as well as the laboratory testing performed. However, unmeasured or residual confounding may remain as administrative data are not equipped to distinguish detailed functional status (eg, ability to cough, chest wall strength) or illness severity (eg, respiratory distress) that might influence antibiotic selection and/or outcomes.

Frthermore, we were unable to account for laboratory, microbiology, or radiology test results, and other management practices (eg, frequency of airway clearance, previous antimicrobial therapy) that may influence outcomes. Future studies should certainly include an examination of the concordance of the antibiotics prescribed with causative organisms, as this undoubtedly affects patient outcomes. Other outcomes are important to examine (eg, time to return to respiratory baseline), but we were unable to do so, given the lack of clinical detail in our database. We randomly selected a single hospitalization for children with multiple admissions; alternative methods could have different results. Although children with NI predominately use children’s hospitals,¹ results may not be generalizable.

 

CONCLUSION

These findings support prior literature that has highlighted the important role anaerobic therapy plays in the treatment of aspiration pneumonia in children with NI. In light of the limitations of our study design, we believe that rigorous clinical trials comparing anaerobic with anaerobic and Gram-negative therapy are an important and necessary next step to determine the optimal treatment for aspiration pneumonia in this population.

Disclosures

The authors do not have any financial relationships relevant to this article to disclose.

Funding

Dr. Thomson was supported by the Agency for Healthcare Research and Quality (AHRQ) under award number K08HS025138. Dr. Ambroggio was supported by the National Institute for Allergy and Infectious Diseases (NIAID) under award number K01AI125413. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ or NIAID.

Neurologic impairment (NI) encompasses static and progressive diseases of the central and/or peripheral nervous systems that result in functional and intellectual impairments.¹ While a variety of neurologic diseases are responsible for NI (eg, hypoxic-ischemic encephalopathy, muscular dystrophy), consequences of these diseases extend beyond neurologic manifestations.¹ These children are at an increased risk for aspiration of oral and gastric contents given their common comorbidities of dysphagia, gastroesophageal reflux, impaired cough, and respiratory muscle weakness.² While aspiration may manifest as a self-resolving pneumonitis, the presence of oral or enteric bacteria in aspirated material may result in the development of bacterial pneumonia. Children with NI hospitalized with aspiration pneumonia have higher complication rates, longer and costlier hospitalizations, and higher readmission rates when compared with children with nonaspiration pneumonia.³

While pediatric aspiration pneumonia is commonly attributed to anaerobic bacteria, this is largely based on extrapolation from epidemiologic studies that were conducted in past decades.^4-8 A single randomized controlled trial found that penicillin and clindamycin, antimicrobials with similar antimicrobial activity against anaerobes, to be equally effective.⁹ However, the recent literature emphasizes the polymicrobial nature of aspiration pneumonia in adults, with the common isolation of Gram-negative enteric bacteria.¹⁰ Further, while Pseudomonas aeruginosa is often identified in respiratory cultures from children with NI and chronic respiratory insufficiency,^11,12 the significance of P. aeruginosa in lower airways remains unclear.

We designed this study to compare hospital outcomes associated with the most commonly prescribed empiric antimicrobial therapies for aspiration pneumonia in children with NI.

MATERIALS AND METHODS

Study Design and Data Source

This multicenter, retrospective cohort study used the Pediatric Health Information System (PHIS) database. PHIS, an administrative database of 50 not-for-profit tertiary care pediatric hospitals, contains data regarding patient demographics, diagnoses and procedures, and daily billed resource utilization, including laboratory and imaging studies. Data quality and reliability are assured through the Children’s Hospital Association (CHA; Lenexa, Kansas) and participating hospitals. Due to incomplete data through the study period and data quality issues, six hospitals were excluded.

STUDY POPULATION

Inclusion Criteria

Children 1-18 years of age who were discharged between July 1, 2007 and June 30, 2015 were included if they had a NI diagnosis,¹ a principal diagnosis indicative of aspiration pneumonia (507.x),^3,13,14 and received antibiotics in the first two calendar days of admission. NI was determined using previously defined International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) diagnosis codes.¹ We only included children who received antibiotics in the first two calendar days of admission to minimize the likelihood of including children admitted for other reasons who acquired aspiration pneumonia after hospitalization. For children with multiple hospitalizations, one admission was randomly selected for inclusion to minimize weighting results toward repeat visits.

 

Exclusion Criteria

Children transferred from another hospital were excluded as records from their initial presentation, including treatment and outcomes, were not available. We also excluded children with tracheostomy^15,16 or chronic ventilator dependence,¹⁷ those with a diagnosis of human immunodeficiency virus or tuberculosis, and children who received chemotherapy during hospitalization given expected differences in etiology, treatment, and outcomes.¹⁸

Exposure

The primary exposure was antibiotic therapy received in the first two days of admission. Antibiotics were classified by their antimicrobial spectra of activity as defined by The Sanford Guide to Antimicrobial Therapy¹⁹ against the most commonly recognized pathogens of aspiration pneumonia: anaerobes, Gram-negatives, and P. aeruginosa (Appendix Table 1).^10,20 For example, penicillin G and clindamycin were among the antibiotics classified as providing anaerobic coverage alone, whereas ceftriaxone was classified as providing Gram-negative coverage alone and ampicillin-sulbactam or as combination therapy with clindamycin and ceftriaxone were classified as providing anaerobic and Gram-negative coverage. Piperacillin-tazobactam and meropenem were classified as providing anaerobic, Gram-negative, and P. aeruginosa coverage. We excluded antibiotics that do not provide coverage against anaerobes, Gram-negative, or P. aeruginosa (eg, ampicillin, azithromycin) or that provide coverage against Gram-negative and P. aeruginosa, but not anaerobes (eg, cefepime, tobramycin), as these therapies were prescribed for <5% of the cohort. We chose not to examine the coverage for Streptococcus pneumonia or Staphylococcus aureus as antibiotics included in this analysis covered these bacteria for 99.9% of our cohort.

OUTCOMES

Outcomes included acute respiratory failure during hospitalization, intensive care unit (ICU) transfer, and hospital length of stay (LOS). Acute respiratory failure during hospitalization was defined as the presence of Clinical Transaction Classification (CTC) or ICD-9 procedure code for noninvasive or invasive mechanical ventilation on day two or later of hospitalization, with or without the need for respiratory support on day 0 or day 1 (Appendix Table 2). Given the variability in hospital policies that may drive ICU admission criteria for complex patients, our outcome of ICU transfer was defined as the requirement for ICU level care on day two or later of hospitalization without ICU admission. Acute respiratory failure and ICU care occurring within the first two hospital days were not classified as outcomes because these early events likely reflect illness severity at presentation rather than outcomes attributable to treatment failure; these were included as markers of severity in the models.

Patient Demographics and Clinical Characteristics

Demographic and clinical characteristics that might influence antibiotic choice and/or hospital outcomes were assessed. Clinical characteristics included complex chronic conditions,^21-23 medical technology assistance,²⁴ performance of diagnostic testing, and markers of severe illness on presentation. Diagnostic testing included bacterial cultures (blood, respiratory, urine) and chest radiograph performance in the first two days of hospitalization. Results of diagnostic testing are not available in the PHIS. Illness severity on presentation included acute respiratory failure, pleural drainage, receipt of vasoactive agents, and transfusion of blood products in the first two days of hospitalization (Appendix Table 2).^17,25,26

STASTICAL ANALYSIS

Continuous data were described with median and interquartile ranges (IQR) due to nonnormal distribution. Categorical data were described with frequencies and percentages. Patient demographics, clinical characteristics, and hospital outcomes were stratified by empiric antimicrobial coverage and compared using chi-square and Kruskal–Wallis tests as appropriate.

 

Generalized linear mixed-effects models with random hospital intercepts were derived to assess the independent effect of antimicrobial spectra of activity on outcomes of acute respiratory failure, ICU transfer, and LOS while adjusting for important differences in demographic and clinical characteristics. LOS had a nonnormal distribution. Thus, we used an exponential distribution. Covariates were chosen a priori given the clinical and biological relevance to exposure and outcomes—age, presence of complex chronic condition diagnoses, the number of complex chronic conditions, technology dependence, the performance of diagnostic tests on presentation, and illness severity on presentation. ICU admission was included as a covariate in acute respiratory failure and LOS outcome models. The results of the model for acute respiratory failure and ICU transfer are presented as adjusted odds ratios (OR) with a 95% CI. LOS results are presented as adjusted rate ratios (RR) with 95% CI.

All analyses were performed with SAS 9.3 (SAS Institute, Cary, North Carolina). P values <.05 were considered statistically significant. Cincinnati Children’s Hospital Medical Center Institutional Review Board considered this deidentified dataset study as not human subjects research.

RESULTS

Study Cohort

At the 44 hospitals included, 4,812 children with NI hospitalized with the diagnosis of aspiration pneumonia met the eligibility criteria. However, 79 received antibiotics with the spectra of activity not examined, leaving 4,733 children in our final analysis (Appendix Figure). Demographic and clinical characteristics of the study cohort are shown in Table 1. Median age was five years (interquartile range [IQR]: 2-11 years). Most subjects were male (53.9%), non-Hispanic white (47.9%), and publicly insured (63.6%). There was a slight variation in the distribution of admissions across seasons (spring 31.6%, summer 19.2%, fall 21.3%, and winter 27.9%). One-third of children had four or more comorbid CCCs (complex chronic conditions; 34.2%). The three most common nonneurologic CCC diagnosis categories were gastrointestinal (63.1%), congenital and/or genetic defects (36.9%), and respiratory (8.9%). Assistance with medical technologies was also common (82%)—particularly gastrointestinal (63.1%) and neurologic/neuromuscular (9.8%) technologies. The vast majority of children (92.5%) had either a chest radiograph (90.5%), respiratory viral study (33.7%), or respiratory culture (10.0%) obtained on presentation. A minority required noninvasive or invasive respiratory support (25.4%), vasoactive agents (8.9%), blood products (1.2%), or pleural drainage (0.3%) in the first two hospital days.

Spectrum of Antimicrobial Coverage

Most children (57.9%) received anaerobic and Gram-negative coverage; 16.2% received anaerobic, Gram-negative and P. aeruginosa coverage; 15.3% received anaerobic coverage alone; and 10.6% received Gram-negative coverage alone. Empiric antimicrobial coverage varied substantially across hospitals: anaerobic coverage was prescribed for 0%-44% of patients; Gram-negative coverage was prescribed for 3%-26% of patients; anaerobic and Gram-negative coverage was prescribed for 25%-90% of patients; and anaerobic, Gram-negative, and P. aeruginosa coverage was prescribed for 0%-65% of patients (Figure 1).

There were several important differences between treatment groups (Table 1). Children receiving anaerobic, Gram-negative, and P. aeruginosa coverage were older, more likely to have certain CCCs (respiratory, gastrointestinal, and malignancy), have ≥4 CCCs, and require assistance with medical technologies (respiratory, gastrointestinal) compared with all other treatment groups. They were also more likely to have respiratory viral testing and bacterial cultures obtained and to have markers of severe illness on presentation.

 

Outcomes

Acute Respiratory Failure

One-quarter (25.4%) of patients had acute respiratory failure on presentation; 22.5% required respiratory support (continued from presentation or were new) on day two or later of hospitalization (Table 2). In the adjusted analysis, children receiving Gram-negative coverage alone had two-fold greater odds (OR 2.15, 95% CI: 1.41-3.27) and children receiving anaerobic and Gram-negative coverage had 1.6-fold greater odds (OR 1.65, 95% CI: 1.19-2.28), of respiratory failure during hospitalization compared with those receiving anaerobic coverage alone (Figure 2). Odds of respiratory failure during hospitalization did not significantly differ for children receiving anaerobic, Gram-negative, and P. aeruginosa coverage compared with those receiving anaerobic coverage alone.

ICU Transfer

Nearly thirty percent (29.0%) of children required ICU admission, with an additional 3.8% requiring ICU transfer following admission (Table 2). In the multivariable analysis, the odds of an ICU transfer were greater for children receiving Gram-negative coverage alone (OR 1.80, 95% CI: 1.03-3.14) compared with those receiving anaerobic coverage alone. There was no statistical difference in ICU transfer for those receiving anaerobic and Gram-negative coverage (with or without P. aeruginosa coverage) compared with those receiving anaerobic coverage alone (Figure 2).

Length of Stay

Median hospital LOS for the total cohort was five days (IQR: 3-9 days; Table 2). In the multivariable analysis, children receiving Gram-negative coverage alone had a longer LOS (RR 1.28; 95% CI: 1.16-1.41) compared with those receiving anaerobic coverage alone, whereas children receiving anaerobic, Gram-negative, and P. aeruginosa coverage had a shorter LOS (RR 0.83; 95% CI: 0.76-0.90) than those receiving anaerobic coverage alone (Figure 2). There was no statistical difference in the LOS between children receiving anaerobic and Gram-negative coverage and those receiving anaerobic coverage alone.

DISCUSSION

In this multicenter study of children with NI hospitalized with aspiration pneumonia, we found substantial variation in empiric antimicrobial coverage for children with aspiration pneumonia. When comparing outcomes across groups, children who received anaerobic and Gram-negative coverage had outcomes similar to children who received anaerobic therapy alone. However, children who did not receive anaerobic coverage (ie, Gram-negative coverage alone) had worse outcomes, most notably a greater than two-fold increase in the odds of experiencing acute respiratory failure during hospitalization when compared with children receiving anaerobic therapy. These findings support prior literature that has highlighted the importance of anaerobic therapy in the treatment of aspiration pneumonia. The benefit of antibiotics targeting Gram-negative organisms, in addition to anaerobes, remains uncertain.

The variability in empiric antimicrobial coverage likely reflects the paucity of available information on oral and/or enteric bacteria required to identify them as causative organisms in aspiration pneumonia. In part, this problem is due to the difficulty in obtaining adequate sputum for culture from pediatric patients.²⁷ While it may be more feasible to obtain tracheal aspirates for respiratory culture in children with a tracheostomy, interpretation of culture results remains challenging because the lower airways of children with tracheostomy are commonly colonized with bacterial pathogens.²⁸ Thus, physicians are often left to choose empiric antimicrobial coverage with inadequate supporting evidence.²⁹ Although the polymicrobial nature of aspiration pneumonia is well recognized in adult and pediatric literature,^10,30 it is less clear which organisms are of pathological significance and require treatment.

The treatment standard for aspiration pneumonia has long included anaerobic therapy.²⁹ The worse outcomes of children not receiving anaerobic therapy (ie, Gram-negative coverage alone) compared with children who received anaerobic therapy support the continued importance of anaerobic therapy in the treatment of aspiration pneumonia for hospitalized children with NI. The role of antibiotics covering Gram-negative organisms is less clear. Recent studies suggest the role of anaerobes is overemphasized in the etiology and treatment of aspiration pneumonia.^10,29,31-38 Multiple studies on aspiration pneumonia bacteriology in hospitalized adults have demonstrated a predominance of Gram-negative organisms (ranging from 37%-71% of isolates identified on respiratory culture) and a relative scarcity of anaerobes (ranging from 0%-16% of isolates).^31-37 A prospective study of 50 children hospitalized with clinical and radiographic evidence of pneumonia with known aspiration risk (eg, neuromuscular disease or dysphagia) found that ~80% of 163 bacterial isolates were Gram-negative.³⁸ However, this study included repeat cultures from the same children, and thus, may overestimate the prevalence of Gram-negative organisms. In our study, children who received both anaerobic and Gram-negative therapy had no differences in ICU transfer or LOS but did experience higher odds of acute respiratory failure. As these results may be due to unmeasured confounding, future studies should further explore the necessity of Gram-negative coverage in addition to anaerobic coverage in this population.

While these recent studies may seem to suggest that anaerobic coverage is not necessary for aspiration pneumonia, there are important limitations worth noting. First, these studies used a variety of sampling techniques. While organisms grown from samples obtained via bronchoalveolar lavage^31-34,36 are likely pathogenic, those grown from tracheal or oral samples obtained via percutaneous transtracheal aspiration,³⁴ a protected specimen brush,^34,36,37 or expectorated sputum^35,38 may not represent lower airway organisms. Second, anaerobic cultures were not obtained in all studies.^31,34,38 Anaerobic organisms are difficult to isolate using traditional clinical specimen collection techniques and aerobic culture media.¹⁸ Furthermore, anaerobes are not easily recovered from lung infections after the receipt of antibiotic therapy.³⁹ Details regarding pretreatment, which are largely lacking from these studies, are necessary to interpret the relative scarcity of anaerobes on respiratory culture. Finally, caution should be taken when extrapolating the results of studies focused on the etiology and treatment of aspiration pneumonia in elderly adults to children. Our results, particularly in the context of the limitation of these more recent studies, suggest that the role of anaerobes has been underestimated.

Recent studies examining populations of children with cerebral palsy and/or tracheostomy have emphasized the high rates of carriage and infection rates with Gram-negative and drug-resistant bacteria; in particular, P. aeruginosa accounts for 50%-72% of pathogenic bacteria.^11,12,38,40These studies note the generally poor outcomes of children with P. aeruginosa—including multiple and longer hospitalizations, frequent readmissions, and the increased severity of pneumonia, including the need for ICU admission, pleural effusions, the need for intubation, and mortality.^{11,12,38,40,41} In our study, nearly 35% of children who received anaerobic, Gram-negative, and P. aeruginosa coverage experienced acute respiratory failure during hospitalization compared with 20% of children who received other therapies. While these results might seem to suggest that broader spectrum therapy is harmful, they must be interpreted in the context of important population differences; children who received a combination of anaerobic, Gram-negative, and P. aeruginosa coverage had greater medical complexity and greater severity of illness on presentation. Such factors may provide the reason for the appropriate prescription of antipseudomonal antibiotics (eg, history of tracheostomy colonization or infection, long-term care facility resident).⁴² When we controlled for population differences, children who received antipseudomonal therapy had a significantly shorter LOS and no differences in outcomes of acute respiratory failure or ICU transfer compared with those receiving anaerobic therapy alone. This result suggests that worse outcomes were associated with antipseudomonal therapy on unadjusted analyses resulting from underlying medical complexity and illness severity rather than from colonization or infection with P. aeruginosa.

Our multicenter observational study has several limitations. We used diagnosis codes to identify patients with aspiration pneumonia. As validated clinical criteria for the diagnosis of aspiration pneumonia do not exist, clinicians may assign a diagnosis of and treatment for aspiration pneumonia by subjective suspicion based on a child’s severe NI or illness severity on presentation leading to selection bias. Although administrative data are not able to verify pneumonia type with absolute certainty, we previously demonstrated that the differences in the outcomes of children with aspiration and nonaspiration pneumonia diagnosis codes persist after accounting for the complexity that might influence the diagnosis.³It is also possible that the diagnosis of aspiration pneumonia was not made upon admission for a subset of patients leading to misclassification of exposure. Some children may have had aspiration pneumonia on admission but were not assigned that diagnosis or treated for presumed aspiration pneumonia until later in the hospital course as they demonstrated treatment failure or clinical worsening. It is also possible that some children had an aspiration event during hospitalization that developed into aspiration pneumonia. We attempted to adjust for medical complexity and illness severity through multivariable adjustment based on the diagnosis and procedure codes, as well as the laboratory testing performed. However, unmeasured or residual confounding may remain as administrative data are not equipped to distinguish detailed functional status (eg, ability to cough, chest wall strength) or illness severity (eg, respiratory distress) that might influence antibiotic selection and/or outcomes.

Frthermore, we were unable to account for laboratory, microbiology, or radiology test results, and other management practices (eg, frequency of airway clearance, previous antimicrobial therapy) that may influence outcomes. Future studies should certainly include an examination of the concordance of the antibiotics prescribed with causative organisms, as this undoubtedly affects patient outcomes. Other outcomes are important to examine (eg, time to return to respiratory baseline), but we were unable to do so, given the lack of clinical detail in our database. We randomly selected a single hospitalization for children with multiple admissions; alternative methods could have different results. Although children with NI predominately use children’s hospitals,¹ results may not be generalizable.

 

CONCLUSION

These findings support prior literature that has highlighted the important role anaerobic therapy plays in the treatment of aspiration pneumonia in children with NI. In light of the limitations of our study design, we believe that rigorous clinical trials comparing anaerobic with anaerobic and Gram-negative therapy are an important and necessary next step to determine the optimal treatment for aspiration pneumonia in this population.

Disclosures

The authors do not have any financial relationships relevant to this article to disclose.

Funding

Dr. Thomson was supported by the Agency for Healthcare Research and Quality (AHRQ) under award number K08HS025138. Dr. Ambroggio was supported by the National Institute for Allergy and Infectious Diseases (NIAID) under award number K01AI125413. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ or NIAID.

Intensive glycemic lowering for the treatment for type 2 diabetes mellitus (T2DM) has been shown to decrease microvascular and macrovascular outcomes in the UK Prospective Diabetes Study (UKPDS) without any risk of increased harm.^1,2 Over the past decade, evidence has shown that the outcomes and risk do not hold true in an older population with additional comorbidities and longer duration of DM. Both the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Veterans Affairs Diabetes Trial (VADT) trials showed no decreased incidence of macrovascular or microvascular complications of DM with intensive glucose lowering but an additional risk of hypoglycemia and even death.^2-4

Patient-specific risk factors, such as age, impaired renal function, and cognitive impairment, have been shown to lead to an increased risk of hypoglycemia independent of hemoglobin A_1c (HbA_1c). Dementia and cognitive impairment are associated with a 2.42 and 1.72 times greater risk of hypoglycemia, respectively, compared with a patient without dementia or cognitive impairment.⁵ A post-hoc analysis of the ACCORD trial that analyzed the risk of hypoglycemia in subgroup populations showed an increased risk of hypoglycemia in those with a serum creatinine (SCr) level > 1.3 mg/dL (hazard ratio, 1.66, P < .01) and increasing age. Risk of hypoglycemia was highest in those aged ≥ 75 years but increased by 3% for every subsequent year (P < .01).⁶ These risk factors should be addressed and considered in individual patients with DM to safely guide therapy.

The evidence from these landmark trials has led to increased HbA_1c goals for specific patient populations in the most recent 2017 VA/DoD Clinical Practice Guideline (CPG) for the Management of Type 2 Diabetes Mellitus in Primary Care.⁷ The majority of patients with T2DM now qualify for HBA_1c goals > 7.0%. According to the 2017 VA/DoD CPG, younger patients with the absence of a major comorbidity and life expectancy of > 10 to 15 years with mild or absent microvascular complications is the only group of patients who should be treated to an A_1c goal of 6.0 to 7.0%.⁷ The use of shared decision making and patient education to set glycemic goals based on “patient capabilities, needs, goals, prior treatment experience, and preferences” also should be used to increase patient education and satisfaction.⁷

In December 2014, the VA introduced the Hypoglycemia Safety Initiative (HSI). The goal of the HSI is to “enable veterans living with diabetes to work more closely with their VA clinicians to personalize health care goals and improve self-management of the disease.”⁸ This goal also aligns with the US Department of Health and Human Services National Action Plan for Adverse Drug Event Prevention. One of 3 initial targets of this plan includes DM agents and the prevention of hypoglycemia.⁹

To combat the risk of hypoglycemia and potentially negative outcomes, as part of the HSI, the VA is implementing a clinical reminder within the Computerized Patient Record System (CPRS) that will prompt the primary care team to screen select patients at risk for hypoglycemia. The purpose of this project was to identify patients at high risk of hypoglycemia, individualize HbA_1c goals, and consider de-escalation in therapy, using shared decision making.

 

Methods

This quality improvement project, conducted at the Fayetteville VA Medical Center (FVAMC), consisted of outpatient services provided at 2 health care centers and 6 community-based outpatient clinics. The project was exempt from institutional review board approval as the protocol met national VA criteria as a quality assurance project.

Patients were identified using the HSI Corporate Data Warehouse (CDR) reports. Once patients were identified, a list was distributed to the appropriate clinical pharmacy specialist (CPS), according to patient aligned care teams (PACTs). The CPS contacted the patient via telephone or in person to conduct hypoglycemia screening. Patients on a sulfonylurea or insulin and an HbA_1c < 7% plus 1 risk factor for hypoglycemia (aged ≥ 75 years, serum creatinine[SCr] ≥1.7 mg/dL, diagnosis of cognitive impairment, or prescribed a cholinesterase inhibitor) were included. These risk factors were chosen to align with the future clinical reminder, which is based on an increased risk of hypoglycemia seen in these patient populations.

Patients were included if they were receiving antidiabetic medications through the FVAMC or outside of the VA and/or prescribed by a non-VA provider. Medications and doses prescribed by a non-VA provider were verified with the patient verbally during the initial interview. Once contacted by the CPS, any patients who no longer met inclusion criteria were excluded.

The CPS used a national VA hypoglycemia screening note template to ask the patient about frequency and severity of hypoglycemia. Hypoglycemia was defined as a self-monitored blood glucose < 70 mg/dL with or without symptoms. An additional definition consisted of typical hypoglycemia symptoms as reported by the patient even if self-monitored blood glucose was not obtained while exhibiting symptoms. Using shared decision making between the CPS and veteran, antidiabetic therapy was either relaxed or continued. Relaxing therapy was defined as decreasing doses or discontinuation of antidiabetic medications that are known for potentiating hypoglycemia (ie, sulfonylurea and insulin).

The CPS had autonomy in deciding how much to lower dose(s) or when to discontinue medication(s). Additional counseling in proper medication administration, including appropriate timing of medication administration, also could have been the sole intervention needed for a given patient who experienced hypoglycemia. Counseling would have been considered continuation of therapy. For example, if a patient was experiencing hypoglycemia while taking a sulfonylurea twice daily, the CPS would provide counseling on proper timing of medication administration 20 to 30 minutes before morning and evening meals rather than the patient’s perceived administration of twice daily without regard to meals. Even in patients who met inclusion criteria but who did not experience any hypoglycemia symptoms, the CPS and patient could use shared decision making with emphasis on appropriate HbA_1c goals to determine whether relaxation in therapy was appropriate.

Data Collection

Baseline demographics, including prespecified risk factors for hypoglycemia, were collected. Data were imported into the HSI CDW from the national VA hypoglycemia screening note template completed by the CPS. From the data CDW, frequency and severity of hypoglycemia were recorded. The CPS interventions were also quantified; HbA_1c data were obtained in patients in whom therapy was relaxed 3- to 6-months postintervention.

 

Statistical Analysis

Descriptive statistics (mean, range) were used for analyzing results. A t test with a 1-tailed distribution was used to analyze the change in HbA_1c after CPS intervention (α = .05).

Results

On August 17, 2017, 839 patients were identified across all FVAMC facilities from the HSI data CDW. Patients were contacted through February 16, 2018. A total of 52 patients were excluded as they no longer met inclusion criteria or were deceased at time of review. 

Of the 787 patients who were included, 619 (79%) were evaluated by the CPS; 168 patients could not be contacted. At baseline, the average age was 75.9 years, average HbA_1c was 6.2%, and most patients who met inclusion criteria were aged ≥ 75 years (Table 1).

The most commonly prescribed antidiabetic prescription was a sulfonylurea (482 prescriptions) followed by basal insulin (319 prescriptions; Table 2). 

