In surgical comanagement (SCM), surgeons and hospitalists share responsibility of care for surgical patients. While SCM has been increasingly utilized, many of the reported models are a modification of the consultation model, in which a group of rotating hospitalists, internists, or geriatricians care for the surgical patients, often after medical complications may have occured.^1-4

In August 2012, we implemented SCM in Orthopedic and Neurosurgery services at our institution.⁵ This model is unique because the same Internal Medicine hospitalists are dedicated year round to the same surgical service. SCM hospitalists see patients on their assigned surgical service only; they do not see patients on the Internal Medicine service. After the first year of implementing SCM, we conducted a propensity score–weighted study with 17,057 discharges in the pre-SCM group (January 2009 to July 2012) and 5,533 discharges in the post-SCM group (September 2012 to September 2013).⁵ In this study, SCM was associated with a decrease in medical complications, length of stay (LOS), medical consultations, 30-day readmissions, and cost.⁵

Since SCM requires ongoing investment by institutions, we now report a follow-up study to explore if there were continued improvements in patient outcomes with SCM. In this study, we evaluate if there was a decrease in medical complications, LOS, number of medical consultations, rapid response team calls, and code blues and an increase in patient satisfaction with SCM in Orthopedic and Neurosurgery services between 2012 and 2018.

We included 26,380 discharges from Orthopedic and Neurosurgery services between September 1, 2012, and June 30, 2018, at our academic medical center. We excluded patients discharged in August 2012 as we transitioned to the SCM model. Our Institutional Review Board exempted this study from further review.

SCM Structure

SCM structure was detailed in a prior article.⁵ We have 3.0 clinical full-time equivalents on the Orthopedic surgery SCM service and 1.2 on the Neurosurgery SCM service. On weekdays, during the day (8 am to 5 pm), there are two SCM hospitalists on Orthopedic surgery service and one on Neurosurgery service. One SCM hospitalist is on call every week and takes after-hours calls from both surgical services and sees patients on both services on the weekend.

During the day, SCM hospitalists receive the first call for medical issues. After 5 pm and on weekends and holidays, surgical services take all calls first and reach out to the on-call SCM hospitalist for any medical issues for which they need assistance. Surgery service is the primary team and does the discharge summaries. SCM hospitalists write any medical orders as needed. Medical students, physician assistant students, medicine housestaff, and geriatric medicine fellows rotate through SCM. SCM hospitalists directly communicate with the surgical service and not through the learners. There are no advanced practice providers on SCM service. Surgery housestaff attend the multidisciplinary team care rounds with the case manager, social worker, rehabilitation services, and pharmacy with ad hoc presence of SCM hospitalists for selected patients. SCM hospitalists often see sick patients with the surgery service at the bedside, and they work together with the surgery service on order sets, quality improvement projects, and scholarly work.

SCM hospitalists screen the entire patient list on their assigned surgery service each day. After screening the patient list, SCM hospitalists formally see select patients with preventable or active medical conditions and write notes on the patient’s chart. There are no set criteria to determine which patients would be seen by SCM. This is because surgeries can decompensate stable medical conditions or new unexpected medical complications may occur. Additionally, in our prior study, we reported that SCM reduced medical complications and LOS regardless of age or patient acuity.⁵

Outcomes

Our primary outcome was proportion of patients with ≥1 medical complication (sepsis, pneumonia, urinary tract infection, delirium, acute kidney injury, atrial fibrillation, or ileus). Our secondary outcomes included mean LOS, proportion of patients with ≥2 medical consultations, rapid response team calls, code blues, and top-box patient satisfaction score. Though cost is an important consideration in implementing SCM, limited financial data were available. However, since LOS is a key component in calculating direct costs,⁶ we estimated the cost savings per discharge using mean direct cost per day and the difference in mean LOS between pre- and post-SCM groups.⁵

We defined medical complications using International Classification of Disease (ICD) Codes 9 or 10 that were coded as “not present on admission” (Appendix 1). We used Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey for three questions for patient satisfaction: Did doctors treat with courtesy and respect, listen carefully, and explain things in a way you could understand?

