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An Automated Electronic Tool to Assess the Risk of 30-Day Readmission: Validation of Predictive Performance
From the Divisions of Hospital Medicine (Drs. Dawson, Chirila, Bhide, and Burton) and Biomedical Statistics and Informatics (Ms. Thomas), Mayo Clinic, Jacksonville, FL, and the Division of Hospital Medicine, Mayo Clinic, Phoenix, AZ (Dr. Cannon).
Abstract
- Objective: To validate an electronic tool created to identify inpatients who are at risk of readmission within 30 days and quantify the predictive performance of the readmission risk score (RRS).
- Methods: Retrospective cohort study including inpa-tients who were discharged between 1 Nov 2012 and 31 Dec 2012. The ability of the RRS to discriminate between those who did and did not have a 30-day urgent readmission was quantified by the c statistic. Calibration was assessed by plotting the observed and predicted probability of 30-day urgent readmission. Predicted probabilities were obtained from generalized estimating equations, clustering on patient.
- Results: Of 1689 hospital inpatient discharges (1515 patients), 159 (9.4%) had a 30-day urgent readmission. The RRS had some discriminative ability (c statistic: 0.612; 95% confidence interval: 0.570–0.655) and good calibration.
- Conclusions: Our study shows that the RRS has some discriminative ability. The automated tool can be used to estimate the probability of a 30-day urgent readmission.
Hospital readmissions are increasingly scrutinized by the Center for Medicare and Medicaid Services and other payers due to their frequency and high cost. It is estimated that up to 25% of all patients discharged from acute care hospitals are readmitted within 30 days [1]. To address this problem, the Center for Medicare and Medicaid Services is using these rates as one of the benchmarks for quality for hospitals and health care organizations and has begun to assess penalties to those institutions with the highest rates. This scrutiny and the desire for better patient care transitions has resulted in most hospitals implementing various initiatives to reduce potentially avoidable readmissions.
Multiple interventions have been shown to reduce readmissions [2,3]. These interventions have varying effectiveness and are often labor intensive and thus costly to the institutions implementing them. In fact, no one intervention has been shown to be effective alone [4], and it may take several concurrent interventions targeting the highest risk patients to improve transitions of care at discharge that result in reduced readmissions. Many experts do recommend risk stratifying patients in order to target interventions to the highest risk patients for effective use of resources [5,6]. Several risk factor assessments have been proposed with varying success [7–13]. Multiple factors can limit the effectiveness of these risk stratification profiles. They may have low sensitivity and specificity, be based solely on retrospective data, be limited to certain populations, or be created from administrative data only without taking psychosocial factors into consideration [14].
An effective risk assessment ideally would encompass multiple known risk factors including certain comorbidities such as malignancy and heart failure, psychosocial factors such as health literacy and social support, and administrative data including payment source and demographics. All of these have been shown in prior studies to contribute to readmissions [7–13]. In addition, availability of the assessment early in the hospitalization would allow for interventions throughout the hospital stay to mitigate the effect of these factors where possible. To address these needs, our institution formed a readmission task force in January 2010 to review published literature on hospital 30-day readmissions and create a readmission risk score (RRS). The aim of this study was to quantify the predictive performance of the RRS after it was first implemented into the electronic medical record (EMR) in November 2012.
Methods
Study Design and Cohort
All consecutive adult inpatients who were discharged between 1 November 2012 and 31 December 2012 were included in this retrospective cohort study. This narrow time frame corresponded to the period from RRS tool implementation to the start of readmission interventions. We excluded hospitalizations if the patient died in the hospital.
Outcome Measures
The primary outcome was a 30-day urgent readmission, which included readmissions categorized as either emergency, urgent, or semi-urgent. Secondary outcomes included any 30-day readmission and 30-day death. Only readmissions to Mayo Clinic were examined.
Predictors
In collaboration with the information technology department, an algorithm was written to extract data from the EMR for each patient within 24 hours of admission to the hospital. This data was retrieved from existing repositories of patient information, such as demographic information, payer source, medication list, problem list, and past medical history. In addition, each patient was interviewed by a nurse at the time of admission, and the nurse completed an “admission profile” in the EMR that confirmed or entered past medical history, medications, social support at home, depression symptoms, and learning styles, among other information (Table 1). The algorithm was able to extract data from this evaluation also, so that each element of the risk score was correlated to at least one data source in the EMR. The algorithm then assigned the correct value to each element, and the total score was electronically calculated and placed in a discrete cell in each patient’s record. The algorithm was automatically run again 48 hours after the initial scoring in order to assure completeness of the information. If the patient had a length of stay greater than 5 days, an additional score was generated to include the length of stay component.
Statistical Analysis
The predictive performance of the RRS was assessed by evaluating the discrimination and calibration. Discrimination is the ability of the RRS to separate those who had a 30-day urgent readmission and those who did not. Discrimination was quantified by the c statistic, which is equivalent to the area under the receiver operating characteristic curve in this study owing to the use of binary endpoints. A c statistic of 1.0 would indicate that the RRS perfectly predicts 30-day urgent readmission while a c statistic of 0.5 would indicate the RRS has no apparent accuracy in predicting 30-day urgent readmission. Calibration assesses how closely predicted outcomes agree with observed outcomes. The predicted probability of 30-day urgent readmission was estimated utilizing a generalized estimating equation model, clustering on patient, with RRS as the only predictor variable. Inpatient discharges were divided into deciles of the predicted probabilities for 30-day urgent readmission. Agreement of the predicted and observed outcomes was displayed graphically according to decile of the predicted outcomes. All analyses were performed using SAS (version 9.3, SAS Institute, Cary, NC) and R statistical software (version 3.1.1, R Foundation for Statistical Computing, Vienna, Austria).
Results
The RRS was significantly associated with 30-day urgent readmission (odds ratio [OR] for 1-point increase in the RRS, 1.07 [95% confidence interval {CI} 1.05–1.10]; P < 0.001). A c statistic of 0.612 (95% CI 0.570–0.655) indicates that the RRS has some ability to discriminate between those with and without a 30-day urgent readmission (Figure, Table 3). The expected and observed probabilities of 30-day urgent readmission were similar in each decile of the RRS. The calibration (Table 4) shows that although there is some deviation between the observed and expected probabilities,
The RRS was also significantly associated with each of the secondary outcome measures. The odds ratios for a 1-point increase in the RRS for any 30-day readmission was 1.06 (95% CI 1.03–1.09, P < 0.001) and the c statistic was 0.591 (95% CI 0.551–0.631, Table 2). The odds ratios for a 1-point increase in the
Discussion
Our study provides evidence that the RRS has some ability to discriminate between patients who did and did not have a 30-day urgent readmission (c statistic 0.612 [95% CI 0.570–0.655]). More importantly the calibration appears to be good particularly in the higher risk patients, which are the most crucial to identify in order to target interventions.
In addition to predicting the risk of readmission, our method of risk evaluation has several other advantages. First, the risk score is assigned to each patient within 24 to 48 hours of admission by using elements available at the time of, or soon after, admission. This early evaluation during the hospitalization identifies patients who could benefit from interventions throughout the stay that could help mitigate the risks and allow for a safer transition. Other studies have used elements available only at discharge, such as lab values and length of stay [7,11]. Donze et al used 7 elements in a validated scoring system, but several of the elements were discharge values and the risk assessment system had a fair discriminatory value with a c statistic of 0.71, similar to our results. The advantage to having the score available at admission is that several of the factors used to compose the RRS could be addressed during the hospitalization, including increased education for those with greater than 7 medications, intensive care management intervention for those with a lack of social support, and increased or modified education for those with low health literacy.
Second, the score is derived entirely from elements available in the EMR, thus the score is calculated automatically within 24 hours of admission and displayed in the chart for all providers to access. This eliminates any need for individual chart review or patient evaluation outside the normal admission process, making this system extremely efficient. Van Walraven et al [9] devised a scoring system using length of stay, acuity of admission, comorbidities and emergency department use (LACE index), with a validation c statistic of 0.684, which again is similar to our results. However, the LACE index uses the Charlson comorbidity index as a measure of patient comorbidity and this can be cumbersome to calculate in clinical practice. Having the score automatically available to all providers caring for the patient increases their awareness of the patient’s level of risk. Allaudeen and colleagues showed that providers are unable to intuitively predict those patients who are at high-risk for readmission [15]; therefore, an objective, readily available risk stratification is necessary to inform the providers.
Third, the risk scoring system uses elements from varied sources to include social, medical, and individual factors, all of which have been shown to increase risk of 30-day readmissions [9,15]. An accurate risk scoring system, ideally, should include elements from multiple sources, and use of the EMR allows for this varied compilation. The risk evaluation is done on every patient, regardless of admitting diagnosis, and in spite of this heterogeneous population, it was still found to be significantly accurate. Prior studies have looked at individual populations [7,10,12,13,16]; however, this can miss many patient populations that are also high-risk. Tailoring individual risk algorithms by diagnosis can also be labor intensive.
Our study has limitations. It is a retrospective study and included a relatively short study period of 2 months. This period was chosen because it represented the time from when the RRS was first implemented to when interventions to reduce readmission according to the RRS began, however, it still encompassed a significant number of discharges. We were only able to evaluate readmissions to our own facility; therefore, patients readmitted to other facilities were not included. Although readmission to any facility is undesirable, having a risk scoring system that can reliably predict readmission to the index admission hospital is still helpful. In addition, we only validated the risk score on patients in our own facility. A larger population from multiple facilities would be helpful for further validation. In spite of this limitation we would expect that most of our readmissions return to our own facility given our community setting. In fact, based on Medicare data for readmissions to all facilities, the difference in readmission rate between our facility and all facilities differs by less than 4%.
In summary, we developed a comprehensive risk scoring system that proved to be moderately predictive of readmission that encompasses multiple factors, is available to all providers early in a hospitalization, and is completely automated via the EMR. Further studies are ongoing to refine this score and improve the predictive performance.
Corresponding author: Nancy L. Dawson, MD, Division of Hospital Medicine, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, dawson.nancy11@mayo.edu.
Financial disclosures: None.
1. Elixhauser A, Steiner C. Statistical Brief #153: Readmissions to U.S. hospitals by diagnosis, 2010. Agency for Healthcare Research and Quality; 2013. Available at www.hcup-us.ahrq.gov/reports/statbriefs/sb153.pdf.
2. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 2009;150:178–87.
3. Boutwell A, Hwu S. Effective interventions to reduce rehospitalizations: a survey of the published evidence. Cambridge, MA: Institute for Healthcare Improvement; 2009. Available at www.ihi.org/resources/Pages/Publications/EffectiveInterventionsReduceRehospitalizationsASurveyPublishedEvidence.aspx.
4. Hansen LO, Young RS, Hinami K, et al. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 2011;155:520–8.
5. Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: current strategies and future directions. Ann Rev Med 2014;65:471–85.
6. Osei-Anto A, Joshi M, Audet AM, et al. Health care leader action guide to reduce avoidable readmissions. Chicago: Health Research & Educational Trust; 2010. Available at www.hret.org/care/projects/resources/readmissions_cp.pdf.
7. Zaya M, Phan A, Schwarz ER. Predictors of re-hospitalization in patients with chronic heart failure. World J Cardiol 2012;4:23–30.
8. Hu J, Gonsahn MD, Nerenz DR. Socioeconomic status and readmissions: evidence from an urban teaching hospital. Health Aff (Millwood) 2014;33:778–85.
9. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ 2010;182:551–7.
10. Rana S, Tran T, Luo W, et al. Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data. Aust Health Rev 2014;38:377–82.
11. Donze J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 2013;173:632–8.
12. Kogon B, Jain A, Oster M, et al. Risk factors associated with readmission after pediatric cardiothoracic surgery. Ann Thorac Surg 2012;94:865–73.
13. Harhay M, Lin E, Pai A, et al. Early rehospitalization after kidney transplantation: assessing preventability and prognosis. Am J Transplant 2013;13:3164–72.
14. Preventing unnecessary readmissions: transcending the hospital’s four walls to achieve collaborative care coordination. The Advisory Board Company; 2010. Available at www.advisory.com/research/physician-executive-council/studies/2010/preventing-unnecessary-readmissions.
15. Allaudeen N, Schnipper JL, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med 2011;26:771–6.
16. Calvillo-King L, Arnold D, Eubank KJ, et al. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med 2013;28:269–82.
From the Divisions of Hospital Medicine (Drs. Dawson, Chirila, Bhide, and Burton) and Biomedical Statistics and Informatics (Ms. Thomas), Mayo Clinic, Jacksonville, FL, and the Division of Hospital Medicine, Mayo Clinic, Phoenix, AZ (Dr. Cannon).
Abstract
- Objective: To validate an electronic tool created to identify inpatients who are at risk of readmission within 30 days and quantify the predictive performance of the readmission risk score (RRS).
- Methods: Retrospective cohort study including inpa-tients who were discharged between 1 Nov 2012 and 31 Dec 2012. The ability of the RRS to discriminate between those who did and did not have a 30-day urgent readmission was quantified by the c statistic. Calibration was assessed by plotting the observed and predicted probability of 30-day urgent readmission. Predicted probabilities were obtained from generalized estimating equations, clustering on patient.
- Results: Of 1689 hospital inpatient discharges (1515 patients), 159 (9.4%) had a 30-day urgent readmission. The RRS had some discriminative ability (c statistic: 0.612; 95% confidence interval: 0.570–0.655) and good calibration.
- Conclusions: Our study shows that the RRS has some discriminative ability. The automated tool can be used to estimate the probability of a 30-day urgent readmission.
Hospital readmissions are increasingly scrutinized by the Center for Medicare and Medicaid Services and other payers due to their frequency and high cost. It is estimated that up to 25% of all patients discharged from acute care hospitals are readmitted within 30 days [1]. To address this problem, the Center for Medicare and Medicaid Services is using these rates as one of the benchmarks for quality for hospitals and health care organizations and has begun to assess penalties to those institutions with the highest rates. This scrutiny and the desire for better patient care transitions has resulted in most hospitals implementing various initiatives to reduce potentially avoidable readmissions.
Multiple interventions have been shown to reduce readmissions [2,3]. These interventions have varying effectiveness and are often labor intensive and thus costly to the institutions implementing them. In fact, no one intervention has been shown to be effective alone [4], and it may take several concurrent interventions targeting the highest risk patients to improve transitions of care at discharge that result in reduced readmissions. Many experts do recommend risk stratifying patients in order to target interventions to the highest risk patients for effective use of resources [5,6]. Several risk factor assessments have been proposed with varying success [7–13]. Multiple factors can limit the effectiveness of these risk stratification profiles. They may have low sensitivity and specificity, be based solely on retrospective data, be limited to certain populations, or be created from administrative data only without taking psychosocial factors into consideration [14].
An effective risk assessment ideally would encompass multiple known risk factors including certain comorbidities such as malignancy and heart failure, psychosocial factors such as health literacy and social support, and administrative data including payment source and demographics. All of these have been shown in prior studies to contribute to readmissions [7–13]. In addition, availability of the assessment early in the hospitalization would allow for interventions throughout the hospital stay to mitigate the effect of these factors where possible. To address these needs, our institution formed a readmission task force in January 2010 to review published literature on hospital 30-day readmissions and create a readmission risk score (RRS). The aim of this study was to quantify the predictive performance of the RRS after it was first implemented into the electronic medical record (EMR) in November 2012.
Methods
Study Design and Cohort
All consecutive adult inpatients who were discharged between 1 November 2012 and 31 December 2012 were included in this retrospective cohort study. This narrow time frame corresponded to the period from RRS tool implementation to the start of readmission interventions. We excluded hospitalizations if the patient died in the hospital.
Outcome Measures
The primary outcome was a 30-day urgent readmission, which included readmissions categorized as either emergency, urgent, or semi-urgent. Secondary outcomes included any 30-day readmission and 30-day death. Only readmissions to Mayo Clinic were examined.
Predictors
In collaboration with the information technology department, an algorithm was written to extract data from the EMR for each patient within 24 hours of admission to the hospital. This data was retrieved from existing repositories of patient information, such as demographic information, payer source, medication list, problem list, and past medical history. In addition, each patient was interviewed by a nurse at the time of admission, and the nurse completed an “admission profile” in the EMR that confirmed or entered past medical history, medications, social support at home, depression symptoms, and learning styles, among other information (Table 1). The algorithm was able to extract data from this evaluation also, so that each element of the risk score was correlated to at least one data source in the EMR. The algorithm then assigned the correct value to each element, and the total score was electronically calculated and placed in a discrete cell in each patient’s record. The algorithm was automatically run again 48 hours after the initial scoring in order to assure completeness of the information. If the patient had a length of stay greater than 5 days, an additional score was generated to include the length of stay component.
Statistical Analysis
The predictive performance of the RRS was assessed by evaluating the discrimination and calibration. Discrimination is the ability of the RRS to separate those who had a 30-day urgent readmission and those who did not. Discrimination was quantified by the c statistic, which is equivalent to the area under the receiver operating characteristic curve in this study owing to the use of binary endpoints. A c statistic of 1.0 would indicate that the RRS perfectly predicts 30-day urgent readmission while a c statistic of 0.5 would indicate the RRS has no apparent accuracy in predicting 30-day urgent readmission. Calibration assesses how closely predicted outcomes agree with observed outcomes. The predicted probability of 30-day urgent readmission was estimated utilizing a generalized estimating equation model, clustering on patient, with RRS as the only predictor variable. Inpatient discharges were divided into deciles of the predicted probabilities for 30-day urgent readmission. Agreement of the predicted and observed outcomes was displayed graphically according to decile of the predicted outcomes. All analyses were performed using SAS (version 9.3, SAS Institute, Cary, NC) and R statistical software (version 3.1.1, R Foundation for Statistical Computing, Vienna, Austria).
Results
The RRS was significantly associated with 30-day urgent readmission (odds ratio [OR] for 1-point increase in the RRS, 1.07 [95% confidence interval {CI} 1.05–1.10]; P < 0.001). A c statistic of 0.612 (95% CI 0.570–0.655) indicates that the RRS has some ability to discriminate between those with and without a 30-day urgent readmission (Figure, Table 3). The expected and observed probabilities of 30-day urgent readmission were similar in each decile of the RRS. The calibration (Table 4) shows that although there is some deviation between the observed and expected probabilities,
The RRS was also significantly associated with each of the secondary outcome measures. The odds ratios for a 1-point increase in the RRS for any 30-day readmission was 1.06 (95% CI 1.03–1.09, P < 0.001) and the c statistic was 0.591 (95% CI 0.551–0.631, Table 2). The odds ratios for a 1-point increase in the
Discussion
Our study provides evidence that the RRS has some ability to discriminate between patients who did and did not have a 30-day urgent readmission (c statistic 0.612 [95% CI 0.570–0.655]). More importantly the calibration appears to be good particularly in the higher risk patients, which are the most crucial to identify in order to target interventions.
In addition to predicting the risk of readmission, our method of risk evaluation has several other advantages. First, the risk score is assigned to each patient within 24 to 48 hours of admission by using elements available at the time of, or soon after, admission. This early evaluation during the hospitalization identifies patients who could benefit from interventions throughout the stay that could help mitigate the risks and allow for a safer transition. Other studies have used elements available only at discharge, such as lab values and length of stay [7,11]. Donze et al used 7 elements in a validated scoring system, but several of the elements were discharge values and the risk assessment system had a fair discriminatory value with a c statistic of 0.71, similar to our results. The advantage to having the score available at admission is that several of the factors used to compose the RRS could be addressed during the hospitalization, including increased education for those with greater than 7 medications, intensive care management intervention for those with a lack of social support, and increased or modified education for those with low health literacy.
Second, the score is derived entirely from elements available in the EMR, thus the score is calculated automatically within 24 hours of admission and displayed in the chart for all providers to access. This eliminates any need for individual chart review or patient evaluation outside the normal admission process, making this system extremely efficient. Van Walraven et al [9] devised a scoring system using length of stay, acuity of admission, comorbidities and emergency department use (LACE index), with a validation c statistic of 0.684, which again is similar to our results. However, the LACE index uses the Charlson comorbidity index as a measure of patient comorbidity and this can be cumbersome to calculate in clinical practice. Having the score automatically available to all providers caring for the patient increases their awareness of the patient’s level of risk. Allaudeen and colleagues showed that providers are unable to intuitively predict those patients who are at high-risk for readmission [15]; therefore, an objective, readily available risk stratification is necessary to inform the providers.