Of the 619 patients evaluated by a CPS, 159 (26%) reported hypoglycemia. Frequency of hypoglycemia was reported as 13% of patients who experienced hypoglycemia once in the past few months, 8% 2 to 3 times per month, 4% once weekly, and < 1% daily (Figure 1). 

Severity of hypoglycemia was analyzed by asking patients how often a hypoglycemic episode was severe enough that they felt that they might pass out. Of the 159 patients reporting hypoglycemia, 132 stated “never,” 23 stated “once,” and 4 stated “2 to 3 times per month.” Eight patients reported a hypoglycemic episode severe enough to require a visit to a clinic, emergency department, or hospital.

The CPS used shared decision making to relax antidiabetic therapy in 102 (16.5%) of the total number of patients contacted (Figure 2). Lab orders were entered for the patient to obtain an HbA_1c in 3 to 6 months in those in whom therapy was relaxed. 

A total of 70 patients obtained a follow-up HbA_1c. Average HbA_1c prior to follow-up was 6.28%, and HbA_1c after pharmacist intervention was 6.57%. This is an average increase in HbA_1c of 0.29% (P < .01). Average time lapse between therapy relaxation and follow-up HbA_1c was 86.6 days.

Discussion

The primary objective of this project was to identify patients at risk for hypoglycemia. Approximately 1 in 4 patients reported any incidence of hypoglycemia, which shows that the prespecified inclusion criteria was an appropriate guide for hypoglycemia screening. The episodes of hypoglycemia were typically infrequent, occurring only once every few months. This could have contributed to a lower rate of therapy changes compared with the rate of hypoglycemia. Overall, hypoglycemia was not severe; 83% of patients did not report any symptoms of faintness. Pharmacists were able to intervene and relax therapy in 102 patients to try to prevent episodes of hypoglycemia and negative outcomes. These interventions led to a statistically significant increase in average HbA_1c in these patients. Throughout these encounters with the CPS and patient, there were also innumerable outcomes secondary to the use of shared decision making. Regardless of medication changes, there was increased patient education concerning hypoglycemia treatment, medication administration times, and HbA_1c goals.

 

This project’s strengths included the large sample size, appropriate inclusion criteria that identified patients at risk for hypoglycemia, and the use of shared decision making. It was also beneficial to obtain HbA_1c levels after a relaxation in therapy for objective outcomes. The increase in HbA_1c levels showed a statistically significant gain, which led to more patients having an HbA_1c closer to a CPG-recommended goal range, given their risk factors for hypoglycemia. This pharmacy initiative fostered increased communication between providers and CPS within the PACT team. The pharmacist was not consulted by the provider for management of these patients with DM, so therapy relaxation was documented in CPRS and was addressed at the patient’s next primary care appointment. Some changes also required discussion with the primary care provider prior to relaxation in therapy. By initiating these discussions with providers, opportunities arose for additional education on appropriate HbA_1c goals and why therapy should be relaxed in select patient populations.

Limitations

Some limitations to this project were the use of telephone encounters and interpharmacist variability. Patients who were contacted via telephone by a pharmacist who was unknown to them were more hesitant to make changes. Patients managed for DM by non-VA providers or patients receiving medications at a non-VA pharmacy were also reluctant to implement changes. Education was the major intervention for these patients. Pharmacists were instructed to use their clinical judgment in addition to shared decision making with the patient when relaxing therapy. There was no protocol for medication changes. Although interpharmacist variability is identified as a weakness, it could be considered more representative of daily practice.

Additionally, despite a statistically significant increase in HbA_1c, which would presumably lead to fewer episodes of hypoglycemia, patients were not contacted again after the intervention to inquire whether hypoglycemia had decreased. Studies targeted at the impact of less frequent hypoglycemia events, including fewer emergency department visits, hospital admissions, or primary care walk-in appointments, would improve the clinical significance of these data. As the HSI is implemented nationally within the VA, more data will be available to better evaluate the applicability of this clinical reminder. Locally, the criteria for the clinical reminder has proved to capture a significant number of patients experiencing hypoglycemia. Using national data will also help to guide the frequency of screening needed in this population.

Conclusion

The implementation of the HSI led to increased provider and patient awareness of hypoglycemia. The CPS interventions have resulted in statistically significant increases in HbA_1c levels, which would seemingly decrease the patient’s risk of adverse outcomes as shown in the ACCORD and VADT trials.

Intensive glycemic lowering for the treatment for type 2 diabetes mellitus (T2DM) has been shown to decrease microvascular and macrovascular outcomes in the UK Prospective Diabetes Study (UKPDS) without any risk of increased harm.^1,2 Over the past decade, evidence has shown that the outcomes and risk do not hold true in an older population with additional comorbidities and longer duration of DM. Both the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Veterans Affairs Diabetes Trial (VADT) trials showed no decreased incidence of macrovascular or microvascular complications of DM with intensive glucose lowering but an additional risk of hypoglycemia and even death.^2-4

Patient-specific risk factors, such as age, impaired renal function, and cognitive impairment, have been shown to lead to an increased risk of hypoglycemia independent of hemoglobin A_1c (HbA_1c). Dementia and cognitive impairment are associated with a 2.42 and 1.72 times greater risk of hypoglycemia, respectively, compared with a patient without dementia or cognitive impairment.⁵ A post-hoc analysis of the ACCORD trial that analyzed the risk of hypoglycemia in subgroup populations showed an increased risk of hypoglycemia in those with a serum creatinine (SCr) level > 1.3 mg/dL (hazard ratio, 1.66, P < .01) and increasing age. Risk of hypoglycemia was highest in those aged ≥ 75 years but increased by 3% for every subsequent year (P < .01).⁶ These risk factors should be addressed and considered in individual patients with DM to safely guide therapy.

The evidence from these landmark trials has led to increased HbA_1c goals for specific patient populations in the most recent 2017 VA/DoD Clinical Practice Guideline (CPG) for the Management of Type 2 Diabetes Mellitus in Primary Care.⁷ The majority of patients with T2DM now qualify for HBA_1c goals > 7.0%. According to the 2017 VA/DoD CPG, younger patients with the absence of a major comorbidity and life expectancy of > 10 to 15 years with mild or absent microvascular complications is the only group of patients who should be treated to an A_1c goal of 6.0 to 7.0%.⁷ The use of shared decision making and patient education to set glycemic goals based on “patient capabilities, needs, goals, prior treatment experience, and preferences” also should be used to increase patient education and satisfaction.⁷

In December 2014, the VA introduced the Hypoglycemia Safety Initiative (HSI). The goal of the HSI is to “enable veterans living with diabetes to work more closely with their VA clinicians to personalize health care goals and improve self-management of the disease.”⁸ This goal also aligns with the US Department of Health and Human Services National Action Plan for Adverse Drug Event Prevention. One of 3 initial targets of this plan includes DM agents and the prevention of hypoglycemia.⁹

To combat the risk of hypoglycemia and potentially negative outcomes, as part of the HSI, the VA is implementing a clinical reminder within the Computerized Patient Record System (CPRS) that will prompt the primary care team to screen select patients at risk for hypoglycemia. The purpose of this project was to identify patients at high risk of hypoglycemia, individualize HbA_1c goals, and consider de-escalation in therapy, using shared decision making.

 

Methods

This quality improvement project, conducted at the Fayetteville VA Medical Center (FVAMC), consisted of outpatient services provided at 2 health care centers and 6 community-based outpatient clinics. The project was exempt from institutional review board approval as the protocol met national VA criteria as a quality assurance project.

Patients were identified using the HSI Corporate Data Warehouse (CDR) reports. Once patients were identified, a list was distributed to the appropriate clinical pharmacy specialist (CPS), according to patient aligned care teams (PACTs). The CPS contacted the patient via telephone or in person to conduct hypoglycemia screening. Patients on a sulfonylurea or insulin and an HbA_1c < 7% plus 1 risk factor for hypoglycemia (aged ≥ 75 years, serum creatinine[SCr] ≥1.7 mg/dL, diagnosis of cognitive impairment, or prescribed a cholinesterase inhibitor) were included. These risk factors were chosen to align with the future clinical reminder, which is based on an increased risk of hypoglycemia seen in these patient populations.

Patients were included if they were receiving antidiabetic medications through the FVAMC or outside of the VA and/or prescribed by a non-VA provider. Medications and doses prescribed by a non-VA provider were verified with the patient verbally during the initial interview. Once contacted by the CPS, any patients who no longer met inclusion criteria were excluded.

The CPS used a national VA hypoglycemia screening note template to ask the patient about frequency and severity of hypoglycemia. Hypoglycemia was defined as a self-monitored blood glucose < 70 mg/dL with or without symptoms. An additional definition consisted of typical hypoglycemia symptoms as reported by the patient even if self-monitored blood glucose was not obtained while exhibiting symptoms. Using shared decision making between the CPS and veteran, antidiabetic therapy was either relaxed or continued. Relaxing therapy was defined as decreasing doses or discontinuation of antidiabetic medications that are known for potentiating hypoglycemia (ie, sulfonylurea and insulin).

The CPS had autonomy in deciding how much to lower dose(s) or when to discontinue medication(s). Additional counseling in proper medication administration, including appropriate timing of medication administration, also could have been the sole intervention needed for a given patient who experienced hypoglycemia. Counseling would have been considered continuation of therapy. For example, if a patient was experiencing hypoglycemia while taking a sulfonylurea twice daily, the CPS would provide counseling on proper timing of medication administration 20 to 30 minutes before morning and evening meals rather than the patient’s perceived administration of twice daily without regard to meals. Even in patients who met inclusion criteria but who did not experience any hypoglycemia symptoms, the CPS and patient could use shared decision making with emphasis on appropriate HbA_1c goals to determine whether relaxation in therapy was appropriate.

Data Collection

Baseline demographics, including prespecified risk factors for hypoglycemia, were collected. Data were imported into the HSI CDW from the national VA hypoglycemia screening note template completed by the CPS. From the data CDW, frequency and severity of hypoglycemia were recorded. The CPS interventions were also quantified; HbA_1c data were obtained in patients in whom therapy was relaxed 3- to 6-months postintervention.

 

Statistical Analysis

Descriptive statistics (mean, range) were used for analyzing results. A t test with a 1-tailed distribution was used to analyze the change in HbA_1c after CPS intervention (α = .05).

Results

On August 17, 2017, 839 patients were identified across all FVAMC facilities from the HSI data CDW. Patients were contacted through February 16, 2018. A total of 52 patients were excluded as they no longer met inclusion criteria or were deceased at time of review. 

Of the 787 patients who were included, 619 (79%) were evaluated by the CPS; 168 patients could not be contacted. At baseline, the average age was 75.9 years, average HbA_1c was 6.2%, and most patients who met inclusion criteria were aged ≥ 75 years (Table 1).

The most commonly prescribed antidiabetic prescription was a sulfonylurea (482 prescriptions) followed by basal insulin (319 prescriptions; Table 2). 

Of the 619 patients evaluated by a CPS, 159 (26%) reported hypoglycemia. Frequency of hypoglycemia was reported as 13% of patients who experienced hypoglycemia once in the past few months, 8% 2 to 3 times per month, 4% once weekly, and < 1% daily (Figure 1). 

Severity of hypoglycemia was analyzed by asking patients how often a hypoglycemic episode was severe enough that they felt that they might pass out. Of the 159 patients reporting hypoglycemia, 132 stated “never,” 23 stated “once,” and 4 stated “2 to 3 times per month.” Eight patients reported a hypoglycemic episode severe enough to require a visit to a clinic, emergency department, or hospital.

The CPS used shared decision making to relax antidiabetic therapy in 102 (16.5%) of the total number of patients contacted (Figure 2). Lab orders were entered for the patient to obtain an HbA_1c in 3 to 6 months in those in whom therapy was relaxed. 

A total of 70 patients obtained a follow-up HbA_1c. Average HbA_1c prior to follow-up was 6.28%, and HbA_1c after pharmacist intervention was 6.57%. This is an average increase in HbA_1c of 0.29% (P < .01). Average time lapse between therapy relaxation and follow-up HbA_1c was 86.6 days.

Discussion

The primary objective of this project was to identify patients at risk for hypoglycemia. Approximately 1 in 4 patients reported any incidence of hypoglycemia, which shows that the prespecified inclusion criteria was an appropriate guide for hypoglycemia screening. The episodes of hypoglycemia were typically infrequent, occurring only once every few months. This could have contributed to a lower rate of therapy changes compared with the rate of hypoglycemia. Overall, hypoglycemia was not severe; 83% of patients did not report any symptoms of faintness. Pharmacists were able to intervene and relax therapy in 102 patients to try to prevent episodes of hypoglycemia and negative outcomes. These interventions led to a statistically significant increase in average HbA_1c in these patients. Throughout these encounters with the CPS and patient, there were also innumerable outcomes secondary to the use of shared decision making. Regardless of medication changes, there was increased patient education concerning hypoglycemia treatment, medication administration times, and HbA_1c goals.

 

This project’s strengths included the large sample size, appropriate inclusion criteria that identified patients at risk for hypoglycemia, and the use of shared decision making. It was also beneficial to obtain HbA_1c levels after a relaxation in therapy for objective outcomes. The increase in HbA_1c levels showed a statistically significant gain, which led to more patients having an HbA_1c closer to a CPG-recommended goal range, given their risk factors for hypoglycemia. This pharmacy initiative fostered increased communication between providers and CPS within the PACT team. The pharmacist was not consulted by the provider for management of these patients with DM, so therapy relaxation was documented in CPRS and was addressed at the patient’s next primary care appointment. Some changes also required discussion with the primary care provider prior to relaxation in therapy. By initiating these discussions with providers, opportunities arose for additional education on appropriate HbA_1c goals and why therapy should be relaxed in select patient populations.

Limitations

Some limitations to this project were the use of telephone encounters and interpharmacist variability. Patients who were contacted via telephone by a pharmacist who was unknown to them were more hesitant to make changes. Patients managed for DM by non-VA providers or patients receiving medications at a non-VA pharmacy were also reluctant to implement changes. Education was the major intervention for these patients. Pharmacists were instructed to use their clinical judgment in addition to shared decision making with the patient when relaxing therapy. There was no protocol for medication changes. Although interpharmacist variability is identified as a weakness, it could be considered more representative of daily practice.

Additionally, despite a statistically significant increase in HbA_1c, which would presumably lead to fewer episodes of hypoglycemia, patients were not contacted again after the intervention to inquire whether hypoglycemia had decreased. Studies targeted at the impact of less frequent hypoglycemia events, including fewer emergency department visits, hospital admissions, or primary care walk-in appointments, would improve the clinical significance of these data. As the HSI is implemented nationally within the VA, more data will be available to better evaluate the applicability of this clinical reminder. Locally, the criteria for the clinical reminder has proved to capture a significant number of patients experiencing hypoglycemia. Using national data will also help to guide the frequency of screening needed in this population.

Conclusion

The implementation of the HSI led to increased provider and patient awareness of hypoglycemia. The CPS interventions have resulted in statistically significant increases in HbA_1c levels, which would seemingly decrease the patient’s risk of adverse outcomes as shown in the ACCORD and VADT trials.

Intensive glycemic lowering for the treatment for type 2 diabetes mellitus (T2DM) has been shown to decrease microvascular and macrovascular outcomes in the UK Prospective Diabetes Study (UKPDS) without any risk of increased harm.^1,2 Over the past decade, evidence has shown that the outcomes and risk do not hold true in an older population with additional comorbidities and longer duration of DM. Both the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Veterans Affairs Diabetes Trial (VADT) trials showed no decreased incidence of macrovascular or microvascular complications of DM with intensive glucose lowering but an additional risk of hypoglycemia and even death.^2-4

Patient-specific risk factors, such as age, impaired renal function, and cognitive impairment, have been shown to lead to an increased risk of hypoglycemia independent of hemoglobin A_1c (HbA_1c). Dementia and cognitive impairment are associated with a 2.42 and 1.72 times greater risk of hypoglycemia, respectively, compared with a patient without dementia or cognitive impairment.⁵ A post-hoc analysis of the ACCORD trial that analyzed the risk of hypoglycemia in subgroup populations showed an increased risk of hypoglycemia in those with a serum creatinine (SCr) level > 1.3 mg/dL (hazard ratio, 1.66, P < .01) and increasing age. Risk of hypoglycemia was highest in those aged ≥ 75 years but increased by 3% for every subsequent year (P < .01).⁶ These risk factors should be addressed and considered in individual patients with DM to safely guide therapy.

The evidence from these landmark trials has led to increased HbA_1c goals for specific patient populations in the most recent 2017 VA/DoD Clinical Practice Guideline (CPG) for the Management of Type 2 Diabetes Mellitus in Primary Care.⁷ The majority of patients with T2DM now qualify for HBA_1c goals > 7.0%. According to the 2017 VA/DoD CPG, younger patients with the absence of a major comorbidity and life expectancy of > 10 to 15 years with mild or absent microvascular complications is the only group of patients who should be treated to an A_1c goal of 6.0 to 7.0%.⁷ The use of shared decision making and patient education to set glycemic goals based on “patient capabilities, needs, goals, prior treatment experience, and preferences” also should be used to increase patient education and satisfaction.⁷

In December 2014, the VA introduced the Hypoglycemia Safety Initiative (HSI). The goal of the HSI is to “enable veterans living with diabetes to work more closely with their VA clinicians to personalize health care goals and improve self-management of the disease.”⁸ This goal also aligns with the US Department of Health and Human Services National Action Plan for Adverse Drug Event Prevention. One of 3 initial targets of this plan includes DM agents and the prevention of hypoglycemia.⁹

To combat the risk of hypoglycemia and potentially negative outcomes, as part of the HSI, the VA is implementing a clinical reminder within the Computerized Patient Record System (CPRS) that will prompt the primary care team to screen select patients at risk for hypoglycemia. The purpose of this project was to identify patients at high risk of hypoglycemia, individualize HbA_1c goals, and consider de-escalation in therapy, using shared decision making.

 

Methods

This quality improvement project, conducted at the Fayetteville VA Medical Center (FVAMC), consisted of outpatient services provided at 2 health care centers and 6 community-based outpatient clinics. The project was exempt from institutional review board approval as the protocol met national VA criteria as a quality assurance project.

Patients were identified using the HSI Corporate Data Warehouse (CDR) reports. Once patients were identified, a list was distributed to the appropriate clinical pharmacy specialist (CPS), according to patient aligned care teams (PACTs). The CPS contacted the patient via telephone or in person to conduct hypoglycemia screening. Patients on a sulfonylurea or insulin and an HbA_1c < 7% plus 1 risk factor for hypoglycemia (aged ≥ 75 years, serum creatinine[SCr] ≥1.7 mg/dL, diagnosis of cognitive impairment, or prescribed a cholinesterase inhibitor) were included. These risk factors were chosen to align with the future clinical reminder, which is based on an increased risk of hypoglycemia seen in these patient populations.

Patients were included if they were receiving antidiabetic medications through the FVAMC or outside of the VA and/or prescribed by a non-VA provider. Medications and doses prescribed by a non-VA provider were verified with the patient verbally during the initial interview. Once contacted by the CPS, any patients who no longer met inclusion criteria were excluded.

The CPS used a national VA hypoglycemia screening note template to ask the patient about frequency and severity of hypoglycemia. Hypoglycemia was defined as a self-monitored blood glucose < 70 mg/dL with or without symptoms. An additional definition consisted of typical hypoglycemia symptoms as reported by the patient even if self-monitored blood glucose was not obtained while exhibiting symptoms. Using shared decision making between the CPS and veteran, antidiabetic therapy was either relaxed or continued. Relaxing therapy was defined as decreasing doses or discontinuation of antidiabetic medications that are known for potentiating hypoglycemia (ie, sulfonylurea and insulin).

The CPS had autonomy in deciding how much to lower dose(s) or when to discontinue medication(s). Additional counseling in proper medication administration, including appropriate timing of medication administration, also could have been the sole intervention needed for a given patient who experienced hypoglycemia. Counseling would have been considered continuation of therapy. For example, if a patient was experiencing hypoglycemia while taking a sulfonylurea twice daily, the CPS would provide counseling on proper timing of medication administration 20 to 30 minutes before morning and evening meals rather than the patient’s perceived administration of twice daily without regard to meals. Even in patients who met inclusion criteria but who did not experience any hypoglycemia symptoms, the CPS and patient could use shared decision making with emphasis on appropriate HbA_1c goals to determine whether relaxation in therapy was appropriate.

Data Collection

Baseline demographics, including prespecified risk factors for hypoglycemia, were collected. Data were imported into the HSI CDW from the national VA hypoglycemia screening note template completed by the CPS. From the data CDW, frequency and severity of hypoglycemia were recorded. The CPS interventions were also quantified; HbA_1c data were obtained in patients in whom therapy was relaxed 3- to 6-months postintervention.

 

Statistical Analysis

Descriptive statistics (mean, range) were used for analyzing results. A t test with a 1-tailed distribution was used to analyze the change in HbA_1c after CPS intervention (α = .05).

Results

On August 17, 2017, 839 patients were identified across all FVAMC facilities from the HSI data CDW. Patients were contacted through February 16, 2018. A total of 52 patients were excluded as they no longer met inclusion criteria or were deceased at time of review. 

Of the 787 patients who were included, 619 (79%) were evaluated by the CPS; 168 patients could not be contacted. At baseline, the average age was 75.9 years, average HbA_1c was 6.2%, and most patients who met inclusion criteria were aged ≥ 75 years (Table 1).

The most commonly prescribed antidiabetic prescription was a sulfonylurea (482 prescriptions) followed by basal insulin (319 prescriptions; Table 2). 

Of the 619 patients evaluated by a CPS, 159 (26%) reported hypoglycemia. Frequency of hypoglycemia was reported as 13% of patients who experienced hypoglycemia once in the past few months, 8% 2 to 3 times per month, 4% once weekly, and < 1% daily (Figure 1). 

Severity of hypoglycemia was analyzed by asking patients how often a hypoglycemic episode was severe enough that they felt that they might pass out. Of the 159 patients reporting hypoglycemia, 132 stated “never,” 23 stated “once,” and 4 stated “2 to 3 times per month.” Eight patients reported a hypoglycemic episode severe enough to require a visit to a clinic, emergency department, or hospital.

The CPS used shared decision making to relax antidiabetic therapy in 102 (16.5%) of the total number of patients contacted (Figure 2). Lab orders were entered for the patient to obtain an HbA_1c in 3 to 6 months in those in whom therapy was relaxed. 

A total of 70 patients obtained a follow-up HbA_1c. Average HbA_1c prior to follow-up was 6.28%, and HbA_1c after pharmacist intervention was 6.57%. This is an average increase in HbA_1c of 0.29% (P < .01). Average time lapse between therapy relaxation and follow-up HbA_1c was 86.6 days.

Discussion

The primary objective of this project was to identify patients at risk for hypoglycemia. Approximately 1 in 4 patients reported any incidence of hypoglycemia, which shows that the prespecified inclusion criteria was an appropriate guide for hypoglycemia screening. The episodes of hypoglycemia were typically infrequent, occurring only once every few months. This could have contributed to a lower rate of therapy changes compared with the rate of hypoglycemia. Overall, hypoglycemia was not severe; 83% of patients did not report any symptoms of faintness. Pharmacists were able to intervene and relax therapy in 102 patients to try to prevent episodes of hypoglycemia and negative outcomes. These interventions led to a statistically significant increase in average HbA_1c in these patients. Throughout these encounters with the CPS and patient, there were also innumerable outcomes secondary to the use of shared decision making. Regardless of medication changes, there was increased patient education concerning hypoglycemia treatment, medication administration times, and HbA_1c goals.

 

This project’s strengths included the large sample size, appropriate inclusion criteria that identified patients at risk for hypoglycemia, and the use of shared decision making. It was also beneficial to obtain HbA_1c levels after a relaxation in therapy for objective outcomes. The increase in HbA_1c levels showed a statistically significant gain, which led to more patients having an HbA_1c closer to a CPG-recommended goal range, given their risk factors for hypoglycemia. This pharmacy initiative fostered increased communication between providers and CPS within the PACT team. The pharmacist was not consulted by the provider for management of these patients with DM, so therapy relaxation was documented in CPRS and was addressed at the patient’s next primary care appointment. Some changes also required discussion with the primary care provider prior to relaxation in therapy. By initiating these discussions with providers, opportunities arose for additional education on appropriate HbA_1c goals and why therapy should be relaxed in select patient populations.

Limitations

Some limitations to this project were the use of telephone encounters and interpharmacist variability. Patients who were contacted via telephone by a pharmacist who was unknown to them were more hesitant to make changes. Patients managed for DM by non-VA providers or patients receiving medications at a non-VA pharmacy were also reluctant to implement changes. Education was the major intervention for these patients. Pharmacists were instructed to use their clinical judgment in addition to shared decision making with the patient when relaxing therapy. There was no protocol for medication changes. Although interpharmacist variability is identified as a weakness, it could be considered more representative of daily practice.

Additionally, despite a statistically significant increase in HbA_1c, which would presumably lead to fewer episodes of hypoglycemia, patients were not contacted again after the intervention to inquire whether hypoglycemia had decreased. Studies targeted at the impact of less frequent hypoglycemia events, including fewer emergency department visits, hospital admissions, or primary care walk-in appointments, would improve the clinical significance of these data. As the HSI is implemented nationally within the VA, more data will be available to better evaluate the applicability of this clinical reminder. Locally, the criteria for the clinical reminder has proved to capture a significant number of patients experiencing hypoglycemia. Using national data will also help to guide the frequency of screening needed in this population.

Conclusion

The implementation of the HSI led to increased provider and patient awareness of hypoglycemia. The CPS interventions have resulted in statistically significant increases in HbA_1c levels, which would seemingly decrease the patient’s risk of adverse outcomes as shown in the ACCORD and VADT trials.

About 30.3 million Americans (9.4%) have diabetes mellitus (DM).¹ Veterans are disproportionately affected—about 1 in 4 of those who receive US Department of Veterans Affairs (VA) care have DM.² The consequences of uncontrolled DM include microvascular complications (eg, retinopathy, neuropathy, and nephropathy) and macrovascular complications (eg, cardiovascular disease).

The American Diabetes Association (ADA) recommends achieving a goal hemoglobin A_1c (HbA_1c) level of < 7% to prevent these complications. However, a goal of < 8% HbA_1c may be more appropriate for certain patients when a more strict goal may be impractical or have the potential to cause harm.³ Furthermore, guidelines developed by the VA and the US Department of Defense suggest a target HbA_1c range of 7.0% to 8.5% for patients with established microvascular or macrovascular disease, comorbid conditions, or a life expectancy of 5 to 10 years.⁴

Despite the existence of evidence showing the importance of glycemic control in preventing morbidity and mortality associated with DM, many patients have inadequate glycemic control. Diabetes mellitus is the seventh leading cause of death in the US. Moreover, DM is a known risk factor for heart disease, stroke, and kidney disease, which are the first, fifth, and ninth leading causes of death in the US, respectively.⁵

Because DM management requires ongoing and comprehensive maintenance and monitoring, the ADA supports a collaborative, multidisciplinary, and patient-centered approach to delivery of care.³ Collaborative teams involving pharmacists have been shown to improve outcomes and cost savings for chronic diseases, including DM.^6-12 In 1995, the VA launched a national policy providing clinical pharmacists with prescribing privileges that would aid in the provision of coordinated medication management for patients with chronic illnesses.¹³ The policy created a framework for collaborative drug therapy management (CDTM) models, which grants pharmacists the ability to perform patient assessments, order laboratory tests, and modify medications within a scope of practice.