Statistical Analysis

We used regression analysis to assess trends in patient characteristics by year (Appendix 2). Logistic regression with logit link was used to assess the yearly change in our binary outcomes (proportion of patients with ≥1 medical complication, those with ≥2 medical consultations, rapid response team calls, code blue, and top-box patient satisfaction score) and reported odds ratios. Gamma regression with identity link was performed for our continuous outcome (LOS). Beta coefficient was reported to estimate the yearly change in LOS under their original scales. Age, primary insurance, race, Charlson comorbidity score, general or regional anesthesia, surgical service, and duration of surgery were adjusted in the regression analyses for outcomes. SAS 9.4 was used for analysis.

Patient characteristics are shown in Table 1. Overall, 62.8% patients were discharged from Orthopedic surgery service, 72.5% patients underwent elective surgery, and 88.8% received general anesthesia. Between 2012 and 2018, there was a significant increase in the median age of patients (from 60 years to 63 years), mean Charlson comorbidity score increased from 1.07 to 1.46, and median case mix index, a measure of patient acuity, increased from 2.10 to 2.36 (Appendix 2).

Comparing pre-SCM unadjusted rates reported in our prior study (January 2009 to July 2012) to post-SCM (September 2012 to June 2018; Appendix 3), patients with ≥1 medical complication decreased from 10.1% to 6.1%, LOS (mean ± standard deviation) changed from 5.4 ± 2.2 days to 4.6 ± 5.8 days, patients with ≥2 medical consultations decreased from 19.4% to 9.2%, rapid response team calls changed from 1% to 0.9%, code blues changed from 0.3% to 0.2%, and patients with top-box patient satisfaction score increased from 86.4% to 94.2%.⁵

In the adjusted analysis from 2012 to 2018, the odds of patients with ≥1 medical complication decreased by 3.8% per year (P = .01), estimated LOS decreased by 0.3 days per year (P < .0001), and the odds of rapid response team calls decreased by 12.2% per year (P = .001; Table 2). Changes over time in the odds of patients with ≥2 medical consultations, code blues, or top-box patient satisfaction score were not statistically significant (Table 2). Based on the LOS reduction pre- to post-SCM, there were estimated average direct cost savings of $3,424 per discharge between 2012 and 2018.

Since the implementation of SCM on Orthopedic and Neurosurgery services at our institution, there was a decrease in medical complications, LOS, and rapid response team calls. To our knowledge, this is one of the largest studies evaluating the benefits of SCM over 5.8 years. Similar to our prior studies on this SCM model of care,^5,7 other studies have reported a decrease in medical complications,^8-10 LOS,^11-13 and cost of care¹⁴ with SCM.

While the changes in the unadjusted rates of outcomes over the years appeared to be small, while our patient population became older and sicker, there were significant changes in several of our outcomes in the adjusted analysis. We believe that SCM hospitalists have developed a skill set and understanding of these surgical patients over time and can manage more medically complex patients without an increase in medical complications or LOS. We attribute this to our unique SCM model in which the same hospitalists stay year round on the same surgical service. SCM hospitalists have built trusting relationships with the surgical team with greater involvement in decision making, care planning, and patient selection. With minimal turnover in the SCM group and with ongoing learning, SCM hospitalists can anticipate fluid or pain medication requirements after specific surgeries and the surgery-specific medical complications. SCM hospitalists are available on the patient units to provide timely intervention in case of medical deterioration; answer any questions from patients, families, or nursing while the surgical teams may be in the operating room; and coordinate with other medical consultants or outpatient providers as needed.

This study has several limitations. This is a single-center study at an academic institution, limited to two surgical services. We did not have a control group and multiple hospital-­wide interventions may have affected these outcomes. This is an observational study in which unobserved variables may bias the results. We used ICD codes to identify medical complications, which relies on the quality of physician documentation. While our response rate of 21.1% for HCAHPS was comparable to the national average of 26.7%, it may not reliably represent our patient population.¹⁵ Lastly, we had limited financial data.