Third, the risk scoring system uses elements from varied sources to include social, medical, and individual factors, all of which have been shown to increase risk of 30-day readmissions [9,15]. An accurate risk scoring system, ideally, should include elements from multiple sources, and use of the EMR allows for this varied compilation. The risk evaluation is done on every patient, regardless of admitting diagnosis, and in spite of this heterogeneous population, it was still found to be significantly accurate. Prior studies have looked at individual populations [7,10,12,13,16]; however, this can miss many patient populations that are also high-risk. Tailoring individual risk algorithms by diagnosis can also be labor intensive.
Our study has limitations. It is a retrospective study and included a relatively short study period of 2 months. This period was chosen because it represented the time from when the RRS was first implemented to when interventions to reduce readmission according to the RRS began, however, it still encompassed a significant number of discharges. We were only able to evaluate readmissions to our own facility; therefore, patients readmitted to other facilities were not included. Although readmission to any facility is undesirable, having a risk scoring system that can reliably predict readmission to the index admission hospital is still helpful. In addition, we only validated the risk score on patients in our own facility. A larger population from multiple facilities would be helpful for further validation. In spite of this limitation we would expect that most of our readmissions return to our own facility given our community setting. In fact, based on Medicare data for readmissions to all facilities, the difference in readmission rate between our facility and all facilities differs by less than 4%.
In summary, we developed a comprehensive risk scoring system that proved to be moderately predictive of readmission that encompasses multiple factors, is available to all providers early in a hospitalization, and is completely automated via the EMR. Further studies are ongoing to refine this score and improve the predictive performance.
Corresponding author: Nancy L. Dawson, MD, Division of Hospital Medicine, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, dawson.nancy11@mayo.edu.
Financial disclosures: None.
From the Divisions of Hospital Medicine (Drs. Dawson, Chirila, Bhide, and Burton) and Biomedical Statistics and Informatics (Ms. Thomas), Mayo Clinic, Jacksonville, FL, and the Division of Hospital Medicine, Mayo Clinic, Phoenix, AZ (Dr. Cannon).
Abstract
- Objective: To validate an electronic tool created to identify inpatients who are at risk of readmission within 30 days and quantify the predictive performance of the readmission risk score (RRS).
- Methods: Retrospective cohort study including inpa-tients who were discharged between 1 Nov 2012 and 31 Dec 2012. The ability of the RRS to discriminate between those who did and did not have a 30-day urgent readmission was quantified by the c statistic. Calibration was assessed by plotting the observed and predicted probability of 30-day urgent readmission. Predicted probabilities were obtained from generalized estimating equations, clustering on patient.
- Results: Of 1689 hospital inpatient discharges (1515 patients), 159 (9.4%) had a 30-day urgent readmission. The RRS had some discriminative ability (c statistic: 0.612; 95% confidence interval: 0.570–0.655) and good calibration.
- Conclusions: Our study shows that the RRS has some discriminative ability. The automated tool can be used to estimate the probability of a 30-day urgent readmission.
Hospital readmissions are increasingly scrutinized by the Center for Medicare and Medicaid Services and other payers due to their frequency and high cost. It is estimated that up to 25% of all patients discharged from acute care hospitals are readmitted within 30 days [1]. To address this problem, the Center for Medicare and Medicaid Services is using these rates as one of the benchmarks for quality for hospitals and health care organizations and has begun to assess penalties to those institutions with the highest rates. This scrutiny and the desire for better patient care transitions has resulted in most hospitals implementing various initiatives to reduce potentially avoidable readmissions.
Multiple interventions have been shown to reduce readmissions [2,3]. These interventions have varying effectiveness and are often labor intensive and thus costly to the institutions implementing them. In fact, no one intervention has been shown to be effective alone [4], and it may take several concurrent interventions targeting the highest risk patients to improve transitions of care at discharge that result in reduced readmissions. Many experts do recommend risk stratifying patients in order to target interventions to the highest risk patients for effective use of resources [5,6]. Several risk factor assessments have been proposed with varying success [7–13]. Multiple factors can limit the effectiveness of these risk stratification profiles. They may have low sensitivity and specificity, be based solely on retrospective data, be limited to certain populations, or be created from administrative data only without taking psychosocial factors into consideration [14].
An effective risk assessment ideally would encompass multiple known risk factors including certain comorbidities such as malignancy and heart failure, psychosocial factors such as health literacy and social support, and administrative data including payment source and demographics. All of these have been shown in prior studies to contribute to readmissions [7–13]. In addition, availability of the assessment early in the hospitalization would allow for interventions throughout the hospital stay to mitigate the effect of these factors where possible. To address these needs, our institution formed a readmission task force in January 2010 to review published literature on hospital 30-day readmissions and create a readmission risk score (RRS). The aim of this study was to quantify the predictive performance of the RRS after it was first implemented into the electronic medical record (EMR) in November 2012.
Methods
Study Design and Cohort
All consecutive adult inpatients who were discharged between 1 November 2012 and 31 December 2012 were included in this retrospective cohort study. This narrow time frame corresponded to the period from RRS tool implementation to the start of readmission interventions. We excluded hospitalizations if the patient died in the hospital.
Outcome Measures
The primary outcome was a 30-day urgent readmission, which included readmissions categorized as either emergency, urgent, or semi-urgent. Secondary outcomes included any 30-day readmission and 30-day death. Only readmissions to Mayo Clinic were examined.
Predictors
In collaboration with the information technology department, an algorithm was written to extract data from the EMR for each patient within 24 hours of admission to the hospital. This data was retrieved from existing repositories of patient information, such as demographic information, payer source, medication list, problem list, and past medical history. In addition, each patient was interviewed by a nurse at the time of admission, and the nurse completed an “admission profile” in the EMR that confirmed or entered past medical history, medications, social support at home, depression symptoms, and learning styles, among other information (Table 1). The algorithm was able to extract data from this evaluation also, so that each element of the risk score was correlated to at least one data source in the EMR. The algorithm then assigned the correct value to each element, and the total score was electronically calculated and placed in a discrete cell in each patient’s record. The algorithm was automatically run again 48 hours after the initial scoring in order to assure completeness of the information. If the patient had a length of stay greater than 5 days, an additional score was generated to include the length of stay component.
Statistical Analysis
The predictive performance of the RRS was assessed by evaluating the discrimination and calibration. Discrimination is the ability of the RRS to separate those who had a 30-day urgent readmission and those who did not. Discrimination was quantified by the c statistic, which is equivalent to the area under the receiver operating characteristic curve in this study owing to the use of binary endpoints. A c statistic of 1.0 would indicate that the RRS perfectly predicts 30-day urgent readmission while a c statistic of 0.5 would indicate the RRS has no apparent accuracy in predicting 30-day urgent readmission. Calibration assesses how closely predicted outcomes agree with observed outcomes. The predicted probability of 30-day urgent readmission was estimated utilizing a generalized estimating equation model, clustering on patient, with RRS as the only predictor variable. Inpatient discharges were divided into deciles of the predicted probabilities for 30-day urgent readmission. Agreement of the predicted and observed outcomes was displayed graphically according to decile of the predicted outcomes. All analyses were performed using SAS (version 9.3, SAS Institute, Cary, NC) and R statistical software (version 3.1.1, R Foundation for Statistical Computing, Vienna, Austria).
Results
The RRS was significantly associated with 30-day urgent readmission (odds ratio [OR] for 1-point increase in the RRS, 1.07 [95% confidence interval {CI} 1.05–1.10]; P < 0.001). A c statistic of 0.612 (95% CI 0.570–0.655) indicates that the RRS has some ability to discriminate between those with and without a 30-day urgent readmission (Figure, Table 3). The expected and observed probabilities of 30-day urgent readmission were similar in each decile of the RRS. The calibration (Table 4) shows that although there is some deviation between the observed and expected probabilities,
The RRS was also significantly associated with each of the secondary outcome measures. The odds ratios for a 1-point increase in the RRS for any 30-day readmission was 1.06 (95% CI 1.03–1.09, P < 0.001) and the c statistic was 0.591 (95% CI 0.551–0.631, Table 2). The odds ratios for a 1-point increase in the
Discussion
Our study provides evidence that the RRS has some ability to discriminate between patients who did and did not have a 30-day urgent readmission (c statistic 0.612 [95% CI 0.570–0.655]). More importantly the calibration appears to be good particularly in the higher risk patients, which are the most crucial to identify in order to target interventions.
In addition to predicting the risk of readmission, our method of risk evaluation has several other advantages. First, the risk score is assigned to each patient within 24 to 48 hours of admission by using elements available at the time of, or soon after, admission. This early evaluation during the hospitalization identifies patients who could benefit from interventions throughout the stay that could help mitigate the risks and allow for a safer transition. Other studies have used elements available only at discharge, such as lab values and length of stay [7,11]. Donze et al used 7 elements in a validated scoring system, but several of the elements were discharge values and the risk assessment system had a fair discriminatory value with a c statistic of 0.71, similar to our results. The advantage to having the score available at admission is that several of the factors used to compose the RRS could be addressed during the hospitalization, including increased education for those with greater than 7 medications, intensive care management intervention for those with a lack of social support, and increased or modified education for those with low health literacy.
Second, the score is derived entirely from elements available in the EMR, thus the score is calculated automatically within 24 hours of admission and displayed in the chart for all providers to access. This eliminates any need for individual chart review or patient evaluation outside the normal admission process, making this system extremely efficient. Van Walraven et al [9] devised a scoring system using length of stay, acuity of admission, comorbidities and emergency department use (LACE index), with a validation c statistic of 0.684, which again is similar to our results. However, the LACE index uses the Charlson comorbidity index as a measure of patient comorbidity and this can be cumbersome to calculate in clinical practice. Having the score automatically available to all providers caring for the patient increases their awareness of the patient’s level of risk. Allaudeen and colleagues showed that providers are unable to intuitively predict those patients who are at high-risk for readmission [15]; therefore, an objective, readily available risk stratification is necessary to inform the providers.
Third, the risk scoring system uses elements from varied sources to include social, medical, and individual factors, all of which have been shown to increase risk of 30-day readmissions [9,15]. An accurate risk scoring system, ideally, should include elements from multiple sources, and use of the EMR allows for this varied compilation. The risk evaluation is done on every patient, regardless of admitting diagnosis, and in spite of this heterogeneous population, it was still found to be significantly accurate. Prior studies have looked at individual populations [7,10,12,13,16]; however, this can miss many patient populations that are also high-risk. Tailoring individual risk algorithms by diagnosis can also be labor intensive.
Our study has limitations. It is a retrospective study and included a relatively short study period of 2 months. This period was chosen because it represented the time from when the RRS was first implemented to when interventions to reduce readmission according to the RRS began, however, it still encompassed a significant number of discharges. We were only able to evaluate readmissions to our own facility; therefore, patients readmitted to other facilities were not included. Although readmission to any facility is undesirable, having a risk scoring system that can reliably predict readmission to the index admission hospital is still helpful. In addition, we only validated the risk score on patients in our own facility. A larger population from multiple facilities would be helpful for further validation. In spite of this limitation we would expect that most of our readmissions return to our own facility given our community setting. In fact, based on Medicare data for readmissions to all facilities, the difference in readmission rate between our facility and all facilities differs by less than 4%.
In summary, we developed a comprehensive risk scoring system that proved to be moderately predictive of readmission that encompasses multiple factors, is available to all providers early in a hospitalization, and is completely automated via the EMR. Further studies are ongoing to refine this score and improve the predictive performance.
Corresponding author: Nancy L. Dawson, MD, Division of Hospital Medicine, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, dawson.nancy11@mayo.edu.
Financial disclosures: None.
1. Elixhauser A, Steiner C. Statistical Brief #153: Readmissions to U.S. hospitals by diagnosis, 2010. Agency for Healthcare Research and Quality; 2013. Available at www.hcup-us.ahrq.gov/reports/statbriefs/sb153.pdf.
2. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 2009;150:178–87.
3. Boutwell A, Hwu S. Effective interventions to reduce rehospitalizations: a survey of the published evidence. Cambridge, MA: Institute for Healthcare Improvement; 2009. Available at www.ihi.org/resources/Pages/Publications/EffectiveInterventionsReduceRehospitalizationsASurveyPublishedEvidence.aspx.
4. Hansen LO, Young RS, Hinami K, et al. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 2011;155:520–8.
5. Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: current strategies and future directions. Ann Rev Med 2014;65:471–85.
6. Osei-Anto A, Joshi M, Audet AM, et al. Health care leader action guide to reduce avoidable readmissions. Chicago: Health Research & Educational Trust; 2010. Available at www.hret.org/care/projects/resources/readmissions_cp.pdf.
7. Zaya M, Phan A, Schwarz ER. Predictors of re-hospitalization in patients with chronic heart failure. World J Cardiol 2012;4:23–30.
8. Hu J, Gonsahn MD, Nerenz DR. Socioeconomic status and readmissions: evidence from an urban teaching hospital. Health Aff (Millwood) 2014;33:778–85.
9. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ 2010;182:551–7.
10. Rana S, Tran T, Luo W, et al. Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data. Aust Health Rev 2014;38:377–82.
11. Donze J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 2013;173:632–8.
12. Kogon B, Jain A, Oster M, et al. Risk factors associated with readmission after pediatric cardiothoracic surgery. Ann Thorac Surg 2012;94:865–73.
13. Harhay M, Lin E, Pai A, et al. Early rehospitalization after kidney transplantation: assessing preventability and prognosis. Am J Transplant 2013;13:3164–72.
14. Preventing unnecessary readmissions: transcending the hospital’s four walls to achieve collaborative care coordination. The Advisory Board Company; 2010. Available at www.advisory.com/research/physician-executive-council/studies/2010/preventing-unnecessary-readmissions.
15. Allaudeen N, Schnipper JL, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med 2011;26:771–6.
16. Calvillo-King L, Arnold D, Eubank KJ, et al. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med 2013;28:269–82.
1. Elixhauser A, Steiner C. Statistical Brief #153: Readmissions to U.S. hospitals by diagnosis, 2010. Agency for Healthcare Research and Quality; 2013. Available at www.hcup-us.ahrq.gov/reports/statbriefs/sb153.pdf.
2. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 2009;150:178–87.
3. Boutwell A, Hwu S. Effective interventions to reduce rehospitalizations: a survey of the published evidence. Cambridge, MA: Institute for Healthcare Improvement; 2009. Available at www.ihi.org/resources/Pages/Publications/EffectiveInterventionsReduceRehospitalizationsASurveyPublishedEvidence.aspx.
4. Hansen LO, Young RS, Hinami K, et al. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 2011;155:520–8.
5. Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: current strategies and future directions. Ann Rev Med 2014;65:471–85.
6. Osei-Anto A, Joshi M, Audet AM, et al. Health care leader action guide to reduce avoidable readmissions. Chicago: Health Research & Educational Trust; 2010. Available at www.hret.org/care/projects/resources/readmissions_cp.pdf.
7. Zaya M, Phan A, Schwarz ER. Predictors of re-hospitalization in patients with chronic heart failure. World J Cardiol 2012;4:23–30.
8. Hu J, Gonsahn MD, Nerenz DR. Socioeconomic status and readmissions: evidence from an urban teaching hospital. Health Aff (Millwood) 2014;33:778–85.
9. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ 2010;182:551–7.
10. Rana S, Tran T, Luo W, et al. Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data. Aust Health Rev 2014;38:377–82.
11. Donze J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 2013;173:632–8.
12. Kogon B, Jain A, Oster M, et al. Risk factors associated with readmission after pediatric cardiothoracic surgery. Ann Thorac Surg 2012;94:865–73.
13. Harhay M, Lin E, Pai A, et al. Early rehospitalization after kidney transplantation: assessing preventability and prognosis. Am J Transplant 2013;13:3164–72.
14. Preventing unnecessary readmissions: transcending the hospital’s four walls to achieve collaborative care coordination. The Advisory Board Company; 2010. Available at www.advisory.com/research/physician-executive-council/studies/2010/preventing-unnecessary-readmissions.
15. Allaudeen N, Schnipper JL, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med 2011;26:771–6.
16. Calvillo-King L, Arnold D, Eubank KJ, et al. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med 2013;28:269–82.
Can Cardiovascular Magnetic Resonance, Myocardial Perfusion Scintigraphy, or NICE Guidelines Prevent Unnecessary Angiography?
Study Overview
Objective. To assess whether noninvasive functional imaging strategies reduced unnecessary angiography compared with UK national guidelines–directed care.
Design. 3–parallel group, multicenter randomized clinical trial using a pragmatic comparative effectiveness design.
Setting and participants. Participants were patients from 6 UK centers (Leeds, Glasgow, Leicester, Bristol, Oxford, London) age 30 years or older with suspected angina pectoris, a coronary heart disease (CHD) pretest likelihood of 10% to 90%, and who were suitable for revascularization. They were randomly assigned at a 1:2:2 allocation ratio to the UK NICE (National Institute for Health Care Excellence) guidelines or to care guided by the results of cardiovascular magnetic resonance (CMR) or myocardial perfusion scintigraphy (MPS).
Main outcome measures. The primary outcome of the study was protocol-defined unnecessary coronary angiography occurring within 12 months, defined by a normal FFR (fractional flow reserve) > 0.8, or quantitative coronary angiography (QCA) showing no percentage diameter stenosis ≥ 70% in 1 view or ≥ 70% in 2 orthogonal views in all vessels 2.5 mm or more in diameter within 12 months. Because of the study design, this included any unnecessary angiography occurring after a false-positive test result, patients with high CHD pretest likelihood sent directly to coronary angiography in the NICE guidelines group, and imaging results that were either inconclusive or negative but overruled by the responsible physician.
Secondary endpoints included positive angiography rates, a composite of major adverse cardiovascular events (MACEs: cardiovascular death, myocardial infarction, unplanned coronary revascularization, and hospital admission for cardiovascular cause), and procedural complications.
Main results. Among 2205 patients assessed for eligibility between 23 November 2012 and 13 March 2015, 1202 patients (55% of eligible) were recruited and allocated to NICE guidelines–directed care (n = 240), or management by CMR (n = 481) or MPS (n = 481). While there were no statistical differences between the 3 groups in terms of baseline characteristics, the study population had a substantial burden of cardiovascular risk factors: 150 patients (12.5%) had diabetes, 458 patients (38.1%) had hypertension, 702 patients (58.4%) were past or current tobacco users, 483 patients (40.2%) had dyslipidemia, and 651 patients (54.2%) had a family history of premature CHD. All patients were symptomatic, with 401 patients (33.4%) reporting typical chest pain and 801 patients (66.6%) reporting atypical chest pain as their primary symptom. Overall, 265 patients (22.0%) underwent at least 1 coronary angiogram and 10 patients underwent 2 angiograms.
The number of patients with invasive coronary angiography after 12 months were as follows: 102 of the 240 patients in the NICE guidelines group (42.5% [95% confidence interval {CI} 36.2%–49.0%]), 85 of the 481 patients in the CMR group (17.7% [95% CI 14.4%–21.4%]), and 78 of the 481 patients in the MPS group (16.2% [95% CI 13.0%–19.8%]). The primary endpoint of unnecessary angiography occurred in 69 patients (28.8%) in the NICE guidelines group, 36 patients (7.5%) in the CMR group, and 34 patients (7.1%) in the MPS group. Using CMR group as reference, adjusted odds ratio (AOR) of unnecessary angiography for CMR group vs. NICE guidelines group was 0.21 (95% CI 0.12–0.34, P < 0.001), and the AOR for CMR group vs. the MPS groups was 1.27 (95% CI 0.79–2.03, P = 0.32).
For the secondary endpoints, positive angiography was observed in 29 patients (12.1% [95% CI 8.2%–16.9%]) in the NICE guidelines group, 47 patients (9.8% [95% CI 7.3%–12.8%]) in the CMR group, and 42 patients (8.7% [95% CI 6.4%–11.6%]) in the MPS group, overall P = 0.36. Annualized MACE rates ware 1.6% in the NICE guidelines group, 2.0% for the CMR group, and 2.0% for the MPS group. Adjusted hazard ratios for MACE were 1.37 (95% CI 0.52–3.57, P = 0.52) for the CMR group vs. NICE guidelines group and 0.95 (95% CI 0.46–1.95, P = 0.88) for the CMR group vs. the MPS group.