Since the initiation of these services, several examples of successful DM management services using clinical pharmacists within the VA exist in the literature.^14-16 However, even with intensive chronic disease and drug therapy management, not all patients who enroll in these services successfully reach clinical goals. Although these pharmacist-driven services seem to demonstrate overall benefit and cost savings to veteran patients and the VA system, little published data exist to help determine patient behaviors that are associated with glycemic goal attainment when using these services.

At the Corporal Michael J. Crescenz VA Medical Center in (CMCVAMC) Philadelphia, Pennsylvania, where this study was performed, primary care providers may refer patients with uncontrolled DM to the pharmacist disease state management (DSM) clinic. The clinic is a form of a CDTM and receives numerous referrals per year, with many patients discharged for successfully meeting glycemic targets.

However, a percentage of patients fail to attain glycemic goals despite involvement in this clinic. We observed specific patient behaviors that delayed glycemic goal attainment. This study examined whether these behaviors correlated with prolonged glycemic goal attainment. The purpose of this study was to identify patient behaviors that led to glycemic goal attainment in insulin-treated patients referred to this pharmacist DSM clinic.

 

Methods

This study was performed as a single-center retrospective chart review. The protocol and data collection documents were approved by the CMCVAMC Institutional Review Board. It included patients referred to a pharmacist-led DSM clinic for insulin titration/optimization from January 1, 2011 through December 31, 2012. Data were collected through June 30, 2013, to allow for 6 months after the last referral date of December 31, 2012.

This study included patients who were on insulin therapy at the time of pharmacy consult, who attended at least 3 consecutive pharmacy DSM clinic visits, and had an HbA_1c ≥ 8% at the time of initial clinic consult. Patients who failed to have 3 consecutive pharmacy DSM clinic visits, were insulin-naïve at the time of referral, aged ≥ 90, lacked at least 1 follow-up HbA_1c result while enrolled in the clinic, or had HbA_1c < 8% were excluded.

Among the patients who met eligibility criteria, charts within the Computerized Patient Record System (CPRS) were reviewed in a chronologic order within the respective study time frame. A convenience sample of 100 patients were enrolled in each treatment arm: the goal-attained arm or the goal-not-attained arm.

The primary study variable was HbA_1c goal attainment, which was defined in this investigation as at least 1 HbA_1c reading of < 8% while enrolled in the DSM clinic during the review period. Secondary variables included specific patient factors such as optimal frequency of self-monitoring of blood glucose (SMBG) testing, adherence to pharmacist’s instructions for changes to glucose-lowering medications, adherence to bringing glucose meter/glucose log book to clinic appointments, and percentage of visits attended. Definitions for each variable are provided in Table 1. 

Data were collected for the 4 months prior to the date of at goal HbA_1c. For patients in the goal-not-attained group, the secondary variables were collected for the 4-month period prior to the end of follow date. This final date was determined as either the last pharmacy visit date due to loss of follow-up or end of data collection time frame of June 30, 2013.

We hypothesized that patients who were more adherent to treatment plans, regularly attend clinic visits, and appropriately monitor their glucose levels were more likely to meet their glycemic goals.

Statistical Analysis

Univariate descriptive statistics described the individual variables/predictors of HbA_1c goal attainment. As the study’s purpose was to identify patient factors and characteristics associated with HbA_1c goal attainment, a logistic regression model framework was used for all covariates to evaluate each measured variable’s independent association with HbA_1c. The univariate tests were used to compare patient characteristics between the 2 study groups: Pearson chi-square test was used for nominal data, and a paired t test (for normally distributed data) or Wilcoxon rank sum test (for non-normally distributed data) was used for continuous variables. Variables having a P value < .2 underwent a multivariate analysis stepwise logistic regression model to identify patient factors and characteristics associated with HbA_1c goal attainment. A Fisher exact test was used to determine gender effect on HbA_1c goal attainment, categoric variables were analyzed using Pearson chi-square test, and an unpaired t test was used for continuous data. The backward elimination approach to inclusion of variables in the model was used to build the most parsimonious and best-fitting model, and the Hosmer-Lemeshow goodness-of-fit tests was used to assess model fit. Data analyses were performed using IBM SPSS, version 18.0 (Armonk, NY).

 

Results

Five hundred eighty-four patient records were reviewed, and 207 patients met inclusion criteria: 102 patient records were reviewed for the goal-attained arm, and 105 patient records for the goal-not-attained arm. Most patients were excluded from the analysis due to not having 3 consecutive visits during the specified period or having an HbA_1c of < 8% at the time of referral to the pharmacist DSM clinic.

The patients in this study had type 2 diabetes for about 11 years, were overwhelmingly male (99%), were aged about 61 years, and were taking on average 13 medications at the time of referral to the pharmacist DSM clinic. Mean HbA_1c at time of enrollment was slightly higher in the goal-not-attained arm vs goal-attained arm (10.7% vs 10.2%, respectively), but the difference was not statistically significant (P = .066). A little more than half the patients in both study arms were on basal + prandial insulin regimens (Table 2).

Patients who attained their goal HbA_1cwere more likely to bring their glucose meter/glucose log book to at least 80% of their appointments (P < .001). Additionally, this same cohort followed insulin dosing instructions at least 80% of the time (P < .001).

  Frequency of glucose testing was not significantly different between goal-attained and goal-not-attained groups (P = .375). Moreover, our analysis showed that, on average, patients who met goal did so within 6 months of referral to the pharmacist-managed clinic (P < .001) (Table 3).

Five variables were included in the multivariate analysis because they had a P value ≤ .2 in univariate analyses: (1) patient adherence to instructions (P < .001); (2) duration in clinic (P < .001); (3) patient bringingglucose meter or glucose log to appointments (P < .001); (4) percentage of scheduled appointments patient attended (P = .015); and (5) baseline HbA_1c (P = .066). 

Following instructions at least 80% of the time and duration in clinic were the only significant contributing factors when entered into the multivariate analysis. Longer duration in the pharmacist-managed clinic was inversely related to goal attainment; in other words, patients in clinic for longer periods were less likely to attain glycemic goals (odds ratio [OR] = 0.895, 95% CI: 0.830-0.964). Following instructions at least 80% of the time increased the likelihood of glycemic goal achievement (OR 17.3; 95% CI: 8.0-37.4).

Discussion

The development and constant modification of clinical practicing guidelines has made DM treatment a focus and priority.^3,4 Additionally, the collaborative approach to health care and creation of VA pharmacist-driven services have demonstrated successful patient outcomes.^6-16 Despite these efforts, further insight is needed to improve the management of DM. Our study identified specific behavioral factors that correlated to veteran patients to attaining their HbA_1c goal of < 8% within a VA pharmacist DSM clinic. Additionally, it highlighted factors that contributed to patients not achieving their glycemic goals.

Our univariate analysis showed behaviors such as showing up for appointments and following directions regimens to correlate with glycemic goal attainment. However, following directions was the only behavioral factor that correlated to glycemic goal attainment in our multivariate analysis. Additionally, our findings indicated that factors for HbA_1c goal attainment included patients who brought their glucose meter/glucose log book and attended clinic appointments at least 80% of the time, respectively.

These findings can help further refine the process for identifying patients who are most likely to achieve glycemic goals when referred to pharmacist DSM clinics or to any DM treatment program. Assessment of a patient’s motivation and ability to attend clinic appointments, bring their glucose meter/glucose log book, and to follow instructions provided at these appointments are reasonable screening questions to ask before referring that patient to a diabetes care program or service. Currently, this is not performed during the consult process to the pharmacist DSM clinic at the respective VA.

Additionally, our findings show that patients who met goal did so, on average, within 6 months of referral to the pharmacist DSM clinic. This finding may have occurred because patients who successfully reach HbA_1c goal in 2 consecutive checks are discharged from the clinic. Patients who do not meet this goal continue with the clinic, thus increasing their duration of enrollment in this service. This finding could help clinical pharmacists estimate how long patients will be followed by the service, thus allowing for a more accurate estimation of workload and clinic capacity. Additionally, this finding provides insight if the patient should remain in clinic or be transferred to another program. Our findings aligned with previous studies showing the link between patient behaviors and glycemic goal attainment.^17-19

 

Limitations

This study has a few notable limitations. First, it is limited to 1 VA medical center, so our findings may not be extrapolated easily to other institutions of the Veterans Health Administration. Ideally, future studies centered on identifying factors that lead to successful glycemic goal attainment would be helpful from multiple VA institutions. This would encourage more factors to be identified and trends to be strengthened. Ultimately, this would allow for more global changes to the consult process from primary care to pharmacist DSM clinics nationally vs at a local VA institution. Additionally, this study was limited to a specific retrospective time frame, therefore limiting its ability to identify trends. This study also relied on some subjective factors, such as the patient’s self-report of properly following the clinic instructions. Another limitation was that our investigation was not designed to characterize the specific pharmacist’s interventions that improved glycemic control. Future studies would benefit from the inclusion of specific interventions and their effect on glycemic goal attainment.

Conclusion

This retrospective study offers insight to specific patient behavioral factors that correlate with glycemic goal attainment in a VA pharmacist DSM clinic. Behavioral factors linked to HbA_1c goal attainment of < 8% included appointment keeping, bringing glucose meter/glucose log book at least 80% of the time to these appointments, and following clinic instructions. This investigation also found that patients who attain glycemic goals generally do so within 6 months of enrollment. Furthermore, this study provided insight that following the clinic instructions a majority of the time strongly contributes to glycemic goal attainment. We believe that an assessment of patients’ behaviors prior to referrals to diabetes management programs will yield useful information about possible barriers to glycemic goal attainment.

About 30.3 million Americans (9.4%) have diabetes mellitus (DM).¹ Veterans are disproportionately affected—about 1 in 4 of those who receive US Department of Veterans Affairs (VA) care have DM.² The consequences of uncontrolled DM include microvascular complications (eg, retinopathy, neuropathy, and nephropathy) and macrovascular complications (eg, cardiovascular disease).

The American Diabetes Association (ADA) recommends achieving a goal hemoglobin A_1c (HbA_1c) level of < 7% to prevent these complications. However, a goal of < 8% HbA_1c may be more appropriate for certain patients when a more strict goal may be impractical or have the potential to cause harm.³ Furthermore, guidelines developed by the VA and the US Department of Defense suggest a target HbA_1c range of 7.0% to 8.5% for patients with established microvascular or macrovascular disease, comorbid conditions, or a life expectancy of 5 to 10 years.⁴

Despite the existence of evidence showing the importance of glycemic control in preventing morbidity and mortality associated with DM, many patients have inadequate glycemic control. Diabetes mellitus is the seventh leading cause of death in the US. Moreover, DM is a known risk factor for heart disease, stroke, and kidney disease, which are the first, fifth, and ninth leading causes of death in the US, respectively.⁵

Because DM management requires ongoing and comprehensive maintenance and monitoring, the ADA supports a collaborative, multidisciplinary, and patient-centered approach to delivery of care.³ Collaborative teams involving pharmacists have been shown to improve outcomes and cost savings for chronic diseases, including DM.^6-12 In 1995, the VA launched a national policy providing clinical pharmacists with prescribing privileges that would aid in the provision of coordinated medication management for patients with chronic illnesses.¹³ The policy created a framework for collaborative drug therapy management (CDTM) models, which grants pharmacists the ability to perform patient assessments, order laboratory tests, and modify medications within a scope of practice.

Since the initiation of these services, several examples of successful DM management services using clinical pharmacists within the VA exist in the literature.^14-16 However, even with intensive chronic disease and drug therapy management, not all patients who enroll in these services successfully reach clinical goals. Although these pharmacist-driven services seem to demonstrate overall benefit and cost savings to veteran patients and the VA system, little published data exist to help determine patient behaviors that are associated with glycemic goal attainment when using these services.

At the Corporal Michael J. Crescenz VA Medical Center in (CMCVAMC) Philadelphia, Pennsylvania, where this study was performed, primary care providers may refer patients with uncontrolled DM to the pharmacist disease state management (DSM) clinic. The clinic is a form of a CDTM and receives numerous referrals per year, with many patients discharged for successfully meeting glycemic targets.

However, a percentage of patients fail to attain glycemic goals despite involvement in this clinic. We observed specific patient behaviors that delayed glycemic goal attainment. This study examined whether these behaviors correlated with prolonged glycemic goal attainment. The purpose of this study was to identify patient behaviors that led to glycemic goal attainment in insulin-treated patients referred to this pharmacist DSM clinic.

 

Methods

This study was performed as a single-center retrospective chart review. The protocol and data collection documents were approved by the CMCVAMC Institutional Review Board. It included patients referred to a pharmacist-led DSM clinic for insulin titration/optimization from January 1, 2011 through December 31, 2012. Data were collected through June 30, 2013, to allow for 6 months after the last referral date of December 31, 2012.

This study included patients who were on insulin therapy at the time of pharmacy consult, who attended at least 3 consecutive pharmacy DSM clinic visits, and had an HbA_1c ≥ 8% at the time of initial clinic consult. Patients who failed to have 3 consecutive pharmacy DSM clinic visits, were insulin-naïve at the time of referral, aged ≥ 90, lacked at least 1 follow-up HbA_1c result while enrolled in the clinic, or had HbA_1c < 8% were excluded.

Among the patients who met eligibility criteria, charts within the Computerized Patient Record System (CPRS) were reviewed in a chronologic order within the respective study time frame. A convenience sample of 100 patients were enrolled in each treatment arm: the goal-attained arm or the goal-not-attained arm.

The primary study variable was HbA_1c goal attainment, which was defined in this investigation as at least 1 HbA_1c reading of < 8% while enrolled in the DSM clinic during the review period. Secondary variables included specific patient factors such as optimal frequency of self-monitoring of blood glucose (SMBG) testing, adherence to pharmacist’s instructions for changes to glucose-lowering medications, adherence to bringing glucose meter/glucose log book to clinic appointments, and percentage of visits attended. Definitions for each variable are provided in Table 1. 

Data were collected for the 4 months prior to the date of at goal HbA_1c. For patients in the goal-not-attained group, the secondary variables were collected for the 4-month period prior to the end of follow date. This final date was determined as either the last pharmacy visit date due to loss of follow-up or end of data collection time frame of June 30, 2013.

We hypothesized that patients who were more adherent to treatment plans, regularly attend clinic visits, and appropriately monitor their glucose levels were more likely to meet their glycemic goals.

Statistical Analysis

Univariate descriptive statistics described the individual variables/predictors of HbA_1c goal attainment. As the study’s purpose was to identify patient factors and characteristics associated with HbA_1c goal attainment, a logistic regression model framework was used for all covariates to evaluate each measured variable’s independent association with HbA_1c. The univariate tests were used to compare patient characteristics between the 2 study groups: Pearson chi-square test was used for nominal data, and a paired t test (for normally distributed data) or Wilcoxon rank sum test (for non-normally distributed data) was used for continuous variables. Variables having a P value < .2 underwent a multivariate analysis stepwise logistic regression model to identify patient factors and characteristics associated with HbA_1c goal attainment. A Fisher exact test was used to determine gender effect on HbA_1c goal attainment, categoric variables were analyzed using Pearson chi-square test, and an unpaired t test was used for continuous data. The backward elimination approach to inclusion of variables in the model was used to build the most parsimonious and best-fitting model, and the Hosmer-Lemeshow goodness-of-fit tests was used to assess model fit. Data analyses were performed using IBM SPSS, version 18.0 (Armonk, NY).

 

Results

Five hundred eighty-four patient records were reviewed, and 207 patients met inclusion criteria: 102 patient records were reviewed for the goal-attained arm, and 105 patient records for the goal-not-attained arm. Most patients were excluded from the analysis due to not having 3 consecutive visits during the specified period or having an HbA_1c of < 8% at the time of referral to the pharmacist DSM clinic.

The patients in this study had type 2 diabetes for about 11 years, were overwhelmingly male (99%), were aged about 61 years, and were taking on average 13 medications at the time of referral to the pharmacist DSM clinic. Mean HbA_1c at time of enrollment was slightly higher in the goal-not-attained arm vs goal-attained arm (10.7% vs 10.2%, respectively), but the difference was not statistically significant (P = .066). A little more than half the patients in both study arms were on basal + prandial insulin regimens (Table 2).

Patients who attained their goal HbA_1cwere more likely to bring their glucose meter/glucose log book to at least 80% of their appointments (P < .001). Additionally, this same cohort followed insulin dosing instructions at least 80% of the time (P < .001).

  Frequency of glucose testing was not significantly different between goal-attained and goal-not-attained groups (P = .375). Moreover, our analysis showed that, on average, patients who met goal did so within 6 months of referral to the pharmacist-managed clinic (P < .001) (Table 3).

Five variables were included in the multivariate analysis because they had a P value ≤ .2 in univariate analyses: (1) patient adherence to instructions (P < .001); (2) duration in clinic (P < .001); (3) patient bringingglucose meter or glucose log to appointments (P < .001); (4) percentage of scheduled appointments patient attended (P = .015); and (5) baseline HbA_1c (P = .066). 

Following instructions at least 80% of the time and duration in clinic were the only significant contributing factors when entered into the multivariate analysis. Longer duration in the pharmacist-managed clinic was inversely related to goal attainment; in other words, patients in clinic for longer periods were less likely to attain glycemic goals (odds ratio [OR] = 0.895, 95% CI: 0.830-0.964). Following instructions at least 80% of the time increased the likelihood of glycemic goal achievement (OR 17.3; 95% CI: 8.0-37.4).

Discussion

The development and constant modification of clinical practicing guidelines has made DM treatment a focus and priority.^3,4 Additionally, the collaborative approach to health care and creation of VA pharmacist-driven services have demonstrated successful patient outcomes.^6-16 Despite these efforts, further insight is needed to improve the management of DM. Our study identified specific behavioral factors that correlated to veteran patients to attaining their HbA_1c goal of < 8% within a VA pharmacist DSM clinic. Additionally, it highlighted factors that contributed to patients not achieving their glycemic goals.

Our univariate analysis showed behaviors such as showing up for appointments and following directions regimens to correlate with glycemic goal attainment. However, following directions was the only behavioral factor that correlated to glycemic goal attainment in our multivariate analysis. Additionally, our findings indicated that factors for HbA_1c goal attainment included patients who brought their glucose meter/glucose log book and attended clinic appointments at least 80% of the time, respectively.

These findings can help further refine the process for identifying patients who are most likely to achieve glycemic goals when referred to pharmacist DSM clinics or to any DM treatment program. Assessment of a patient’s motivation and ability to attend clinic appointments, bring their glucose meter/glucose log book, and to follow instructions provided at these appointments are reasonable screening questions to ask before referring that patient to a diabetes care program or service. Currently, this is not performed during the consult process to the pharmacist DSM clinic at the respective VA.

Additionally, our findings show that patients who met goal did so, on average, within 6 months of referral to the pharmacist DSM clinic. This finding may have occurred because patients who successfully reach HbA_1c goal in 2 consecutive checks are discharged from the clinic. Patients who do not meet this goal continue with the clinic, thus increasing their duration of enrollment in this service. This finding could help clinical pharmacists estimate how long patients will be followed by the service, thus allowing for a more accurate estimation of workload and clinic capacity. Additionally, this finding provides insight if the patient should remain in clinic or be transferred to another program. Our findings aligned with previous studies showing the link between patient behaviors and glycemic goal attainment.^17-19

 

Limitations

This study has a few notable limitations. First, it is limited to 1 VA medical center, so our findings may not be extrapolated easily to other institutions of the Veterans Health Administration. Ideally, future studies centered on identifying factors that lead to successful glycemic goal attainment would be helpful from multiple VA institutions. This would encourage more factors to be identified and trends to be strengthened. Ultimately, this would allow for more global changes to the consult process from primary care to pharmacist DSM clinics nationally vs at a local VA institution. Additionally, this study was limited to a specific retrospective time frame, therefore limiting its ability to identify trends. This study also relied on some subjective factors, such as the patient’s self-report of properly following the clinic instructions. Another limitation was that our investigation was not designed to characterize the specific pharmacist’s interventions that improved glycemic control. Future studies would benefit from the inclusion of specific interventions and their effect on glycemic goal attainment.

Conclusion

This retrospective study offers insight to specific patient behavioral factors that correlate with glycemic goal attainment in a VA pharmacist DSM clinic. Behavioral factors linked to HbA_1c goal attainment of < 8% included appointment keeping, bringing glucose meter/glucose log book at least 80% of the time to these appointments, and following clinic instructions. This investigation also found that patients who attain glycemic goals generally do so within 6 months of enrollment. Furthermore, this study provided insight that following the clinic instructions a majority of the time strongly contributes to glycemic goal attainment. We believe that an assessment of patients’ behaviors prior to referrals to diabetes management programs will yield useful information about possible barriers to glycemic goal attainment.

About 30.3 million Americans (9.4%) have diabetes mellitus (DM).¹ Veterans are disproportionately affected—about 1 in 4 of those who receive US Department of Veterans Affairs (VA) care have DM.² The consequences of uncontrolled DM include microvascular complications (eg, retinopathy, neuropathy, and nephropathy) and macrovascular complications (eg, cardiovascular disease).

The American Diabetes Association (ADA) recommends achieving a goal hemoglobin A_1c (HbA_1c) level of < 7% to prevent these complications. However, a goal of < 8% HbA_1c may be more appropriate for certain patients when a more strict goal may be impractical or have the potential to cause harm.³ Furthermore, guidelines developed by the VA and the US Department of Defense suggest a target HbA_1c range of 7.0% to 8.5% for patients with established microvascular or macrovascular disease, comorbid conditions, or a life expectancy of 5 to 10 years.⁴

Despite the existence of evidence showing the importance of glycemic control in preventing morbidity and mortality associated with DM, many patients have inadequate glycemic control. Diabetes mellitus is the seventh leading cause of death in the US. Moreover, DM is a known risk factor for heart disease, stroke, and kidney disease, which are the first, fifth, and ninth leading causes of death in the US, respectively.⁵

Because DM management requires ongoing and comprehensive maintenance and monitoring, the ADA supports a collaborative, multidisciplinary, and patient-centered approach to delivery of care.³ Collaborative teams involving pharmacists have been shown to improve outcomes and cost savings for chronic diseases, including DM.^6-12 In 1995, the VA launched a national policy providing clinical pharmacists with prescribing privileges that would aid in the provision of coordinated medication management for patients with chronic illnesses.¹³ The policy created a framework for collaborative drug therapy management (CDTM) models, which grants pharmacists the ability to perform patient assessments, order laboratory tests, and modify medications within a scope of practice.

Since the initiation of these services, several examples of successful DM management services using clinical pharmacists within the VA exist in the literature.^14-16 However, even with intensive chronic disease and drug therapy management, not all patients who enroll in these services successfully reach clinical goals. Although these pharmacist-driven services seem to demonstrate overall benefit and cost savings to veteran patients and the VA system, little published data exist to help determine patient behaviors that are associated with glycemic goal attainment when using these services.

At the Corporal Michael J. Crescenz VA Medical Center in (CMCVAMC) Philadelphia, Pennsylvania, where this study was performed, primary care providers may refer patients with uncontrolled DM to the pharmacist disease state management (DSM) clinic. The clinic is a form of a CDTM and receives numerous referrals per year, with many patients discharged for successfully meeting glycemic targets.

However, a percentage of patients fail to attain glycemic goals despite involvement in this clinic. We observed specific patient behaviors that delayed glycemic goal attainment. This study examined whether these behaviors correlated with prolonged glycemic goal attainment. The purpose of this study was to identify patient behaviors that led to glycemic goal attainment in insulin-treated patients referred to this pharmacist DSM clinic.

 

Methods

This study was performed as a single-center retrospective chart review. The protocol and data collection documents were approved by the CMCVAMC Institutional Review Board. It included patients referred to a pharmacist-led DSM clinic for insulin titration/optimization from January 1, 2011 through December 31, 2012. Data were collected through June 30, 2013, to allow for 6 months after the last referral date of December 31, 2012.

This study included patients who were on insulin therapy at the time of pharmacy consult, who attended at least 3 consecutive pharmacy DSM clinic visits, and had an HbA_1c ≥ 8% at the time of initial clinic consult. Patients who failed to have 3 consecutive pharmacy DSM clinic visits, were insulin-naïve at the time of referral, aged ≥ 90, lacked at least 1 follow-up HbA_1c result while enrolled in the clinic, or had HbA_1c < 8% were excluded.

Among the patients who met eligibility criteria, charts within the Computerized Patient Record System (CPRS) were reviewed in a chronologic order within the respective study time frame. A convenience sample of 100 patients were enrolled in each treatment arm: the goal-attained arm or the goal-not-attained arm.

The primary study variable was HbA_1c goal attainment, which was defined in this investigation as at least 1 HbA_1c reading of < 8% while enrolled in the DSM clinic during the review period. Secondary variables included specific patient factors such as optimal frequency of self-monitoring of blood glucose (SMBG) testing, adherence to pharmacist’s instructions for changes to glucose-lowering medications, adherence to bringing glucose meter/glucose log book to clinic appointments, and percentage of visits attended. Definitions for each variable are provided in Table 1. 

Data were collected for the 4 months prior to the date of at goal HbA_1c. For patients in the goal-not-attained group, the secondary variables were collected for the 4-month period prior to the end of follow date. This final date was determined as either the last pharmacy visit date due to loss of follow-up or end of data collection time frame of June 30, 2013.

We hypothesized that patients who were more adherent to treatment plans, regularly attend clinic visits, and appropriately monitor their glucose levels were more likely to meet their glycemic goals.

Statistical Analysis

Univariate descriptive statistics described the individual variables/predictors of HbA_1c goal attainment. As the study’s purpose was to identify patient factors and characteristics associated with HbA_1c goal attainment, a logistic regression model framework was used for all covariates to evaluate each measured variable’s independent association with HbA_1c. The univariate tests were used to compare patient characteristics between the 2 study groups: Pearson chi-square test was used for nominal data, and a paired t test (for normally distributed data) or Wilcoxon rank sum test (for non-normally distributed data) was used for continuous variables. Variables having a P value < .2 underwent a multivariate analysis stepwise logistic regression model to identify patient factors and characteristics associated with HbA_1c goal attainment. A Fisher exact test was used to determine gender effect on HbA_1c goal attainment, categoric variables were analyzed using Pearson chi-square test, and an unpaired t test was used for continuous data. The backward elimination approach to inclusion of variables in the model was used to build the most parsimonious and best-fitting model, and the Hosmer-Lemeshow goodness-of-fit tests was used to assess model fit. Data analyses were performed using IBM SPSS, version 18.0 (Armonk, NY).

 

Results

Five hundred eighty-four patient records were reviewed, and 207 patients met inclusion criteria: 102 patient records were reviewed for the goal-attained arm, and 105 patient records for the goal-not-attained arm. Most patients were excluded from the analysis due to not having 3 consecutive visits during the specified period or having an HbA_1c of < 8% at the time of referral to the pharmacist DSM clinic.

The patients in this study had type 2 diabetes for about 11 years, were overwhelmingly male (99%), were aged about 61 years, and were taking on average 13 medications at the time of referral to the pharmacist DSM clinic. Mean HbA_1c at time of enrollment was slightly higher in the goal-not-attained arm vs goal-attained arm (10.7% vs 10.2%, respectively), but the difference was not statistically significant (P = .066). A little more than half the patients in both study arms were on basal + prandial insulin regimens (Table 2).