With the move toward value-based payment and increasing medical complexity of surgical patients, SCM by hospitalists may deliver high-quality care.

In surgical comanagement (SCM), surgeons and hospitalists share responsibility of care for surgical patients. While SCM has been increasingly utilized, many of the reported models are a modification of the consultation model, in which a group of rotating hospitalists, internists, or geriatricians care for the surgical patients, often after medical complications may have occured.^1-4

In August 2012, we implemented SCM in Orthopedic and Neurosurgery services at our institution.⁵ This model is unique because the same Internal Medicine hospitalists are dedicated year round to the same surgical service. SCM hospitalists see patients on their assigned surgical service only; they do not see patients on the Internal Medicine service. After the first year of implementing SCM, we conducted a propensity score–weighted study with 17,057 discharges in the pre-SCM group (January 2009 to July 2012) and 5,533 discharges in the post-SCM group (September 2012 to September 2013).⁵ In this study, SCM was associated with a decrease in medical complications, length of stay (LOS), medical consultations, 30-day readmissions, and cost.⁵

Since SCM requires ongoing investment by institutions, we now report a follow-up study to explore if there were continued improvements in patient outcomes with SCM. In this study, we evaluate if there was a decrease in medical complications, LOS, number of medical consultations, rapid response team calls, and code blues and an increase in patient satisfaction with SCM in Orthopedic and Neurosurgery services between 2012 and 2018.

We included 26,380 discharges from Orthopedic and Neurosurgery services between September 1, 2012, and June 30, 2018, at our academic medical center. We excluded patients discharged in August 2012 as we transitioned to the SCM model. Our Institutional Review Board exempted this study from further review.

SCM Structure

SCM structure was detailed in a prior article.⁵ We have 3.0 clinical full-time equivalents on the Orthopedic surgery SCM service and 1.2 on the Neurosurgery SCM service. On weekdays, during the day (8 am to 5 pm), there are two SCM hospitalists on Orthopedic surgery service and one on Neurosurgery service. One SCM hospitalist is on call every week and takes after-hours calls from both surgical services and sees patients on both services on the weekend.

During the day, SCM hospitalists receive the first call for medical issues. After 5 pm and on weekends and holidays, surgical services take all calls first and reach out to the on-call SCM hospitalist for any medical issues for which they need assistance. Surgery service is the primary team and does the discharge summaries. SCM hospitalists write any medical orders as needed. Medical students, physician assistant students, medicine housestaff, and geriatric medicine fellows rotate through SCM. SCM hospitalists directly communicate with the surgical service and not through the learners. There are no advanced practice providers on SCM service. Surgery housestaff attend the multidisciplinary team care rounds with the case manager, social worker, rehabilitation services, and pharmacy with ad hoc presence of SCM hospitalists for selected patients. SCM hospitalists often see sick patients with the surgery service at the bedside, and they work together with the surgery service on order sets, quality improvement projects, and scholarly work.

SCM hospitalists screen the entire patient list on their assigned surgery service each day. After screening the patient list, SCM hospitalists formally see select patients with preventable or active medical conditions and write notes on the patient’s chart. There are no set criteria to determine which patients would be seen by SCM. This is because surgeries can decompensate stable medical conditions or new unexpected medical complications may occur. Additionally, in our prior study, we reported that SCM reduced medical complications and LOS regardless of age or patient acuity.⁵

Outcomes

Our primary outcome was proportion of patients with ≥1 medical complication (sepsis, pneumonia, urinary tract infection, delirium, acute kidney injury, atrial fibrillation, or ileus). Our secondary outcomes included mean LOS, proportion of patients with ≥2 medical consultations, rapid response team calls, code blues, and top-box patient satisfaction score. Though cost is an important consideration in implementing SCM, limited financial data were available. However, since LOS is a key component in calculating direct costs,⁶ we estimated the cost savings per discharge using mean direct cost per day and the difference in mean LOS between pre- and post-SCM groups.⁵

We defined medical complications using International Classification of Disease (ICD) Codes 9 or 10 that were coded as “not present on admission” (Appendix 1). We used Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey for three questions for patient satisfaction: Did doctors treat with courtesy and respect, listen carefully, and explain things in a way you could understand?