Conclusion. In patients with suspected CHD, investigation by CMR or MPS resulted in lower probability of unnecessary angiography within 12 months of care than using the NICE guideline–directed care. There was no difference in adverse outcomes as measured by MACE by using NICE guidelines, CMR, or MPS.
Commentary
Coronary heart disease is a leading cause of morbidity and mortality worldwide. Despite the advancement in noninvasive imaging and recommendations in international guidelines, invasive coronary angiography is still commonly used early in diagnostic pathways in patients with suspected CHD [1]. Previous studies demonstrated that majority of patients presenting with chest pain will not have significant obstructive coronary disease; a large US study reported that approximately 60% of elective cardiac catheterizations found no obstructive CHD [2]. Thus, avoiding unnecessary angiography should reduce patient risk and provide significant financial savings. Current guidelines for investigation of stable chest pain rely on pretest likelihood of CHD. These pretest likelihood models can overestimate CHD risk, resulting in the increase in probability of invasive coronary angiography [1,3].
The current study by Greenwood et al investigated whether CMR-guided care is superior to MPS or NICE guidelines–directed care in reducing the occurrence of unnecessary angiography within 12 months. Overall, rates of disease detection based on positive angiogram were comparable for the 3 strategies. In addition, there was no difference in adverse events as measured by a composite of MACE.
While this was an excellently performed multicenter study, there were several major limitations. First, the study population was predominately white northern European (92% were classified ethnically as white), and therefore the results may not translate to other populations. Second, the NICE guidelines for estimation of high-risk CHD changed after initiation of the study due to overestimation, and recent guidelines have adopted a recalibrated risk model [4,5]. Finally, MACE is not a proxy for a missed diagnosis or treatment. It remains debatable whether revascularization for stable angina has prognostic benefit over optimal medical therapy.
Applications for Clinical Practice
This multicenter randomized clinical trial provides strong evidence to use either cardiovascular magnetic resonance–guided care or myocardial perfusion scintigraphy–guided care instead of NICE guidelines–directed care for symptomatic patients with suspected CHD in reducing unnecessary angiography.
—Ka Ming Gordon Ngai, MD, MPH
1. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease. Circulation 2012;126:e354–e471.
2. Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med 2010;362:
886–95.
3. Fox KA, McLean S. Nice guidance on the investigation of chest pain. Heart 2010;96:903–6.
4. Montalescot G, Sechtem U, Achenbach S, et al. 2013 ESC guidelines on the management of stable coronary artery disease. Eur Heart J 2013;34:2949–3003.
5. Genders TSS, Steyerberg EW, Alkadhi H, et al. A clinical prediction rule for the diagnosis of coronary artery disease. Eur Heart J 2011;32:1316–30.
Study Overview
Objective. To assess whether noninvasive functional imaging strategies reduced unnecessary angiography compared with UK national guidelines–directed care.
Design. 3–parallel group, multicenter randomized clinical trial using a pragmatic comparative effectiveness design.
Setting and participants. Participants were patients from 6 UK centers (Leeds, Glasgow, Leicester, Bristol, Oxford, London) age 30 years or older with suspected angina pectoris, a coronary heart disease (CHD) pretest likelihood of 10% to 90%, and who were suitable for revascularization. They were randomly assigned at a 1:2:2 allocation ratio to the UK NICE (National Institute for Health Care Excellence) guidelines or to care guided by the results of cardiovascular magnetic resonance (CMR) or myocardial perfusion scintigraphy (MPS).
Main outcome measures. The primary outcome of the study was protocol-defined unnecessary coronary angiography occurring within 12 months, defined by a normal FFR (fractional flow reserve) > 0.8, or quantitative coronary angiography (QCA) showing no percentage diameter stenosis ≥ 70% in 1 view or ≥ 70% in 2 orthogonal views in all vessels 2.5 mm or more in diameter within 12 months. Because of the study design, this included any unnecessary angiography occurring after a false-positive test result, patients with high CHD pretest likelihood sent directly to coronary angiography in the NICE guidelines group, and imaging results that were either inconclusive or negative but overruled by the responsible physician.
Secondary endpoints included positive angiography rates, a composite of major adverse cardiovascular events (MACEs: cardiovascular death, myocardial infarction, unplanned coronary revascularization, and hospital admission for cardiovascular cause), and procedural complications.
Main results. Among 2205 patients assessed for eligibility between 23 November 2012 and 13 March 2015, 1202 patients (55% of eligible) were recruited and allocated to NICE guidelines–directed care (n = 240), or management by CMR (n = 481) or MPS (n = 481). While there were no statistical differences between the 3 groups in terms of baseline characteristics, the study population had a substantial burden of cardiovascular risk factors: 150 patients (12.5%) had diabetes, 458 patients (38.1%) had hypertension, 702 patients (58.4%) were past or current tobacco users, 483 patients (40.2%) had dyslipidemia, and 651 patients (54.2%) had a family history of premature CHD. All patients were symptomatic, with 401 patients (33.4%) reporting typical chest pain and 801 patients (66.6%) reporting atypical chest pain as their primary symptom. Overall, 265 patients (22.0%) underwent at least 1 coronary angiogram and 10 patients underwent 2 angiograms.
The number of patients with invasive coronary angiography after 12 months were as follows: 102 of the 240 patients in the NICE guidelines group (42.5% [95% confidence interval {CI} 36.2%–49.0%]), 85 of the 481 patients in the CMR group (17.7% [95% CI 14.4%–21.4%]), and 78 of the 481 patients in the MPS group (16.2% [95% CI 13.0%–19.8%]). The primary endpoint of unnecessary angiography occurred in 69 patients (28.8%) in the NICE guidelines group, 36 patients (7.5%) in the CMR group, and 34 patients (7.1%) in the MPS group. Using CMR group as reference, adjusted odds ratio (AOR) of unnecessary angiography for CMR group vs. NICE guidelines group was 0.21 (95% CI 0.12–0.34, P < 0.001), and the AOR for CMR group vs. the MPS groups was 1.27 (95% CI 0.79–2.03, P = 0.32).
For the secondary endpoints, positive angiography was observed in 29 patients (12.1% [95% CI 8.2%–16.9%]) in the NICE guidelines group, 47 patients (9.8% [95% CI 7.3%–12.8%]) in the CMR group, and 42 patients (8.7% [95% CI 6.4%–11.6%]) in the MPS group, overall P = 0.36. Annualized MACE rates ware 1.6% in the NICE guidelines group, 2.0% for the CMR group, and 2.0% for the MPS group. Adjusted hazard ratios for MACE were 1.37 (95% CI 0.52–3.57, P = 0.52) for the CMR group vs. NICE guidelines group and 0.95 (95% CI 0.46–1.95, P = 0.88) for the CMR group vs. the MPS group.
Conclusion. In patients with suspected CHD, investigation by CMR or MPS resulted in lower probability of unnecessary angiography within 12 months of care than using the NICE guideline–directed care. There was no difference in adverse outcomes as measured by MACE by using NICE guidelines, CMR, or MPS.
Commentary
Coronary heart disease is a leading cause of morbidity and mortality worldwide. Despite the advancement in noninvasive imaging and recommendations in international guidelines, invasive coronary angiography is still commonly used early in diagnostic pathways in patients with suspected CHD [1]. Previous studies demonstrated that majority of patients presenting with chest pain will not have significant obstructive coronary disease; a large US study reported that approximately 60% of elective cardiac catheterizations found no obstructive CHD [2]. Thus, avoiding unnecessary angiography should reduce patient risk and provide significant financial savings. Current guidelines for investigation of stable chest pain rely on pretest likelihood of CHD. These pretest likelihood models can overestimate CHD risk, resulting in the increase in probability of invasive coronary angiography [1,3].
The current study by Greenwood et al investigated whether CMR-guided care is superior to MPS or NICE guidelines–directed care in reducing the occurrence of unnecessary angiography within 12 months. Overall, rates of disease detection based on positive angiogram were comparable for the 3 strategies. In addition, there was no difference in adverse events as measured by a composite of MACE.
While this was an excellently performed multicenter study, there were several major limitations. First, the study population was predominately white northern European (92% were classified ethnically as white), and therefore the results may not translate to other populations. Second, the NICE guidelines for estimation of high-risk CHD changed after initiation of the study due to overestimation, and recent guidelines have adopted a recalibrated risk model [4,5]. Finally, MACE is not a proxy for a missed diagnosis or treatment. It remains debatable whether revascularization for stable angina has prognostic benefit over optimal medical therapy.
Applications for Clinical Practice
This multicenter randomized clinical trial provides strong evidence to use either cardiovascular magnetic resonance–guided care or myocardial perfusion scintigraphy–guided care instead of NICE guidelines–directed care for symptomatic patients with suspected CHD in reducing unnecessary angiography.
—Ka Ming Gordon Ngai, MD, MPH
Study Overview
Objective. To assess whether noninvasive functional imaging strategies reduced unnecessary angiography compared with UK national guidelines–directed care.
Design. 3–parallel group, multicenter randomized clinical trial using a pragmatic comparative effectiveness design.
Setting and participants. Participants were patients from 6 UK centers (Leeds, Glasgow, Leicester, Bristol, Oxford, London) age 30 years or older with suspected angina pectoris, a coronary heart disease (CHD) pretest likelihood of 10% to 90%, and who were suitable for revascularization. They were randomly assigned at a 1:2:2 allocation ratio to the UK NICE (National Institute for Health Care Excellence) guidelines or to care guided by the results of cardiovascular magnetic resonance (CMR) or myocardial perfusion scintigraphy (MPS).
Main outcome measures. The primary outcome of the study was protocol-defined unnecessary coronary angiography occurring within 12 months, defined by a normal FFR (fractional flow reserve) > 0.8, or quantitative coronary angiography (QCA) showing no percentage diameter stenosis ≥ 70% in 1 view or ≥ 70% in 2 orthogonal views in all vessels 2.5 mm or more in diameter within 12 months. Because of the study design, this included any unnecessary angiography occurring after a false-positive test result, patients with high CHD pretest likelihood sent directly to coronary angiography in the NICE guidelines group, and imaging results that were either inconclusive or negative but overruled by the responsible physician.
Secondary endpoints included positive angiography rates, a composite of major adverse cardiovascular events (MACEs: cardiovascular death, myocardial infarction, unplanned coronary revascularization, and hospital admission for cardiovascular cause), and procedural complications.
Main results. Among 2205 patients assessed for eligibility between 23 November 2012 and 13 March 2015, 1202 patients (55% of eligible) were recruited and allocated to NICE guidelines–directed care (n = 240), or management by CMR (n = 481) or MPS (n = 481). While there were no statistical differences between the 3 groups in terms of baseline characteristics, the study population had a substantial burden of cardiovascular risk factors: 150 patients (12.5%) had diabetes, 458 patients (38.1%) had hypertension, 702 patients (58.4%) were past or current tobacco users, 483 patients (40.2%) had dyslipidemia, and 651 patients (54.2%) had a family history of premature CHD. All patients were symptomatic, with 401 patients (33.4%) reporting typical chest pain and 801 patients (66.6%) reporting atypical chest pain as their primary symptom. Overall, 265 patients (22.0%) underwent at least 1 coronary angiogram and 10 patients underwent 2 angiograms.
The number of patients with invasive coronary angiography after 12 months were as follows: 102 of the 240 patients in the NICE guidelines group (42.5% [95% confidence interval {CI} 36.2%–49.0%]), 85 of the 481 patients in the CMR group (17.7% [95% CI 14.4%–21.4%]), and 78 of the 481 patients in the MPS group (16.2% [95% CI 13.0%–19.8%]). The primary endpoint of unnecessary angiography occurred in 69 patients (28.8%) in the NICE guidelines group, 36 patients (7.5%) in the CMR group, and 34 patients (7.1%) in the MPS group. Using CMR group as reference, adjusted odds ratio (AOR) of unnecessary angiography for CMR group vs. NICE guidelines group was 0.21 (95% CI 0.12–0.34, P < 0.001), and the AOR for CMR group vs. the MPS groups was 1.27 (95% CI 0.79–2.03, P = 0.32).
For the secondary endpoints, positive angiography was observed in 29 patients (12.1% [95% CI 8.2%–16.9%]) in the NICE guidelines group, 47 patients (9.8% [95% CI 7.3%–12.8%]) in the CMR group, and 42 patients (8.7% [95% CI 6.4%–11.6%]) in the MPS group, overall P = 0.36. Annualized MACE rates ware 1.6% in the NICE guidelines group, 2.0% for the CMR group, and 2.0% for the MPS group. Adjusted hazard ratios for MACE were 1.37 (95% CI 0.52–3.57, P = 0.52) for the CMR group vs. NICE guidelines group and 0.95 (95% CI 0.46–1.95, P = 0.88) for the CMR group vs. the MPS group.
Conclusion. In patients with suspected CHD, investigation by CMR or MPS resulted in lower probability of unnecessary angiography within 12 months of care than using the NICE guideline–directed care. There was no difference in adverse outcomes as measured by MACE by using NICE guidelines, CMR, or MPS.
Commentary
Coronary heart disease is a leading cause of morbidity and mortality worldwide. Despite the advancement in noninvasive imaging and recommendations in international guidelines, invasive coronary angiography is still commonly used early in diagnostic pathways in patients with suspected CHD [1]. Previous studies demonstrated that majority of patients presenting with chest pain will not have significant obstructive coronary disease; a large US study reported that approximately 60% of elective cardiac catheterizations found no obstructive CHD [2]. Thus, avoiding unnecessary angiography should reduce patient risk and provide significant financial savings. Current guidelines for investigation of stable chest pain rely on pretest likelihood of CHD. These pretest likelihood models can overestimate CHD risk, resulting in the increase in probability of invasive coronary angiography [1,3].
The current study by Greenwood et al investigated whether CMR-guided care is superior to MPS or NICE guidelines–directed care in reducing the occurrence of unnecessary angiography within 12 months. Overall, rates of disease detection based on positive angiogram were comparable for the 3 strategies. In addition, there was no difference in adverse events as measured by a composite of MACE.
While this was an excellently performed multicenter study, there were several major limitations. First, the study population was predominately white northern European (92% were classified ethnically as white), and therefore the results may not translate to other populations. Second, the NICE guidelines for estimation of high-risk CHD changed after initiation of the study due to overestimation, and recent guidelines have adopted a recalibrated risk model [4,5]. Finally, MACE is not a proxy for a missed diagnosis or treatment. It remains debatable whether revascularization for stable angina has prognostic benefit over optimal medical therapy.
Applications for Clinical Practice
This multicenter randomized clinical trial provides strong evidence to use either cardiovascular magnetic resonance–guided care or myocardial perfusion scintigraphy–guided care instead of NICE guidelines–directed care for symptomatic patients with suspected CHD in reducing unnecessary angiography.
—Ka Ming Gordon Ngai, MD, MPH
1. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease. Circulation 2012;126:e354–e471.
2. Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med 2010;362:
886–95.
3. Fox KA, McLean S. Nice guidance on the investigation of chest pain. Heart 2010;96:903–6.
4. Montalescot G, Sechtem U, Achenbach S, et al. 2013 ESC guidelines on the management of stable coronary artery disease. Eur Heart J 2013;34:2949–3003.
5. Genders TSS, Steyerberg EW, Alkadhi H, et al. A clinical prediction rule for the diagnosis of coronary artery disease. Eur Heart J 2011;32:1316–30.
1. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease. Circulation 2012;126:e354–e471.
2. Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med 2010;362:
886–95.
3. Fox KA, McLean S. Nice guidance on the investigation of chest pain. Heart 2010;96:903–6.
4. Montalescot G, Sechtem U, Achenbach S, et al. 2013 ESC guidelines on the management of stable coronary artery disease. Eur Heart J 2013;34:2949–3003.
5. Genders TSS, Steyerberg EW, Alkadhi H, et al. A clinical prediction rule for the diagnosis of coronary artery disease. Eur Heart J 2011;32:1316–30.
Is There a Dose-Response Relationship Between Weight Loss and Symptom Improvement in Persons With Knee Osteoarthritis?
Study Overview
Objective. To determine if there is an additive benefit of weight loss for pain and functioning in patients with established symptomatic osteoarthritis (OA) of the knee.
Design. Cohort study.
Setting and participants. Participants living in Australia who had completed the Osteoarthritis Healthy Weight For Life program (OAHWFL), a program run by Prima Health Solutions on behalf of participating health funds in Australia and New Zealand; its full cost is borne by the insurance/health care fund. Patients in the program are invited to enroll based on age (≥ 50) and claims data indicating knee OA; patients wishing to enroll must obtain a referral from their doctor confirming weight and height and radiographic or arthroscopic diagnosis of knee OA. Participants in the program had a body mass index (BMI) > 28 kg/m2 and met 1986 American College of Rheumatology clinical criteria for knee OA. Further, participants were deemed to clinically require referral to orthopedic surgeon and were surgical candidates by medical opinion.
Intervention. The OAHWFL program is a specialized knee and hip OA management program that focuses on weight loss, utilizing a portion-controlled eating plan with meal replacements, an activity plan, a personalized online tracker, and personal support. It is delivered remotely via phone, texts, email, message board, and mail. The 18-week program consists of 3 phases. During the first 6-week phase, participants were instructed to consume a nutritionally complete very low calorie meal replacement (KicStart, Prima Health Solutions) for 2 meals per day with controlled portions and “free foods” (eg, berries and leafy greens). During the second 6-week phase, participants were transitioned off the meal replacements onto a portion-controlled meal plan, with 1 meal replacement per day. In the final phase, participants consumed portion-controlled whole foods for all 3 meals. All phases included recommendations for moderate aerobic exercise 3 times per week for an increasing time period and intensity, online healthy eating and lifestyle education, and telephone motivation and support at predetermined intervals and on demand.
Main outcome measure. The main outcome measure was percentage of body weight lost from baseline to 18 weeks. Additionally, the validated Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire was administered to all participants. The 5 KOOS subscales (pain, other symptoms, function in daily living, function in recreation, and knee-related quality of life) were co-primary outcomes. The validated Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) function score was derived from KOOS. The dose-response relationship was assessed using weight change categories (< 2.5%, 2.5–5.0%, 5.1–7.5%, 7.6%–10%, and > 10%) and change in KOOS scores.
Main results. At the time of analysis, 3827 persons with knee or hip OA were approved by their doctor to participate. Of these 155 had not yet started the program, 728 were undergoing the program, and 846 had discontinued or were lost to follow-up. Of the 2098 who completed the program, 715 were excluded because of incomplete data or OA of the hip, leaving 1383 participants. Overall the baseline mean weight was 95.12 ± 17.2 kg with a mean BMI of 34.39 ± 5.17. Average age was 64 ± 8.7.
94.2% (1304 participants) had a greater than 2.5% reduction in body weight at the end of 18 weeks. 31.1% lost ≥ 10% body weight, 22.9% lost between 7.5 and 10%, 24% lost between 5 and 7.5%, 16.1% lost between 2.5–5%, and 5.7% of participants lost ≤ 2.5%. The greatest amount of weight loss was associated with the greatest improvement of both KOOS and WOMAC scores, with a significant dose-response relationship between weight loss and knee OA symptoms. This persisted in regression analysis adjusted for baseline KOOS and weight, sex, and age. Those with the largest weight loss improved their KOOS scores by 16.17 ± 16.1. The second highest weight loss group has an improvement in KOOS scores by 13.3 ± 15.1, then next highest 12.0 ± 17.1, followed by 9.9 ± 16.8 and finally an improvement of 6.1 ± 13.0 in the weight loss of ≤ 2.5% cohort.
Conclusion. This study showed a relationship between weight loss and improvement in knee OA pain and functioning, with greater weight loss resulting in greater improvement in both categories. Those who were better functioning at the commencement of the study required less weight loss to reach a meaningful improvement in functioning and pain compared to those who started with worse functional status. The OAHWFL intervention was shown to be an effective method of weight loss over an 18-month period.
Commentary
OA is the most common form of arthritis in the United States and the incidence has been rising. A recent study conducted by the Mayo Clinic found OA to be the second most common reason for ambulatory primary care visits, second only to dermatologic complaints [1].It is estimated that the average direct cost of OA per patient is $2600 per year [2], with job-related costs of $3.4 to $13.2 billion per year [3]. Knee replacements alone amounted to $28.5 billion in 2009 [4]. Aside from the financial burden of OA is its impact on quality of life. While genetic predisposition is important in disease pathogenesis, there are well established modifiable risk factors for OA. Among these is maintenance of a healthy weight and physical activity, both of which were addressed in this study.