Patients who attained their goal HbA_1cwere more likely to bring their glucose meter/glucose log book to at least 80% of their appointments (P < .001). Additionally, this same cohort followed insulin dosing instructions at least 80% of the time (P < .001).

  Frequency of glucose testing was not significantly different between goal-attained and goal-not-attained groups (P = .375). Moreover, our analysis showed that, on average, patients who met goal did so within 6 months of referral to the pharmacist-managed clinic (P < .001) (Table 3).

Five variables were included in the multivariate analysis because they had a P value ≤ .2 in univariate analyses: (1) patient adherence to instructions (P < .001); (2) duration in clinic (P < .001); (3) patient bringingglucose meter or glucose log to appointments (P < .001); (4) percentage of scheduled appointments patient attended (P = .015); and (5) baseline HbA_1c (P = .066). 

Following instructions at least 80% of the time and duration in clinic were the only significant contributing factors when entered into the multivariate analysis. Longer duration in the pharmacist-managed clinic was inversely related to goal attainment; in other words, patients in clinic for longer periods were less likely to attain glycemic goals (odds ratio [OR] = 0.895, 95% CI: 0.830-0.964). Following instructions at least 80% of the time increased the likelihood of glycemic goal achievement (OR 17.3; 95% CI: 8.0-37.4).

Discussion

The development and constant modification of clinical practicing guidelines has made DM treatment a focus and priority.^3,4 Additionally, the collaborative approach to health care and creation of VA pharmacist-driven services have demonstrated successful patient outcomes.^6-16 Despite these efforts, further insight is needed to improve the management of DM. Our study identified specific behavioral factors that correlated to veteran patients to attaining their HbA_1c goal of < 8% within a VA pharmacist DSM clinic. Additionally, it highlighted factors that contributed to patients not achieving their glycemic goals.

Our univariate analysis showed behaviors such as showing up for appointments and following directions regimens to correlate with glycemic goal attainment. However, following directions was the only behavioral factor that correlated to glycemic goal attainment in our multivariate analysis. Additionally, our findings indicated that factors for HbA_1c goal attainment included patients who brought their glucose meter/glucose log book and attended clinic appointments at least 80% of the time, respectively.

These findings can help further refine the process for identifying patients who are most likely to achieve glycemic goals when referred to pharmacist DSM clinics or to any DM treatment program. Assessment of a patient’s motivation and ability to attend clinic appointments, bring their glucose meter/glucose log book, and to follow instructions provided at these appointments are reasonable screening questions to ask before referring that patient to a diabetes care program or service. Currently, this is not performed during the consult process to the pharmacist DSM clinic at the respective VA.

Additionally, our findings show that patients who met goal did so, on average, within 6 months of referral to the pharmacist DSM clinic. This finding may have occurred because patients who successfully reach HbA_1c goal in 2 consecutive checks are discharged from the clinic. Patients who do not meet this goal continue with the clinic, thus increasing their duration of enrollment in this service. This finding could help clinical pharmacists estimate how long patients will be followed by the service, thus allowing for a more accurate estimation of workload and clinic capacity. Additionally, this finding provides insight if the patient should remain in clinic or be transferred to another program. Our findings aligned with previous studies showing the link between patient behaviors and glycemic goal attainment.^17-19

 

Limitations

This study has a few notable limitations. First, it is limited to 1 VA medical center, so our findings may not be extrapolated easily to other institutions of the Veterans Health Administration. Ideally, future studies centered on identifying factors that lead to successful glycemic goal attainment would be helpful from multiple VA institutions. This would encourage more factors to be identified and trends to be strengthened. Ultimately, this would allow for more global changes to the consult process from primary care to pharmacist DSM clinics nationally vs at a local VA institution. Additionally, this study was limited to a specific retrospective time frame, therefore limiting its ability to identify trends. This study also relied on some subjective factors, such as the patient’s self-report of properly following the clinic instructions. Another limitation was that our investigation was not designed to characterize the specific pharmacist’s interventions that improved glycemic control. Future studies would benefit from the inclusion of specific interventions and their effect on glycemic goal attainment.

Conclusion

This retrospective study offers insight to specific patient behavioral factors that correlate with glycemic goal attainment in a VA pharmacist DSM clinic. Behavioral factors linked to HbA_1c goal attainment of < 8% included appointment keeping, bringing glucose meter/glucose log book at least 80% of the time to these appointments, and following clinic instructions. This investigation also found that patients who attain glycemic goals generally do so within 6 months of enrollment. Furthermore, this study provided insight that following the clinic instructions a majority of the time strongly contributes to glycemic goal attainment. We believe that an assessment of patients’ behaviors prior to referrals to diabetes management programs will yield useful information about possible barriers to glycemic goal attainment.

In 2050, persons aged ≥ 85 years, also known as the oldest old, are projected to reach 18 million, accounting for 4.5% of the US population, up from 2.5% in 2030.¹ These patients are the fastest growing segment of the US population.

Advances in treating cardiovascular (CV) disease over the past 2 decades have led to an increased incidence of heart failure (HF) and hospitalizations among older patients.² Total costs of care for persons with HF have exceeded $30 billion annually and are expected to rise to more than $70 billion by 2030 due to growth of the aging population.^3,4 Moreover, the Framingham Study reported mortality increases with advancing age (HR 1.27 and 1.61 per decade in men and women, respectively).⁵

The prevalence of HF is also high and increasing over time. The National Health and Nutrition Examination Survey reported that about 5.7 million Americans have HF.⁶ The prevalence of HF is expected to reach 8 million by 2030.⁶ The higher numbers of HF among patients with advanced age is associated with age-related changes in CV structure and function, including reduced responsiveness to β-adrenergic stimulation, impaired left ventricular diastolic filling, and increased vascular stiffness. In addition, age-related changes in other systems might contribute to a HF diagnosis or worsening of the condition.⁷

Older adults experience physiologic changes in pharmacokinetics and pharmacodynamics, including decreased volume of distribution and creatinine clearance, which lead to significant changes in drug concentration and effectiveness.⁸

Geriatric patients aged > 65 years who have comorbidities and those who reside in long-term care settings are underrepresented in clinical trials, leading clinicians to make treatment decisions based on data from younger, community-dwelling individuals. Researchers have questioned whether to include elderly patients and those with comorbidities in clinical trials, given that their diminished response may produce less conclusive results with smaller treatment effects. Exclusion criteria based on comorbid conditions or functional status disqualify many older adults from clinical trials.

This article reviews evidence from major randomized controlled trials over the past 2 decades and explores their applicability to support HF treatment guidelines in patients with advanced age (Table). 

This article also offers a practical approach to managing HF in these patients while advocating for bridging the gap between research and real-world clinical practice.

Pharmacotherapy for Heart Failure

Angiotensin-Converting Enzyme Inhibitors

Several randomized clinical trials have found that angiotensin-converting enzyme (ACE) inhibitors improve symptoms in patients with HF. The CooperativeNorth Scandinavian Enalapril Survival Study (CONSENSUS), demonstrated that enalapril improves survival in patients with New York Heart Association (NYHA) class IV HF with reduced ejection fraction (HFrEF) when added to standard therapy.⁹ However, the duration of beneficial effect of reduced mortality could not be assessed because the benefit of enalapril in NYHA class I to III HF was not evaluated, and follow-up data are limited. The average age of patients in the study was 71 years, and individuals with significant comorbidities were excluded.

ACE inhibitors also were found to reduce mortality even in asymptomatic patients with HFrEF in the Studies of Left Ventricular Dysfunction trial (SOLVD).¹⁰ Enalapril was found to reduce 4-year mortality by 16% and decrease HF hospitalizations when added to conventional therapy consisting primarily of digitalis, diuretics, and nitrates in patients with HFrEF. In this trial, patients aged ≥ 80 years were excluded as well as those with serum creatinine > 2 mg/dL or other conditions that could shorten survival or otherwise impede participation in a long-term trial.

PARADIGM-HF trial patients with HFrEF were randomized to enalapril or the angiotensin receptor-neprilysin inhibitor LCZ696. After a median of 27 months of follow-up, treatment with the angiotensin receptor-neprilysin inhibitor demonstrated greater reduction in CV mortality and HF hospitalizations than enalapril did and was associated with reduced all-cause mortality.¹¹ The trial was stopped early because of evidence of overwhelming benefit with LCZ696. This study of mainly white men included no patients aged ≥ 75 years.

 

Angiotensin Receptor Blockers

Although less studied than ACE inhibitors, angiotensin receptor blockers (ARBs) share similar benefits. Among patients with symptomatic HFrEF taking an ACE inhibitor, the addition of candesartan reduced the risk of CV death and HF hospitalization as demonstrated in the Candesartan in Heart Failure Assessment of Reduction Mortality and Morbidity (CHARM-added and CHARM-alternative trials).^12,13 The CHARM-added trial targeted patients with left ventricular ejection fraction (LVEF) ≤ 40% and NYHA class II to IV HF symptoms who were taking an ACE inhibitor. Adding candesartan reduced CV mortality by 37.9% and HF hospitalization by 42.3% compared with that of placebo.

The CHARM-alternative study found that use of candesartan in symptomatic HFrEF patients who do not tolerate ACE inhibitors,resulted in a 20% reduction in CV mortality as well as a 40% reduction in hospitalization for HF. Among patients with HF with preserved ejection fraction (HFpEF) and NYHA class II to IV symptoms, adding candesartan modestly reduced the rate of HF-related hospitalizations and had no effect on CV mortality in the CHARM-preserved study.¹⁴ The CHARM trials examined mostly white men, but 26% of patients were aged > 75 years. However, there was no subgroup analysis for patients aged > 75 years. The study excluded patients with serum creatinine > 2 mg/dL.

Other ARB trials included the following:

• The I-PRESERVE trial, which found that irbesartan did not improve outcomes of patients with HF with preserved ejection fraction (HFpEF).¹⁵ The study of mostly white patients did not include patients aged ≥ 80 years.
• A randomized trial of valsartan in HF improved symptoms and mortality in NYHA II to IV HF but showed no benefit when added to ACE inhibitors.¹⁶ The trial had no patients aged ≥ 75 years and excluded those with several common comorbidities.
• A randomized, double-blind trial studied the effects of high-dose vs low-dose losartan on clinical outcomes in 3,846 patients with HF and demonstrated that high-dose losartan (150 mg/d) reduces all-cause mortality and hospitalization for HF more effectively than does low-dose losartan (50 mg/d).¹⁷ The study, however, had several exclusion criteria, and no patients were aged ≥ 75 years.

Mineralocorticoid Receptor Antagonists

Major studies of aldosterone antagonists demonstrated extra benefit when added to ACE inhibitors/ARBs in patients with HFrEF and NYHA class II HF.^18,19

In the RALES study, spironolactone was found to reduce all-cause mortality by 30% and symptoms in NYHA III HF without a significant increase in the risk of serious hyperkalemia or renal failure.¹⁸ Most patients were white men aged < 80 years. This study demonstrated the importance of closely following serum potassium levels after initiating aldosterone antagonists in patients with subclinical renal disease because extensive structural damage within the kidney occurs before serum creatinine increases. Patients with advanced renal failure or those who cannot have close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists. Patients with cancer and liver failure were excluded from this trial.

In the Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure study, (EMPHASIS-HF Study) eplerenone was found to reduce all-cause mortality and hospitalization for HFrEF.¹⁹ Similar to RALES, patients were mostly white males aged < 80 years, and patients with clinically significant, coexisting conditions were excluded.

The 2014 Treatment of Preserved Cardiac FunctionHeart Failure with an Aldosterone Antagonist Trial (TOPCAT) randomized 3,445 patients with well-controlled blood pressure to spironolactone or placebo.²⁰ Inclusion criteria were LVEF ≥ 45%, findings of HF, and either a HF hospitalization or elevated B-type natriuretic peptide level. There was no difference in the primary composite outcome of CV mortality, aborted cardiac arrest, or HF hospitalization over the 3.3-year follow-up period. The study found that among patients with HFpEF, spironolactone does not reduce the composite endpoint of CV mortality, aborted cardiac arrest, or HF hospitalizations compared with that of placebo.²⁰ In the trial, 29% of patients were aged > 75 years, and most were white men. There was no subgroup analysis for older patients.²⁰ In all 3 trials, patients with kidney injury (serum creatinine of ≥ 2.5 or estimated glomerular filtration rate of ≤ 30 mL/min) were excluded because of the risk of hyperkalemia.

An observational study after the RALES trial demonstrated a nearly 4-fold increase in admissions for hyperkalemia with a 6-fold increase in associated mortality in patients taking spirolactone.²¹ Therefore, it is important to closely follow serum potassium levels after initiating aldosterone antagonists in older patients with subclinical renal disease. Patients with advanced renal failure or those without close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists.

 

Antithrombotic Therapy

The large multicenter, double-blind randomized trial WARCEF found no added benefit with warfarin vs aspirin for patients with HFrEF in sinus rhythm.²² There was no reduced time to first stroke or death, and the reduced ischemic stroke risk was offset by an increase in major hemorrhage. It is not clear whether subgroup analysis for the etiology of patients’ HF was performed in WARCEF.

The Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial (N = 1,587) found that treatment with warfarin resulted in significantly fewer strokes in patients with ischemic cardiomyopathy.²³ Randomization was not stratified by age group in both trials, and baseline characteristics included mostly white men, and no patients were older than aged > 75 years.

The risk of bleeding with prophylactic aspirin use for CV disease is dose dependent and increases with higher aspirin doses.²⁴ The use of aspirin, 325 mg/d, in the WARCEF study might have contributed to the increased risk of hemorrhage.

Recently published results of COMMANDER HF found that the addition of rivaroxaban at a dose of 2.5 mg twice daily to standard care, including clinically selected antiplatelet therapies was not associated with a significantly lower rate of the composite primary outcome composite outcome of death, myocardial infarction (MI), or stroke among 5,022 patients with a recent episode of worsening heart failure compared with that of placebo.²⁵

Several medical conditions are known to increase bleeding risk, including hypertension, cerebrovascular disease, ischemic stroke, serious heart disease, diabetes mellitus, renal insufficiency, alcoholism, liver disease, and falls.²⁶ Many of these conditions are common among very old patients and should be considered when estimating risk–benefit ratio of oral anticoagulation therapy.

β-blockers

In several large studies, β-blockers have been shown to be effective in reducing mortality in patients with HFrEF. In the Cardiac Insufficiency Bisoprolol Study II, bisoprolol improved all-cause mortality and all-cause hospitalizations, and reduced sudden death in patients with NYHA III or IV HF.²⁷ In the Carvedilol or Metoprolol European Trial (COMET), carvedilol was superior to metoprolol in reducing all-cause mortality for patients with NYHA II or IV HF.²⁸ Both trials included mostly white men; patients with several comorbidities were excluded, and no patients were aged > 80 years.

COMET compared carvedilol with metoprolol tartrate, the short-acting form of metoprolol that has not shown a survival benefit for patients with HF. However, the Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure trial demonstrated survival benefits with metoprolol CR/XL and included patients aged > 80 years.²⁹

In the SENIORS study, patients treated with nebivolol had a 4.2% absolute risk reduction in a composite of mortality or hospital admission at a mean follow-up of 21 months.³⁰ It is reasonable to use nebivolol for managing HF in older patients. Careful monitoring of heart rate is necessary when prescribing β-blockers for older patients.

Cardiac Glycosides

Digoxin with diuretics was the first-line treatment for HF for many decades and the mainstay of HF therapy until the first large HF trials were performed in the 1980s. One trial initiated by the Digoxin Investigation Group (DIG) studied patients with HFrEF who were already receiving treatment for HF (including 94% taking ACE inhibitors and 82% on diuretics) and randomized them to either digoxin or placebo.³¹ The study found no significant difference in mortality between the groups at the 3-year follow-up; however, the digoxin group had significantly fewer hospitalizations compared with that of the placebo group.

 

A post-hoc analysis of patients by age found no difference in mortality between patients aged 70 to 79 years and those ≥ 80 years, with a persistent benefit in fewer hospitalizations. Digoxin continues to be recommended as a reasonable medication for treating symptomatic HFrEF. However, caution is advised in older patients, especially women, who are at higher risk of digoxin toxicity.

No current evidence exists that digoxin adds any benefits for patients with HFpEF of any age and therefore, it should not be used.

Diuretics

Diuretic therapy is important for managing shortness of breath and congestion related to fluid volume overload in patients with HF. Although diuretics have not been shown to reduce mortality in patients with HF, they are the mainstay treatment for patients with HFpEF.³² In a post-hoc analysis of the DIG study, diuretic use was associated with increased risk of mortality and hospitalizations in patients aged > 65 years.³³ Hyponatremia is one of the most serious adverse effects (AEs) with these agents and occurs in about one-fifth of elderly patients taking diuretics.

In severe cases hyponatremia can cause a range of problems, including weakness, confusion, postural giddiness, postural hypotension, falls, transient hemiparesis, and seizures. In older patients with diminished renal reserve, diuretics are more likely to precipitate prerenal uremia than it does in younger patients. Prerequisites for diuretic use are an accurate diagnosis, careful monitoring of blood pressure and serum electrolytes, and regular review of their efficacy, AEs, and the need for continued treatment.

Statins

The Controlled Rosuvastatin Multinational Trial in Heart Failure demonstrated that low-dose rosuvastatin (10 mg/d) does not improve survival among patients with moderate-to-severe ischemic cardiomyopathy but could reduce the rate of CV hospitalizations.³⁴ Patients in this study had a mean age of 73 years, and 41% of them were aged ≥ 75 years. However, the study used a low-dose rosuvastatin, and patients with several common comorbidities were excluded. Evidence exists that treatment with other statins may improve outcomes in patients with HF. There is also evidence that among elderly patients with HF, low serum total cholesterol is independently associated with a worse prognosis.³⁵

Comorbidities

Anemia

In patients with iron-deficiency anemia (ferritin 15-100 ng/mL or 100-299 ng/mL with transferrin saturation < 20%) and symptomatic HFrEF (LVEF ≤ 40% with NYHA II to IV HF), oral iron replacement had no effect on exercise capacity as measured using change in peak oxygen uptake.³⁶ However, IV iron replacement might be a reasonable option to improve functional status and quality of life (QOL) for patients with HF.³⁷ In these studies, participants were aged < 75 years, and there is no evidence that treating other types of anemia improves outcomes in patients with HF.

Hypertension

The Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated that controlling blood pressure to a goal systolic pressure of < 120 mm Hg is associated with significant reduction in the mortality among patients with increased CV risk (aged > 75 years, vascular disease, kidney injury, or a Framingham Risk Score >15%).³⁸ The SPRINT study included patients aged > 75 (25%); however, the study excluded older adults living in nursing homes and those with diabetes mellitus, symptomatic HF, dementia, or stroke. The subgroup analysis did not stratify patients based on age nor provided sufficient evidence regarding treatment targets for this vulnerable population. Therefore, clinicians cannot draw any conclusions about managing hypertension among patients with HF from this study.

 

Sleep Apnea

Sleep apnea is common among patients with HF. A study of adults with chronic HF treated with evidence-based therapies found that 61% of participants had central or obstructive sleep apnea.³⁹ In elderly patients, sleep apnea is further complicated by insomnia and disturbance of sleep cycle that often occur with the aging process.

It is crucial to differentiate central sleep apnea from obstructive sleep apnea, because the treatment approaches differ. Central sleep apnea is associated with poor prognosis in patients with HF.⁴⁰ Adaptive servo ventilation for central sleep apnea uses a noninvasive ventilator to delivering servo controlled inspiratory pressure support on top of expiratory positive airway pressure. Adaptive servo ventilation for central sleep apnea is associated with higher all-cause mortality and CV mortality.⁴¹ Continuous positive airway pressure for obstructive sleep apnea improves sleep quality, reduces the apnea-hypopnea index, and improves nocturnal oxygenation.⁴²

Depression

Clinically significant depression occurs in 21% of patients with HF, and the relationship between depression and poor HF outcomes is consistent and strong across several endpoints. However, in a randomized, 12-week study, the selective serotonin reuptake inhibitor sertraline did not improve depression symptoms or clinical status among patients with HF.⁴³ Depression symptoms might overlap with fatigue and low energy expenditure experienced by oldest old patients with HF who do not have depression.

Furthermore, studies describing depression treatments among patients with HF are too small and heterogeneous to permit definitive conclusions about intervention effectiveness. These results identify areas requiring further development, raise questions regarding the association between depression and clinical outcomes in patients with HF, and provide information on depression prevalence that may help researchers design studies with appropriate depression measures and adequately powered sample sizes.

Frailty

Although frailty is prevalent in the elderly and is independently associated with poor outcomes, there is no standardized definition for frailty. The Fried Frailty Index is a widely used scale that incorporates criteria including weakness, slowness, exhaustion, and low physical activity in the diagnosis of frailty.⁴⁴ However these symptoms are common among patients with advanced HF with and without depression or frailty.

Frailty should be defined collaboratively by the clinician and the patient and should include multidimensional aspects of health, function, and well-being. The treatment goal for patients with HF with frailty is to establish patient-centered goals based on preferences of care.⁴⁵

Discussion

Although several novel approaches to improve outcomes of patients with HF have been developed, it continues to be the leading cause of cardiovascular death among older patients and the leading cause of hospital admissions.⁴⁶ About 50% of newly diagnosed patients with HF die within 5 years.⁴⁷ Current guidelines for managing HF are based on clinical trials that either include few or completely exclude patients aged > 80 years, minorities, and patients with comorbidities clinicians encounter daily in clinical practice.

Furthermore, most clinical trials are designed with mortality as the primary endpoint, which might be as important to our patients with advanced age as their ability to function with a reasonable QOL and less dependence on caregivers.

Decision making in managing HF in our oldest patients should start with an open discussion of the disease and its prognosis, goals of care, and available treatment options. The discussion should also cover all dimensions of suffering, including physical, spiritual, and psychosocial domains. Interviews of patients dying of HF and their caregivers conducted in the United Kingdom identified several communication and transition of care challenges specific to treating this population.⁴⁸ The study revealed in most cases, patients did not recall receiving any written information about the severity of their disease and often did not understand the association among symptoms, such as shortness of breath, edema, and HF. Patients and caregivers did not feel involved in the decision-making process regarding their illness.

The concurrent presence of comorbidity, frailty, and cognitive impairment in our aging population with HF might add to the burden of the primary condition. Care often is perceived as fragmented. Polypharmacy negatively impacts HF management by increasing risk of drug nonadherence, drug interactions, and AEs in an already vulnerable population. There is a need for more effective interpersonal and easy to understand communication and resources.

In many situations, support services might be best facilitated by a dedicated palliative medicine team with significant experience in managing patients with HF.Although palliative medicine should always be considered for patients with HF with advanced age,consultations often are not obtained unless the patient decides to forgo medical treatment or until the last month of life.⁴⁹It is important to note that older adults are not a homogeneous group, and the conventional viewpoint that all patients with HF value symptom control and QOL over longevity may not be true. In a large study three-fourths of elderly outpatients with HF were not willing to trade survival time for improved QOL; and their preferences changed over the course of illness.⁵⁰

Although not all end-of-life symptoms can realistically be palliated, earlier involvement of multidisciplinary palliative medicine specialists may improve symptom control, functional status, and QOL. The team may help patients and caregivers cope with uncertainty, and make informed decisions that are person centered based on value system and beliefs.⁵¹

 

Conclusion

Randomized control trials as well as thoughtful observational studies of HF in patients with advanced age and comorbidities, although challenging, are needed to create the evidence base for treatment interventions and assessing their impact on mortality, morbidity, and QOL in this rapidly growing segment of our population.

Given the lack of evidence for HF treatment in patients with advanced age, the clinician should weigh the knowledge of the effect of aging on the CV system, and the lived experience of patients with HF, with the evidence that exists for making the best decision to relieve bothersome symptoms and improve outcomes of care as determined by patients and their caregivers.

Often the most important intervention we can offer our patients, especially those nearing the end of life, is dedicating our time to truly and actively listen with empathy, understating, and respect for their autonomy and for their decision making. And in doing so we accept our own limitations with humility.
 

Acknowledgments
Dr. Kheirbek received funds from the Veterans Affairs Capitol Health Care Network to establish the Center for Health and Aging at the Washington DC VA Medical Center.

In 2050, persons aged ≥ 85 years, also known as the oldest old, are projected to reach 18 million, accounting for 4.5% of the US population, up from 2.5% in 2030.¹ These patients are the fastest growing segment of the US population.

Advances in treating cardiovascular (CV) disease over the past 2 decades have led to an increased incidence of heart failure (HF) and hospitalizations among older patients.² Total costs of care for persons with HF have exceeded $30 billion annually and are expected to rise to more than $70 billion by 2030 due to growth of the aging population.^3,4 Moreover, the Framingham Study reported mortality increases with advancing age (HR 1.27 and 1.61 per decade in men and women, respectively).⁵

The prevalence of HF is also high and increasing over time. The National Health and Nutrition Examination Survey reported that about 5.7 million Americans have HF.⁶ The prevalence of HF is expected to reach 8 million by 2030.⁶ The higher numbers of HF among patients with advanced age is associated with age-related changes in CV structure and function, including reduced responsiveness to β-adrenergic stimulation, impaired left ventricular diastolic filling, and increased vascular stiffness. In addition, age-related changes in other systems might contribute to a HF diagnosis or worsening of the condition.⁷

Older adults experience physiologic changes in pharmacokinetics and pharmacodynamics, including decreased volume of distribution and creatinine clearance, which lead to significant changes in drug concentration and effectiveness.⁸

Geriatric patients aged > 65 years who have comorbidities and those who reside in long-term care settings are underrepresented in clinical trials, leading clinicians to make treatment decisions based on data from younger, community-dwelling individuals. Researchers have questioned whether to include elderly patients and those with comorbidities in clinical trials, given that their diminished response may produce less conclusive results with smaller treatment effects. Exclusion criteria based on comorbid conditions or functional status disqualify many older adults from clinical trials.

This article reviews evidence from major randomized controlled trials over the past 2 decades and explores their applicability to support HF treatment guidelines in patients with advanced age (Table). 

This article also offers a practical approach to managing HF in these patients while advocating for bridging the gap between research and real-world clinical practice.

Pharmacotherapy for Heart Failure

Angiotensin-Converting Enzyme Inhibitors

Several randomized clinical trials have found that angiotensin-converting enzyme (ACE) inhibitors improve symptoms in patients with HF. The CooperativeNorth Scandinavian Enalapril Survival Study (CONSENSUS), demonstrated that enalapril improves survival in patients with New York Heart Association (NYHA) class IV HF with reduced ejection fraction (HFrEF) when added to standard therapy.⁹ However, the duration of beneficial effect of reduced mortality could not be assessed because the benefit of enalapril in NYHA class I to III HF was not evaluated, and follow-up data are limited. The average age of patients in the study was 71 years, and individuals with significant comorbidities were excluded.