Statistical Analysis

We used regression analysis to assess trends in patient characteristics by year (Appendix 2). Logistic regression with logit link was used to assess the yearly change in our binary outcomes (proportion of patients with ≥1 medical complication, those with ≥2 medical consultations, rapid response team calls, code blue, and top-box patient satisfaction score) and reported odds ratios. Gamma regression with identity link was performed for our continuous outcome (LOS). Beta coefficient was reported to estimate the yearly change in LOS under their original scales. Age, primary insurance, race, Charlson comorbidity score, general or regional anesthesia, surgical service, and duration of surgery were adjusted in the regression analyses for outcomes. SAS 9.4 was used for analysis.

Patient characteristics are shown in Table 1. Overall, 62.8% patients were discharged from Orthopedic surgery service, 72.5% patients underwent elective surgery, and 88.8% received general anesthesia. Between 2012 and 2018, there was a significant increase in the median age of patients (from 60 years to 63 years), mean Charlson comorbidity score increased from 1.07 to 1.46, and median case mix index, a measure of patient acuity, increased from 2.10 to 2.36 (Appendix 2).

Comparing pre-SCM unadjusted rates reported in our prior study (January 2009 to July 2012) to post-SCM (September 2012 to June 2018; Appendix 3), patients with ≥1 medical complication decreased from 10.1% to 6.1%, LOS (mean ± standard deviation) changed from 5.4 ± 2.2 days to 4.6 ± 5.8 days, patients with ≥2 medical consultations decreased from 19.4% to 9.2%, rapid response team calls changed from 1% to 0.9%, code blues changed from 0.3% to 0.2%, and patients with top-box patient satisfaction score increased from 86.4% to 94.2%.⁵

In the adjusted analysis from 2012 to 2018, the odds of patients with ≥1 medical complication decreased by 3.8% per year (P = .01), estimated LOS decreased by 0.3 days per year (P < .0001), and the odds of rapid response team calls decreased by 12.2% per year (P = .001; Table 2). Changes over time in the odds of patients with ≥2 medical consultations, code blues, or top-box patient satisfaction score were not statistically significant (Table 2). Based on the LOS reduction pre- to post-SCM, there were estimated average direct cost savings of $3,424 per discharge between 2012 and 2018.

Since the implementation of SCM on Orthopedic and Neurosurgery services at our institution, there was a decrease in medical complications, LOS, and rapid response team calls. To our knowledge, this is one of the largest studies evaluating the benefits of SCM over 5.8 years. Similar to our prior studies on this SCM model of care,^5,7 other studies have reported a decrease in medical complications,^8-10 LOS,^11-13 and cost of care¹⁴ with SCM.

While the changes in the unadjusted rates of outcomes over the years appeared to be small, while our patient population became older and sicker, there were significant changes in several of our outcomes in the adjusted analysis. We believe that SCM hospitalists have developed a skill set and understanding of these surgical patients over time and can manage more medically complex patients without an increase in medical complications or LOS. We attribute this to our unique SCM model in which the same hospitalists stay year round on the same surgical service. SCM hospitalists have built trusting relationships with the surgical team with greater involvement in decision making, care planning, and patient selection. With minimal turnover in the SCM group and with ongoing learning, SCM hospitalists can anticipate fluid or pain medication requirements after specific surgeries and the surgery-specific medical complications. SCM hospitalists are available on the patient units to provide timely intervention in case of medical deterioration; answer any questions from patients, families, or nursing while the surgical teams may be in the operating room; and coordinate with other medical consultants or outpatient providers as needed.