There is high-quality evidence that weight loss improves the symptoms of knee OA [5]. The current study evaluated whether a dietary intervention for knee OA would be effective in a real-world setting, outside the controlled conditions of a randomized trial. Short-term weight loss did provide pain relief and increase functioning; however, the study does not report weight trajectory after cessation of the intervention. It would be more meaningful to know how many of the participants maintained weight loss after a longer period of time. In addition, it is unclear if the gain in function and pain control was from the weight loss or regular physical activity. A control group that participated in the physical activity without significant weight loss would have strengthened the association between weight loss and KOOS and WOMAC measures.
Though this study took place in a community setting and was tested in both rural and urban settings, the results may not be generalizable to patients who are not already motivated to lose weight, as patients self-nominated themselves to enroll in the program. This study also made use of meal supplements, which were supplied at no cost to patients. Without dedicated funding to supply the meal replacements in addition to the support program, it would be difficult to replicate these results. However, some insurance carriers will cover similar programs that provide validated methods for weight loss, which may be a feasible alternative. Other limitations to the study included lack of a control group, reliance on self-reported weight loss data, and that persons who discontinued the program were not included in the analysis.
Applications for Clinical Practice
Body mechanics and increased inflammation associated with obesity both contribute to worsening of knee OA. The dose-response relationship shown in this study of weight loss in overweight or obese people with OA of the knee is encouraging. Previous studies have shown a clear relationship between weight loss and improvement in pain. The most well-known is perhaps the 4-pound weight rule, which states that for every pound of weight lost, there is a 4-pound reduction in the load exerted on the knee for each step taken [5].Concrete examples of the benefits of weight loss that providers can share with their patients makes discussion about weight loss tangible. Further, the study teased out that those with better physical functioning at the start of the study required less weight loss to achieve gains in pain reduction and functional status. As the hazards of obesity continue to come to light, more community-based weight loss programs are becoming available. Most of the participants in this study successfully lost weight using a community-based approach, highlighting the usefulness of these programs. Weight loss in a community setting is a challenge to all providers. Knowing which patients will benefit the most from a weight loss program can help direct providers to personalized recommendations.
—Christina Downey, MD,
Geisinger Medical Center, Danville, PA.
1. St. Sauver JL, Warner DO, Yawn BP, et al. Why patients visit their doctors: assessing the most prevalent conditions in a defined American population. Mayo Clin Proc 2013;88:56–67.
2. Maetzel A, Li LC, Pencharz J, et al. The economic burden associated with osteoarthritis, rheumatoid arthritis, and hypertension: a comparative study. Ann Rheum Dis 2004;63:395–401.
3. Buckwalter JA, Saltzman C, Brown T. The impact of osteoarthritis. Clin Orthoped Rel Res 2004;42 7S:S6–S15.
4. Murphy L, Helmick CG.The impact of osteoarthritis in the United States: a population-health perspective. Am J Nurs 2012;112(3 Suppl 1):S13–9.
5. Messier SP1, Gutekunst DJ, Davis C, DeVita P. Weight loss reduces knee-joint loads in overweight and obese older adults with knee osteoarthritis. Arthrit Rheum 2005;52:2026–32.
Study Overview
Objective. To determine if there is an additive benefit of weight loss for pain and functioning in patients with established symptomatic osteoarthritis (OA) of the knee.
Design. Cohort study.
Setting and participants. Participants living in Australia who had completed the Osteoarthritis Healthy Weight For Life program (OAHWFL), a program run by Prima Health Solutions on behalf of participating health funds in Australia and New Zealand; its full cost is borne by the insurance/health care fund. Patients in the program are invited to enroll based on age (≥ 50) and claims data indicating knee OA; patients wishing to enroll must obtain a referral from their doctor confirming weight and height and radiographic or arthroscopic diagnosis of knee OA. Participants in the program had a body mass index (BMI) > 28 kg/m2 and met 1986 American College of Rheumatology clinical criteria for knee OA. Further, participants were deemed to clinically require referral to orthopedic surgeon and were surgical candidates by medical opinion.
Intervention. The OAHWFL program is a specialized knee and hip OA management program that focuses on weight loss, utilizing a portion-controlled eating plan with meal replacements, an activity plan, a personalized online tracker, and personal support. It is delivered remotely via phone, texts, email, message board, and mail. The 18-week program consists of 3 phases. During the first 6-week phase, participants were instructed to consume a nutritionally complete very low calorie meal replacement (KicStart, Prima Health Solutions) for 2 meals per day with controlled portions and “free foods” (eg, berries and leafy greens). During the second 6-week phase, participants were transitioned off the meal replacements onto a portion-controlled meal plan, with 1 meal replacement per day. In the final phase, participants consumed portion-controlled whole foods for all 3 meals. All phases included recommendations for moderate aerobic exercise 3 times per week for an increasing time period and intensity, online healthy eating and lifestyle education, and telephone motivation and support at predetermined intervals and on demand.
Main outcome measure. The main outcome measure was percentage of body weight lost from baseline to 18 weeks. Additionally, the validated Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire was administered to all participants. The 5 KOOS subscales (pain, other symptoms, function in daily living, function in recreation, and knee-related quality of life) were co-primary outcomes. The validated Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) function score was derived from KOOS. The dose-response relationship was assessed using weight change categories (< 2.5%, 2.5–5.0%, 5.1–7.5%, 7.6%–10%, and > 10%) and change in KOOS scores.
Main results. At the time of analysis, 3827 persons with knee or hip OA were approved by their doctor to participate. Of these 155 had not yet started the program, 728 were undergoing the program, and 846 had discontinued or were lost to follow-up. Of the 2098 who completed the program, 715 were excluded because of incomplete data or OA of the hip, leaving 1383 participants. Overall the baseline mean weight was 95.12 ± 17.2 kg with a mean BMI of 34.39 ± 5.17. Average age was 64 ± 8.7.
94.2% (1304 participants) had a greater than 2.5% reduction in body weight at the end of 18 weeks. 31.1% lost ≥ 10% body weight, 22.9% lost between 7.5 and 10%, 24% lost between 5 and 7.5%, 16.1% lost between 2.5–5%, and 5.7% of participants lost ≤ 2.5%. The greatest amount of weight loss was associated with the greatest improvement of both KOOS and WOMAC scores, with a significant dose-response relationship between weight loss and knee OA symptoms. This persisted in regression analysis adjusted for baseline KOOS and weight, sex, and age. Those with the largest weight loss improved their KOOS scores by 16.17 ± 16.1. The second highest weight loss group has an improvement in KOOS scores by 13.3 ± 15.1, then next highest 12.0 ± 17.1, followed by 9.9 ± 16.8 and finally an improvement of 6.1 ± 13.0 in the weight loss of ≤ 2.5% cohort.
Conclusion. This study showed a relationship between weight loss and improvement in knee OA pain and functioning, with greater weight loss resulting in greater improvement in both categories. Those who were better functioning at the commencement of the study required less weight loss to reach a meaningful improvement in functioning and pain compared to those who started with worse functional status. The OAHWFL intervention was shown to be an effective method of weight loss over an 18-month period.
Commentary
OA is the most common form of arthritis in the United States and the incidence has been rising. A recent study conducted by the Mayo Clinic found OA to be the second most common reason for ambulatory primary care visits, second only to dermatologic complaints [1].It is estimated that the average direct cost of OA per patient is $2600 per year [2], with job-related costs of $3.4 to $13.2 billion per year [3]. Knee replacements alone amounted to $28.5 billion in 2009 [4]. Aside from the financial burden of OA is its impact on quality of life. While genetic predisposition is important in disease pathogenesis, there are well established modifiable risk factors for OA. Among these is maintenance of a healthy weight and physical activity, both of which were addressed in this study.
There is high-quality evidence that weight loss improves the symptoms of knee OA [5]. The current study evaluated whether a dietary intervention for knee OA would be effective in a real-world setting, outside the controlled conditions of a randomized trial. Short-term weight loss did provide pain relief and increase functioning; however, the study does not report weight trajectory after cessation of the intervention. It would be more meaningful to know how many of the participants maintained weight loss after a longer period of time. In addition, it is unclear if the gain in function and pain control was from the weight loss or regular physical activity. A control group that participated in the physical activity without significant weight loss would have strengthened the association between weight loss and KOOS and WOMAC measures.
Though this study took place in a community setting and was tested in both rural and urban settings, the results may not be generalizable to patients who are not already motivated to lose weight, as patients self-nominated themselves to enroll in the program. This study also made use of meal supplements, which were supplied at no cost to patients. Without dedicated funding to supply the meal replacements in addition to the support program, it would be difficult to replicate these results. However, some insurance carriers will cover similar programs that provide validated methods for weight loss, which may be a feasible alternative. Other limitations to the study included lack of a control group, reliance on self-reported weight loss data, and that persons who discontinued the program were not included in the analysis.
Applications for Clinical Practice
Body mechanics and increased inflammation associated with obesity both contribute to worsening of knee OA. The dose-response relationship shown in this study of weight loss in overweight or obese people with OA of the knee is encouraging. Previous studies have shown a clear relationship between weight loss and improvement in pain. The most well-known is perhaps the 4-pound weight rule, which states that for every pound of weight lost, there is a 4-pound reduction in the load exerted on the knee for each step taken [5].Concrete examples of the benefits of weight loss that providers can share with their patients makes discussion about weight loss tangible. Further, the study teased out that those with better physical functioning at the start of the study required less weight loss to achieve gains in pain reduction and functional status. As the hazards of obesity continue to come to light, more community-based weight loss programs are becoming available. Most of the participants in this study successfully lost weight using a community-based approach, highlighting the usefulness of these programs. Weight loss in a community setting is a challenge to all providers. Knowing which patients will benefit the most from a weight loss program can help direct providers to personalized recommendations.
—Christina Downey, MD,
Geisinger Medical Center, Danville, PA.
Study Overview
Objective. To determine if there is an additive benefit of weight loss for pain and functioning in patients with established symptomatic osteoarthritis (OA) of the knee.
Design. Cohort study.
Setting and participants. Participants living in Australia who had completed the Osteoarthritis Healthy Weight For Life program (OAHWFL), a program run by Prima Health Solutions on behalf of participating health funds in Australia and New Zealand; its full cost is borne by the insurance/health care fund. Patients in the program are invited to enroll based on age (≥ 50) and claims data indicating knee OA; patients wishing to enroll must obtain a referral from their doctor confirming weight and height and radiographic or arthroscopic diagnosis of knee OA. Participants in the program had a body mass index (BMI) > 28 kg/m2 and met 1986 American College of Rheumatology clinical criteria for knee OA. Further, participants were deemed to clinically require referral to orthopedic surgeon and were surgical candidates by medical opinion.
Intervention. The OAHWFL program is a specialized knee and hip OA management program that focuses on weight loss, utilizing a portion-controlled eating plan with meal replacements, an activity plan, a personalized online tracker, and personal support. It is delivered remotely via phone, texts, email, message board, and mail. The 18-week program consists of 3 phases. During the first 6-week phase, participants were instructed to consume a nutritionally complete very low calorie meal replacement (KicStart, Prima Health Solutions) for 2 meals per day with controlled portions and “free foods” (eg, berries and leafy greens). During the second 6-week phase, participants were transitioned off the meal replacements onto a portion-controlled meal plan, with 1 meal replacement per day. In the final phase, participants consumed portion-controlled whole foods for all 3 meals. All phases included recommendations for moderate aerobic exercise 3 times per week for an increasing time period and intensity, online healthy eating and lifestyle education, and telephone motivation and support at predetermined intervals and on demand.
Main outcome measure. The main outcome measure was percentage of body weight lost from baseline to 18 weeks. Additionally, the validated Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire was administered to all participants. The 5 KOOS subscales (pain, other symptoms, function in daily living, function in recreation, and knee-related quality of life) were co-primary outcomes. The validated Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) function score was derived from KOOS. The dose-response relationship was assessed using weight change categories (< 2.5%, 2.5–5.0%, 5.1–7.5%, 7.6%–10%, and > 10%) and change in KOOS scores.
Main results. At the time of analysis, 3827 persons with knee or hip OA were approved by their doctor to participate. Of these 155 had not yet started the program, 728 were undergoing the program, and 846 had discontinued or were lost to follow-up. Of the 2098 who completed the program, 715 were excluded because of incomplete data or OA of the hip, leaving 1383 participants. Overall the baseline mean weight was 95.12 ± 17.2 kg with a mean BMI of 34.39 ± 5.17. Average age was 64 ± 8.7.
94.2% (1304 participants) had a greater than 2.5% reduction in body weight at the end of 18 weeks. 31.1% lost ≥ 10% body weight, 22.9% lost between 7.5 and 10%, 24% lost between 5 and 7.5%, 16.1% lost between 2.5–5%, and 5.7% of participants lost ≤ 2.5%. The greatest amount of weight loss was associated with the greatest improvement of both KOOS and WOMAC scores, with a significant dose-response relationship between weight loss and knee OA symptoms. This persisted in regression analysis adjusted for baseline KOOS and weight, sex, and age. Those with the largest weight loss improved their KOOS scores by 16.17 ± 16.1. The second highest weight loss group has an improvement in KOOS scores by 13.3 ± 15.1, then next highest 12.0 ± 17.1, followed by 9.9 ± 16.8 and finally an improvement of 6.1 ± 13.0 in the weight loss of ≤ 2.5% cohort.
Conclusion. This study showed a relationship between weight loss and improvement in knee OA pain and functioning, with greater weight loss resulting in greater improvement in both categories. Those who were better functioning at the commencement of the study required less weight loss to reach a meaningful improvement in functioning and pain compared to those who started with worse functional status. The OAHWFL intervention was shown to be an effective method of weight loss over an 18-month period.
Commentary
OA is the most common form of arthritis in the United States and the incidence has been rising. A recent study conducted by the Mayo Clinic found OA to be the second most common reason for ambulatory primary care visits, second only to dermatologic complaints [1].It is estimated that the average direct cost of OA per patient is $2600 per year [2], with job-related costs of $3.4 to $13.2 billion per year [3]. Knee replacements alone amounted to $28.5 billion in 2009 [4]. Aside from the financial burden of OA is its impact on quality of life. While genetic predisposition is important in disease pathogenesis, there are well established modifiable risk factors for OA. Among these is maintenance of a healthy weight and physical activity, both of which were addressed in this study.
There is high-quality evidence that weight loss improves the symptoms of knee OA [5]. The current study evaluated whether a dietary intervention for knee OA would be effective in a real-world setting, outside the controlled conditions of a randomized trial. Short-term weight loss did provide pain relief and increase functioning; however, the study does not report weight trajectory after cessation of the intervention. It would be more meaningful to know how many of the participants maintained weight loss after a longer period of time. In addition, it is unclear if the gain in function and pain control was from the weight loss or regular physical activity. A control group that participated in the physical activity without significant weight loss would have strengthened the association between weight loss and KOOS and WOMAC measures.
Though this study took place in a community setting and was tested in both rural and urban settings, the results may not be generalizable to patients who are not already motivated to lose weight, as patients self-nominated themselves to enroll in the program. This study also made use of meal supplements, which were supplied at no cost to patients. Without dedicated funding to supply the meal replacements in addition to the support program, it would be difficult to replicate these results. However, some insurance carriers will cover similar programs that provide validated methods for weight loss, which may be a feasible alternative. Other limitations to the study included lack of a control group, reliance on self-reported weight loss data, and that persons who discontinued the program were not included in the analysis.
Applications for Clinical Practice
Body mechanics and increased inflammation associated with obesity both contribute to worsening of knee OA. The dose-response relationship shown in this study of weight loss in overweight or obese people with OA of the knee is encouraging. Previous studies have shown a clear relationship between weight loss and improvement in pain. The most well-known is perhaps the 4-pound weight rule, which states that for every pound of weight lost, there is a 4-pound reduction in the load exerted on the knee for each step taken [5].Concrete examples of the benefits of weight loss that providers can share with their patients makes discussion about weight loss tangible. Further, the study teased out that those with better physical functioning at the start of the study required less weight loss to achieve gains in pain reduction and functional status. As the hazards of obesity continue to come to light, more community-based weight loss programs are becoming available. Most of the participants in this study successfully lost weight using a community-based approach, highlighting the usefulness of these programs. Weight loss in a community setting is a challenge to all providers. Knowing which patients will benefit the most from a weight loss program can help direct providers to personalized recommendations.
—Christina Downey, MD,
Geisinger Medical Center, Danville, PA.
1. St. Sauver JL, Warner DO, Yawn BP, et al. Why patients visit their doctors: assessing the most prevalent conditions in a defined American population. Mayo Clin Proc 2013;88:56–67.
2. Maetzel A, Li LC, Pencharz J, et al. The economic burden associated with osteoarthritis, rheumatoid arthritis, and hypertension: a comparative study. Ann Rheum Dis 2004;63:395–401.
3. Buckwalter JA, Saltzman C, Brown T. The impact of osteoarthritis. Clin Orthoped Rel Res 2004;42 7S:S6–S15.
4. Murphy L, Helmick CG.The impact of osteoarthritis in the United States: a population-health perspective. Am J Nurs 2012;112(3 Suppl 1):S13–9.
5. Messier SP1, Gutekunst DJ, Davis C, DeVita P. Weight loss reduces knee-joint loads in overweight and obese older adults with knee osteoarthritis. Arthrit Rheum 2005;52:2026–32.
1. St. Sauver JL, Warner DO, Yawn BP, et al. Why patients visit their doctors: assessing the most prevalent conditions in a defined American population. Mayo Clin Proc 2013;88:56–67.
2. Maetzel A, Li LC, Pencharz J, et al. The economic burden associated with osteoarthritis, rheumatoid arthritis, and hypertension: a comparative study. Ann Rheum Dis 2004;63:395–401.
3. Buckwalter JA, Saltzman C, Brown T. The impact of osteoarthritis. Clin Orthoped Rel Res 2004;42 7S:S6–S15.
4. Murphy L, Helmick CG.The impact of osteoarthritis in the United States: a population-health perspective. Am J Nurs 2012;112(3 Suppl 1):S13–9.
5. Messier SP1, Gutekunst DJ, Davis C, DeVita P. Weight loss reduces knee-joint loads in overweight and obese older adults with knee osteoarthritis. Arthrit Rheum 2005;52:2026–32.
Managing Pain in Postoperative Patients: What the Hospitalist Needs to Know
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You Can Earn CME with shmConsults
Appropriate Use of Targeted Oral Anticoagulants to Prevent Stroke in Patients with Nonvalvular Atrial Fibrillation
This activity includes thorough discussions on initial management of patients with nonvalvular atrial fibrillation (NVAF), appropriate situations for oral anticoagulation in the presence of NVAF, appropriate choice of oral anticoagulant, reversal of oral anticoagulation, and guidelines for oral anticoagulation and stroke prevention in NVAF patients and special-population NVAF patients.
Four modules addressing various aspects of anticoagulation/thrombosis have been updated and include:
- Target-Specific Oral Anticoagulants for Stroke Prophylaxis in Patients with NVAF
- Management of Postoperative Atrial Fibrillation
- Perioperative Bridging of Anticoagulant Therapy
- Perioperative Management of Anticoagulation
Appropriate Use of Targeted Oral Anticoagulants to Prevent Stroke in Patients with Nonvalvular Atrial Fibrillation
This activity includes thorough discussions on initial management of patients with nonvalvular atrial fibrillation (NVAF), appropriate situations for oral anticoagulation in the presence of NVAF, appropriate choice of oral anticoagulant, reversal of oral anticoagulation, and guidelines for oral anticoagulation and stroke prevention in NVAF patients and special-population NVAF patients.
Four modules addressing various aspects of anticoagulation/thrombosis have been updated and include:
- Target-Specific Oral Anticoagulants for Stroke Prophylaxis in Patients with NVAF
- Management of Postoperative Atrial Fibrillation
- Perioperative Bridging of Anticoagulant Therapy
- Perioperative Management of Anticoagulation
Appropriate Use of Targeted Oral Anticoagulants to Prevent Stroke in Patients with Nonvalvular Atrial Fibrillation
This activity includes thorough discussions on initial management of patients with nonvalvular atrial fibrillation (NVAF), appropriate situations for oral anticoagulation in the presence of NVAF, appropriate choice of oral anticoagulant, reversal of oral anticoagulation, and guidelines for oral anticoagulation and stroke prevention in NVAF patients and special-population NVAF patients.