ACE inhibitors also were found to reduce mortality even in asymptomatic patients with HFrEF in the Studies of Left Ventricular Dysfunction trial (SOLVD).¹⁰ Enalapril was found to reduce 4-year mortality by 16% and decrease HF hospitalizations when added to conventional therapy consisting primarily of digitalis, diuretics, and nitrates in patients with HFrEF. In this trial, patients aged ≥ 80 years were excluded as well as those with serum creatinine > 2 mg/dL or other conditions that could shorten survival or otherwise impede participation in a long-term trial.

PARADIGM-HF trial patients with HFrEF were randomized to enalapril or the angiotensin receptor-neprilysin inhibitor LCZ696. After a median of 27 months of follow-up, treatment with the angiotensin receptor-neprilysin inhibitor demonstrated greater reduction in CV mortality and HF hospitalizations than enalapril did and was associated with reduced all-cause mortality.¹¹ The trial was stopped early because of evidence of overwhelming benefit with LCZ696. This study of mainly white men included no patients aged ≥ 75 years.

 

Angiotensin Receptor Blockers

Although less studied than ACE inhibitors, angiotensin receptor blockers (ARBs) share similar benefits. Among patients with symptomatic HFrEF taking an ACE inhibitor, the addition of candesartan reduced the risk of CV death and HF hospitalization as demonstrated in the Candesartan in Heart Failure Assessment of Reduction Mortality and Morbidity (CHARM-added and CHARM-alternative trials).^12,13 The CHARM-added trial targeted patients with left ventricular ejection fraction (LVEF) ≤ 40% and NYHA class II to IV HF symptoms who were taking an ACE inhibitor. Adding candesartan reduced CV mortality by 37.9% and HF hospitalization by 42.3% compared with that of placebo.

The CHARM-alternative study found that use of candesartan in symptomatic HFrEF patients who do not tolerate ACE inhibitors,resulted in a 20% reduction in CV mortality as well as a 40% reduction in hospitalization for HF. Among patients with HF with preserved ejection fraction (HFpEF) and NYHA class II to IV symptoms, adding candesartan modestly reduced the rate of HF-related hospitalizations and had no effect on CV mortality in the CHARM-preserved study.¹⁴ The CHARM trials examined mostly white men, but 26% of patients were aged > 75 years. However, there was no subgroup analysis for patients aged > 75 years. The study excluded patients with serum creatinine > 2 mg/dL.

Other ARB trials included the following:

• The I-PRESERVE trial, which found that irbesartan did not improve outcomes of patients with HF with preserved ejection fraction (HFpEF).¹⁵ The study of mostly white patients did not include patients aged ≥ 80 years.
• A randomized trial of valsartan in HF improved symptoms and mortality in NYHA II to IV HF but showed no benefit when added to ACE inhibitors.¹⁶ The trial had no patients aged ≥ 75 years and excluded those with several common comorbidities.
• A randomized, double-blind trial studied the effects of high-dose vs low-dose losartan on clinical outcomes in 3,846 patients with HF and demonstrated that high-dose losartan (150 mg/d) reduces all-cause mortality and hospitalization for HF more effectively than does low-dose losartan (50 mg/d).¹⁷ The study, however, had several exclusion criteria, and no patients were aged ≥ 75 years.

Mineralocorticoid Receptor Antagonists

Major studies of aldosterone antagonists demonstrated extra benefit when added to ACE inhibitors/ARBs in patients with HFrEF and NYHA class II HF.^18,19

In the RALES study, spironolactone was found to reduce all-cause mortality by 30% and symptoms in NYHA III HF without a significant increase in the risk of serious hyperkalemia or renal failure.¹⁸ Most patients were white men aged < 80 years. This study demonstrated the importance of closely following serum potassium levels after initiating aldosterone antagonists in patients with subclinical renal disease because extensive structural damage within the kidney occurs before serum creatinine increases. Patients with advanced renal failure or those who cannot have close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists. Patients with cancer and liver failure were excluded from this trial.

In the Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure study, (EMPHASIS-HF Study) eplerenone was found to reduce all-cause mortality and hospitalization for HFrEF.¹⁹ Similar to RALES, patients were mostly white males aged < 80 years, and patients with clinically significant, coexisting conditions were excluded.

The 2014 Treatment of Preserved Cardiac FunctionHeart Failure with an Aldosterone Antagonist Trial (TOPCAT) randomized 3,445 patients with well-controlled blood pressure to spironolactone or placebo.²⁰ Inclusion criteria were LVEF ≥ 45%, findings of HF, and either a HF hospitalization or elevated B-type natriuretic peptide level. There was no difference in the primary composite outcome of CV mortality, aborted cardiac arrest, or HF hospitalization over the 3.3-year follow-up period. The study found that among patients with HFpEF, spironolactone does not reduce the composite endpoint of CV mortality, aborted cardiac arrest, or HF hospitalizations compared with that of placebo.²⁰ In the trial, 29% of patients were aged > 75 years, and most were white men. There was no subgroup analysis for older patients.²⁰ In all 3 trials, patients with kidney injury (serum creatinine of ≥ 2.5 or estimated glomerular filtration rate of ≤ 30 mL/min) were excluded because of the risk of hyperkalemia.

An observational study after the RALES trial demonstrated a nearly 4-fold increase in admissions for hyperkalemia with a 6-fold increase in associated mortality in patients taking spirolactone.²¹ Therefore, it is important to closely follow serum potassium levels after initiating aldosterone antagonists in older patients with subclinical renal disease. Patients with advanced renal failure or those without close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists.

 

Antithrombotic Therapy

The large multicenter, double-blind randomized trial WARCEF found no added benefit with warfarin vs aspirin for patients with HFrEF in sinus rhythm.²² There was no reduced time to first stroke or death, and the reduced ischemic stroke risk was offset by an increase in major hemorrhage. It is not clear whether subgroup analysis for the etiology of patients’ HF was performed in WARCEF.

The Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial (N = 1,587) found that treatment with warfarin resulted in significantly fewer strokes in patients with ischemic cardiomyopathy.²³ Randomization was not stratified by age group in both trials, and baseline characteristics included mostly white men, and no patients were older than aged > 75 years.

The risk of bleeding with prophylactic aspirin use for CV disease is dose dependent and increases with higher aspirin doses.²⁴ The use of aspirin, 325 mg/d, in the WARCEF study might have contributed to the increased risk of hemorrhage.

Recently published results of COMMANDER HF found that the addition of rivaroxaban at a dose of 2.5 mg twice daily to standard care, including clinically selected antiplatelet therapies was not associated with a significantly lower rate of the composite primary outcome composite outcome of death, myocardial infarction (MI), or stroke among 5,022 patients with a recent episode of worsening heart failure compared with that of placebo.²⁵

Several medical conditions are known to increase bleeding risk, including hypertension, cerebrovascular disease, ischemic stroke, serious heart disease, diabetes mellitus, renal insufficiency, alcoholism, liver disease, and falls.²⁶ Many of these conditions are common among very old patients and should be considered when estimating risk–benefit ratio of oral anticoagulation therapy.

β-blockers

In several large studies, β-blockers have been shown to be effective in reducing mortality in patients with HFrEF. In the Cardiac Insufficiency Bisoprolol Study II, bisoprolol improved all-cause mortality and all-cause hospitalizations, and reduced sudden death in patients with NYHA III or IV HF.²⁷ In the Carvedilol or Metoprolol European Trial (COMET), carvedilol was superior to metoprolol in reducing all-cause mortality for patients with NYHA II or IV HF.²⁸ Both trials included mostly white men; patients with several comorbidities were excluded, and no patients were aged > 80 years.

COMET compared carvedilol with metoprolol tartrate, the short-acting form of metoprolol that has not shown a survival benefit for patients with HF. However, the Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure trial demonstrated survival benefits with metoprolol CR/XL and included patients aged > 80 years.²⁹

In the SENIORS study, patients treated with nebivolol had a 4.2% absolute risk reduction in a composite of mortality or hospital admission at a mean follow-up of 21 months.³⁰ It is reasonable to use nebivolol for managing HF in older patients. Careful monitoring of heart rate is necessary when prescribing β-blockers for older patients.

Cardiac Glycosides

Digoxin with diuretics was the first-line treatment for HF for many decades and the mainstay of HF therapy until the first large HF trials were performed in the 1980s. One trial initiated by the Digoxin Investigation Group (DIG) studied patients with HFrEF who were already receiving treatment for HF (including 94% taking ACE inhibitors and 82% on diuretics) and randomized them to either digoxin or placebo.³¹ The study found no significant difference in mortality between the groups at the 3-year follow-up; however, the digoxin group had significantly fewer hospitalizations compared with that of the placebo group.

 

A post-hoc analysis of patients by age found no difference in mortality between patients aged 70 to 79 years and those ≥ 80 years, with a persistent benefit in fewer hospitalizations. Digoxin continues to be recommended as a reasonable medication for treating symptomatic HFrEF. However, caution is advised in older patients, especially women, who are at higher risk of digoxin toxicity.

No current evidence exists that digoxin adds any benefits for patients with HFpEF of any age and therefore, it should not be used.

Diuretics

Diuretic therapy is important for managing shortness of breath and congestion related to fluid volume overload in patients with HF. Although diuretics have not been shown to reduce mortality in patients with HF, they are the mainstay treatment for patients with HFpEF.³² In a post-hoc analysis of the DIG study, diuretic use was associated with increased risk of mortality and hospitalizations in patients aged > 65 years.³³ Hyponatremia is one of the most serious adverse effects (AEs) with these agents and occurs in about one-fifth of elderly patients taking diuretics.

In severe cases hyponatremia can cause a range of problems, including weakness, confusion, postural giddiness, postural hypotension, falls, transient hemiparesis, and seizures. In older patients with diminished renal reserve, diuretics are more likely to precipitate prerenal uremia than it does in younger patients. Prerequisites for diuretic use are an accurate diagnosis, careful monitoring of blood pressure and serum electrolytes, and regular review of their efficacy, AEs, and the need for continued treatment.

Statins

The Controlled Rosuvastatin Multinational Trial in Heart Failure demonstrated that low-dose rosuvastatin (10 mg/d) does not improve survival among patients with moderate-to-severe ischemic cardiomyopathy but could reduce the rate of CV hospitalizations.³⁴ Patients in this study had a mean age of 73 years, and 41% of them were aged ≥ 75 years. However, the study used a low-dose rosuvastatin, and patients with several common comorbidities were excluded. Evidence exists that treatment with other statins may improve outcomes in patients with HF. There is also evidence that among elderly patients with HF, low serum total cholesterol is independently associated with a worse prognosis.³⁵

Comorbidities

Anemia

In patients with iron-deficiency anemia (ferritin 15-100 ng/mL or 100-299 ng/mL with transferrin saturation < 20%) and symptomatic HFrEF (LVEF ≤ 40% with NYHA II to IV HF), oral iron replacement had no effect on exercise capacity as measured using change in peak oxygen uptake.³⁶ However, IV iron replacement might be a reasonable option to improve functional status and quality of life (QOL) for patients with HF.³⁷ In these studies, participants were aged < 75 years, and there is no evidence that treating other types of anemia improves outcomes in patients with HF.

Hypertension

The Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated that controlling blood pressure to a goal systolic pressure of < 120 mm Hg is associated with significant reduction in the mortality among patients with increased CV risk (aged > 75 years, vascular disease, kidney injury, or a Framingham Risk Score >15%).³⁸ The SPRINT study included patients aged > 75 (25%); however, the study excluded older adults living in nursing homes and those with diabetes mellitus, symptomatic HF, dementia, or stroke. The subgroup analysis did not stratify patients based on age nor provided sufficient evidence regarding treatment targets for this vulnerable population. Therefore, clinicians cannot draw any conclusions about managing hypertension among patients with HF from this study.

 

Sleep Apnea

Sleep apnea is common among patients with HF. A study of adults with chronic HF treated with evidence-based therapies found that 61% of participants had central or obstructive sleep apnea.³⁹ In elderly patients, sleep apnea is further complicated by insomnia and disturbance of sleep cycle that often occur with the aging process.

It is crucial to differentiate central sleep apnea from obstructive sleep apnea, because the treatment approaches differ. Central sleep apnea is associated with poor prognosis in patients with HF.⁴⁰ Adaptive servo ventilation for central sleep apnea uses a noninvasive ventilator to delivering servo controlled inspiratory pressure support on top of expiratory positive airway pressure. Adaptive servo ventilation for central sleep apnea is associated with higher all-cause mortality and CV mortality.⁴¹ Continuous positive airway pressure for obstructive sleep apnea improves sleep quality, reduces the apnea-hypopnea index, and improves nocturnal oxygenation.⁴²

Depression

Clinically significant depression occurs in 21% of patients with HF, and the relationship between depression and poor HF outcomes is consistent and strong across several endpoints. However, in a randomized, 12-week study, the selective serotonin reuptake inhibitor sertraline did not improve depression symptoms or clinical status among patients with HF.⁴³ Depression symptoms might overlap with fatigue and low energy expenditure experienced by oldest old patients with HF who do not have depression.

Furthermore, studies describing depression treatments among patients with HF are too small and heterogeneous to permit definitive conclusions about intervention effectiveness. These results identify areas requiring further development, raise questions regarding the association between depression and clinical outcomes in patients with HF, and provide information on depression prevalence that may help researchers design studies with appropriate depression measures and adequately powered sample sizes.

Frailty

Although frailty is prevalent in the elderly and is independently associated with poor outcomes, there is no standardized definition for frailty. The Fried Frailty Index is a widely used scale that incorporates criteria including weakness, slowness, exhaustion, and low physical activity in the diagnosis of frailty.⁴⁴ However these symptoms are common among patients with advanced HF with and without depression or frailty.

Frailty should be defined collaboratively by the clinician and the patient and should include multidimensional aspects of health, function, and well-being. The treatment goal for patients with HF with frailty is to establish patient-centered goals based on preferences of care.⁴⁵

Discussion

Although several novel approaches to improve outcomes of patients with HF have been developed, it continues to be the leading cause of cardiovascular death among older patients and the leading cause of hospital admissions.⁴⁶ About 50% of newly diagnosed patients with HF die within 5 years.⁴⁷ Current guidelines for managing HF are based on clinical trials that either include few or completely exclude patients aged > 80 years, minorities, and patients with comorbidities clinicians encounter daily in clinical practice.

Furthermore, most clinical trials are designed with mortality as the primary endpoint, which might be as important to our patients with advanced age as their ability to function with a reasonable QOL and less dependence on caregivers.

Decision making in managing HF in our oldest patients should start with an open discussion of the disease and its prognosis, goals of care, and available treatment options. The discussion should also cover all dimensions of suffering, including physical, spiritual, and psychosocial domains. Interviews of patients dying of HF and their caregivers conducted in the United Kingdom identified several communication and transition of care challenges specific to treating this population.⁴⁸ The study revealed in most cases, patients did not recall receiving any written information about the severity of their disease and often did not understand the association among symptoms, such as shortness of breath, edema, and HF. Patients and caregivers did not feel involved in the decision-making process regarding their illness.

The concurrent presence of comorbidity, frailty, and cognitive impairment in our aging population with HF might add to the burden of the primary condition. Care often is perceived as fragmented. Polypharmacy negatively impacts HF management by increasing risk of drug nonadherence, drug interactions, and AEs in an already vulnerable population. There is a need for more effective interpersonal and easy to understand communication and resources.

In many situations, support services might be best facilitated by a dedicated palliative medicine team with significant experience in managing patients with HF.Although palliative medicine should always be considered for patients with HF with advanced age,consultations often are not obtained unless the patient decides to forgo medical treatment or until the last month of life.⁴⁹It is important to note that older adults are not a homogeneous group, and the conventional viewpoint that all patients with HF value symptom control and QOL over longevity may not be true. In a large study three-fourths of elderly outpatients with HF were not willing to trade survival time for improved QOL; and their preferences changed over the course of illness.⁵⁰

Although not all end-of-life symptoms can realistically be palliated, earlier involvement of multidisciplinary palliative medicine specialists may improve symptom control, functional status, and QOL. The team may help patients and caregivers cope with uncertainty, and make informed decisions that are person centered based on value system and beliefs.⁵¹

 

Conclusion

Randomized control trials as well as thoughtful observational studies of HF in patients with advanced age and comorbidities, although challenging, are needed to create the evidence base for treatment interventions and assessing their impact on mortality, morbidity, and QOL in this rapidly growing segment of our population.

Given the lack of evidence for HF treatment in patients with advanced age, the clinician should weigh the knowledge of the effect of aging on the CV system, and the lived experience of patients with HF, with the evidence that exists for making the best decision to relieve bothersome symptoms and improve outcomes of care as determined by patients and their caregivers.

Often the most important intervention we can offer our patients, especially those nearing the end of life, is dedicating our time to truly and actively listen with empathy, understating, and respect for their autonomy and for their decision making. And in doing so we accept our own limitations with humility.
 

Acknowledgments
Dr. Kheirbek received funds from the Veterans Affairs Capitol Health Care Network to establish the Center for Health and Aging at the Washington DC VA Medical Center.

In 2050, persons aged ≥ 85 years, also known as the oldest old, are projected to reach 18 million, accounting for 4.5% of the US population, up from 2.5% in 2030.¹ These patients are the fastest growing segment of the US population.

Advances in treating cardiovascular (CV) disease over the past 2 decades have led to an increased incidence of heart failure (HF) and hospitalizations among older patients.² Total costs of care for persons with HF have exceeded $30 billion annually and are expected to rise to more than $70 billion by 2030 due to growth of the aging population.^3,4 Moreover, the Framingham Study reported mortality increases with advancing age (HR 1.27 and 1.61 per decade in men and women, respectively).⁵

The prevalence of HF is also high and increasing over time. The National Health and Nutrition Examination Survey reported that about 5.7 million Americans have HF.⁶ The prevalence of HF is expected to reach 8 million by 2030.⁶ The higher numbers of HF among patients with advanced age is associated with age-related changes in CV structure and function, including reduced responsiveness to β-adrenergic stimulation, impaired left ventricular diastolic filling, and increased vascular stiffness. In addition, age-related changes in other systems might contribute to a HF diagnosis or worsening of the condition.⁷

Older adults experience physiologic changes in pharmacokinetics and pharmacodynamics, including decreased volume of distribution and creatinine clearance, which lead to significant changes in drug concentration and effectiveness.⁸

Geriatric patients aged > 65 years who have comorbidities and those who reside in long-term care settings are underrepresented in clinical trials, leading clinicians to make treatment decisions based on data from younger, community-dwelling individuals. Researchers have questioned whether to include elderly patients and those with comorbidities in clinical trials, given that their diminished response may produce less conclusive results with smaller treatment effects. Exclusion criteria based on comorbid conditions or functional status disqualify many older adults from clinical trials.

This article reviews evidence from major randomized controlled trials over the past 2 decades and explores their applicability to support HF treatment guidelines in patients with advanced age (Table). 

This article also offers a practical approach to managing HF in these patients while advocating for bridging the gap between research and real-world clinical practice.

Pharmacotherapy for Heart Failure

Angiotensin-Converting Enzyme Inhibitors

Several randomized clinical trials have found that angiotensin-converting enzyme (ACE) inhibitors improve symptoms in patients with HF. The CooperativeNorth Scandinavian Enalapril Survival Study (CONSENSUS), demonstrated that enalapril improves survival in patients with New York Heart Association (NYHA) class IV HF with reduced ejection fraction (HFrEF) when added to standard therapy.⁹ However, the duration of beneficial effect of reduced mortality could not be assessed because the benefit of enalapril in NYHA class I to III HF was not evaluated, and follow-up data are limited. The average age of patients in the study was 71 years, and individuals with significant comorbidities were excluded.

ACE inhibitors also were found to reduce mortality even in asymptomatic patients with HFrEF in the Studies of Left Ventricular Dysfunction trial (SOLVD).¹⁰ Enalapril was found to reduce 4-year mortality by 16% and decrease HF hospitalizations when added to conventional therapy consisting primarily of digitalis, diuretics, and nitrates in patients with HFrEF. In this trial, patients aged ≥ 80 years were excluded as well as those with serum creatinine > 2 mg/dL or other conditions that could shorten survival or otherwise impede participation in a long-term trial.

PARADIGM-HF trial patients with HFrEF were randomized to enalapril or the angiotensin receptor-neprilysin inhibitor LCZ696. After a median of 27 months of follow-up, treatment with the angiotensin receptor-neprilysin inhibitor demonstrated greater reduction in CV mortality and HF hospitalizations than enalapril did and was associated with reduced all-cause mortality.¹¹ The trial was stopped early because of evidence of overwhelming benefit with LCZ696. This study of mainly white men included no patients aged ≥ 75 years.

 

Angiotensin Receptor Blockers

Although less studied than ACE inhibitors, angiotensin receptor blockers (ARBs) share similar benefits. Among patients with symptomatic HFrEF taking an ACE inhibitor, the addition of candesartan reduced the risk of CV death and HF hospitalization as demonstrated in the Candesartan in Heart Failure Assessment of Reduction Mortality and Morbidity (CHARM-added and CHARM-alternative trials).^12,13 The CHARM-added trial targeted patients with left ventricular ejection fraction (LVEF) ≤ 40% and NYHA class II to IV HF symptoms who were taking an ACE inhibitor. Adding candesartan reduced CV mortality by 37.9% and HF hospitalization by 42.3% compared with that of placebo.

The CHARM-alternative study found that use of candesartan in symptomatic HFrEF patients who do not tolerate ACE inhibitors,resulted in a 20% reduction in CV mortality as well as a 40% reduction in hospitalization for HF. Among patients with HF with preserved ejection fraction (HFpEF) and NYHA class II to IV symptoms, adding candesartan modestly reduced the rate of HF-related hospitalizations and had no effect on CV mortality in the CHARM-preserved study.¹⁴ The CHARM trials examined mostly white men, but 26% of patients were aged > 75 years. However, there was no subgroup analysis for patients aged > 75 years. The study excluded patients with serum creatinine > 2 mg/dL.

Other ARB trials included the following:

• The I-PRESERVE trial, which found that irbesartan did not improve outcomes of patients with HF with preserved ejection fraction (HFpEF).¹⁵ The study of mostly white patients did not include patients aged ≥ 80 years.
• A randomized trial of valsartan in HF improved symptoms and mortality in NYHA II to IV HF but showed no benefit when added to ACE inhibitors.¹⁶ The trial had no patients aged ≥ 75 years and excluded those with several common comorbidities.
• A randomized, double-blind trial studied the effects of high-dose vs low-dose losartan on clinical outcomes in 3,846 patients with HF and demonstrated that high-dose losartan (150 mg/d) reduces all-cause mortality and hospitalization for HF more effectively than does low-dose losartan (50 mg/d).¹⁷ The study, however, had several exclusion criteria, and no patients were aged ≥ 75 years.

Mineralocorticoid Receptor Antagonists

Major studies of aldosterone antagonists demonstrated extra benefit when added to ACE inhibitors/ARBs in patients with HFrEF and NYHA class II HF.^18,19

In the RALES study, spironolactone was found to reduce all-cause mortality by 30% and symptoms in NYHA III HF without a significant increase in the risk of serious hyperkalemia or renal failure.¹⁸ Most patients were white men aged < 80 years. This study demonstrated the importance of closely following serum potassium levels after initiating aldosterone antagonists in patients with subclinical renal disease because extensive structural damage within the kidney occurs before serum creatinine increases. Patients with advanced renal failure or those who cannot have close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists. Patients with cancer and liver failure were excluded from this trial.

In the Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure study, (EMPHASIS-HF Study) eplerenone was found to reduce all-cause mortality and hospitalization for HFrEF.¹⁹ Similar to RALES, patients were mostly white males aged < 80 years, and patients with clinically significant, coexisting conditions were excluded.

The 2014 Treatment of Preserved Cardiac FunctionHeart Failure with an Aldosterone Antagonist Trial (TOPCAT) randomized 3,445 patients with well-controlled blood pressure to spironolactone or placebo.²⁰ Inclusion criteria were LVEF ≥ 45%, findings of HF, and either a HF hospitalization or elevated B-type natriuretic peptide level. There was no difference in the primary composite outcome of CV mortality, aborted cardiac arrest, or HF hospitalization over the 3.3-year follow-up period. The study found that among patients with HFpEF, spironolactone does not reduce the composite endpoint of CV mortality, aborted cardiac arrest, or HF hospitalizations compared with that of placebo.²⁰ In the trial, 29% of patients were aged > 75 years, and most were white men. There was no subgroup analysis for older patients.²⁰ In all 3 trials, patients with kidney injury (serum creatinine of ≥ 2.5 or estimated glomerular filtration rate of ≤ 30 mL/min) were excluded because of the risk of hyperkalemia.

An observational study after the RALES trial demonstrated a nearly 4-fold increase in admissions for hyperkalemia with a 6-fold increase in associated mortality in patients taking spirolactone.²¹ Therefore, it is important to closely follow serum potassium levels after initiating aldosterone antagonists in older patients with subclinical renal disease. Patients with advanced renal failure or those without close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists.

 

Antithrombotic Therapy

The large multicenter, double-blind randomized trial WARCEF found no added benefit with warfarin vs aspirin for patients with HFrEF in sinus rhythm.²² There was no reduced time to first stroke or death, and the reduced ischemic stroke risk was offset by an increase in major hemorrhage. It is not clear whether subgroup analysis for the etiology of patients’ HF was performed in WARCEF.

The Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial (N = 1,587) found that treatment with warfarin resulted in significantly fewer strokes in patients with ischemic cardiomyopathy.²³ Randomization was not stratified by age group in both trials, and baseline characteristics included mostly white men, and no patients were older than aged > 75 years.

The risk of bleeding with prophylactic aspirin use for CV disease is dose dependent and increases with higher aspirin doses.²⁴ The use of aspirin, 325 mg/d, in the WARCEF study might have contributed to the increased risk of hemorrhage.

Recently published results of COMMANDER HF found that the addition of rivaroxaban at a dose of 2.5 mg twice daily to standard care, including clinically selected antiplatelet therapies was not associated with a significantly lower rate of the composite primary outcome composite outcome of death, myocardial infarction (MI), or stroke among 5,022 patients with a recent episode of worsening heart failure compared with that of placebo.²⁵

Several medical conditions are known to increase bleeding risk, including hypertension, cerebrovascular disease, ischemic stroke, serious heart disease, diabetes mellitus, renal insufficiency, alcoholism, liver disease, and falls.²⁶ Many of these conditions are common among very old patients and should be considered when estimating risk–benefit ratio of oral anticoagulation therapy.

β-blockers

In several large studies, β-blockers have been shown to be effective in reducing mortality in patients with HFrEF. In the Cardiac Insufficiency Bisoprolol Study II, bisoprolol improved all-cause mortality and all-cause hospitalizations, and reduced sudden death in patients with NYHA III or IV HF.²⁷ In the Carvedilol or Metoprolol European Trial (COMET), carvedilol was superior to metoprolol in reducing all-cause mortality for patients with NYHA II or IV HF.²⁸ Both trials included mostly white men; patients with several comorbidities were excluded, and no patients were aged > 80 years.

COMET compared carvedilol with metoprolol tartrate, the short-acting form of metoprolol that has not shown a survival benefit for patients with HF. However, the Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure trial demonstrated survival benefits with metoprolol CR/XL and included patients aged > 80 years.²⁹

In the SENIORS study, patients treated with nebivolol had a 4.2% absolute risk reduction in a composite of mortality or hospital admission at a mean follow-up of 21 months.³⁰ It is reasonable to use nebivolol for managing HF in older patients. Careful monitoring of heart rate is necessary when prescribing β-blockers for older patients.