Four modules addressing various aspects of anticoagulation/thrombosis have been updated and include:
- Target-Specific Oral Anticoagulants for Stroke Prophylaxis in Patients with NVAF
- Management of Postoperative Atrial Fibrillation
- Perioperative Bridging of Anticoagulant Therapy
- Perioperative Management of Anticoagulation
FDA clears use of coagulation analyzer
The US Food and Drug Administration (FDA) has granted 510(k) clearance for the Xprecia Stride Coagulation Analyzer from Siemens Healthcare Diagnostics.
This hand-held, portable coagulation analyzer is designed to deliver prothrombin time/international normalized ratio (PT/INR) testing for point-of-care (POC) monitoring and management of oral anticoagulation therapy with the vitamin K antagonist warfarin.
The Xprecia Stride Coagulation Analyzer is the first POC PT/INR device cleared by the FDA based on the new rules published in March 2016.
The analyzer uses fresh capillary whole blood, and results are expressed as INR. The Xprecia Stride Coagulation Analyzer utilizes the same Dade® Innovin® reagent used by Siemens Healthineers central lab analyzers to minimize any potential for variability.
Research has shown the performance of the Xprecia Stride Coagulation Analyzer to be equivalent to a reference laboratory hemostasis system,* with results available within minutes.
According to Siemens, the Xprecia Stride Coagulation Analyzer includes a number of innovations and features not found on most other POC analyzers.
The Xprecia Stride Coagulation Analyzer has a touchscreen interface with step-by-step instructions that help guide the user.
To further enhance usability, the analyzer features simple icons and animation in a color display more commonly found in mobile devices than medical instruments.
The Xprecia Stride Coagulation Analyzer is no bigger than a smartphone and weighs just 10.5 oz, so it can be held at virtually any angle and brought directly to the patient’s finger for blood sample collection.
The analyzer has an integrated barcode scanner intended to simplify data capture and improve patient workflow. The scanner offers patient and operator ID entry.
The Xprecia Stride Coagulation Analyzer has an operator lockout feature that restricts the analyzer’s use to trained staff only.
And the analyzer includes a first-of-its kind test strip eject button that allows the user to eject a used test strip and easily dispose of it without touching it, minimizing potential biohazard exposure.
For more information on the Xprecia Stride Coagulation Analyzer, visit www.siemens.com/xprecia.
The US Food and Drug Administration (FDA) has granted 510(k) clearance for the Xprecia Stride Coagulation Analyzer from Siemens Healthcare Diagnostics.
This hand-held, portable coagulation analyzer is designed to deliver prothrombin time/international normalized ratio (PT/INR) testing for point-of-care (POC) monitoring and management of oral anticoagulation therapy with the vitamin K antagonist warfarin.
The Xprecia Stride Coagulation Analyzer is the first POC PT/INR device cleared by the FDA based on the new rules published in March 2016.
The analyzer uses fresh capillary whole blood, and results are expressed as INR. The Xprecia Stride Coagulation Analyzer utilizes the same Dade® Innovin® reagent used by Siemens Healthineers central lab analyzers to minimize any potential for variability.
Research has shown the performance of the Xprecia Stride Coagulation Analyzer to be equivalent to a reference laboratory hemostasis system,* with results available within minutes.
According to Siemens, the Xprecia Stride Coagulation Analyzer includes a number of innovations and features not found on most other POC analyzers.
The Xprecia Stride Coagulation Analyzer has a touchscreen interface with step-by-step instructions that help guide the user.
To further enhance usability, the analyzer features simple icons and animation in a color display more commonly found in mobile devices than medical instruments.
The Xprecia Stride Coagulation Analyzer is no bigger than a smartphone and weighs just 10.5 oz, so it can be held at virtually any angle and brought directly to the patient’s finger for blood sample collection.
The analyzer has an integrated barcode scanner intended to simplify data capture and improve patient workflow. The scanner offers patient and operator ID entry.
The Xprecia Stride Coagulation Analyzer has an operator lockout feature that restricts the analyzer’s use to trained staff only.
And the analyzer includes a first-of-its kind test strip eject button that allows the user to eject a used test strip and easily dispose of it without touching it, minimizing potential biohazard exposure.
For more information on the Xprecia Stride Coagulation Analyzer, visit www.siemens.com/xprecia.
The US Food and Drug Administration (FDA) has granted 510(k) clearance for the Xprecia Stride Coagulation Analyzer from Siemens Healthcare Diagnostics.
This hand-held, portable coagulation analyzer is designed to deliver prothrombin time/international normalized ratio (PT/INR) testing for point-of-care (POC) monitoring and management of oral anticoagulation therapy with the vitamin K antagonist warfarin.
The Xprecia Stride Coagulation Analyzer is the first POC PT/INR device cleared by the FDA based on the new rules published in March 2016.
The analyzer uses fresh capillary whole blood, and results are expressed as INR. The Xprecia Stride Coagulation Analyzer utilizes the same Dade® Innovin® reagent used by Siemens Healthineers central lab analyzers to minimize any potential for variability.
Research has shown the performance of the Xprecia Stride Coagulation Analyzer to be equivalent to a reference laboratory hemostasis system,* with results available within minutes.
According to Siemens, the Xprecia Stride Coagulation Analyzer includes a number of innovations and features not found on most other POC analyzers.
The Xprecia Stride Coagulation Analyzer has a touchscreen interface with step-by-step instructions that help guide the user.
To further enhance usability, the analyzer features simple icons and animation in a color display more commonly found in mobile devices than medical instruments.
The Xprecia Stride Coagulation Analyzer is no bigger than a smartphone and weighs just 10.5 oz, so it can be held at virtually any angle and brought directly to the patient’s finger for blood sample collection.
The analyzer has an integrated barcode scanner intended to simplify data capture and improve patient workflow. The scanner offers patient and operator ID entry.
The Xprecia Stride Coagulation Analyzer has an operator lockout feature that restricts the analyzer’s use to trained staff only.
And the analyzer includes a first-of-its kind test strip eject button that allows the user to eject a used test strip and easily dispose of it without touching it, minimizing potential biohazard exposure.
For more information on the Xprecia Stride Coagulation Analyzer, visit www.siemens.com/xprecia.
Clinical Challenges - October 2016: Boerhaave’s syndrome (spontaneous rupture of the esophagus)
What's Your Diagnosis?
The diagnosis
Gastrografin swallow (Figure C) demonstrated rupture of the distal esophagus, with leakage of gastrografin into the mediastinum (arrow). Upper gastrointestinal endoscopy confirmed rupture of the left posterolateral wall of the distal esophagus consistent with Boerhaave’s syndrome (Figure D), and a self-expanding covered metal stent was placed.
Broad-spectrum antibiotics and nasogastric feeding were commenced, and the left pleural effusion drained with a tube thoracostomy. Unfortunately, despite initial improvement, the patient subsequently deteriorated and died 30 days after admission.
Boerhaave’s is a rare clinical entity defined as spontaneous esophageal rupture, excluding perforations resulting from foreign bodies or iatrogenic instrumentation.
Mackler’s triad of vomiting, lower chest pain, and subcutaneous emphysema is the classical presentation but is seen in only a minority of cases; thus, diagnostic errors are common.2 Importantly, the chest radiograph is almost always abnormal, with pleural effusions or pneumomediastinum often seen.3 Surgical repair is the definitive treatment, but in patients considered unfit for surgery, conservative or endoscopic management is advocated. Mortality remains greater than 30%, and rises sharply if diagnosis is delayed,2 emphasizing the importance of awareness of this unusual diagnosis.
References
1. Lucendo, A.J., Fringal-Ruiz, A.B., Rodriguez, B. Boerhaave’s syndrome as the primary manifestation of adult eosinophilic esophagitis. (Two case reports and a review of the literature.) Dis Esophagus. 2011 Feb;24:E11-5.
2. Brauer, R.B., Liebermann-Meffert, D., Stein, H.J., et al. Boerhaave’s syndrome: Analysis of the literature and report of 18 new cases. Dis Esophagus. 1997 Jan;10:64-8.
3. Pate, J.W., Walker, W.A., Cole, F.H. Jr, et al. Spontaneous rupture of the esophagus: a 30-year experience. Ann Thorac Surg. 1989 May;47:689-92.
The diagnosis
Gastrografin swallow (Figure C) demonstrated rupture of the distal esophagus, with leakage of gastrografin into the mediastinum (arrow). Upper gastrointestinal endoscopy confirmed rupture of the left posterolateral wall of the distal esophagus consistent with Boerhaave’s syndrome (Figure D), and a self-expanding covered metal stent was placed.
Broad-spectrum antibiotics and nasogastric feeding were commenced, and the left pleural effusion drained with a tube thoracostomy. Unfortunately, despite initial improvement, the patient subsequently deteriorated and died 30 days after admission.
Boerhaave’s is a rare clinical entity defined as spontaneous esophageal rupture, excluding perforations resulting from foreign bodies or iatrogenic instrumentation.
Mackler’s triad of vomiting, lower chest pain, and subcutaneous emphysema is the classical presentation but is seen in only a minority of cases; thus, diagnostic errors are common.2 Importantly, the chest radiograph is almost always abnormal, with pleural effusions or pneumomediastinum often seen.3 Surgical repair is the definitive treatment, but in patients considered unfit for surgery, conservative or endoscopic management is advocated. Mortality remains greater than 30%, and rises sharply if diagnosis is delayed,2 emphasizing the importance of awareness of this unusual diagnosis.
References
1. Lucendo, A.J., Fringal-Ruiz, A.B., Rodriguez, B. Boerhaave’s syndrome as the primary manifestation of adult eosinophilic esophagitis. (Two case reports and a review of the literature.) Dis Esophagus. 2011 Feb;24:E11-5.
2. Brauer, R.B., Liebermann-Meffert, D., Stein, H.J., et al. Boerhaave’s syndrome: Analysis of the literature and report of 18 new cases. Dis Esophagus. 1997 Jan;10:64-8.
3. Pate, J.W., Walker, W.A., Cole, F.H. Jr, et al. Spontaneous rupture of the esophagus: a 30-year experience. Ann Thorac Surg. 1989 May;47:689-92.
The diagnosis
Gastrografin swallow (Figure C) demonstrated rupture of the distal esophagus, with leakage of gastrografin into the mediastinum (arrow). Upper gastrointestinal endoscopy confirmed rupture of the left posterolateral wall of the distal esophagus consistent with Boerhaave’s syndrome (Figure D), and a self-expanding covered metal stent was placed.
Broad-spectrum antibiotics and nasogastric feeding were commenced, and the left pleural effusion drained with a tube thoracostomy. Unfortunately, despite initial improvement, the patient subsequently deteriorated and died 30 days after admission.
Boerhaave’s is a rare clinical entity defined as spontaneous esophageal rupture, excluding perforations resulting from foreign bodies or iatrogenic instrumentation.
Mackler’s triad of vomiting, lower chest pain, and subcutaneous emphysema is the classical presentation but is seen in only a minority of cases; thus, diagnostic errors are common.2 Importantly, the chest radiograph is almost always abnormal, with pleural effusions or pneumomediastinum often seen.3 Surgical repair is the definitive treatment, but in patients considered unfit for surgery, conservative or endoscopic management is advocated. Mortality remains greater than 30%, and rises sharply if diagnosis is delayed,2 emphasizing the importance of awareness of this unusual diagnosis.
References
1. Lucendo, A.J., Fringal-Ruiz, A.B., Rodriguez, B. Boerhaave’s syndrome as the primary manifestation of adult eosinophilic esophagitis. (Two case reports and a review of the literature.) Dis Esophagus. 2011 Feb;24:E11-5.
2. Brauer, R.B., Liebermann-Meffert, D., Stein, H.J., et al. Boerhaave’s syndrome: Analysis of the literature and report of 18 new cases. Dis Esophagus. 1997 Jan;10:64-8.
3. Pate, J.W., Walker, W.A., Cole, F.H. Jr, et al. Spontaneous rupture of the esophagus: a 30-year experience. Ann Thorac Surg. 1989 May;47:689-92.
What's Your Diagnosis?
What's Your Diagnosis?
What's Your Diagnosis?
By Thomas P. Chapman, MD, David A. Gorard, MBBS, MD, and Emily A. Johns, MD. Published previously in Gastroenterology (2012;143:1438, 1692).
An 84-year-old man presented to the emergency department with acute left-sided chest pain, after a recent diarrheal and vomiting illness. He had a background of severe Alzheimer’s dementia and was a resident in a care home. On arrival in the emergency department, he was unable to give a clear history and was distressed by the chest pain.
Why Required Pediatric Hospital Medicine Fellowships Are Unnecessary
The Joint Council of Pediatric Hospital Medicine (JCPHM), successor to the Strategic Planning (STP) Committee, recently recommended submitting a petition for two-year pediatric hospital medicine (PHM) fellowship certification to the American Board of Pediatrics (ABP), which was completed in 2014. In December 2015, the ABP Board of Directors voted to (1) approve the proposal for a two-year PHM fellowship incorporating scholarly activity with the provision that entrustable professional activities (EPAs) be used as the framework for assessing competencies and (2) not require those who achieve and maintain PHM certification to maintain general pediatrics certification. The proposal for certification of a two-year PHM fellowship will now be submitted to the American Board of Medical Specialties (ABMS). Concerns regarding the formal certification of PHM as an ABMS-recognized subspecialty have been raised by many stakeholders, including community pediatric hospitalists, pediatric residency program directors, and med-peds physicians.
We feel that the “first, do no harm” guiding principle seems to have been forgotten by the ABP as it attempts to formalize the training of pediatric hospitalists. In December 2015, the ABP voted in favor of a two-year ACGME-accredited PHM fellowship. The intent was to “assure the best care of hospitalized children,” “assure the public,” “accelerate improvements and innovation in quality improvement,” and “raise the level of care of all hospitalized children by establishing best practices in clinical care.” To be clear, these goals are shared by all of us (although there is no indication that the public is seeking additional assurance). Prior to launching broad-scale, time-intensive, and financially costly initiatives, we should ensure that our efforts would achieve—rather than obstruct—their intended aims. In addition to a lack of evidence supporting that subspecialty certification will advance our path toward achieving these goals, there are numerous reasons a required PHM fellowship is unnecessary and potentially even harmful to the hospitalist workforce. The negative unintended consequences need to be weighed heavily.
We have found no data to support that children would receive inferior inpatient care from pediatric hospitalists due to lack of formal certification. Hospital medicine physicians are paving the way in quality improvement, high-value care, medical education, palliative care, and global health, supported in part through training in various non-accredited hospital medicine fellowships. There is nothing stopping pediatric hospitalists from establishing and disseminating best practices in clinical care. Hospitalists are already making strides in providing high-quality care at low costs, as demonstrated by the abundant PHM scholarly work described in the ABP application to the ABMS. The alleged problem of needing to build trust within the community is yet to be demonstrated, as we have leaders at local, regional, and national levels. The chief medical officer of the Centers for Medicare & Medicaid Services is a hospitalist as is our surgeon general. Hospital medicine is the fastest-growing specialty in the history of medicine,1 and we should seek to propel rather than fetter our future colleagues.
Below are our reasons for opposing this formal certification.
We already have a fellowship system.
As we all know, advanced training opportunities already exist for those interested in pursuing extra research and quality improvement training. Similar to other pediatric subspecialty fellowships, these PHM fellowships are undersubscribed (20% of PHM fellowships did not fill in 2016),2 with the majority of graduating pediatric residents transitioning to hospitalists opting not to pursue fellowship training. We should continue to let graduating pediatric residents vote with their feet without the undue influence of subspecialty certification.
Subspecialization has opportunity costs that may reduce the PHM pipeline.
Even if we assume an adequate number of fellowship programs could be developed and funded, our fear is that the decision to turn PHM into an accredited subspecialty could paradoxically reduce the pipeline of inpatient providers. Residency is already a three- to four-year endeavor (pediatrics and med-peds) that is poorly compensated and time-intensive. In the absence of evidence supporting the value of additional training, tacking on another two years seems unreasonable in the face of the student loan debt crisis, reduced compensation, and lost time for career advancement. These are significant opportunity costs. While most specialties lead to a significant pay raise to compensate for the added training time, pediatrics remains the lowest-paid physician specialty.3 Should PHM follow the trend of most pediatric subspecialties, pursuit of fellowship training would be a negative financial decision for residency graduates.4 For the health system, increasing debt-to-income ratios runs the risk of creating a medical education bubble market.5
More than 25% of med-peds graduates pursue careers in hospital medicine, a percentage that continues to grow, accounting for more than 100 new hospitalists per year.6 As a result, med-peds-trained hospitalists constitute more than 10% of the pediatric hospitalist workforce.6 Requiring PHM fellowship training may reduce this crucial pipeline of practitioners. In a 2014 unpublished survey of 225 med-peds practitioners, 78% of residents and 96% of attendings responded that they would not consider pursuing an ACGME-accredited PHM fellowship.7 This is compounded by a lack of parity with the practice of adult hospital medicine both in compensation and required training and is heightened by the fact that the training in question does not incorporate care for adult patients. There is clear consensus by 96% of med-peds hospitalists that the creation of an ACGME-certified PHM subspecialty will negatively affect the likelihood of med-peds providers pursuing PHM.7
Certification will pose a potential risk to specific patient populations.
We are also concerned that a reduced PHM workforce could disproportionately impact young adults with special healthcare needs and those children cared for in rural or community-based hospitals. Med-peds training equips providers to care for children with chronic diseases that then transition into adulthood; more than 25% provide care for young adults with special healthcare needs.6 With the increasing number of children with chronic health conditions surviving into adulthood,8 med-peds hospitalists serve essential roles in providing care and coordination for this vulnerable population. Furthermore, hospital medicine groups in medical systems that cannot support a full-time categorical pediatric hospitalist tend to employ med-peds physicians or family practitioners. Concerns with PHM certification are thus extended to those family medicine physicians who practice PHM.
Pediatric residency trains pediatricians in inpatient care.
We feel that the decision to move forward on PHM subspecialty certification calls into question the value of pediatric residency training. There is no evidence that clinical inpatient training in pediatrics residency is inadequate. If one leaves residency trained to do anything, it is practicing hospital medicine. A significant portion of residency takes place inpatient, both on wards and in the intensive care units. The 2009 ABP Foundation–funded study of PHM reported that 94% of pediatric hospitalist respondents rated their training in general clinical skills during residency as fully adequate, 85% rated their training in communication skills as fully adequate, and 73% did not believe any additional training beyond residency should be required.9 With respect to med-peds graduates, more than 90% feel equipped to care for children and adults upon residency completion.10 If the ABMS carries forward with this decision, the only clinical work one would be “certified” to do after residency is primary care. However, after completion of residency training, most of us feel at least as comfortable, if not more comfortable, caring for children in the inpatient setting.
Primary care should require subspecialty certification as well.
Furthermore, the decision to create a certified subspecialty begs the question as to why fellowship should not be mandated for those entering the field of primary care. Does the field of primary care not require research to move it forward? Does the field of primary care not require providers who can adeptly apply quality improvement methodologies to improve primary-care delivery? Does the public not require the same type of assurance? By these measures, primary care should require subspecialty certification as well. These arguments could easily be construed as an indictment of residency training.
The target should be residency training.
The PHM ABMS application describes a clinical curriculum consisting of eight core clinical rotations in various settings. That small number emphasizes the fact that extra clinical training is really not needed and that we do not require a complete overhaul of the current training system. The skills in question for the accredited PHM fellowship include communication, negotiation, leadership, quality improvement, pain management, sedation, procedures, transport, billing/coding, autonomous decision making, and scholarly practice. Are most of these not skills that we should foster in all practicing pediatricians? If graduating pediatric residents lack competence in core pediatric skills (e.g., communication, pain management, autonomous decision making), we should target improvements in residency education rather than require years of further training. Pediatrics residency training already requires training in quality improvement and is incorporating “tracks” that target areas of perceived deficiency. Those physicians who actually require specialized hospital-based skills (e.g., sedation, procedures, and transport) could receive core training during residency (e.g., through PHM tracks or electives) and further hone these skills through faculty development efforts. While non-PhD researchers may benefit from additional training in research methodologies, this training comes at the expense of time spent caring for patients on the wards and should not be required training for the majority of pediatric hospitalists pursuing purely clinical roles.