Cardiac Glycosides

Digoxin with diuretics was the first-line treatment for HF for many decades and the mainstay of HF therapy until the first large HF trials were performed in the 1980s. One trial initiated by the Digoxin Investigation Group (DIG) studied patients with HFrEF who were already receiving treatment for HF (including 94% taking ACE inhibitors and 82% on diuretics) and randomized them to either digoxin or placebo.³¹ The study found no significant difference in mortality between the groups at the 3-year follow-up; however, the digoxin group had significantly fewer hospitalizations compared with that of the placebo group.

 

A post-hoc analysis of patients by age found no difference in mortality between patients aged 70 to 79 years and those ≥ 80 years, with a persistent benefit in fewer hospitalizations. Digoxin continues to be recommended as a reasonable medication for treating symptomatic HFrEF. However, caution is advised in older patients, especially women, who are at higher risk of digoxin toxicity.

No current evidence exists that digoxin adds any benefits for patients with HFpEF of any age and therefore, it should not be used.

Diuretics

Diuretic therapy is important for managing shortness of breath and congestion related to fluid volume overload in patients with HF. Although diuretics have not been shown to reduce mortality in patients with HF, they are the mainstay treatment for patients with HFpEF.³² In a post-hoc analysis of the DIG study, diuretic use was associated with increased risk of mortality and hospitalizations in patients aged > 65 years.³³ Hyponatremia is one of the most serious adverse effects (AEs) with these agents and occurs in about one-fifth of elderly patients taking diuretics.

In severe cases hyponatremia can cause a range of problems, including weakness, confusion, postural giddiness, postural hypotension, falls, transient hemiparesis, and seizures. In older patients with diminished renal reserve, diuretics are more likely to precipitate prerenal uremia than it does in younger patients. Prerequisites for diuretic use are an accurate diagnosis, careful monitoring of blood pressure and serum electrolytes, and regular review of their efficacy, AEs, and the need for continued treatment.

Statins

The Controlled Rosuvastatin Multinational Trial in Heart Failure demonstrated that low-dose rosuvastatin (10 mg/d) does not improve survival among patients with moderate-to-severe ischemic cardiomyopathy but could reduce the rate of CV hospitalizations.³⁴ Patients in this study had a mean age of 73 years, and 41% of them were aged ≥ 75 years. However, the study used a low-dose rosuvastatin, and patients with several common comorbidities were excluded. Evidence exists that treatment with other statins may improve outcomes in patients with HF. There is also evidence that among elderly patients with HF, low serum total cholesterol is independently associated with a worse prognosis.³⁵

Comorbidities

Anemia

In patients with iron-deficiency anemia (ferritin 15-100 ng/mL or 100-299 ng/mL with transferrin saturation < 20%) and symptomatic HFrEF (LVEF ≤ 40% with NYHA II to IV HF), oral iron replacement had no effect on exercise capacity as measured using change in peak oxygen uptake.³⁶ However, IV iron replacement might be a reasonable option to improve functional status and quality of life (QOL) for patients with HF.³⁷ In these studies, participants were aged < 75 years, and there is no evidence that treating other types of anemia improves outcomes in patients with HF.

Hypertension

The Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated that controlling blood pressure to a goal systolic pressure of < 120 mm Hg is associated with significant reduction in the mortality among patients with increased CV risk (aged > 75 years, vascular disease, kidney injury, or a Framingham Risk Score >15%).³⁸ The SPRINT study included patients aged > 75 (25%); however, the study excluded older adults living in nursing homes and those with diabetes mellitus, symptomatic HF, dementia, or stroke. The subgroup analysis did not stratify patients based on age nor provided sufficient evidence regarding treatment targets for this vulnerable population. Therefore, clinicians cannot draw any conclusions about managing hypertension among patients with HF from this study.

 

Sleep Apnea

Sleep apnea is common among patients with HF. A study of adults with chronic HF treated with evidence-based therapies found that 61% of participants had central or obstructive sleep apnea.³⁹ In elderly patients, sleep apnea is further complicated by insomnia and disturbance of sleep cycle that often occur with the aging process.

It is crucial to differentiate central sleep apnea from obstructive sleep apnea, because the treatment approaches differ. Central sleep apnea is associated with poor prognosis in patients with HF.⁴⁰ Adaptive servo ventilation for central sleep apnea uses a noninvasive ventilator to delivering servo controlled inspiratory pressure support on top of expiratory positive airway pressure. Adaptive servo ventilation for central sleep apnea is associated with higher all-cause mortality and CV mortality.⁴¹ Continuous positive airway pressure for obstructive sleep apnea improves sleep quality, reduces the apnea-hypopnea index, and improves nocturnal oxygenation.⁴²

Depression

Clinically significant depression occurs in 21% of patients with HF, and the relationship between depression and poor HF outcomes is consistent and strong across several endpoints. However, in a randomized, 12-week study, the selective serotonin reuptake inhibitor sertraline did not improve depression symptoms or clinical status among patients with HF.⁴³ Depression symptoms might overlap with fatigue and low energy expenditure experienced by oldest old patients with HF who do not have depression.

Furthermore, studies describing depression treatments among patients with HF are too small and heterogeneous to permit definitive conclusions about intervention effectiveness. These results identify areas requiring further development, raise questions regarding the association between depression and clinical outcomes in patients with HF, and provide information on depression prevalence that may help researchers design studies with appropriate depression measures and adequately powered sample sizes.

Frailty

Although frailty is prevalent in the elderly and is independently associated with poor outcomes, there is no standardized definition for frailty. The Fried Frailty Index is a widely used scale that incorporates criteria including weakness, slowness, exhaustion, and low physical activity in the diagnosis of frailty.⁴⁴ However these symptoms are common among patients with advanced HF with and without depression or frailty.

Frailty should be defined collaboratively by the clinician and the patient and should include multidimensional aspects of health, function, and well-being. The treatment goal for patients with HF with frailty is to establish patient-centered goals based on preferences of care.⁴⁵

Discussion

Although several novel approaches to improve outcomes of patients with HF have been developed, it continues to be the leading cause of cardiovascular death among older patients and the leading cause of hospital admissions.⁴⁶ About 50% of newly diagnosed patients with HF die within 5 years.⁴⁷ Current guidelines for managing HF are based on clinical trials that either include few or completely exclude patients aged > 80 years, minorities, and patients with comorbidities clinicians encounter daily in clinical practice.

Furthermore, most clinical trials are designed with mortality as the primary endpoint, which might be as important to our patients with advanced age as their ability to function with a reasonable QOL and less dependence on caregivers.

Decision making in managing HF in our oldest patients should start with an open discussion of the disease and its prognosis, goals of care, and available treatment options. The discussion should also cover all dimensions of suffering, including physical, spiritual, and psychosocial domains. Interviews of patients dying of HF and their caregivers conducted in the United Kingdom identified several communication and transition of care challenges specific to treating this population.⁴⁸ The study revealed in most cases, patients did not recall receiving any written information about the severity of their disease and often did not understand the association among symptoms, such as shortness of breath, edema, and HF. Patients and caregivers did not feel involved in the decision-making process regarding their illness.

The concurrent presence of comorbidity, frailty, and cognitive impairment in our aging population with HF might add to the burden of the primary condition. Care often is perceived as fragmented. Polypharmacy negatively impacts HF management by increasing risk of drug nonadherence, drug interactions, and AEs in an already vulnerable population. There is a need for more effective interpersonal and easy to understand communication and resources.

In many situations, support services might be best facilitated by a dedicated palliative medicine team with significant experience in managing patients with HF.Although palliative medicine should always be considered for patients with HF with advanced age,consultations often are not obtained unless the patient decides to forgo medical treatment or until the last month of life.⁴⁹It is important to note that older adults are not a homogeneous group, and the conventional viewpoint that all patients with HF value symptom control and QOL over longevity may not be true. In a large study three-fourths of elderly outpatients with HF were not willing to trade survival time for improved QOL; and their preferences changed over the course of illness.⁵⁰

Although not all end-of-life symptoms can realistically be palliated, earlier involvement of multidisciplinary palliative medicine specialists may improve symptom control, functional status, and QOL. The team may help patients and caregivers cope with uncertainty, and make informed decisions that are person centered based on value system and beliefs.⁵¹

 

Conclusion

Randomized control trials as well as thoughtful observational studies of HF in patients with advanced age and comorbidities, although challenging, are needed to create the evidence base for treatment interventions and assessing their impact on mortality, morbidity, and QOL in this rapidly growing segment of our population.

Given the lack of evidence for HF treatment in patients with advanced age, the clinician should weigh the knowledge of the effect of aging on the CV system, and the lived experience of patients with HF, with the evidence that exists for making the best decision to relieve bothersome symptoms and improve outcomes of care as determined by patients and their caregivers.

Often the most important intervention we can offer our patients, especially those nearing the end of life, is dedicating our time to truly and actively listen with empathy, understating, and respect for their autonomy and for their decision making. And in doing so we accept our own limitations with humility.
 

Acknowledgments
Dr. Kheirbek received funds from the Veterans Affairs Capitol Health Care Network to establish the Center for Health and Aging at the Washington DC VA Medical Center.

Cardiovascular disease (CVD) remains the leading cause of global mortality. In 2015, 41.5% of the US population had at least 1 form of CVD and CVD accounted for nearly 18 million deaths worldwide.1,2 The major disease categories represented include myocardial infarction (MI), sudden death, strokes, calcific aortic valve stenosis (CAVS), and peripheral vascular disease.^1,2 In terms of health care costs, quality of life, and caregiver burden, the overall impact of disease prevalence continues to rise.^1,3-6 There is an urgent need for more precise and earlier CVD risk assessment to guide lifestyle and therapeutic interventions for prevention of disease progression as well as potential reversal of preclinical disease. Even at a young age, visible coronary atherosclerosis has been found in up to 11% of “healthy” active individuals during autopsies for trauma fatalities.^7,8

The impact of CVD on the US and global populations is profound. In 2011, CVD prevalence was predicted to reach 40% by 2030.9 That estimate was exceeded in 2015, and it is now predicted that by 2035, 45% of the US population will suffer from some form of clinical or preclinical CVD. In 2015, the decadeslong decline in CVD mortality was reversed for the first time since 1969, showing a 1% increase in deaths from CVD.¹ Nearly 300,000 of those using US Department of Veterans Affairs (VA) services were hospitalized for CVD between 2010 and 2014.¹⁰ The annual direct and indirect costs related to CVD in the US are estimated at $329.7 billion, and these costs are predicted to top $1 trillion by 2035.¹ Heart attack, coronary atherosclerosis, and stroke accounted for 3 of the 10 most expensive conditions treated in US hospitals in 2013.¹¹ Globally, the estimate for CVD-related direct and indirect costs was $863 billion in 2010 and may exceed $1 trillion by 2030.¹²

The nature of military service adds additional risk factors, such as posttraumatic stress disorder, depression, sleep disorders and physical trauma which increase CVD morbidity/ mortality in service members, veterans, and their families.^13-16 In addition, living in lowerincome areas (countries or neighborhoods) can increase the risk of both CVD incidence and fatalities, particularly in younger individuals.^17-20 The Military Health System (MHS) and VA are responsible for the care of those individuals who have voluntarily taken on these additional risks through their time in service. This responsibility calls for rapid translation to practice tools and resources that can support interventions to minimize as many modifiable risk factors as possible and improve longterm health. This strategy aligns with the World Health Organization’s (WHO) focus on prevention of disease progression through interventions targeting modifiable risk.^3-6,21-23 The driving force behind the launch of the US Department of Health and Human Services (HHS) Million Hearts program was the goal of preventing 1 million heart attacks and strokes by 2017 with risk reduction through aspirin, blood pressure control, cholesterol management, smoking cessation, sodium reduction, and physical activity.^24,25 While some reductions in CVD events have been documented, the outcomes fell short of the goals set, highlighting both the need and value of continued and expanded efforts for CVD risk reduction.²⁶

More precise assessment of risk factors during preventative care, as well as after a diagnosis of CVD, may improve the timeliness and precision of earlier interventions (both lifestyle and therapeutic) that reduce CVD morbidity and mortality^.27 Personalized or precision medicine approaches take into account differences in socioeconomic, environmental, and lifestyle factors that are potentially reversible, as well as gender, race, and ethnicity.^28-31 Current methods of predicting CVD risk have considerable room for improvement.²⁷ About 40% of patients with newly diagnosed CVD have normal traditional cholesterol profiles, including those whose first cardiac event proves fatal.^29-33 Currently available risk scores (hundreds have been described in the literature) mischaracterize risk in minority populations and women, and have shown deficiencies in identifying preclinical atherosclerosis.^34,35 The failure to recognize preclinical CVD in military personnel during their active duty life cycle results in missed opportunities for improved health and readiness sustainment.

Most CVD risk prediction models incorporate some form of blood lipids. Total cholesterol (TC) is most commonly used in clinical practice, along with high-density lipoprotein (HDLC), low-density lipoprotein (LDLC), and triglycerides (TG).^23,27,36 High LDLC and/or TC are well established as lipid-related CVD risk factors and are incorporated into many CVD risk scoring systems/models described in the literature.²⁷ LDLC reduction is commonly recommended as CVD prevention, but even with optimal statin treatment, there is still considerable residual risk for new and recurrent CVD events.^{28,32,34,35,37-42}

Incorporating novel biomarkers and alternative lipid measurements may improve risk prediction and aid targeted treatment, ultimately reducing CVD events.²⁷ Apolipoprotein B (ApoB) is a major atherogenic component embedded in LDL and VLDL correlating to non-HDLC and may be useful in the setting of triglycerides ≥ 200 mg/d as levels > 130 mg/ dL appear to be risk-enhancing, but measurements may be unreliable.⁴³ According to the 2018 Cholesterol Guidelines, lipoprotein(a) [Lp(a)] elevation also is recognized as a risk-enhancing factor that is particularly implicated when there is a strong family history of premature atherosclerotic CVD or personal history of CVD not explained by major risk factors.⁴³

Lp(a) elevation is a largely underrecognized category of lipid disorder that impacts up to 20% to 30% of the population globally and within the US, although there is considerable variability by geographic location and ethnicity.⁴⁴ Globally, Lp(a) elevation places > 1 billion people at moderate to high risk for CVD.44 Lp(a) has a strong genetic component and is recognized as a distinct and independent risk factor for MI, sudden death, strokes and CAVS. Lp(a) has an extensive body of evidence to support its distinct role both as a causal factor in CVD and as an augmentation to traditional risk factors.^44-48

Lipoproteni(a) Elevation Use For Diagnosis

The importance of Lp(a) elevation as a clinical diagnosis rather than a laboratory abnormality alone was brought forward by the Lipoprotein(a) Foundation. Its founder, Sandra Tremulis, is a survivor of an acute coronary event that occurred when she was 39-years old, despite running marathons and having none of the traditional CVD lifestyle risk factors.⁴⁹ This experience inspired her to create the Lipoprotein(a) Foundation to give a voice to families living with or at risk for CVD due to Lp(a) elevation.

As often happens in the progress of medicine, patients and their families drive change based on their personal experiences with the gaps in standard clinical practice. It was this foundation—not a member of the medical establishment—that submitted the formal request for the addition of new ICD-10-CM diagnostic and family history codes for Lp(a) elevation during the Centers for Disease Control and Prevention (CDC) September 2017 ICD-10-CM Coordination and Maintenance Committee meeting.⁵⁰ In June 2018, the final ICD-10-CM code addenda for 2019 was released and included the new codes E78.⁴¹ (Elevated Lp[a]) and Z83.430 (Family history of elevated Lp[a]).⁵² After the new codes were approved, both the American Heart Association and the National Lipid Association added recommendations regarding Lp(a) testing to their clinical practice guidelines.^43,52

Practically, these codes standardize billing and payment for legitimate clinical work and laboratory testing. Prior to the addition of Lp(a) elevation as a clinical diagnosis, testing and treatment of Lp(a) elevation was considered experimental and not medically necessary until after a cardiovascular event had already occurred. Services for Lp(a) elevation were therefore not reimbursed by many healthcare organizations and insurance companies. The new ICD-10-CM codes encourage the assessment of Lp(a) both in individuals with early onset major CVD events and in presumably fit, healthy individuals, particularly when there is a family history of Lp(a) elevation. Given that Lp(a) levels do not change significantly over time, the current understanding is that only a single measurement is needed to define the individual risk over a lifetime.^41,42,44,45 As therapies targeting Lp(a) levels evolve, repeated measurements may be indicated to monitor response and direct changes in management. “Elevated Lipoprotein(a)” is the first laboratory testing abnormality that has achieved the status of a clinical diagnosis.

Lp(a) Measurements

There is considerable complexity to the measurement of lipoproteins in blood samples due to heterogeneity in both density and size of particles as illustrated in the Figure.⁵³

For traditional lipids measured in clinical practice, the size and density ranges from small high-density lipoprotein (HDL) through LDLC and intermediate- density lipoprotein (IDL) to the largest least dense particles in the very low-density lipoprotein (VLDL) and chylomicron remnant fractions. Standard lipid profiles consist of mass concentration measurements (mg/dL) of TC, TG, HDLC, and LDLC.⁵³ Non-HDLC (calculated as: TC−HDLC) consists of all cholesterol found in atherogenic lipoproteins, including remnant-C and Lp(a). Until recently, the cholesterol content of Lp(a), corresponding to about 30% of Lp(a) total mass, was included in the TC, non-HDLC and LDLC measurements with no separate reporting by the majority of clinical laboratories.

 

After > 50 years of research on the structure and biochemistry of Lp(a), the physiology and biological functions of these complex and polymorphic lipoprotein particles are not fully understood. Lp(a) is composed of a lipoprotein particle similar in composition to LDL (protein and lipid), containing 1 molecule of ApoB wrapped around a core of cholesteryl ester and triglyceride with phospholipids and unesterified cholesterol at its surface.⁴⁸ The presence of a unique hydrophilic, highly glycosylated protein referred to as apolopoprotienA (apo[a]), covalently attached to ApoB-100 by a single disulfide bridge, differentiates Lp(a) from LDL.⁴⁸ Cholesterol rich ApoB is an important component within many lipoproteins pathogenic for atherosclerosis and CVD.^45,47,53

The apo(a) contributes to the increased density of Lp(a) compared to LDLC with associated reduced binding affinity to the LDL receptor. This reduced receptor binding affinity is a presumed mechanism for the lack of Lp(a) plasma level response to statin therapies, which increase hepatic LDL receptor activity.⁴⁷ Apo(a) evolved from the plasminogen gene through duplication and remodeling and demonstrates extensive heterogeneity in protein size, with > 40 different apo(a) isoforms resulting in > 40 different Lp(a) particle sizes. Size of the apo(a) particle is determined by the number of pleated structures known as kringles. Most people (> 80%) carry 2 different-sized apo(a) isoforms. Plasma Lp(a) level is determined by the net production of apo(a) in each isoform, and the smaller apo(a) isoforms are associated with higher plasma levels of Lp(a).⁴⁵

Given the heterogeneity in Lp(a) molecular weight, which can vary even within individuals, recommendations have been made for reporting results as particle numbers or concentrations (nmol/L or mmol/L) rather than as mass concentration (mg/dL).⁵⁵ However, the majority of the large CVD morbidity and mortality outcomes studies used Lp(a) mass concentration levels in mg/ dL to characterize risk levels.^56,57 There is no standardized method to convert Lp(a) measurements from mg/dL to nmol/L.⁵⁵ Current assays using WHO standardized reagents and controls are reliable for categorizing risk levels.⁵⁸

The European Atherosclerosis Society consensus panel recommended that desirable Lp(a) levels should be below the 80th percentile (< 50 mg/dL or < 125 nmol/L) in patients with intermediate or high CVD risk.⁵⁹ Subsequent epidemiological and Mendelian randomization studies have been performed in general populations with no history of CVD and demonstrated that increased CVD risk can be detected with Lp(a) levels as low as 25 to 30 mg/dL.^56,60-63 In secondary prevention populations with prior CVD and optimal treatment (statins, antiplatelet drugs), recurrent event risk was also increased with elevated Lp(a).^63-66

Using immunoturbidometric assays, Varvel and colleagues reported the prevalence of elevated Lp(a) mass concentration levels (mg/dL) in > 500,000 US patients undergoing clinical evaluations based on data from a referral laboratory of patients.⁵⁸ The mean Lp(a) levels were 34.0 mg/dL with median (interquartile range [IQR]) levels at 17 (7-47) mg/dL and overall range of 0 to 907 mg/dL.⁵⁸ Females had higher Lp(a) levels compared to males but no ethnic or racial breakdown was provided. Lp(a) levels > 30 mg/dL and > 50 mg/dL were present in 35% and 24% of subjects, respectively. Table 1 displays the relationship between various Lp(a) level cut-offs to mean levels of LDLC, estimated LDLC corrected for Lp(a), TC, HDLC, and TG.⁵⁸ The data demonstrate that Lp(a) elevation cannot be inferred from LDLC levels nor from any of the other traditional lipoprotein measures. Patients with high risk Lp(a) levels may have normal LDLC. While Lp(a) thresholds have been identified for stratification of CVD risk, the target levels for risk reduction have not been specifically defined, particularly since therapies are not widely available for reduction of Lp(a). Table 2 provides an overview of clinical lipoprotein measurements that may be reasonable targets for therapeutic interventions and reduction of CVD risk.^44,53,55 In general, existing studies suggest that radical reduction (> 80%) is required to impact long-term outcomes, particularly in individuals with severe disease.^68,69

LDLC reduction alone leaves a residual CVD risk that is greater than the risk reduced.40 In addition, the autoimmune inflammation and lipid specific autoantibodies play an important role in increased CVD morbidity and mortality risk.^70,71 The presence of autoantibodies such as antiphospholipid antibodies (without a specific autoimmune disease diagnosis) increases the risk of subclinical atherosclerosis.^72,73 Certain autoimmune diseases such as systemic lupus erythematosus are recognized as independent risk factors for CVD.^74,75 Autoantibodies appear to mediate CVD events and mortality risk, independent of traditional therapies for risk reduction.⁷³ Further research is needed to clarify the role of autoantibodies as markers of increased or decreased CVD risk and their mechanism of action.

Autoantibodies directed at new antigens in lipoproteins within atherosclerotic lesions can modulate the impact of atherosclerosis via activation of the innate and adaptive immune system.⁷⁶ The lipid-associated neopeptides are recognized as damage-associated or danger- associated molecular patterns (DAMPs), also known as alarmins, which signal molecules that can trigger and perpetuate noninfectious inflammatory responses.^77-79 Plasma autoantibodies (immunoglobulin M and G [IgM, IgG]) modify proinflammatory oxidation-specific epitopes on oxidized phospholipids (oxPL) within lipoproteins and are linked with markers of inflammation and CVD events.^80-82 Modified LDLC and ApoB-100 immune complexes with specific autoantibodies in the IgG class are associated with increased CVD.⁷⁶ These and other risk-modulating autoantibodies may explain some of the variability in CVD outcomes by ethnicity and between individuals.

Some antibodies to oxidized LDL (ox-LDL) may have a protective role in the development of atherosclerosis.^83,84 In a cohort of > 500 women, the number of carotid atherosclerotic plaques and total carotid plaque area were inversely correlated with a specific IgM autoantibody (MDA-p210).⁸⁴ High concentrations of Lp(a)- containing circulating immune complexes and Lp(a)-specific IgM and IgG have been described in patients with coronary heart disease (CHD).⁸⁵ Like ox-LDL, oxidized Lp(a) [ox-Lp(a)] is more potent than native Lp(a) in increasing atherosclerosis risk and is increased in patients with CHD compared to healthy controls.^86-88 Ox-Lp(a) levels may represent an even stronger risk marker for CVD than ox-LDL.⁸⁵

Possible Mechanisms of Pathogenesis

While the precise quantification of Lp(a) in human plasma (or serum) has been challenging, current clinical laboratories use standardized international reference reagents and controls in their assays. Most current Lp(a) assays are based on immunological methods (eg, immunonephelometry, immunoturbidimetry, or enzyme linked immunosorbent assay [ELISA]) using antibodies against apo(a).⁸⁹ Apo(a) contains 10 subtypes of kringle IV and 1 copy of kringle V. Some assays use antibodies against kringle-IV type 2; however, it has been recommended that newer methods should use antibodies against the specific bridging kringle-IV Type 9 domain, which has a more stable bond and is present as a single copy.^48,89 Other approaches to Lp(a) measurement include ultraperformance liquid chromatography/mass spectrometry that can determine both the concentration and particle size of apo(a).^48,90 For routine clinical care, currently available assays reporting in mg/dL can be considered fairly accurate for separating low-risk from moderate-to-high-risk patients.⁴⁵

The physiologic role of Lp(a) in humans remains to be fully defined and individuals with extremely low plasma Lp(a) levels present no disease or deficiency syndromes.⁹¹ Lp(a) accumulates in endothelial injuries and binds to components of the vessel wall and subendothelial matrix, presumably due to the strong lysine binding site in apo(a).⁴⁶ Mediated by apo(a), the binding stimulates chemotactic activation of monocytes/macrophages and thereby modulating angiogenesis and inflammation.⁸⁹ Lp(a) may contribute to CVD and CAVS via its LDL-like component, with proinflammatory effects of oxidized phospholipids (OxPL) on both ApoB and apo(a) and antifibrinolytic/prothrombotic effects of apo(a).⁹² In Vitro studies have demonstrated that apo(a) modifies cellular function of cultured vascular endothelial cells (promoting stress fiber formation, endothelial contraction and vascular permeability), smooth muscles, and monocytes/ macrophages (promoting differentiation of proinflammatory M1-1 type macrophages) via complex mechanisms of cell signaling and cytokine production.89 Lp(a) is the only monogenetic risk factor for aortic valve calcification and stenosis⁹³ and is strongly linked specifically with the single nucleotide polymorphism (SNP) rs10455872 in the gene LPA encoding for apo(a).⁹⁴

CVD Risk Predictive Value

There are a large number of studies demonstrating that Lp(a) elevations are an independent predictor of adverse cardiovascular outcomes including MI, sudden death, strokes, calcific aortic valve stenosis and peripheral vascular disease (Table 3). The Copenhagen City Heart Study and Copenhagen General Population Study are well known prospective population- based cohort studies that track outcomes through national patient registries.⁹⁵ These studies demonstrate increased risk for MI, CHD, CAVS, and heart failure when subjects with very high Lp(a) levels (50-115 mg/dL) are compared with subjects with very low Lp(a) levels (< 5 mg/dL).^96-100 Subjects with less extreme Lp(a) elevations (> 30 mg/dL) also show increased risk of CVD when they have comorbid LDLC elevations.¹⁰¹ However, the Copenhagen studies are composed exclusively of white subjects and the effects of Lp(a) are known to vary with race or ethnicity.

The Multi-Ethnic Study of Atherosclerosis (MESA) recruited an ethnically diverse sample of > 6,000 Americans, aged 45 to 84 years, without CVD, into an ongoing prospective cohort study. Research using subjects from this study has found consistently increased risk of CHD, heart failure, subclinical aortic valve calcification, and more severe CAVS in white subjects with elevated Lp(a).^60,102,103 Black subjects with elevated Lp(a) had increased risk of CHD and more severe CAVS and Hispanic subjects with Lp(a) elevation were at higher risk for CHD.^60,102 So far, no studies of MESA subjects have identified a relationship between Lp(a) elevation and CVD events for Asian-Americans subjects (predominantly of Chinese descent). There is a need for ongoing research to more precisely define relevant cut-off levels by race, ethnicity and sex.