Broad-based support for a PHM subspecialty has not been demonstrated.
While approximately 40 pediatric hospitalists originated the PHM certification petition, we have not seen clear support for subspecialty certification from the community. PHM certification runs the risk of alienating the general pediatrics community, as many outpatient pediatricians continue to care for their patients in the inpatient setting. Furthermore, at tertiary-care medical centers, pediatric subspecialists often serve as hospitalists, yet this stakeholder group has not entered into this conversation. Importantly, the Association of Pediatric Program Directors (APPD) did not endorse this proposal. Many of the APPD members were quite concerned about the harm this certification could cause. While the APA Board and the AAP Board of Directors support PHM subspecialty certification, it is not clear that the rank-and-file members do. The Society of Hospital Medicine did not support or oppose certification. In an era of controversy surrounding certification requirements, prior to making a decision that will alter the direction of an entire field and impact all future residency graduates interested in entering that field, we should ensure there is broad-based support for this decision.
An alternative path has already been established and validated.
A more prudent, cost-effective, and universally acceptable approach would be to follow in the footsteps of the American Board of Internal Medicine (ABIM) and American Board of Family Medicine (ABFM) in establishing a Focused Practice in Pediatric Hospital Medicine program. This approach respects the unique body of knowledge required of those who care for hospitalized children while maintaining the required flexibility to nurture and help to mature existing training pipelines. Core hospital medicine skills should be further honed through residency curricular changes and faculty development efforts, while hospital-based physicians interested in developing niche skills could still do so via already existing fellowships.
When it comes to pediatric hospital medicine, first, do no harm.
Pediatric hospitalists are inpatient generalists by training and clinical approach. Our practices vary from large academic medical centers with every imaginable subspecialty consult service available to remote rural settings that require hospitalists to possess unique and specific skills. Some pediatric hospitalists participate in newborn care, some perform sedations, and some perform a variety of diagnostic and therapeutic procedures. The current system is meeting the needs of the vast majority of our PHM community. Changes to the residency curriculum that are already under way can address any clinical and quality improvement gaps. More than enough PHM fellowships are available to those who choose to pursue them. The public is not requesting reassurance, and the field is already advancing at a rapid rate both clinically and scholarly. Subspecialty recognition is not necessary and will likely lead to negative unintended consequences. Given the financial constraints on our current system and the need for pediatric hospitalists to be stewards of high-value care, we should make collective decisions that will clearly benefit our patients and health system. As medical professionals, our priority should always be first, do no harm.
Weijen W. Chang, MD, is chief of the Division of Pediatric Hospital Medicine at Baystate Children’s Hospital and associate professor of pediatrics at the University of Massachusetts Medical School.
Leonard Samuel Feldman, MD, is director of the Medicine-Pediatrics Urban Health Residency Program and associate professor of medicine and pediatrics at Johns Hopkins School of Medicine.
Bradley Monash, MD, is associate chief of medicine at University of California, San Francisco and assistant clinical professor of medicine and pediatrics at UCSF School of Medicine.
Archna Eniasivam, MD, is assistant clinical professor of medicine at UCSF School of Medicine.
References
- Chen C, Eagle S. “Should Pediatric HM Pursue Subspecialty Certification, Required Fellowship Training?” The Hospitalist. July 31, 2012
- Results and Data: Specialties Matching Service 2016 Appointment Year. National Resident Matching Program website. Accessed May 15, 2016.
- Medscape Pediatrician Compensation Report 2015. Medscape website. Accessed April 29, 2016.
- Rochlin JM, Simon HK. Does fellowship pay: what is the long-term financial impact of subspecialty training in pediatrics? Pediatrics. 2001;127(2):254-260.
- Asch DA, Nicholson S, Vujicic M. Are we in a medical education bubble market? N Engl J Med. 2013;369(21):1973-1975.
- O’Toole JK, Friedland AR, Gonzaga AM, et al. The practice patterns of recently graduated internal medicine-pediatric hospitalists. Hosp Pediatr. 2015;5(6):309-314.
- Society of Hospital Medicine: Survey of Med-Peds Physicians about PHM Certification. May 2014 (unpublished).
- Goodman DM, Hall M, Levin A, et al. Adults with chronic health conditions originating in childhood: inpatient experience in children’s hospitals. Pediatrics. 2011;128(1):5-13.
- Freed GL, Dunham KM, Research Advisory Committee of the American Board of P. Pediatric hospitalists: training, current practice, and career goals. J Hosp Med. 2009;4(3):179-186.
- Donnelly MJ, Lubrano L, Radabaugh CL, Lukela MP, Friedland AR, Ruch-Ross HS. The med-peds hospitalist workforce: results from the American Academy of Pediatrics Workforce Survey. Hosp Pediatr. 2015;5(11):574-579.
The Joint Council of Pediatric Hospital Medicine (JCPHM), successor to the Strategic Planning (STP) Committee, recently recommended submitting a petition for two-year pediatric hospital medicine (PHM) fellowship certification to the American Board of Pediatrics (ABP), which was completed in 2014. In December 2015, the ABP Board of Directors voted to (1) approve the proposal for a two-year PHM fellowship incorporating scholarly activity with the provision that entrustable professional activities (EPAs) be used as the framework for assessing competencies and (2) not require those who achieve and maintain PHM certification to maintain general pediatrics certification. The proposal for certification of a two-year PHM fellowship will now be submitted to the American Board of Medical Specialties (ABMS). Concerns regarding the formal certification of PHM as an ABMS-recognized subspecialty have been raised by many stakeholders, including community pediatric hospitalists, pediatric residency program directors, and med-peds physicians.
We feel that the “first, do no harm” guiding principle seems to have been forgotten by the ABP as it attempts to formalize the training of pediatric hospitalists. In December 2015, the ABP voted in favor of a two-year ACGME-accredited PHM fellowship. The intent was to “assure the best care of hospitalized children,” “assure the public,” “accelerate improvements and innovation in quality improvement,” and “raise the level of care of all hospitalized children by establishing best practices in clinical care.” To be clear, these goals are shared by all of us (although there is no indication that the public is seeking additional assurance). Prior to launching broad-scale, time-intensive, and financially costly initiatives, we should ensure that our efforts would achieve—rather than obstruct—their intended aims. In addition to a lack of evidence supporting that subspecialty certification will advance our path toward achieving these goals, there are numerous reasons a required PHM fellowship is unnecessary and potentially even harmful to the hospitalist workforce. The negative unintended consequences need to be weighed heavily.
We have found no data to support that children would receive inferior inpatient care from pediatric hospitalists due to lack of formal certification. Hospital medicine physicians are paving the way in quality improvement, high-value care, medical education, palliative care, and global health, supported in part through training in various non-accredited hospital medicine fellowships. There is nothing stopping pediatric hospitalists from establishing and disseminating best practices in clinical care. Hospitalists are already making strides in providing high-quality care at low costs, as demonstrated by the abundant PHM scholarly work described in the ABP application to the ABMS. The alleged problem of needing to build trust within the community is yet to be demonstrated, as we have leaders at local, regional, and national levels. The chief medical officer of the Centers for Medicare & Medicaid Services is a hospitalist as is our surgeon general. Hospital medicine is the fastest-growing specialty in the history of medicine,1 and we should seek to propel rather than fetter our future colleagues.
Below are our reasons for opposing this formal certification.
We already have a fellowship system.
As we all know, advanced training opportunities already exist for those interested in pursuing extra research and quality improvement training. Similar to other pediatric subspecialty fellowships, these PHM fellowships are undersubscribed (20% of PHM fellowships did not fill in 2016),2 with the majority of graduating pediatric residents transitioning to hospitalists opting not to pursue fellowship training. We should continue to let graduating pediatric residents vote with their feet without the undue influence of subspecialty certification.
Subspecialization has opportunity costs that may reduce the PHM pipeline.
Even if we assume an adequate number of fellowship programs could be developed and funded, our fear is that the decision to turn PHM into an accredited subspecialty could paradoxically reduce the pipeline of inpatient providers. Residency is already a three- to four-year endeavor (pediatrics and med-peds) that is poorly compensated and time-intensive. In the absence of evidence supporting the value of additional training, tacking on another two years seems unreasonable in the face of the student loan debt crisis, reduced compensation, and lost time for career advancement. These are significant opportunity costs. While most specialties lead to a significant pay raise to compensate for the added training time, pediatrics remains the lowest-paid physician specialty.3 Should PHM follow the trend of most pediatric subspecialties, pursuit of fellowship training would be a negative financial decision for residency graduates.4 For the health system, increasing debt-to-income ratios runs the risk of creating a medical education bubble market.5
More than 25% of med-peds graduates pursue careers in hospital medicine, a percentage that continues to grow, accounting for more than 100 new hospitalists per year.6 As a result, med-peds-trained hospitalists constitute more than 10% of the pediatric hospitalist workforce.6 Requiring PHM fellowship training may reduce this crucial pipeline of practitioners. In a 2014 unpublished survey of 225 med-peds practitioners, 78% of residents and 96% of attendings responded that they would not consider pursuing an ACGME-accredited PHM fellowship.7 This is compounded by a lack of parity with the practice of adult hospital medicine both in compensation and required training and is heightened by the fact that the training in question does not incorporate care for adult patients. There is clear consensus by 96% of med-peds hospitalists that the creation of an ACGME-certified PHM subspecialty will negatively affect the likelihood of med-peds providers pursuing PHM.7
Certification will pose a potential risk to specific patient populations.
We are also concerned that a reduced PHM workforce could disproportionately impact young adults with special healthcare needs and those children cared for in rural or community-based hospitals. Med-peds training equips providers to care for children with chronic diseases that then transition into adulthood; more than 25% provide care for young adults with special healthcare needs.6 With the increasing number of children with chronic health conditions surviving into adulthood,8 med-peds hospitalists serve essential roles in providing care and coordination for this vulnerable population. Furthermore, hospital medicine groups in medical systems that cannot support a full-time categorical pediatric hospitalist tend to employ med-peds physicians or family practitioners. Concerns with PHM certification are thus extended to those family medicine physicians who practice PHM.
Pediatric residency trains pediatricians in inpatient care.
We feel that the decision to move forward on PHM subspecialty certification calls into question the value of pediatric residency training. There is no evidence that clinical inpatient training in pediatrics residency is inadequate. If one leaves residency trained to do anything, it is practicing hospital medicine. A significant portion of residency takes place inpatient, both on wards and in the intensive care units. The 2009 ABP Foundation–funded study of PHM reported that 94% of pediatric hospitalist respondents rated their training in general clinical skills during residency as fully adequate, 85% rated their training in communication skills as fully adequate, and 73% did not believe any additional training beyond residency should be required.9 With respect to med-peds graduates, more than 90% feel equipped to care for children and adults upon residency completion.10 If the ABMS carries forward with this decision, the only clinical work one would be “certified” to do after residency is primary care. However, after completion of residency training, most of us feel at least as comfortable, if not more comfortable, caring for children in the inpatient setting.
Primary care should require subspecialty certification as well.
Furthermore, the decision to create a certified subspecialty begs the question as to why fellowship should not be mandated for those entering the field of primary care. Does the field of primary care not require research to move it forward? Does the field of primary care not require providers who can adeptly apply quality improvement methodologies to improve primary-care delivery? Does the public not require the same type of assurance? By these measures, primary care should require subspecialty certification as well. These arguments could easily be construed as an indictment of residency training.
The target should be residency training.
The PHM ABMS application describes a clinical curriculum consisting of eight core clinical rotations in various settings. That small number emphasizes the fact that extra clinical training is really not needed and that we do not require a complete overhaul of the current training system. The skills in question for the accredited PHM fellowship include communication, negotiation, leadership, quality improvement, pain management, sedation, procedures, transport, billing/coding, autonomous decision making, and scholarly practice. Are most of these not skills that we should foster in all practicing pediatricians? If graduating pediatric residents lack competence in core pediatric skills (e.g., communication, pain management, autonomous decision making), we should target improvements in residency education rather than require years of further training. Pediatrics residency training already requires training in quality improvement and is incorporating “tracks” that target areas of perceived deficiency. Those physicians who actually require specialized hospital-based skills (e.g., sedation, procedures, and transport) could receive core training during residency (e.g., through PHM tracks or electives) and further hone these skills through faculty development efforts. While non-PhD researchers may benefit from additional training in research methodologies, this training comes at the expense of time spent caring for patients on the wards and should not be required training for the majority of pediatric hospitalists pursuing purely clinical roles.
Broad-based support for a PHM subspecialty has not been demonstrated.
While approximately 40 pediatric hospitalists originated the PHM certification petition, we have not seen clear support for subspecialty certification from the community. PHM certification runs the risk of alienating the general pediatrics community, as many outpatient pediatricians continue to care for their patients in the inpatient setting. Furthermore, at tertiary-care medical centers, pediatric subspecialists often serve as hospitalists, yet this stakeholder group has not entered into this conversation. Importantly, the Association of Pediatric Program Directors (APPD) did not endorse this proposal. Many of the APPD members were quite concerned about the harm this certification could cause. While the APA Board and the AAP Board of Directors support PHM subspecialty certification, it is not clear that the rank-and-file members do. The Society of Hospital Medicine did not support or oppose certification. In an era of controversy surrounding certification requirements, prior to making a decision that will alter the direction of an entire field and impact all future residency graduates interested in entering that field, we should ensure there is broad-based support for this decision.
An alternative path has already been established and validated.
A more prudent, cost-effective, and universally acceptable approach would be to follow in the footsteps of the American Board of Internal Medicine (ABIM) and American Board of Family Medicine (ABFM) in establishing a Focused Practice in Pediatric Hospital Medicine program. This approach respects the unique body of knowledge required of those who care for hospitalized children while maintaining the required flexibility to nurture and help to mature existing training pipelines. Core hospital medicine skills should be further honed through residency curricular changes and faculty development efforts, while hospital-based physicians interested in developing niche skills could still do so via already existing fellowships.
When it comes to pediatric hospital medicine, first, do no harm.
Pediatric hospitalists are inpatient generalists by training and clinical approach. Our practices vary from large academic medical centers with every imaginable subspecialty consult service available to remote rural settings that require hospitalists to possess unique and specific skills. Some pediatric hospitalists participate in newborn care, some perform sedations, and some perform a variety of diagnostic and therapeutic procedures. The current system is meeting the needs of the vast majority of our PHM community. Changes to the residency curriculum that are already under way can address any clinical and quality improvement gaps. More than enough PHM fellowships are available to those who choose to pursue them. The public is not requesting reassurance, and the field is already advancing at a rapid rate both clinically and scholarly. Subspecialty recognition is not necessary and will likely lead to negative unintended consequences. Given the financial constraints on our current system and the need for pediatric hospitalists to be stewards of high-value care, we should make collective decisions that will clearly benefit our patients and health system. As medical professionals, our priority should always be first, do no harm.
Weijen W. Chang, MD, is chief of the Division of Pediatric Hospital Medicine at Baystate Children’s Hospital and associate professor of pediatrics at the University of Massachusetts Medical School.
Leonard Samuel Feldman, MD, is director of the Medicine-Pediatrics Urban Health Residency Program and associate professor of medicine and pediatrics at Johns Hopkins School of Medicine.
Bradley Monash, MD, is associate chief of medicine at University of California, San Francisco and assistant clinical professor of medicine and pediatrics at UCSF School of Medicine.
Archna Eniasivam, MD, is assistant clinical professor of medicine at UCSF School of Medicine.
References
- Chen C, Eagle S. “Should Pediatric HM Pursue Subspecialty Certification, Required Fellowship Training?” The Hospitalist. July 31, 2012
- Results and Data: Specialties Matching Service 2016 Appointment Year. National Resident Matching Program website. Accessed May 15, 2016.
- Medscape Pediatrician Compensation Report 2015. Medscape website. Accessed April 29, 2016.
- Rochlin JM, Simon HK. Does fellowship pay: what is the long-term financial impact of subspecialty training in pediatrics? Pediatrics. 2001;127(2):254-260.
- Asch DA, Nicholson S, Vujicic M. Are we in a medical education bubble market? N Engl J Med. 2013;369(21):1973-1975.
- O’Toole JK, Friedland AR, Gonzaga AM, et al. The practice patterns of recently graduated internal medicine-pediatric hospitalists. Hosp Pediatr. 2015;5(6):309-314.
- Society of Hospital Medicine: Survey of Med-Peds Physicians about PHM Certification. May 2014 (unpublished).
- Goodman DM, Hall M, Levin A, et al. Adults with chronic health conditions originating in childhood: inpatient experience in children’s hospitals. Pediatrics. 2011;128(1):5-13.
- Freed GL, Dunham KM, Research Advisory Committee of the American Board of P. Pediatric hospitalists: training, current practice, and career goals. J Hosp Med. 2009;4(3):179-186.
- Donnelly MJ, Lubrano L, Radabaugh CL, Lukela MP, Friedland AR, Ruch-Ross HS. The med-peds hospitalist workforce: results from the American Academy of Pediatrics Workforce Survey. Hosp Pediatr. 2015;5(11):574-579.
The Joint Council of Pediatric Hospital Medicine (JCPHM), successor to the Strategic Planning (STP) Committee, recently recommended submitting a petition for two-year pediatric hospital medicine (PHM) fellowship certification to the American Board of Pediatrics (ABP), which was completed in 2014. In December 2015, the ABP Board of Directors voted to (1) approve the proposal for a two-year PHM fellowship incorporating scholarly activity with the provision that entrustable professional activities (EPAs) be used as the framework for assessing competencies and (2) not require those who achieve and maintain PHM certification to maintain general pediatrics certification. The proposal for certification of a two-year PHM fellowship will now be submitted to the American Board of Medical Specialties (ABMS). Concerns regarding the formal certification of PHM as an ABMS-recognized subspecialty have been raised by many stakeholders, including community pediatric hospitalists, pediatric residency program directors, and med-peds physicians.
We feel that the “first, do no harm” guiding principle seems to have been forgotten by the ABP as it attempts to formalize the training of pediatric hospitalists. In December 2015, the ABP voted in favor of a two-year ACGME-accredited PHM fellowship. The intent was to “assure the best care of hospitalized children,” “assure the public,” “accelerate improvements and innovation in quality improvement,” and “raise the level of care of all hospitalized children by establishing best practices in clinical care.” To be clear, these goals are shared by all of us (although there is no indication that the public is seeking additional assurance). Prior to launching broad-scale, time-intensive, and financially costly initiatives, we should ensure that our efforts would achieve—rather than obstruct—their intended aims. In addition to a lack of evidence supporting that subspecialty certification will advance our path toward achieving these goals, there are numerous reasons a required PHM fellowship is unnecessary and potentially even harmful to the hospitalist workforce. The negative unintended consequences need to be weighed heavily.
We have found no data to support that children would receive inferior inpatient care from pediatric hospitalists due to lack of formal certification. Hospital medicine physicians are paving the way in quality improvement, high-value care, medical education, palliative care, and global health, supported in part through training in various non-accredited hospital medicine fellowships. There is nothing stopping pediatric hospitalists from establishing and disseminating best practices in clinical care. Hospitalists are already making strides in providing high-quality care at low costs, as demonstrated by the abundant PHM scholarly work described in the ABP application to the ABMS. The alleged problem of needing to build trust within the community is yet to be demonstrated, as we have leaders at local, regional, and national levels. The chief medical officer of the Centers for Medicare & Medicaid Services is a hospitalist as is our surgeon general. Hospital medicine is the fastest-growing specialty in the history of medicine,1 and we should seek to propel rather than fetter our future colleagues.
Below are our reasons for opposing this formal certification.
We already have a fellowship system.
As we all know, advanced training opportunities already exist for those interested in pursuing extra research and quality improvement training. Similar to other pediatric subspecialty fellowships, these PHM fellowships are undersubscribed (20% of PHM fellowships did not fill in 2016),2 with the majority of graduating pediatric residents transitioning to hospitalists opting not to pursue fellowship training. We should continue to let graduating pediatric residents vote with their feet without the undue influence of subspecialty certification.
Subspecialization has opportunity costs that may reduce the PHM pipeline.
Even if we assume an adequate number of fellowship programs could be developed and funded, our fear is that the decision to turn PHM into an accredited subspecialty could paradoxically reduce the pipeline of inpatient providers. Residency is already a three- to four-year endeavor (pediatrics and med-peds) that is poorly compensated and time-intensive. In the absence of evidence supporting the value of additional training, tacking on another two years seems unreasonable in the face of the student loan debt crisis, reduced compensation, and lost time for career advancement. These are significant opportunity costs. While most specialties lead to a significant pay raise to compensate for the added training time, pediatrics remains the lowest-paid physician specialty.3 Should PHM follow the trend of most pediatric subspecialties, pursuit of fellowship training would be a negative financial decision for residency graduates.4 For the health system, increasing debt-to-income ratios runs the risk of creating a medical education bubble market.5
More than 25% of med-peds graduates pursue careers in hospital medicine, a percentage that continues to grow, accounting for more than 100 new hospitalists per year.6 As a result, med-peds-trained hospitalists constitute more than 10% of the pediatric hospitalist workforce.6 Requiring PHM fellowship training may reduce this crucial pipeline of practitioners. In a 2014 unpublished survey of 225 med-peds practitioners, 78% of residents and 96% of attendings responded that they would not consider pursuing an ACGME-accredited PHM fellowship.7 This is compounded by a lack of parity with the practice of adult hospital medicine both in compensation and required training and is heightened by the fact that the training in question does not incorporate care for adult patients. There is clear consensus by 96% of med-peds hospitalists that the creation of an ACGME-certified PHM subspecialty will negatively affect the likelihood of med-peds providers pursuing PHM.7
Certification will pose a potential risk to specific patient populations.
We are also concerned that a reduced PHM workforce could disproportionately impact young adults with special healthcare needs and those children cared for in rural or community-based hospitals. Med-peds training equips providers to care for children with chronic diseases that then transition into adulthood; more than 25% provide care for young adults with special healthcare needs.6 With the increasing number of children with chronic health conditions surviving into adulthood,8 med-peds hospitalists serve essential roles in providing care and coordination for this vulnerable population. Furthermore, hospital medicine groups in medical systems that cannot support a full-time categorical pediatric hospitalist tend to employ med-peds physicians or family practitioners. Concerns with PHM certification are thus extended to those family medicine physicians who practice PHM.
Pediatric residency trains pediatricians in inpatient care.
We feel that the decision to move forward on PHM subspecialty certification calls into question the value of pediatric residency training. There is no evidence that clinical inpatient training in pediatrics residency is inadequate. If one leaves residency trained to do anything, it is practicing hospital medicine. A significant portion of residency takes place inpatient, both on wards and in the intensive care units. The 2009 ABP Foundation–funded study of PHM reported that 94% of pediatric hospitalist respondents rated their training in general clinical skills during residency as fully adequate, 85% rated their training in communication skills as fully adequate, and 73% did not believe any additional training beyond residency should be required.9 With respect to med-peds graduates, more than 90% feel equipped to care for children and adults upon residency completion.10 If the ABMS carries forward with this decision, the only clinical work one would be “certified” to do after residency is primary care. However, after completion of residency training, most of us feel at least as comfortable, if not more comfortable, caring for children in the inpatient setting.
Primary care should require subspecialty certification as well.
Furthermore, the decision to create a certified subspecialty begs the question as to why fellowship should not be mandated for those entering the field of primary care. Does the field of primary care not require research to move it forward? Does the field of primary care not require providers who can adeptly apply quality improvement methodologies to improve primary-care delivery? Does the public not require the same type of assurance? By these measures, primary care should require subspecialty certification as well. These arguments could easily be construed as an indictment of residency training.
The target should be residency training.
The PHM ABMS application describes a clinical curriculum consisting of eight core clinical rotations in various settings. That small number emphasizes the fact that extra clinical training is really not needed and that we do not require a complete overhaul of the current training system. The skills in question for the accredited PHM fellowship include communication, negotiation, leadership, quality improvement, pain management, sedation, procedures, transport, billing/coding, autonomous decision making, and scholarly practice. Are most of these not skills that we should foster in all practicing pediatricians? If graduating pediatric residents lack competence in core pediatric skills (e.g., communication, pain management, autonomous decision making), we should target improvements in residency education rather than require years of further training. Pediatrics residency training already requires training in quality improvement and is incorporating “tracks” that target areas of perceived deficiency. Those physicians who actually require specialized hospital-based skills (e.g., sedation, procedures, and transport) could receive core training during residency (e.g., through PHM tracks or electives) and further hone these skills through faculty development efforts. While non-PhD researchers may benefit from additional training in research methodologies, this training comes at the expense of time spent caring for patients on the wards and should not be required training for the majority of pediatric hospitalists pursuing purely clinical roles.
Broad-based support for a PHM subspecialty has not been demonstrated.
While approximately 40 pediatric hospitalists originated the PHM certification petition, we have not seen clear support for subspecialty certification from the community. PHM certification runs the risk of alienating the general pediatrics community, as many outpatient pediatricians continue to care for their patients in the inpatient setting. Furthermore, at tertiary-care medical centers, pediatric subspecialists often serve as hospitalists, yet this stakeholder group has not entered into this conversation. Importantly, the Association of Pediatric Program Directors (APPD) did not endorse this proposal. Many of the APPD members were quite concerned about the harm this certification could cause. While the APA Board and the AAP Board of Directors support PHM subspecialty certification, it is not clear that the rank-and-file members do. The Society of Hospital Medicine did not support or oppose certification. In an era of controversy surrounding certification requirements, prior to making a decision that will alter the direction of an entire field and impact all future residency graduates interested in entering that field, we should ensure there is broad-based support for this decision.
An alternative path has already been established and validated.
A more prudent, cost-effective, and universally acceptable approach would be to follow in the footsteps of the American Board of Internal Medicine (ABIM) and American Board of Family Medicine (ABFM) in establishing a Focused Practice in Pediatric Hospital Medicine program. This approach respects the unique body of knowledge required of those who care for hospitalized children while maintaining the required flexibility to nurture and help to mature existing training pipelines. Core hospital medicine skills should be further honed through residency curricular changes and faculty development efforts, while hospital-based physicians interested in developing niche skills could still do so via already existing fellowships.
When it comes to pediatric hospital medicine, first, do no harm.
Pediatric hospitalists are inpatient generalists by training and clinical approach. Our practices vary from large academic medical centers with every imaginable subspecialty consult service available to remote rural settings that require hospitalists to possess unique and specific skills. Some pediatric hospitalists participate in newborn care, some perform sedations, and some perform a variety of diagnostic and therapeutic procedures. The current system is meeting the needs of the vast majority of our PHM community. Changes to the residency curriculum that are already under way can address any clinical and quality improvement gaps. More than enough PHM fellowships are available to those who choose to pursue them. The public is not requesting reassurance, and the field is already advancing at a rapid rate both clinically and scholarly. Subspecialty recognition is not necessary and will likely lead to negative unintended consequences. Given the financial constraints on our current system and the need for pediatric hospitalists to be stewards of high-value care, we should make collective decisions that will clearly benefit our patients and health system. As medical professionals, our priority should always be first, do no harm.
Weijen W. Chang, MD, is chief of the Division of Pediatric Hospital Medicine at Baystate Children’s Hospital and associate professor of pediatrics at the University of Massachusetts Medical School.
Leonard Samuel Feldman, MD, is director of the Medicine-Pediatrics Urban Health Residency Program and associate professor of medicine and pediatrics at Johns Hopkins School of Medicine.
Bradley Monash, MD, is associate chief of medicine at University of California, San Francisco and assistant clinical professor of medicine and pediatrics at UCSF School of Medicine.
Archna Eniasivam, MD, is assistant clinical professor of medicine at UCSF School of Medicine.
References
- Chen C, Eagle S. “Should Pediatric HM Pursue Subspecialty Certification, Required Fellowship Training?” The Hospitalist. July 31, 2012
- Results and Data: Specialties Matching Service 2016 Appointment Year. National Resident Matching Program website. Accessed May 15, 2016.
- Medscape Pediatrician Compensation Report 2015. Medscape website. Accessed April 29, 2016.
- Rochlin JM, Simon HK. Does fellowship pay: what is the long-term financial impact of subspecialty training in pediatrics? Pediatrics. 2001;127(2):254-260.
- Asch DA, Nicholson S, Vujicic M. Are we in a medical education bubble market? N Engl J Med. 2013;369(21):1973-1975.
- O’Toole JK, Friedland AR, Gonzaga AM, et al. The practice patterns of recently graduated internal medicine-pediatric hospitalists. Hosp Pediatr. 2015;5(6):309-314.
- Society of Hospital Medicine: Survey of Med-Peds Physicians about PHM Certification. May 2014 (unpublished).
- Goodman DM, Hall M, Levin A, et al. Adults with chronic health conditions originating in childhood: inpatient experience in children’s hospitals. Pediatrics. 2011;128(1):5-13.
- Freed GL, Dunham KM, Research Advisory Committee of the American Board of P. Pediatric hospitalists: training, current practice, and career goals. J Hosp Med. 2009;4(3):179-186.
- Donnelly MJ, Lubrano L, Radabaugh CL, Lukela MP, Friedland AR, Ruch-Ross HS. The med-peds hospitalist workforce: results from the American Academy of Pediatrics Workforce Survey. Hosp Pediatr. 2015;5(11):574-579.
Supportive care isn’t palliative care, speaker says
Photo courtesy of NCI
NEW YORK—Two presentations at the NCCN 11th Annual Congress: Hematologic Malignancies addressed the importance of supportive care in the treatment of patients with T-cell lymphomas and multiple myeloma.
Erin Kopp, ACNP-BC, of City of Hope Comprehensive Cancer Center in Duarte, California, reminded the audience that supportive care is not palliative care.
Supportive care “complements critical care so that the patient doesn’t have to stop treatment,” she said.
Kopp focused primarily on cutaneous T-cell lymphoma (CTCL) in her presentation, with some recommendations for managing tumor lysis syndrome in patients undergoing therapy for peripheral T-cell lymphoma (PTCL).
And Kathleen Colson, RN, of the Dana-Farber Cancer Institute in Boston, Massachusetts, discussed supportive care for patients with multiple myeloma (MM).
T-cell lymphomas
Most T-cell lymphoma patients will require multiple treatment regimens over their lifetimes, Kopp said. And each type of therapy brings different treatment-related toxicities, which in turn require distinct supportive care measures to manage them.
Topical steroids, for example, may cause skin-thinning, stretch marks, skin irritation, and may be absorbed systemically when a high-potency formulation is used. So the lowest potency steroid that provides the maximum efficacy should be utilized. Practitioners should assess systemic effects if high-potency steroids are utilized.
Topical nitrogen mustard can darken the skin, which often occurs as the lesions resolve, Kopp said. She cautioned that patients experiencing hyperpigmentation often stop treatment without telling their physicians.
So Kopp recommends appropriate patient education to go along with the treatment. With nitrogen mustard, this includes applying a thin layer only to the affected areas and refrigerating the topical ointment to increase soothing.
Topical retinoids may cause redness, itching, warmth, swelling, burning, scaling or other irritation. They also increase the patients’ sensitivity to light. Kopp indicated that for the first week, topical retinoids should be applied once every other day and then titrated as tolerated.
Phototherapy with PUVA or narrowband-UVB may also cause itching, in addition to skin burn, nausea, and other side effects.
“Do not underestimate emollients,” Kopp said, for relief of pruritus. And skin baths with bleach significantly decrease infections that may result from treatment.
Systemic therapy with retinoids, interferon, cytotoxic agents, monoclonal antibodies, and HDAC inhibitors may also cause distinct reactions. For example, the retinoid bexarotene may cause primary hypothyroidism and major lipid abnormalities. Therefore, TSH, free T4, and triglycerides should be monitored every 8 weeks.
Cytotoxic agents such as pralatrexate and methotrexate significantly increase the risk for infection.
Monoclonal antibodies can reactivate previous viral infection, induce tumor lysis syndrome (TLS), and cause progressive multifocal leukoencephalopathy.
HDAC inhibitors such as vorinostat and romidepsin may cause QT prolongation and myelosuppression, among other side effects.
Practitioners need to assess symptoms and side effects thoroughly and often and provide options for supportive care management.
PTCL is an under recognized risk for TLS, Kopp said.
“It should be addressed aggressively,” she added, with monitoring and correction of electrolyte imbalance.
Patients should be rigorously hydrated, and allopurinol should be administered 2-3 days prior to treatment and adjusted based on the patient response and uric acid level.
Multiple myeloma
Colson described supportive care as “keeping all the pieces together.” MM itself can result in a broad spectrum of clinical manifestations, including renal compromise, neuropathy, infection, hypercalcemia, bone pain, lytic lesions, and anemia.
To preserve renal health, patients should drink plenty of water and avoid certain medications, such as IV contrast and nonsteroidal anti-inflammatory drugs.
Peripheral neuropathy can be a side effect of treatment or be caused by the disease itself. Bortezomib-related neuropathy can be reduced with weekly instead of twice weekly dosing and with subcutaneous administration.
Duration of higher doses of thalidomide treatment also impacts neuropathy. Carfilzomib and pomalidomide have a lower incidence of neuropathy.
Myeloma patients have a 15-fold increased risk of recurrent infection because white blood cell production is decreased and the normal immune role of plasma cells is lost.
Supportive therapy includes antibiotics and IVIG therapy. In addition, Colson said pneumonia and influenza vaccines should be considered, as well as prophylaxis for Pneumocystis carinii, herpes zoster, and fungal infections.
Hypercalcemia results from increased bone deterioration. Symptoms include loss of appetite, fatigue, vomiting, muscle weakness, confusion, constipation, increased thirst, and increased urine output. Supportive measures are adequate hydration, furosemide, bisphosphonates, and steroids.
Supportive therapy for bone pain includes bisphosphonates, radiation, pain medication, kyphoplasty, and vertebroplasty. Bisphosphonates, such as pamidronate and zoledronic acid, inhibit bone destruction and are recommended for all myeloma patients with bone disease. However, patients should be monitored for renal dysfunction and osteonecrosis of the jaw when taking bisphosphonates.
And Colson advises, “Hold bisphosphonate therapy if the patient needs a root canal or extraction.” Additionally, dental implants are not recommended for MM patients.
Anemia is another common presenting symptom of myeloma and may also be a result of decreased kidney function. Colson said the use of red blood cell supplements may be used with caution to ameliorate the symptom. Red blood cell transfusion may be considered and a reduction in the medication dose may be required.
MM is a hypercoagulable disease, and measures should be taken to avoid deep vein thrombosis (DVT) and pulmonary embolism (PE). Patients should wear anti-embolism stockings, exercise regularly, take low-dose aspirin, and move about frequently instead of sitting for long periods. Immunomodulatory medications may be adjusted to reduce the risk of a blot clot forming.
Infusion-related reactions are also a risk of therapy, and symptoms of a reaction need to be managed immediately and appropriately, with antihistamines, corticosteroids, interruption of the infusion, slowing of the infusion rate after symptom resolution, and permanent discontinuation in the case of grade 4 reactions.
The potential for longer survival exists, Colson said, due to appropriate supportive care measures.
Photo courtesy of NCI
NEW YORK—Two presentations at the NCCN 11th Annual Congress: Hematologic Malignancies addressed the importance of supportive care in the treatment of patients with T-cell lymphomas and multiple myeloma.
Erin Kopp, ACNP-BC, of City of Hope Comprehensive Cancer Center in Duarte, California, reminded the audience that supportive care is not palliative care.
Supportive care “complements critical care so that the patient doesn’t have to stop treatment,” she said.
Kopp focused primarily on cutaneous T-cell lymphoma (CTCL) in her presentation, with some recommendations for managing tumor lysis syndrome in patients undergoing therapy for peripheral T-cell lymphoma (PTCL).
And Kathleen Colson, RN, of the Dana-Farber Cancer Institute in Boston, Massachusetts, discussed supportive care for patients with multiple myeloma (MM).
T-cell lymphomas
Most T-cell lymphoma patients will require multiple treatment regimens over their lifetimes, Kopp said. And each type of therapy brings different treatment-related toxicities, which in turn require distinct supportive care measures to manage them.
Topical steroids, for example, may cause skin-thinning, stretch marks, skin irritation, and may be absorbed systemically when a high-potency formulation is used. So the lowest potency steroid that provides the maximum efficacy should be utilized. Practitioners should assess systemic effects if high-potency steroids are utilized.
Topical nitrogen mustard can darken the skin, which often occurs as the lesions resolve, Kopp said. She cautioned that patients experiencing hyperpigmentation often stop treatment without telling their physicians.
So Kopp recommends appropriate patient education to go along with the treatment. With nitrogen mustard, this includes applying a thin layer only to the affected areas and refrigerating the topical ointment to increase soothing.
Topical retinoids may cause redness, itching, warmth, swelling, burning, scaling or other irritation. They also increase the patients’ sensitivity to light. Kopp indicated that for the first week, topical retinoids should be applied once every other day and then titrated as tolerated.
Phototherapy with PUVA or narrowband-UVB may also cause itching, in addition to skin burn, nausea, and other side effects.
“Do not underestimate emollients,” Kopp said, for relief of pruritus. And skin baths with bleach significantly decrease infections that may result from treatment.
Systemic therapy with retinoids, interferon, cytotoxic agents, monoclonal antibodies, and HDAC inhibitors may also cause distinct reactions. For example, the retinoid bexarotene may cause primary hypothyroidism and major lipid abnormalities. Therefore, TSH, free T4, and triglycerides should be monitored every 8 weeks.
Cytotoxic agents such as pralatrexate and methotrexate significantly increase the risk for infection.
Monoclonal antibodies can reactivate previous viral infection, induce tumor lysis syndrome (TLS), and cause progressive multifocal leukoencephalopathy.
HDAC inhibitors such as vorinostat and romidepsin may cause QT prolongation and myelosuppression, among other side effects.
Practitioners need to assess symptoms and side effects thoroughly and often and provide options for supportive care management.
PTCL is an under recognized risk for TLS, Kopp said.
“It should be addressed aggressively,” she added, with monitoring and correction of electrolyte imbalance.
Patients should be rigorously hydrated, and allopurinol should be administered 2-3 days prior to treatment and adjusted based on the patient response and uric acid level.
Multiple myeloma
Colson described supportive care as “keeping all the pieces together.” MM itself can result in a broad spectrum of clinical manifestations, including renal compromise, neuropathy, infection, hypercalcemia, bone pain, lytic lesions, and anemia.
To preserve renal health, patients should drink plenty of water and avoid certain medications, such as IV contrast and nonsteroidal anti-inflammatory drugs.
Peripheral neuropathy can be a side effect of treatment or be caused by the disease itself. Bortezomib-related neuropathy can be reduced with weekly instead of twice weekly dosing and with subcutaneous administration.
Duration of higher doses of thalidomide treatment also impacts neuropathy. Carfilzomib and pomalidomide have a lower incidence of neuropathy.
Myeloma patients have a 15-fold increased risk of recurrent infection because white blood cell production is decreased and the normal immune role of plasma cells is lost.
Supportive therapy includes antibiotics and IVIG therapy. In addition, Colson said pneumonia and influenza vaccines should be considered, as well as prophylaxis for Pneumocystis carinii, herpes zoster, and fungal infections.
Hypercalcemia results from increased bone deterioration. Symptoms include loss of appetite, fatigue, vomiting, muscle weakness, confusion, constipation, increased thirst, and increased urine output. Supportive measures are adequate hydration, furosemide, bisphosphonates, and steroids.
Supportive therapy for bone pain includes bisphosphonates, radiation, pain medication, kyphoplasty, and vertebroplasty. Bisphosphonates, such as pamidronate and zoledronic acid, inhibit bone destruction and are recommended for all myeloma patients with bone disease. However, patients should be monitored for renal dysfunction and osteonecrosis of the jaw when taking bisphosphonates.
And Colson advises, “Hold bisphosphonate therapy if the patient needs a root canal or extraction.” Additionally, dental implants are not recommended for MM patients.
Anemia is another common presenting symptom of myeloma and may also be a result of decreased kidney function. Colson said the use of red blood cell supplements may be used with caution to ameliorate the symptom. Red blood cell transfusion may be considered and a reduction in the medication dose may be required.
MM is a hypercoagulable disease, and measures should be taken to avoid deep vein thrombosis (DVT) and pulmonary embolism (PE). Patients should wear anti-embolism stockings, exercise regularly, take low-dose aspirin, and move about frequently instead of sitting for long periods. Immunomodulatory medications may be adjusted to reduce the risk of a blot clot forming.