The Atherosclerosis Risk in Communities (ARIC) Study was a prospective multiethnic cohort study including > 15,000 US adults, aged 45 to 64 years.¹⁰³ Lp(a) elevations in this cohort were associated with greater risks for first CVD events, heart failure, and recurrent CVD events.^61,64,105 The risk of stroke for subjects with elevated Lp(a) was greater for black and white women, and for black men.^61,106 However, a meta-analysis of case-control studies showed increased ischemic stroke risk in both men and women with elevated Lp(a).⁵⁷

A recent European meta-analysis collected blood samples and outcome data from > 50,000 subjects in 7 prospective cohort studies. Using a central laboratory to standardize Lp(a) measurements, researchers found increased risk of major coronary events and new CVD in subjects with Lp(a) > 50 mg/dL compared to those below that threshold.¹⁰⁷

Although many of these studies show modest increases in risk of CVD events with Lp(a) elevation, it should be noted that other studies do not demonstrate such consistent associations. This is particularly true in studies of women and nonwhite ethnic groups.^103,108-112 The variability of study results may be due to other confounding factors such as autoantibodies that either upregulate or downregulate atherogenicity of LDLC and potentially other lipoproteins. This is particularly relevant to women who have an increased risk for autoimmune disease.

Lp(a) has significant genetic heritability—75% in Europeans and 85% in African Americans.¹¹³ In whites, the LPA gene on chromosome 6p26- 27 with the polymorphism genetic variants rs10455872 and rs3798220 is consistently associated with elevated Lp(a) levels.^63,100,113 However, the degree of Lp(a) elevation associated with these specific genetic variants varies by ethnicity.^78,113,115

Lifestyle and Cardiovascular Health

It is noteworthy that the Lp(a) genetic risks can also be modified by lifestyle risk reduction even in the absence of significant blood level reductions. For example, Khera and colleagues constructed a genetic risk profile for CVD that included genes related to Lp(a).¹¹⁶ Subjects with high genetic risk were more likely to experience CVD events compared with subjects with low genetic risk. However, risks for CVD were attenuated by 4 healthy lifestyle factors: current nonsmoker, body mass index < 30, at least weekly physical activity, and a healthy diet. Subjects with high genetic risk and an unhealthy lifestyle (0 or 1 of the 4 healthy lifestyle factors) were the most likely to develop CVD (Hazard ratio [HR], 3.5), but that risk was lower for subjects with healthy (3 or 4 of the 4 healthy lifestyle factors) and intermediate lifestyles (2 of the 4 healthy lifestyle factors) (HR, 1.9 and 2.2, respectively), despite despite high genetic risk for CVD.

While the independent CVD risk associated with elevated Lp(a) does not appear to be responsive to lifestyle risk reduction alone, certainly elevated LDLC and traditional risk factors can increase the overall CVD risk and are worthy of preventive interventions. In particular, inflammation from any source exacerbates CVD risk. Proatherogenic diet, insufficient sleep, lack of exercise, and maladaptive stress responses are other targets for personalized CVD risk reduction. ^28,117 Studies of dietary modifications and other lifestyle factors have shown reduced risk of CVD events, despite lack of reduction in Lp(a) levels.^119,120 It is noteworthy that statin therapy (with or without ezetimibe) fails to impact CAVS progression, likely because statins either raise or have no effect on Lp(a) levels.^92,119

Until recently, there has been no evidence supporting any therapeutic intervention causing clinically meaningful reductions in Lp(a). Table 4 lists major drug classes and their effects on Lp(a) and CVD outcomes; however, a detailed discussion of each of these therapies is beyond the scope of this review. Drugs that reduce Lp(a) by 20-30% have varying effects on CVD outcomes, from no effect^122,123 to a 10% to 20% decrease in CVD events when compared with a placebo.^124,125 Because these drugs also produce substantial reductions in LDLC, it is not possible to determine how much of the beneficial effects are due to reductions in Lp(a).

Lipoprotein apheresis produces profound reductions in Lp(a) of 60 to 80% in very highrisk populations.^69,126 Within-subjects comparisons show up to 80% reductions in CVD events, relative to event rates prior to treatment initiation.^69,127 Early trials of antisense oligonucleotide against apo(a) therapies show potential to produce similar outcomes.^128,129 These treatments may be particularly effective in patients with isolated Lp(a) elevations.

Summary

Lp(a) elevation is a major contributor to cardiovascular disease risk and has been recognized as an ICD-10-CM coded clinical diagnosis, the first laboratory abnormality to be defined a clinical disease in the asymptomatic healthy young individuals. This change addresses currently under- diagnosed CVD risk independent of LDLC reduction strategies. A brief overview of recent guidelines for the clinical use of Lp(a) testing from the American Heart Association^43,151 and the National Lipid Association52 can be found in Table 5. Although drug therapies for lowering Lp(a) levels remain limited, new treatment options are actively being developed.

Many Americans with high Lp(a) have not yet been identified. Expanded one-time screening can inform these patients of their cardiovascular risk and increase their access to early, aggressive lifestyle modification and optimal lipid-lowering therapy. Given the further increased CVD risk factors for military service members and veterans, a case can be made for broader screening and enhanced surveillance of elevated Lp(a) in these presumably healthy and fit individuals as well as management focused on modifiable risk factors.

Acknowledgements

This program initiative was conducted by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. as part of the Integrative Cardiac Health Project at Walter Reed National Military Medical Center (WRNMMC), and is made possible by a cooperative agreement that was awarded and administered by the US Army Medical Research & Materiel Command (USAMRMC), at Fort Detrick under Contract Number: W81XWH-16-2-0007. It reflects literature review preparatory work for a research protocol but does not involve an actual research project. The work in this manuscript was supported by the staff of the Integrative Cardiac Health Project (ICHP) with special thanks to Claire Fuller, Elaine Walizer, Dr. Mariam Kashani and the entire health coaching team.

Cardiovascular disease (CVD) remains the leading cause of global mortality. In 2015, 41.5% of the US population had at least 1 form of CVD and CVD accounted for nearly 18 million deaths worldwide.1,2 The major disease categories represented include myocardial infarction (MI), sudden death, strokes, calcific aortic valve stenosis (CAVS), and peripheral vascular disease.^1,2 In terms of health care costs, quality of life, and caregiver burden, the overall impact of disease prevalence continues to rise.^1,3-6 There is an urgent need for more precise and earlier CVD risk assessment to guide lifestyle and therapeutic interventions for prevention of disease progression as well as potential reversal of preclinical disease. Even at a young age, visible coronary atherosclerosis has been found in up to 11% of “healthy” active individuals during autopsies for trauma fatalities.^7,8

The impact of CVD on the US and global populations is profound. In 2011, CVD prevalence was predicted to reach 40% by 2030.9 That estimate was exceeded in 2015, and it is now predicted that by 2035, 45% of the US population will suffer from some form of clinical or preclinical CVD. In 2015, the decadeslong decline in CVD mortality was reversed for the first time since 1969, showing a 1% increase in deaths from CVD.¹ Nearly 300,000 of those using US Department of Veterans Affairs (VA) services were hospitalized for CVD between 2010 and 2014.¹⁰ The annual direct and indirect costs related to CVD in the US are estimated at $329.7 billion, and these costs are predicted to top $1 trillion by 2035.¹ Heart attack, coronary atherosclerosis, and stroke accounted for 3 of the 10 most expensive conditions treated in US hospitals in 2013.¹¹ Globally, the estimate for CVD-related direct and indirect costs was $863 billion in 2010 and may exceed $1 trillion by 2030.¹²

The nature of military service adds additional risk factors, such as posttraumatic stress disorder, depression, sleep disorders and physical trauma which increase CVD morbidity/ mortality in service members, veterans, and their families.^13-16 In addition, living in lowerincome areas (countries or neighborhoods) can increase the risk of both CVD incidence and fatalities, particularly in younger individuals.^17-20 The Military Health System (MHS) and VA are responsible for the care of those individuals who have voluntarily taken on these additional risks through their time in service. This responsibility calls for rapid translation to practice tools and resources that can support interventions to minimize as many modifiable risk factors as possible and improve longterm health. This strategy aligns with the World Health Organization’s (WHO) focus on prevention of disease progression through interventions targeting modifiable risk.^3-6,21-23 The driving force behind the launch of the US Department of Health and Human Services (HHS) Million Hearts program was the goal of preventing 1 million heart attacks and strokes by 2017 with risk reduction through aspirin, blood pressure control, cholesterol management, smoking cessation, sodium reduction, and physical activity.^24,25 While some reductions in CVD events have been documented, the outcomes fell short of the goals set, highlighting both the need and value of continued and expanded efforts for CVD risk reduction.²⁶

More precise assessment of risk factors during preventative care, as well as after a diagnosis of CVD, may improve the timeliness and precision of earlier interventions (both lifestyle and therapeutic) that reduce CVD morbidity and mortality^.27 Personalized or precision medicine approaches take into account differences in socioeconomic, environmental, and lifestyle factors that are potentially reversible, as well as gender, race, and ethnicity.^28-31 Current methods of predicting CVD risk have considerable room for improvement.²⁷ About 40% of patients with newly diagnosed CVD have normal traditional cholesterol profiles, including those whose first cardiac event proves fatal.^29-33 Currently available risk scores (hundreds have been described in the literature) mischaracterize risk in minority populations and women, and have shown deficiencies in identifying preclinical atherosclerosis.^34,35 The failure to recognize preclinical CVD in military personnel during their active duty life cycle results in missed opportunities for improved health and readiness sustainment.

Most CVD risk prediction models incorporate some form of blood lipids. Total cholesterol (TC) is most commonly used in clinical practice, along with high-density lipoprotein (HDLC), low-density lipoprotein (LDLC), and triglycerides (TG).^23,27,36 High LDLC and/or TC are well established as lipid-related CVD risk factors and are incorporated into many CVD risk scoring systems/models described in the literature.²⁷ LDLC reduction is commonly recommended as CVD prevention, but even with optimal statin treatment, there is still considerable residual risk for new and recurrent CVD events.^{28,32,34,35,37-42}

Incorporating novel biomarkers and alternative lipid measurements may improve risk prediction and aid targeted treatment, ultimately reducing CVD events.²⁷ Apolipoprotein B (ApoB) is a major atherogenic component embedded in LDL and VLDL correlating to non-HDLC and may be useful in the setting of triglycerides ≥ 200 mg/d as levels > 130 mg/ dL appear to be risk-enhancing, but measurements may be unreliable.⁴³ According to the 2018 Cholesterol Guidelines, lipoprotein(a) [Lp(a)] elevation also is recognized as a risk-enhancing factor that is particularly implicated when there is a strong family history of premature atherosclerotic CVD or personal history of CVD not explained by major risk factors.⁴³

Lp(a) elevation is a largely underrecognized category of lipid disorder that impacts up to 20% to 30% of the population globally and within the US, although there is considerable variability by geographic location and ethnicity.⁴⁴ Globally, Lp(a) elevation places > 1 billion people at moderate to high risk for CVD.44 Lp(a) has a strong genetic component and is recognized as a distinct and independent risk factor for MI, sudden death, strokes and CAVS. Lp(a) has an extensive body of evidence to support its distinct role both as a causal factor in CVD and as an augmentation to traditional risk factors.^44-48

Lipoproteni(a) Elevation Use For Diagnosis

The importance of Lp(a) elevation as a clinical diagnosis rather than a laboratory abnormality alone was brought forward by the Lipoprotein(a) Foundation. Its founder, Sandra Tremulis, is a survivor of an acute coronary event that occurred when she was 39-years old, despite running marathons and having none of the traditional CVD lifestyle risk factors.⁴⁹ This experience inspired her to create the Lipoprotein(a) Foundation to give a voice to families living with or at risk for CVD due to Lp(a) elevation.

As often happens in the progress of medicine, patients and their families drive change based on their personal experiences with the gaps in standard clinical practice. It was this foundation—not a member of the medical establishment—that submitted the formal request for the addition of new ICD-10-CM diagnostic and family history codes for Lp(a) elevation during the Centers for Disease Control and Prevention (CDC) September 2017 ICD-10-CM Coordination and Maintenance Committee meeting.⁵⁰ In June 2018, the final ICD-10-CM code addenda for 2019 was released and included the new codes E78.⁴¹ (Elevated Lp[a]) and Z83.430 (Family history of elevated Lp[a]).⁵² After the new codes were approved, both the American Heart Association and the National Lipid Association added recommendations regarding Lp(a) testing to their clinical practice guidelines.^43,52

Practically, these codes standardize billing and payment for legitimate clinical work and laboratory testing. Prior to the addition of Lp(a) elevation as a clinical diagnosis, testing and treatment of Lp(a) elevation was considered experimental and not medically necessary until after a cardiovascular event had already occurred. Services for Lp(a) elevation were therefore not reimbursed by many healthcare organizations and insurance companies. The new ICD-10-CM codes encourage the assessment of Lp(a) both in individuals with early onset major CVD events and in presumably fit, healthy individuals, particularly when there is a family history of Lp(a) elevation. Given that Lp(a) levels do not change significantly over time, the current understanding is that only a single measurement is needed to define the individual risk over a lifetime.^41,42,44,45 As therapies targeting Lp(a) levels evolve, repeated measurements may be indicated to monitor response and direct changes in management. “Elevated Lipoprotein(a)” is the first laboratory testing abnormality that has achieved the status of a clinical diagnosis.

Lp(a) Measurements

There is considerable complexity to the measurement of lipoproteins in blood samples due to heterogeneity in both density and size of particles as illustrated in the Figure.⁵³

For traditional lipids measured in clinical practice, the size and density ranges from small high-density lipoprotein (HDL) through LDLC and intermediate- density lipoprotein (IDL) to the largest least dense particles in the very low-density lipoprotein (VLDL) and chylomicron remnant fractions. Standard lipid profiles consist of mass concentration measurements (mg/dL) of TC, TG, HDLC, and LDLC.⁵³ Non-HDLC (calculated as: TC−HDLC) consists of all cholesterol found in atherogenic lipoproteins, including remnant-C and Lp(a). Until recently, the cholesterol content of Lp(a), corresponding to about 30% of Lp(a) total mass, was included in the TC, non-HDLC and LDLC measurements with no separate reporting by the majority of clinical laboratories.

 

After > 50 years of research on the structure and biochemistry of Lp(a), the physiology and biological functions of these complex and polymorphic lipoprotein particles are not fully understood. Lp(a) is composed of a lipoprotein particle similar in composition to LDL (protein and lipid), containing 1 molecule of ApoB wrapped around a core of cholesteryl ester and triglyceride with phospholipids and unesterified cholesterol at its surface.⁴⁸ The presence of a unique hydrophilic, highly glycosylated protein referred to as apolopoprotienA (apo[a]), covalently attached to ApoB-100 by a single disulfide bridge, differentiates Lp(a) from LDL.⁴⁸ Cholesterol rich ApoB is an important component within many lipoproteins pathogenic for atherosclerosis and CVD.^45,47,53

The apo(a) contributes to the increased density of Lp(a) compared to LDLC with associated reduced binding affinity to the LDL receptor. This reduced receptor binding affinity is a presumed mechanism for the lack of Lp(a) plasma level response to statin therapies, which increase hepatic LDL receptor activity.⁴⁷ Apo(a) evolved from the plasminogen gene through duplication and remodeling and demonstrates extensive heterogeneity in protein size, with > 40 different apo(a) isoforms resulting in > 40 different Lp(a) particle sizes. Size of the apo(a) particle is determined by the number of pleated structures known as kringles. Most people (> 80%) carry 2 different-sized apo(a) isoforms. Plasma Lp(a) level is determined by the net production of apo(a) in each isoform, and the smaller apo(a) isoforms are associated with higher plasma levels of Lp(a).⁴⁵

Given the heterogeneity in Lp(a) molecular weight, which can vary even within individuals, recommendations have been made for reporting results as particle numbers or concentrations (nmol/L or mmol/L) rather than as mass concentration (mg/dL).⁵⁵ However, the majority of the large CVD morbidity and mortality outcomes studies used Lp(a) mass concentration levels in mg/ dL to characterize risk levels.^56,57 There is no standardized method to convert Lp(a) measurements from mg/dL to nmol/L.⁵⁵ Current assays using WHO standardized reagents and controls are reliable for categorizing risk levels.⁵⁸

The European Atherosclerosis Society consensus panel recommended that desirable Lp(a) levels should be below the 80th percentile (< 50 mg/dL or < 125 nmol/L) in patients with intermediate or high CVD risk.⁵⁹ Subsequent epidemiological and Mendelian randomization studies have been performed in general populations with no history of CVD and demonstrated that increased CVD risk can be detected with Lp(a) levels as low as 25 to 30 mg/dL.^56,60-63 In secondary prevention populations with prior CVD and optimal treatment (statins, antiplatelet drugs), recurrent event risk was also increased with elevated Lp(a).^63-66

Using immunoturbidometric assays, Varvel and colleagues reported the prevalence of elevated Lp(a) mass concentration levels (mg/dL) in > 500,000 US patients undergoing clinical evaluations based on data from a referral laboratory of patients.⁵⁸ The mean Lp(a) levels were 34.0 mg/dL with median (interquartile range [IQR]) levels at 17 (7-47) mg/dL and overall range of 0 to 907 mg/dL.⁵⁸ Females had higher Lp(a) levels compared to males but no ethnic or racial breakdown was provided. Lp(a) levels > 30 mg/dL and > 50 mg/dL were present in 35% and 24% of subjects, respectively. Table 1 displays the relationship between various Lp(a) level cut-offs to mean levels of LDLC, estimated LDLC corrected for Lp(a), TC, HDLC, and TG.⁵⁸ The data demonstrate that Lp(a) elevation cannot be inferred from LDLC levels nor from any of the other traditional lipoprotein measures. Patients with high risk Lp(a) levels may have normal LDLC. While Lp(a) thresholds have been identified for stratification of CVD risk, the target levels for risk reduction have not been specifically defined, particularly since therapies are not widely available for reduction of Lp(a). Table 2 provides an overview of clinical lipoprotein measurements that may be reasonable targets for therapeutic interventions and reduction of CVD risk.^44,53,55 In general, existing studies suggest that radical reduction (> 80%) is required to impact long-term outcomes, particularly in individuals with severe disease.^68,69

LDLC reduction alone leaves a residual CVD risk that is greater than the risk reduced.40 In addition, the autoimmune inflammation and lipid specific autoantibodies play an important role in increased CVD morbidity and mortality risk.^70,71 The presence of autoantibodies such as antiphospholipid antibodies (without a specific autoimmune disease diagnosis) increases the risk of subclinical atherosclerosis.^72,73 Certain autoimmune diseases such as systemic lupus erythematosus are recognized as independent risk factors for CVD.^74,75 Autoantibodies appear to mediate CVD events and mortality risk, independent of traditional therapies for risk reduction.⁷³ Further research is needed to clarify the role of autoantibodies as markers of increased or decreased CVD risk and their mechanism of action.

Autoantibodies directed at new antigens in lipoproteins within atherosclerotic lesions can modulate the impact of atherosclerosis via activation of the innate and adaptive immune system.⁷⁶ The lipid-associated neopeptides are recognized as damage-associated or danger- associated molecular patterns (DAMPs), also known as alarmins, which signal molecules that can trigger and perpetuate noninfectious inflammatory responses.^77-79 Plasma autoantibodies (immunoglobulin M and G [IgM, IgG]) modify proinflammatory oxidation-specific epitopes on oxidized phospholipids (oxPL) within lipoproteins and are linked with markers of inflammation and CVD events.^80-82 Modified LDLC and ApoB-100 immune complexes with specific autoantibodies in the IgG class are associated with increased CVD.⁷⁶ These and other risk-modulating autoantibodies may explain some of the variability in CVD outcomes by ethnicity and between individuals.

Some antibodies to oxidized LDL (ox-LDL) may have a protective role in the development of atherosclerosis.^83,84 In a cohort of > 500 women, the number of carotid atherosclerotic plaques and total carotid plaque area were inversely correlated with a specific IgM autoantibody (MDA-p210).⁸⁴ High concentrations of Lp(a)- containing circulating immune complexes and Lp(a)-specific IgM and IgG have been described in patients with coronary heart disease (CHD).⁸⁵ Like ox-LDL, oxidized Lp(a) [ox-Lp(a)] is more potent than native Lp(a) in increasing atherosclerosis risk and is increased in patients with CHD compared to healthy controls.^86-88 Ox-Lp(a) levels may represent an even stronger risk marker for CVD than ox-LDL.⁸⁵

Possible Mechanisms of Pathogenesis

While the precise quantification of Lp(a) in human plasma (or serum) has been challenging, current clinical laboratories use standardized international reference reagents and controls in their assays. Most current Lp(a) assays are based on immunological methods (eg, immunonephelometry, immunoturbidimetry, or enzyme linked immunosorbent assay [ELISA]) using antibodies against apo(a).⁸⁹ Apo(a) contains 10 subtypes of kringle IV and 1 copy of kringle V. Some assays use antibodies against kringle-IV type 2; however, it has been recommended that newer methods should use antibodies against the specific bridging kringle-IV Type 9 domain, which has a more stable bond and is present as a single copy.^48,89 Other approaches to Lp(a) measurement include ultraperformance liquid chromatography/mass spectrometry that can determine both the concentration and particle size of apo(a).^48,90 For routine clinical care, currently available assays reporting in mg/dL can be considered fairly accurate for separating low-risk from moderate-to-high-risk patients.⁴⁵

The physiologic role of Lp(a) in humans remains to be fully defined and individuals with extremely low plasma Lp(a) levels present no disease or deficiency syndromes.⁹¹ Lp(a) accumulates in endothelial injuries and binds to components of the vessel wall and subendothelial matrix, presumably due to the strong lysine binding site in apo(a).⁴⁶ Mediated by apo(a), the binding stimulates chemotactic activation of monocytes/macrophages and thereby modulating angiogenesis and inflammation.⁸⁹ Lp(a) may contribute to CVD and CAVS via its LDL-like component, with proinflammatory effects of oxidized phospholipids (OxPL) on both ApoB and apo(a) and antifibrinolytic/prothrombotic effects of apo(a).⁹² In Vitro studies have demonstrated that apo(a) modifies cellular function of cultured vascular endothelial cells (promoting stress fiber formation, endothelial contraction and vascular permeability), smooth muscles, and monocytes/ macrophages (promoting differentiation of proinflammatory M1-1 type macrophages) via complex mechanisms of cell signaling and cytokine production.89 Lp(a) is the only monogenetic risk factor for aortic valve calcification and stenosis⁹³ and is strongly linked specifically with the single nucleotide polymorphism (SNP) rs10455872 in the gene LPA encoding for apo(a).⁹⁴

CVD Risk Predictive Value

There are a large number of studies demonstrating that Lp(a) elevations are an independent predictor of adverse cardiovascular outcomes including MI, sudden death, strokes, calcific aortic valve stenosis and peripheral vascular disease (Table 3). The Copenhagen City Heart Study and Copenhagen General Population Study are well known prospective population- based cohort studies that track outcomes through national patient registries.⁹⁵ These studies demonstrate increased risk for MI, CHD, CAVS, and heart failure when subjects with very high Lp(a) levels (50-115 mg/dL) are compared with subjects with very low Lp(a) levels (< 5 mg/dL).^96-100 Subjects with less extreme Lp(a) elevations (> 30 mg/dL) also show increased risk of CVD when they have comorbid LDLC elevations.¹⁰¹ However, the Copenhagen studies are composed exclusively of white subjects and the effects of Lp(a) are known to vary with race or ethnicity.

The Multi-Ethnic Study of Atherosclerosis (MESA) recruited an ethnically diverse sample of > 6,000 Americans, aged 45 to 84 years, without CVD, into an ongoing prospective cohort study. Research using subjects from this study has found consistently increased risk of CHD, heart failure, subclinical aortic valve calcification, and more severe CAVS in white subjects with elevated Lp(a).^60,102,103 Black subjects with elevated Lp(a) had increased risk of CHD and more severe CAVS and Hispanic subjects with Lp(a) elevation were at higher risk for CHD.^60,102 So far, no studies of MESA subjects have identified a relationship between Lp(a) elevation and CVD events for Asian-Americans subjects (predominantly of Chinese descent). There is a need for ongoing research to more precisely define relevant cut-off levels by race, ethnicity and sex.

The Atherosclerosis Risk in Communities (ARIC) Study was a prospective multiethnic cohort study including > 15,000 US adults, aged 45 to 64 years.¹⁰³ Lp(a) elevations in this cohort were associated with greater risks for first CVD events, heart failure, and recurrent CVD events.^61,64,105 The risk of stroke for subjects with elevated Lp(a) was greater for black and white women, and for black men.^61,106 However, a meta-analysis of case-control studies showed increased ischemic stroke risk in both men and women with elevated Lp(a).⁵⁷

A recent European meta-analysis collected blood samples and outcome data from > 50,000 subjects in 7 prospective cohort studies. Using a central laboratory to standardize Lp(a) measurements, researchers found increased risk of major coronary events and new CVD in subjects with Lp(a) > 50 mg/dL compared to those below that threshold.¹⁰⁷

Although many of these studies show modest increases in risk of CVD events with Lp(a) elevation, it should be noted that other studies do not demonstrate such consistent associations. This is particularly true in studies of women and nonwhite ethnic groups.^103,108-112 The variability of study results may be due to other confounding factors such as autoantibodies that either upregulate or downregulate atherogenicity of LDLC and potentially other lipoproteins. This is particularly relevant to women who have an increased risk for autoimmune disease.

Lp(a) has significant genetic heritability—75% in Europeans and 85% in African Americans.¹¹³ In whites, the LPA gene on chromosome 6p26- 27 with the polymorphism genetic variants rs10455872 and rs3798220 is consistently associated with elevated Lp(a) levels.^63,100,113 However, the degree of Lp(a) elevation associated with these specific genetic variants varies by ethnicity.^78,113,115

Lifestyle and Cardiovascular Health

It is noteworthy that the Lp(a) genetic risks can also be modified by lifestyle risk reduction even in the absence of significant blood level reductions. For example, Khera and colleagues constructed a genetic risk profile for CVD that included genes related to Lp(a).¹¹⁶ Subjects with high genetic risk were more likely to experience CVD events compared with subjects with low genetic risk. However, risks for CVD were attenuated by 4 healthy lifestyle factors: current nonsmoker, body mass index < 30, at least weekly physical activity, and a healthy diet. Subjects with high genetic risk and an unhealthy lifestyle (0 or 1 of the 4 healthy lifestyle factors) were the most likely to develop CVD (Hazard ratio [HR], 3.5), but that risk was lower for subjects with healthy (3 or 4 of the 4 healthy lifestyle factors) and intermediate lifestyles (2 of the 4 healthy lifestyle factors) (HR, 1.9 and 2.2, respectively), despite despite high genetic risk for CVD.

While the independent CVD risk associated with elevated Lp(a) does not appear to be responsive to lifestyle risk reduction alone, certainly elevated LDLC and traditional risk factors can increase the overall CVD risk and are worthy of preventive interventions. In particular, inflammation from any source exacerbates CVD risk. Proatherogenic diet, insufficient sleep, lack of exercise, and maladaptive stress responses are other targets for personalized CVD risk reduction. ^28,117 Studies of dietary modifications and other lifestyle factors have shown reduced risk of CVD events, despite lack of reduction in Lp(a) levels.^119,120 It is noteworthy that statin therapy (with or without ezetimibe) fails to impact CAVS progression, likely because statins either raise or have no effect on Lp(a) levels.^92,119

Until recently, there has been no evidence supporting any therapeutic intervention causing clinically meaningful reductions in Lp(a). Table 4 lists major drug classes and their effects on Lp(a) and CVD outcomes; however, a detailed discussion of each of these therapies is beyond the scope of this review. Drugs that reduce Lp(a) by 20-30% have varying effects on CVD outcomes, from no effect^122,123 to a 10% to 20% decrease in CVD events when compared with a placebo.^124,125 Because these drugs also produce substantial reductions in LDLC, it is not possible to determine how much of the beneficial effects are due to reductions in Lp(a).

Lipoprotein apheresis produces profound reductions in Lp(a) of 60 to 80% in very highrisk populations.^69,126 Within-subjects comparisons show up to 80% reductions in CVD events, relative to event rates prior to treatment initiation.^69,127 Early trials of antisense oligonucleotide against apo(a) therapies show potential to produce similar outcomes.^128,129 These treatments may be particularly effective in patients with isolated Lp(a) elevations.

Summary

Lp(a) elevation is a major contributor to cardiovascular disease risk and has been recognized as an ICD-10-CM coded clinical diagnosis, the first laboratory abnormality to be defined a clinical disease in the asymptomatic healthy young individuals. This change addresses currently under- diagnosed CVD risk independent of LDLC reduction strategies. A brief overview of recent guidelines for the clinical use of Lp(a) testing from the American Heart Association^43,151 and the National Lipid Association52 can be found in Table 5. Although drug therapies for lowering Lp(a) levels remain limited, new treatment options are actively being developed.

Many Americans with high Lp(a) have not yet been identified. Expanded one-time screening can inform these patients of their cardiovascular risk and increase their access to early, aggressive lifestyle modification and optimal lipid-lowering therapy. Given the further increased CVD risk factors for military service members and veterans, a case can be made for broader screening and enhanced surveillance of elevated Lp(a) in these presumably healthy and fit individuals as well as management focused on modifiable risk factors.

Acknowledgements

This program initiative was conducted by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. as part of the Integrative Cardiac Health Project at Walter Reed National Military Medical Center (WRNMMC), and is made possible by a cooperative agreement that was awarded and administered by the US Army Medical Research & Materiel Command (USAMRMC), at Fort Detrick under Contract Number: W81XWH-16-2-0007. It reflects literature review preparatory work for a research protocol but does not involve an actual research project. The work in this manuscript was supported by the staff of the Integrative Cardiac Health Project (ICHP) with special thanks to Claire Fuller, Elaine Walizer, Dr. Mariam Kashani and the entire health coaching team.

Cardiovascular disease (CVD) remains the leading cause of global mortality. In 2015, 41.5% of the US population had at least 1 form of CVD and CVD accounted for nearly 18 million deaths worldwide.1,2 The major disease categories represented include myocardial infarction (MI), sudden death, strokes, calcific aortic valve stenosis (CAVS), and peripheral vascular disease.^1,2 In terms of health care costs, quality of life, and caregiver burden, the overall impact of disease prevalence continues to rise.^1,3-6 There is an urgent need for more precise and earlier CVD risk assessment to guide lifestyle and therapeutic interventions for prevention of disease progression as well as potential reversal of preclinical disease. Even at a young age, visible coronary atherosclerosis has been found in up to 11% of “healthy” active individuals during autopsies for trauma fatalities.^7,8

The impact of CVD on the US and global populations is profound. In 2011, CVD prevalence was predicted to reach 40% by 2030.9 That estimate was exceeded in 2015, and it is now predicted that by 2035, 45% of the US population will suffer from some form of clinical or preclinical CVD. In 2015, the decadeslong decline in CVD mortality was reversed for the first time since 1969, showing a 1% increase in deaths from CVD.¹ Nearly 300,000 of those using US Department of Veterans Affairs (VA) services were hospitalized for CVD between 2010 and 2014.¹⁰ The annual direct and indirect costs related to CVD in the US are estimated at $329.7 billion, and these costs are predicted to top $1 trillion by 2035.¹ Heart attack, coronary atherosclerosis, and stroke accounted for 3 of the 10 most expensive conditions treated in US hospitals in 2013.¹¹ Globally, the estimate for CVD-related direct and indirect costs was $863 billion in 2010 and may exceed $1 trillion by 2030.¹²

The nature of military service adds additional risk factors, such as posttraumatic stress disorder, depression, sleep disorders and physical trauma which increase CVD morbidity/ mortality in service members, veterans, and their families.^13-16 In addition, living in lowerincome areas (countries or neighborhoods) can increase the risk of both CVD incidence and fatalities, particularly in younger individuals.^17-20 The Military Health System (MHS) and VA are responsible for the care of those individuals who have voluntarily taken on these additional risks through their time in service. This responsibility calls for rapid translation to practice tools and resources that can support interventions to minimize as many modifiable risk factors as possible and improve longterm health. This strategy aligns with the World Health Organization’s (WHO) focus on prevention of disease progression through interventions targeting modifiable risk.^3-6,21-23 The driving force behind the launch of the US Department of Health and Human Services (HHS) Million Hearts program was the goal of preventing 1 million heart attacks and strokes by 2017 with risk reduction through aspirin, blood pressure control, cholesterol management, smoking cessation, sodium reduction, and physical activity.^24,25 While some reductions in CVD events have been documented, the outcomes fell short of the goals set, highlighting both the need and value of continued and expanded efforts for CVD risk reduction.²⁶

More precise assessment of risk factors during preventative care, as well as after a diagnosis of CVD, may improve the timeliness and precision of earlier interventions (both lifestyle and therapeutic) that reduce CVD morbidity and mortality^.27 Personalized or precision medicine approaches take into account differences in socioeconomic, environmental, and lifestyle factors that are potentially reversible, as well as gender, race, and ethnicity.^28-31 Current methods of predicting CVD risk have considerable room for improvement.²⁷ About 40% of patients with newly diagnosed CVD have normal traditional cholesterol profiles, including those whose first cardiac event proves fatal.^29-33 Currently available risk scores (hundreds have been described in the literature) mischaracterize risk in minority populations and women, and have shown deficiencies in identifying preclinical atherosclerosis.^34,35 The failure to recognize preclinical CVD in military personnel during their active duty life cycle results in missed opportunities for improved health and readiness sustainment.

Most CVD risk prediction models incorporate some form of blood lipids. Total cholesterol (TC) is most commonly used in clinical practice, along with high-density lipoprotein (HDLC), low-density lipoprotein (LDLC), and triglycerides (TG).^23,27,36 High LDLC and/or TC are well established as lipid-related CVD risk factors and are incorporated into many CVD risk scoring systems/models described in the literature.²⁷ LDLC reduction is commonly recommended as CVD prevention, but even with optimal statin treatment, there is still considerable residual risk for new and recurrent CVD events.^{28,32,34,35,37-42}

Incorporating novel biomarkers and alternative lipid measurements may improve risk prediction and aid targeted treatment, ultimately reducing CVD events.²⁷ Apolipoprotein B (ApoB) is a major atherogenic component embedded in LDL and VLDL correlating to non-HDLC and may be useful in the setting of triglycerides ≥ 200 mg/d as levels > 130 mg/ dL appear to be risk-enhancing, but measurements may be unreliable.⁴³ According to the 2018 Cholesterol Guidelines, lipoprotein(a) [Lp(a)] elevation also is recognized as a risk-enhancing factor that is particularly implicated when there is a strong family history of premature atherosclerotic CVD or personal history of CVD not explained by major risk factors.⁴³

Lp(a) elevation is a largely underrecognized category of lipid disorder that impacts up to 20% to 30% of the population globally and within the US, although there is considerable variability by geographic location and ethnicity.⁴⁴ Globally, Lp(a) elevation places > 1 billion people at moderate to high risk for CVD.44 Lp(a) has a strong genetic component and is recognized as a distinct and independent risk factor for MI, sudden death, strokes and CAVS. Lp(a) has an extensive body of evidence to support its distinct role both as a causal factor in CVD and as an augmentation to traditional risk factors.^44-48

Lipoproteni(a) Elevation Use For Diagnosis

The importance of Lp(a) elevation as a clinical diagnosis rather than a laboratory abnormality alone was brought forward by the Lipoprotein(a) Foundation. Its founder, Sandra Tremulis, is a survivor of an acute coronary event that occurred when she was 39-years old, despite running marathons and having none of the traditional CVD lifestyle risk factors.⁴⁹ This experience inspired her to create the Lipoprotein(a) Foundation to give a voice to families living with or at risk for CVD due to Lp(a) elevation.

As often happens in the progress of medicine, patients and their families drive change based on their personal experiences with the gaps in standard clinical practice. It was this foundation—not a member of the medical establishment—that submitted the formal request for the addition of new ICD-10-CM diagnostic and family history codes for Lp(a) elevation during the Centers for Disease Control and Prevention (CDC) September 2017 ICD-10-CM Coordination and Maintenance Committee meeting.⁵⁰ In June 2018, the final ICD-10-CM code addenda for 2019 was released and included the new codes E78.⁴¹ (Elevated Lp[a]) and Z83.430 (Family history of elevated Lp[a]).⁵² After the new codes were approved, both the American Heart Association and the National Lipid Association added recommendations regarding Lp(a) testing to their clinical practice guidelines.^43,52

Practically, these codes standardize billing and payment for legitimate clinical work and laboratory testing. Prior to the addition of Lp(a) elevation as a clinical diagnosis, testing and treatment of Lp(a) elevation was considered experimental and not medically necessary until after a cardiovascular event had already occurred. Services for Lp(a) elevation were therefore not reimbursed by many healthcare organizations and insurance companies. The new ICD-10-CM codes encourage the assessment of Lp(a) both in individuals with early onset major CVD events and in presumably fit, healthy individuals, particularly when there is a family history of Lp(a) elevation. Given that Lp(a) levels do not change significantly over time, the current understanding is that only a single measurement is needed to define the individual risk over a lifetime.^41,42,44,45 As therapies targeting Lp(a) levels evolve, repeated measurements may be indicated to monitor response and direct changes in management. “Elevated Lipoprotein(a)” is the first laboratory testing abnormality that has achieved the status of a clinical diagnosis.

Lp(a) Measurements

There is considerable complexity to the measurement of lipoproteins in blood samples due to heterogeneity in both density and size of particles as illustrated in the Figure.⁵³

For traditional lipids measured in clinical practice, the size and density ranges from small high-density lipoprotein (HDL) through LDLC and intermediate- density lipoprotein (IDL) to the largest least dense particles in the very low-density lipoprotein (VLDL) and chylomicron remnant fractions. Standard lipid profiles consist of mass concentration measurements (mg/dL) of TC, TG, HDLC, and LDLC.⁵³ Non-HDLC (calculated as: TC−HDLC) consists of all cholesterol found in atherogenic lipoproteins, including remnant-C and Lp(a). Until recently, the cholesterol content of Lp(a), corresponding to about 30% of Lp(a) total mass, was included in the TC, non-HDLC and LDLC measurements with no separate reporting by the majority of clinical laboratories.

 

After > 50 years of research on the structure and biochemistry of Lp(a), the physiology and biological functions of these complex and polymorphic lipoprotein particles are not fully understood. Lp(a) is composed of a lipoprotein particle similar in composition to LDL (protein and lipid), containing 1 molecule of ApoB wrapped around a core of cholesteryl ester and triglyceride with phospholipids and unesterified cholesterol at its surface.⁴⁸ The presence of a unique hydrophilic, highly glycosylated protein referred to as apolopoprotienA (apo[a]), covalently attached to ApoB-100 by a single disulfide bridge, differentiates Lp(a) from LDL.⁴⁸ Cholesterol rich ApoB is an important component within many lipoproteins pathogenic for atherosclerosis and CVD.^45,47,53

The apo(a) contributes to the increased density of Lp(a) compared to LDLC with associated reduced binding affinity to the LDL receptor. This reduced receptor binding affinity is a presumed mechanism for the lack of Lp(a) plasma level response to statin therapies, which increase hepatic LDL receptor activity.⁴⁷ Apo(a) evolved from the plasminogen gene through duplication and remodeling and demonstrates extensive heterogeneity in protein size, with > 40 different apo(a) isoforms resulting in > 40 different Lp(a) particle sizes. Size of the apo(a) particle is determined by the number of pleated structures known as kringles. Most people (> 80%) carry 2 different-sized apo(a) isoforms. Plasma Lp(a) level is determined by the net production of apo(a) in each isoform, and the smaller apo(a) isoforms are associated with higher plasma levels of Lp(a).⁴⁵

Given the heterogeneity in Lp(a) molecular weight, which can vary even within individuals, recommendations have been made for reporting results as particle numbers or concentrations (nmol/L or mmol/L) rather than as mass concentration (mg/dL).⁵⁵ However, the majority of the large CVD morbidity and mortality outcomes studies used Lp(a) mass concentration levels in mg/ dL to characterize risk levels.^56,57 There is no standardized method to convert Lp(a) measurements from mg/dL to nmol/L.⁵⁵ Current assays using WHO standardized reagents and controls are reliable for categorizing risk levels.⁵⁸

The European Atherosclerosis Society consensus panel recommended that desirable Lp(a) levels should be below the 80th percentile (< 50 mg/dL or < 125 nmol/L) in patients with intermediate or high CVD risk.⁵⁹ Subsequent epidemiological and Mendelian randomization studies have been performed in general populations with no history of CVD and demonstrated that increased CVD risk can be detected with Lp(a) levels as low as 25 to 30 mg/dL.^56,60-63 In secondary prevention populations with prior CVD and optimal treatment (statins, antiplatelet drugs), recurrent event risk was also increased with elevated Lp(a).^63-66

Using immunoturbidometric assays, Varvel and colleagues reported the prevalence of elevated Lp(a) mass concentration levels (mg/dL) in > 500,000 US patients undergoing clinical evaluations based on data from a referral laboratory of patients.⁵⁸ The mean Lp(a) levels were 34.0 mg/dL with median (interquartile range [IQR]) levels at 17 (7-47) mg/dL and overall range of 0 to 907 mg/dL.⁵⁸ Females had higher Lp(a) levels compared to males but no ethnic or racial breakdown was provided. Lp(a) levels > 30 mg/dL and > 50 mg/dL were present in 35% and 24% of subjects, respectively. Table 1 displays the relationship between various Lp(a) level cut-offs to mean levels of LDLC, estimated LDLC corrected for Lp(a), TC, HDLC, and TG.⁵⁸ The data demonstrate that Lp(a) elevation cannot be inferred from LDLC levels nor from any of the other traditional lipoprotein measures. Patients with high risk Lp(a) levels may have normal LDLC. While Lp(a) thresholds have been identified for stratification of CVD risk, the target levels for risk reduction have not been specifically defined, particularly since therapies are not widely available for reduction of Lp(a). Table 2 provides an overview of clinical lipoprotein measurements that may be reasonable targets for therapeutic interventions and reduction of CVD risk.^44,53,55 In general, existing studies suggest that radical reduction (> 80%) is required to impact long-term outcomes, particularly in individuals with severe disease.^68,69

LDLC reduction alone leaves a residual CVD risk that is greater than the risk reduced.40 In addition, the autoimmune inflammation and lipid specific autoantibodies play an important role in increased CVD morbidity and mortality risk.^70,71 The presence of autoantibodies such as antiphospholipid antibodies (without a specific autoimmune disease diagnosis) increases the risk of subclinical atherosclerosis.^72,73 Certain autoimmune diseases such as systemic lupus erythematosus are recognized as independent risk factors for CVD.^74,75 Autoantibodies appear to mediate CVD events and mortality risk, independent of traditional therapies for risk reduction.⁷³ Further research is needed to clarify the role of autoantibodies as markers of increased or decreased CVD risk and their mechanism of action.

Autoantibodies directed at new antigens in lipoproteins within atherosclerotic lesions can modulate the impact of atherosclerosis via activation of the innate and adaptive immune system.⁷⁶ The lipid-associated neopeptides are recognized as damage-associated or danger- associated molecular patterns (DAMPs), also known as alarmins, which signal molecules that can trigger and perpetuate noninfectious inflammatory responses.^77-79 Plasma autoantibodies (immunoglobulin M and G [IgM, IgG]) modify proinflammatory oxidation-specific epitopes on oxidized phospholipids (oxPL) within lipoproteins and are linked with markers of inflammation and CVD events.^80-82 Modified LDLC and ApoB-100 immune complexes with specific autoantibodies in the IgG class are associated with increased CVD.⁷⁶ These and other risk-modulating autoantibodies may explain some of the variability in CVD outcomes by ethnicity and between individuals.

Some antibodies to oxidized LDL (ox-LDL) may have a protective role in the development of atherosclerosis.^83,84 In a cohort of > 500 women, the number of carotid atherosclerotic plaques and total carotid plaque area were inversely correlated with a specific IgM autoantibody (MDA-p210).⁸⁴ High concentrations of Lp(a)- containing circulating immune complexes and Lp(a)-specific IgM and IgG have been described in patients with coronary heart disease (CHD).⁸⁵ Like ox-LDL, oxidized Lp(a) [ox-Lp(a)] is more potent than native Lp(a) in increasing atherosclerosis risk and is increased in patients with CHD compared to healthy controls.^86-88 Ox-Lp(a) levels may represent an even stronger risk marker for CVD than ox-LDL.⁸⁵

Possible Mechanisms of Pathogenesis

While the precise quantification of Lp(a) in human plasma (or serum) has been challenging, current clinical laboratories use standardized international reference reagents and controls in their assays. Most current Lp(a) assays are based on immunological methods (eg, immunonephelometry, immunoturbidimetry, or enzyme linked immunosorbent assay [ELISA]) using antibodies against apo(a).⁸⁹ Apo(a) contains 10 subtypes of kringle IV and 1 copy of kringle V. Some assays use antibodies against kringle-IV type 2; however, it has been recommended that newer methods should use antibodies against the specific bridging kringle-IV Type 9 domain, which has a more stable bond and is present as a single copy.^48,89 Other approaches to Lp(a) measurement include ultraperformance liquid chromatography/mass spectrometry that can determine both the concentration and particle size of apo(a).^48,90 For routine clinical care, currently available assays reporting in mg/dL can be considered fairly accurate for separating low-risk from moderate-to-high-risk patients.⁴⁵

The physiologic role of Lp(a) in humans remains to be fully defined and individuals with extremely low plasma Lp(a) levels present no disease or deficiency syndromes.⁹¹ Lp(a) accumulates in endothelial injuries and binds to components of the vessel wall and subendothelial matrix, presumably due to the strong lysine binding site in apo(a).⁴⁶ Mediated by apo(a), the binding stimulates chemotactic activation of monocytes/macrophages and thereby modulating angiogenesis and inflammation.⁸⁹ Lp(a) may contribute to CVD and CAVS via its LDL-like component, with proinflammatory effects of oxidized phospholipids (OxPL) on both ApoB and apo(a) and antifibrinolytic/prothrombotic effects of apo(a).⁹² In Vitro studies have demonstrated that apo(a) modifies cellular function of cultured vascular endothelial cells (promoting stress fiber formation, endothelial contraction and vascular permeability), smooth muscles, and monocytes/ macrophages (promoting differentiation of proinflammatory M1-1 type macrophages) via complex mechanisms of cell signaling and cytokine production.89 Lp(a) is the only monogenetic risk factor for aortic valve calcification and stenosis⁹³ and is strongly linked specifically with the single nucleotide polymorphism (SNP) rs10455872 in the gene LPA encoding for apo(a).⁹⁴

CVD Risk Predictive Value

There are a large number of studies demonstrating that Lp(a) elevations are an independent predictor of adverse cardiovascular outcomes including MI, sudden death, strokes, calcific aortic valve stenosis and peripheral vascular disease (Table 3). The Copenhagen City Heart Study and Copenhagen General Population Study are well known prospective population- based cohort studies that track outcomes through national patient registries.⁹⁵ These studies demonstrate increased risk for MI, CHD, CAVS, and heart failure when subjects with very high Lp(a) levels (50-115 mg/dL) are compared with subjects with very low Lp(a) levels (< 5 mg/dL).^96-100 Subjects with less extreme Lp(a) elevations (> 30 mg/dL) also show increased risk of CVD when they have comorbid LDLC elevations.¹⁰¹ However, the Copenhagen studies are composed exclusively of white subjects and the effects of Lp(a) are known to vary with race or ethnicity.

The Multi-Ethnic Study of Atherosclerosis (MESA) recruited an ethnically diverse sample of > 6,000 Americans, aged 45 to 84 years, without CVD, into an ongoing prospective cohort study. Research using subjects from this study has found consistently increased risk of CHD, heart failure, subclinical aortic valve calcification, and more severe CAVS in white subjects with elevated Lp(a).^60,102,103 Black subjects with elevated Lp(a) had increased risk of CHD and more severe CAVS and Hispanic subjects with Lp(a) elevation were at higher risk for CHD.^60,102 So far, no studies of MESA subjects have identified a relationship between Lp(a) elevation and CVD events for Asian-Americans subjects (predominantly of Chinese descent). There is a need for ongoing research to more precisely define relevant cut-off levels by race, ethnicity and sex.

The Atherosclerosis Risk in Communities (ARIC) Study was a prospective multiethnic cohort study including > 15,000 US adults, aged 45 to 64 years.¹⁰³ Lp(a) elevations in this cohort were associated with greater risks for first CVD events, heart failure, and recurrent CVD events.^61,64,105 The risk of stroke for subjects with elevated Lp(a) was greater for black and white women, and for black men.^61,106 However, a meta-analysis of case-control studies showed increased ischemic stroke risk in both men and women with elevated Lp(a).⁵⁷

A recent European meta-analysis collected blood samples and outcome data from > 50,000 subjects in 7 prospective cohort studies. Using a central laboratory to standardize Lp(a) measurements, researchers found increased risk of major coronary events and new CVD in subjects with Lp(a) > 50 mg/dL compared to those below that threshold.¹⁰⁷

Although many of these studies show modest increases in risk of CVD events with Lp(a) elevation, it should be noted that other studies do not demonstrate such consistent associations. This is particularly true in studies of women and nonwhite ethnic groups.^103,108-112 The variability of study results may be due to other confounding factors such as autoantibodies that either upregulate or downregulate atherogenicity of LDLC and potentially other lipoproteins. This is particularly relevant to women who have an increased risk for autoimmune disease.

Lp(a) has significant genetic heritability—75% in Europeans and 85% in African Americans.¹¹³ In whites, the LPA gene on chromosome 6p26- 27 with the polymorphism genetic variants rs10455872 and rs3798220 is consistently associated with elevated Lp(a) levels.^63,100,113 However, the degree of Lp(a) elevation associated with these specific genetic variants varies by ethnicity.^78,113,115

Lifestyle and Cardiovascular Health

It is noteworthy that the Lp(a) genetic risks can also be modified by lifestyle risk reduction even in the absence of significant blood level reductions. For example, Khera and colleagues constructed a genetic risk profile for CVD that included genes related to Lp(a).¹¹⁶ Subjects with high genetic risk were more likely to experience CVD events compared with subjects with low genetic risk. However, risks for CVD were attenuated by 4 healthy lifestyle factors: current nonsmoker, body mass index < 30, at least weekly physical activity, and a healthy diet. Subjects with high genetic risk and an unhealthy lifestyle (0 or 1 of the 4 healthy lifestyle factors) were the most likely to develop CVD (Hazard ratio [HR], 3.5), but that risk was lower for subjects with healthy (3 or 4 of the 4 healthy lifestyle factors) and intermediate lifestyles (2 of the 4 healthy lifestyle factors) (HR, 1.9 and 2.2, respectively), despite despite high genetic risk for CVD.

While the independent CVD risk associated with elevated Lp(a) does not appear to be responsive to lifestyle risk reduction alone, certainly elevated LDLC and traditional risk factors can increase the overall CVD risk and are worthy of preventive interventions. In particular, inflammation from any source exacerbates CVD risk. Proatherogenic diet, insufficient sleep, lack of exercise, and maladaptive stress responses are other targets for personalized CVD risk reduction. ^28,117 Studies of dietary modifications and other lifestyle factors have shown reduced risk of CVD events, despite lack of reduction in Lp(a) levels.^119,120 It is noteworthy that statin therapy (with or without ezetimibe) fails to impact CAVS progression, likely because statins either raise or have no effect on Lp(a) levels.^92,119

Until recently, there has been no evidence supporting any therapeutic intervention causing clinically meaningful reductions in Lp(a). Table 4 lists major drug classes and their effects on Lp(a) and CVD outcomes; however, a detailed discussion of each of these therapies is beyond the scope of this review. Drugs that reduce Lp(a) by 20-30% have varying effects on CVD outcomes, from no effect^122,123 to a 10% to 20% decrease in CVD events when compared with a placebo.^124,125 Because these drugs also produce substantial reductions in LDLC, it is not possible to determine how much of the beneficial effects are due to reductions in Lp(a).

Lipoprotein apheresis produces profound reductions in Lp(a) of 60 to 80% in very highrisk populations.^69,126 Within-subjects comparisons show up to 80% reductions in CVD events, relative to event rates prior to treatment initiation.^69,127 Early trials of antisense oligonucleotide against apo(a) therapies show potential to produce similar outcomes.^128,129 These treatments may be particularly effective in patients with isolated Lp(a) elevations.

Summary

Lp(a) elevation is a major contributor to cardiovascular disease risk and has been recognized as an ICD-10-CM coded clinical diagnosis, the first laboratory abnormality to be defined a clinical disease in the asymptomatic healthy young individuals. This change addresses currently under- diagnosed CVD risk independent of LDLC reduction strategies. A brief overview of recent guidelines for the clinical use of Lp(a) testing from the American Heart Association^43,151 and the National Lipid Association52 can be found in Table 5. Although drug therapies for lowering Lp(a) levels remain limited, new treatment options are actively being developed.

Many Americans with high Lp(a) have not yet been identified. Expanded one-time screening can inform these patients of their cardiovascular risk and increase their access to early, aggressive lifestyle modification and optimal lipid-lowering therapy. Given the further increased CVD risk factors for military service members and veterans, a case can be made for broader screening and enhanced surveillance of elevated Lp(a) in these presumably healthy and fit individuals as well as management focused on modifiable risk factors.

Acknowledgements

This program initiative was conducted by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. as part of the Integrative Cardiac Health Project at Walter Reed National Military Medical Center (WRNMMC), and is made possible by a cooperative agreement that was awarded and administered by the US Army Medical Research & Materiel Command (USAMRMC), at Fort Detrick under Contract Number: W81XWH-16-2-0007. It reflects literature review preparatory work for a research protocol but does not involve an actual research project. The work in this manuscript was supported by the staff of the Integrative Cardiac Health Project (ICHP) with special thanks to Claire Fuller, Elaine Walizer, Dr. Mariam Kashani and the entire health coaching team.