Infusion-related reactions are also a risk of therapy, and symptoms of a reaction need to be managed immediately and appropriately, with antihistamines, corticosteroids, interruption of the infusion, slowing of the infusion rate after symptom resolution, and permanent discontinuation in the case of grade 4 reactions.
The potential for longer survival exists, Colson said, due to appropriate supportive care measures.
Photo courtesy of NCI
NEW YORK—Two presentations at the NCCN 11th Annual Congress: Hematologic Malignancies addressed the importance of supportive care in the treatment of patients with T-cell lymphomas and multiple myeloma.
Erin Kopp, ACNP-BC, of City of Hope Comprehensive Cancer Center in Duarte, California, reminded the audience that supportive care is not palliative care.
Supportive care “complements critical care so that the patient doesn’t have to stop treatment,” she said.
Kopp focused primarily on cutaneous T-cell lymphoma (CTCL) in her presentation, with some recommendations for managing tumor lysis syndrome in patients undergoing therapy for peripheral T-cell lymphoma (PTCL).
And Kathleen Colson, RN, of the Dana-Farber Cancer Institute in Boston, Massachusetts, discussed supportive care for patients with multiple myeloma (MM).
T-cell lymphomas
Most T-cell lymphoma patients will require multiple treatment regimens over their lifetimes, Kopp said. And each type of therapy brings different treatment-related toxicities, which in turn require distinct supportive care measures to manage them.
Topical steroids, for example, may cause skin-thinning, stretch marks, skin irritation, and may be absorbed systemically when a high-potency formulation is used. So the lowest potency steroid that provides the maximum efficacy should be utilized. Practitioners should assess systemic effects if high-potency steroids are utilized.
Topical nitrogen mustard can darken the skin, which often occurs as the lesions resolve, Kopp said. She cautioned that patients experiencing hyperpigmentation often stop treatment without telling their physicians.
So Kopp recommends appropriate patient education to go along with the treatment. With nitrogen mustard, this includes applying a thin layer only to the affected areas and refrigerating the topical ointment to increase soothing.
Topical retinoids may cause redness, itching, warmth, swelling, burning, scaling or other irritation. They also increase the patients’ sensitivity to light. Kopp indicated that for the first week, topical retinoids should be applied once every other day and then titrated as tolerated.
Phototherapy with PUVA or narrowband-UVB may also cause itching, in addition to skin burn, nausea, and other side effects.
“Do not underestimate emollients,” Kopp said, for relief of pruritus. And skin baths with bleach significantly decrease infections that may result from treatment.
Systemic therapy with retinoids, interferon, cytotoxic agents, monoclonal antibodies, and HDAC inhibitors may also cause distinct reactions. For example, the retinoid bexarotene may cause primary hypothyroidism and major lipid abnormalities. Therefore, TSH, free T4, and triglycerides should be monitored every 8 weeks.
Cytotoxic agents such as pralatrexate and methotrexate significantly increase the risk for infection.
Monoclonal antibodies can reactivate previous viral infection, induce tumor lysis syndrome (TLS), and cause progressive multifocal leukoencephalopathy.
HDAC inhibitors such as vorinostat and romidepsin may cause QT prolongation and myelosuppression, among other side effects.
Practitioners need to assess symptoms and side effects thoroughly and often and provide options for supportive care management.
PTCL is an under recognized risk for TLS, Kopp said.
“It should be addressed aggressively,” she added, with monitoring and correction of electrolyte imbalance.
Patients should be rigorously hydrated, and allopurinol should be administered 2-3 days prior to treatment and adjusted based on the patient response and uric acid level.
Multiple myeloma
Colson described supportive care as “keeping all the pieces together.” MM itself can result in a broad spectrum of clinical manifestations, including renal compromise, neuropathy, infection, hypercalcemia, bone pain, lytic lesions, and anemia.
To preserve renal health, patients should drink plenty of water and avoid certain medications, such as IV contrast and nonsteroidal anti-inflammatory drugs.
Peripheral neuropathy can be a side effect of treatment or be caused by the disease itself. Bortezomib-related neuropathy can be reduced with weekly instead of twice weekly dosing and with subcutaneous administration.
Duration of higher doses of thalidomide treatment also impacts neuropathy. Carfilzomib and pomalidomide have a lower incidence of neuropathy.
Myeloma patients have a 15-fold increased risk of recurrent infection because white blood cell production is decreased and the normal immune role of plasma cells is lost.
Supportive therapy includes antibiotics and IVIG therapy. In addition, Colson said pneumonia and influenza vaccines should be considered, as well as prophylaxis for Pneumocystis carinii, herpes zoster, and fungal infections.
Hypercalcemia results from increased bone deterioration. Symptoms include loss of appetite, fatigue, vomiting, muscle weakness, confusion, constipation, increased thirst, and increased urine output. Supportive measures are adequate hydration, furosemide, bisphosphonates, and steroids.
Supportive therapy for bone pain includes bisphosphonates, radiation, pain medication, kyphoplasty, and vertebroplasty. Bisphosphonates, such as pamidronate and zoledronic acid, inhibit bone destruction and are recommended for all myeloma patients with bone disease. However, patients should be monitored for renal dysfunction and osteonecrosis of the jaw when taking bisphosphonates.
And Colson advises, “Hold bisphosphonate therapy if the patient needs a root canal or extraction.” Additionally, dental implants are not recommended for MM patients.
Anemia is another common presenting symptom of myeloma and may also be a result of decreased kidney function. Colson said the use of red blood cell supplements may be used with caution to ameliorate the symptom. Red blood cell transfusion may be considered and a reduction in the medication dose may be required.
MM is a hypercoagulable disease, and measures should be taken to avoid deep vein thrombosis (DVT) and pulmonary embolism (PE). Patients should wear anti-embolism stockings, exercise regularly, take low-dose aspirin, and move about frequently instead of sitting for long periods. Immunomodulatory medications may be adjusted to reduce the risk of a blot clot forming.
Infusion-related reactions are also a risk of therapy, and symptoms of a reaction need to be managed immediately and appropriately, with antihistamines, corticosteroids, interruption of the infusion, slowing of the infusion rate after symptom resolution, and permanent discontinuation in the case of grade 4 reactions.
The potential for longer survival exists, Colson said, due to appropriate supportive care measures.
Technology underused in psychiatry, but changes are ahead
Editors’ Note: The intent of this new column is to discuss topics at the intersection of technology and psychiatry – “Techiatry.” We’ve enlisted two leaders in this field to write for the column. Steven R. Daviss, MD, DFAPA (@HITshrink), is the chief medical informatics officer at M3 Information and chairs the American Psychiatric Association’s Committee on Mental Health Information Technology. James (Jay) H. Shore, MD, MPH, chairs the APA Committee on Telepsychiatry, is director of telemedicine at the Helen & Arthur E. Johnson Depression Center, and an associate professor of psychiatry at the University of Colorado at Denver, Aurora. Email them at cpnews@frontlinemedcom.com.
Medicine is late to the game when it comes to technology, specifically information technology. And psychiatry, even more so. Jay will talk in future columns about early use of telepsychiatry in the 1960s and since. But here in 2016, a surprisingly low percentage of us are using it to deliver care, despite the fact that half of the counties in the United States lack psychiatrists – and telemedicine has been shown to improve access to care.
Nonetheless, telemedicine and other uses of technology across all specialties is growing quickly, as usability, mobile technology, economics, and policy-making all converge. The integration of mental health care (including addiction treatment) with primary care is one of the driving forces in expanding access to the expertise that physicians trained in psychiatry possess. The collaborative care model of integrated care has the most evidence, making regular access to psychiatric consultants a weekly event.
This exchange of information and knowledge between primary care and psychiatry is being formally incentivized by the Centers for Medicare & Medicaid Services (CMS) with proposed new codes to pay for this exchange, while the American Psychiatric Association has received a large grant from CMS to train 10% of its members in this care model.
Information technology is fundamental to this care model, because the efficient exchange of clinical information is important to optimize the capabilities and comprehensiveness of the clinical decision support provided by the psychiatrist to the primary care team.
As the team members learn what questions are asked and how the consultant arrives at her recommendations, they will become better at making these decisions on their own. They will learn how psychiatrists think and make decisions, weighing other medical, personal, social, family, and logistical aspects to guide the decision making process with the patient.
While this model of care is certainly helpful in expanding access to psychiatric expertise, there are other ways to achieve this access to expert knowledge. One of them is through electronic clinical decision support (CDS) tools. These are tools that apply clinical rules, algorithms, and other knowledge discovery processes to the information within the electronic health record (EHR) about a patient, with the goal of assessing and filling gaps in available patient information so that a set of possible recommendations can be delivered to the clinician.
Knowledge-based CDS tools apply clinical knowledge that comes from practice guidelines, textbooks, and the medical literature to what is known about the patient. The simplest CDS tool might be a rule that says, “IF patient is on lithium for bipolar disorder AND patient has current mood symptoms AND has not had a recent lithium level, THEN check a lithium level.” Applying and coding this rule into an EHR is fairly straightforward. A much more complex CDS tool could help the clinician think through the question, “What should I do next for this 32yo woman with hypertension and moderate depression who is symptomatic?”
Non–knowledge-based CDS tools use machine learning techniques, like neural networks, genetic algorithms, and natural language processing, to “learn” new clinical rules by going through a training process that inputs a large amount of clinical data and uses experts to “train” the system. Such a system was recently developed by IBM Watson and Memorial Sloan-Kettering Cancer Center to aid in developing recommendations for treating oncology patients.
The APA recently formed the CDS Product Workgroup (which I chair) to explore the feasibility of developing an electronic clinical decision support (CDS) tool that leverages the information and knowledge within the APA’s series of Practice Guidelines, DSM library, and other reference materials. This group will consider the necessary important clinical information sources – such as EHRs, personal health records, health information exchanges, claims and utilization data, patient generated data, mobile health apps, and clinical registries – from which to analyze patient-specific data and produce a set of ranked, evidence-based, annotated clinical suggestions.
The goal is to develop a CDS tool that is designed in a manner that ultimately benefits patients being treated by primary care practitioners, emergency practitioners, psychiatrists, and other medical specialists who treat patients with mental health and substance use disorders. Tools like this are being developed now in many specialties. Given the vast amount of psychiatric expertise within the APA, as well as the trove of content that exists within the publishing arm of the APA, the opportunity to make this more broadly available to medical practitioners is one that demands consideration.
Such an undertaking would require substantial time and commitment of resources, thus the task of the workgroup is to understand the pros and cons of developing this tool, and to explore its feasibility, including various business models to ensure that this CDS tool becomes a maintainable and sustainable product.
Bringing our expertise to the primary care settings, where most of our patients are treated, should greatly benefit the care of our patients, whether this is through collaborative care, clinical decision support, or telepsychiatry. In the same way that many people with diabetes do not require an endocrinologist, many with mental health conditions do not require a psychiatrist. Yet, primary care practitioners would certainly benefit from more help from us.
I will update readers of this column on our workgroup’s progress at the end of the year.
Editors’ Note: The intent of this new column is to discuss topics at the intersection of technology and psychiatry – “Techiatry.” We’ve enlisted two leaders in this field to write for the column. Steven R. Daviss, MD, DFAPA (@HITshrink), is the chief medical informatics officer at M3 Information and chairs the American Psychiatric Association’s Committee on Mental Health Information Technology. James (Jay) H. Shore, MD, MPH, chairs the APA Committee on Telepsychiatry, is director of telemedicine at the Helen & Arthur E. Johnson Depression Center, and an associate professor of psychiatry at the University of Colorado at Denver, Aurora. Email them at cpnews@frontlinemedcom.com.
Medicine is late to the game when it comes to technology, specifically information technology. And psychiatry, even more so. Jay will talk in future columns about early use of telepsychiatry in the 1960s and since. But here in 2016, a surprisingly low percentage of us are using it to deliver care, despite the fact that half of the counties in the United States lack psychiatrists – and telemedicine has been shown to improve access to care.
Nonetheless, telemedicine and other uses of technology across all specialties is growing quickly, as usability, mobile technology, economics, and policy-making all converge. The integration of mental health care (including addiction treatment) with primary care is one of the driving forces in expanding access to the expertise that physicians trained in psychiatry possess. The collaborative care model of integrated care has the most evidence, making regular access to psychiatric consultants a weekly event.
This exchange of information and knowledge between primary care and psychiatry is being formally incentivized by the Centers for Medicare & Medicaid Services (CMS) with proposed new codes to pay for this exchange, while the American Psychiatric Association has received a large grant from CMS to train 10% of its members in this care model.
Information technology is fundamental to this care model, because the efficient exchange of clinical information is important to optimize the capabilities and comprehensiveness of the clinical decision support provided by the psychiatrist to the primary care team.
As the team members learn what questions are asked and how the consultant arrives at her recommendations, they will become better at making these decisions on their own. They will learn how psychiatrists think and make decisions, weighing other medical, personal, social, family, and logistical aspects to guide the decision making process with the patient.
While this model of care is certainly helpful in expanding access to psychiatric expertise, there are other ways to achieve this access to expert knowledge. One of them is through electronic clinical decision support (CDS) tools. These are tools that apply clinical rules, algorithms, and other knowledge discovery processes to the information within the electronic health record (EHR) about a patient, with the goal of assessing and filling gaps in available patient information so that a set of possible recommendations can be delivered to the clinician.
Knowledge-based CDS tools apply clinical knowledge that comes from practice guidelines, textbooks, and the medical literature to what is known about the patient. The simplest CDS tool might be a rule that says, “IF patient is on lithium for bipolar disorder AND patient has current mood symptoms AND has not had a recent lithium level, THEN check a lithium level.” Applying and coding this rule into an EHR is fairly straightforward. A much more complex CDS tool could help the clinician think through the question, “What should I do next for this 32yo woman with hypertension and moderate depression who is symptomatic?”
Non–knowledge-based CDS tools use machine learning techniques, like neural networks, genetic algorithms, and natural language processing, to “learn” new clinical rules by going through a training process that inputs a large amount of clinical data and uses experts to “train” the system. Such a system was recently developed by IBM Watson and Memorial Sloan-Kettering Cancer Center to aid in developing recommendations for treating oncology patients.
The APA recently formed the CDS Product Workgroup (which I chair) to explore the feasibility of developing an electronic clinical decision support (CDS) tool that leverages the information and knowledge within the APA’s series of Practice Guidelines, DSM library, and other reference materials. This group will consider the necessary important clinical information sources – such as EHRs, personal health records, health information exchanges, claims and utilization data, patient generated data, mobile health apps, and clinical registries – from which to analyze patient-specific data and produce a set of ranked, evidence-based, annotated clinical suggestions.
The goal is to develop a CDS tool that is designed in a manner that ultimately benefits patients being treated by primary care practitioners, emergency practitioners, psychiatrists, and other medical specialists who treat patients with mental health and substance use disorders. Tools like this are being developed now in many specialties. Given the vast amount of psychiatric expertise within the APA, as well as the trove of content that exists within the publishing arm of the APA, the opportunity to make this more broadly available to medical practitioners is one that demands consideration.
Such an undertaking would require substantial time and commitment of resources, thus the task of the workgroup is to understand the pros and cons of developing this tool, and to explore its feasibility, including various business models to ensure that this CDS tool becomes a maintainable and sustainable product.
Bringing our expertise to the primary care settings, where most of our patients are treated, should greatly benefit the care of our patients, whether this is through collaborative care, clinical decision support, or telepsychiatry. In the same way that many people with diabetes do not require an endocrinologist, many with mental health conditions do not require a psychiatrist. Yet, primary care practitioners would certainly benefit from more help from us.
I will update readers of this column on our workgroup’s progress at the end of the year.
Editors’ Note: The intent of this new column is to discuss topics at the intersection of technology and psychiatry – “Techiatry.” We’ve enlisted two leaders in this field to write for the column. Steven R. Daviss, MD, DFAPA (@HITshrink), is the chief medical informatics officer at M3 Information and chairs the American Psychiatric Association’s Committee on Mental Health Information Technology. James (Jay) H. Shore, MD, MPH, chairs the APA Committee on Telepsychiatry, is director of telemedicine at the Helen & Arthur E. Johnson Depression Center, and an associate professor of psychiatry at the University of Colorado at Denver, Aurora. Email them at cpnews@frontlinemedcom.com.
Medicine is late to the game when it comes to technology, specifically information technology. And psychiatry, even more so. Jay will talk in future columns about early use of telepsychiatry in the 1960s and since. But here in 2016, a surprisingly low percentage of us are using it to deliver care, despite the fact that half of the counties in the United States lack psychiatrists – and telemedicine has been shown to improve access to care.
Nonetheless, telemedicine and other uses of technology across all specialties is growing quickly, as usability, mobile technology, economics, and policy-making all converge. The integration of mental health care (including addiction treatment) with primary care is one of the driving forces in expanding access to the expertise that physicians trained in psychiatry possess. The collaborative care model of integrated care has the most evidence, making regular access to psychiatric consultants a weekly event.
This exchange of information and knowledge between primary care and psychiatry is being formally incentivized by the Centers for Medicare & Medicaid Services (CMS) with proposed new codes to pay for this exchange, while the American Psychiatric Association has received a large grant from CMS to train 10% of its members in this care model.
Information technology is fundamental to this care model, because the efficient exchange of clinical information is important to optimize the capabilities and comprehensiveness of the clinical decision support provided by the psychiatrist to the primary care team.
As the team members learn what questions are asked and how the consultant arrives at her recommendations, they will become better at making these decisions on their own. They will learn how psychiatrists think and make decisions, weighing other medical, personal, social, family, and logistical aspects to guide the decision making process with the patient.
While this model of care is certainly helpful in expanding access to psychiatric expertise, there are other ways to achieve this access to expert knowledge. One of them is through electronic clinical decision support (CDS) tools. These are tools that apply clinical rules, algorithms, and other knowledge discovery processes to the information within the electronic health record (EHR) about a patient, with the goal of assessing and filling gaps in available patient information so that a set of possible recommendations can be delivered to the clinician.
Knowledge-based CDS tools apply clinical knowledge that comes from practice guidelines, textbooks, and the medical literature to what is known about the patient. The simplest CDS tool might be a rule that says, “IF patient is on lithium for bipolar disorder AND patient has current mood symptoms AND has not had a recent lithium level, THEN check a lithium level.” Applying and coding this rule into an EHR is fairly straightforward. A much more complex CDS tool could help the clinician think through the question, “What should I do next for this 32yo woman with hypertension and moderate depression who is symptomatic?”
Non–knowledge-based CDS tools use machine learning techniques, like neural networks, genetic algorithms, and natural language processing, to “learn” new clinical rules by going through a training process that inputs a large amount of clinical data and uses experts to “train” the system. Such a system was recently developed by IBM Watson and Memorial Sloan-Kettering Cancer Center to aid in developing recommendations for treating oncology patients.
The APA recently formed the CDS Product Workgroup (which I chair) to explore the feasibility of developing an electronic clinical decision support (CDS) tool that leverages the information and knowledge within the APA’s series of Practice Guidelines, DSM library, and other reference materials. This group will consider the necessary important clinical information sources – such as EHRs, personal health records, health information exchanges, claims and utilization data, patient generated data, mobile health apps, and clinical registries – from which to analyze patient-specific data and produce a set of ranked, evidence-based, annotated clinical suggestions.
The goal is to develop a CDS tool that is designed in a manner that ultimately benefits patients being treated by primary care practitioners, emergency practitioners, psychiatrists, and other medical specialists who treat patients with mental health and substance use disorders. Tools like this are being developed now in many specialties. Given the vast amount of psychiatric expertise within the APA, as well as the trove of content that exists within the publishing arm of the APA, the opportunity to make this more broadly available to medical practitioners is one that demands consideration.
Such an undertaking would require substantial time and commitment of resources, thus the task of the workgroup is to understand the pros and cons of developing this tool, and to explore its feasibility, including various business models to ensure that this CDS tool becomes a maintainable and sustainable product.
Bringing our expertise to the primary care settings, where most of our patients are treated, should greatly benefit the care of our patients, whether this is through collaborative care, clinical decision support, or telepsychiatry. In the same way that many people with diabetes do not require an endocrinologist, many with mental health conditions do not require a psychiatrist. Yet, primary care practitioners would certainly benefit from more help from us.
I will update readers of this column on our workgroup’s progress at the end of the year.