This study has several limitations. This is a single-center study at an academic institution, limited to two surgical services. We did not have a control group and multiple hospital-­wide interventions may have affected these outcomes. This is an observational study in which unobserved variables may bias the results. We used ICD codes to identify medical complications, which relies on the quality of physician documentation. While our response rate of 21.1% for HCAHPS was comparable to the national average of 26.7%, it may not reliably represent our patient population.¹⁵ Lastly, we had limited financial data.

With the move toward value-based payment and increasing medical complexity of surgical patients, SCM by hospitalists may deliver high-quality care.

In surgical comanagement (SCM), surgeons and hospitalists share responsibility of care for surgical patients. While SCM has been increasingly utilized, many of the reported models are a modification of the consultation model, in which a group of rotating hospitalists, internists, or geriatricians care for the surgical patients, often after medical complications may have occured.^1-4

In August 2012, we implemented SCM in Orthopedic and Neurosurgery services at our institution.⁵ This model is unique because the same Internal Medicine hospitalists are dedicated year round to the same surgical service. SCM hospitalists see patients on their assigned surgical service only; they do not see patients on the Internal Medicine service. After the first year of implementing SCM, we conducted a propensity score–weighted study with 17,057 discharges in the pre-SCM group (January 2009 to July 2012) and 5,533 discharges in the post-SCM group (September 2012 to September 2013).⁵ In this study, SCM was associated with a decrease in medical complications, length of stay (LOS), medical consultations, 30-day readmissions, and cost.⁵

Since SCM requires ongoing investment by institutions, we now report a follow-up study to explore if there were continued improvements in patient outcomes with SCM. In this study, we evaluate if there was a decrease in medical complications, LOS, number of medical consultations, rapid response team calls, and code blues and an increase in patient satisfaction with SCM in Orthopedic and Neurosurgery services between 2012 and 2018.

We included 26,380 discharges from Orthopedic and Neurosurgery services between September 1, 2012, and June 30, 2018, at our academic medical center. We excluded patients discharged in August 2012 as we transitioned to the SCM model. Our Institutional Review Board exempted this study from further review.

SCM Structure

SCM structure was detailed in a prior article.⁵ We have 3.0 clinical full-time equivalents on the Orthopedic surgery SCM service and 1.2 on the Neurosurgery SCM service. On weekdays, during the day (8 am to 5 pm), there are two SCM hospitalists on Orthopedic surgery service and one on Neurosurgery service. One SCM hospitalist is on call every week and takes after-hours calls from both surgical services and sees patients on both services on the weekend.

During the day, SCM hospitalists receive the first call for medical issues. After 5 pm and on weekends and holidays, surgical services take all calls first and reach out to the on-call SCM hospitalist for any medical issues for which they need assistance. Surgery service is the primary team and does the discharge summaries. SCM hospitalists write any medical orders as needed. Medical students, physician assistant students, medicine housestaff, and geriatric medicine fellows rotate through SCM. SCM hospitalists directly communicate with the surgical service and not through the learners. There are no advanced practice providers on SCM service. Surgery housestaff attend the multidisciplinary team care rounds with the case manager, social worker, rehabilitation services, and pharmacy with ad hoc presence of SCM hospitalists for selected patients. SCM hospitalists often see sick patients with the surgery service at the bedside, and they work together with the surgery service on order sets, quality improvement projects, and scholarly work.

SCM hospitalists screen the entire patient list on their assigned surgery service each day. After screening the patient list, SCM hospitalists formally see select patients with preventable or active medical conditions and write notes on the patient’s chart. There are no set criteria to determine which patients would be seen by SCM. This is because surgeries can decompensate stable medical conditions or new unexpected medical complications may occur. Additionally, in our prior study, we reported that SCM reduced medical complications and LOS regardless of age or patient acuity.⁵

Outcomes

Our primary outcome was proportion of patients with ≥1 medical complication (sepsis, pneumonia, urinary tract infection, delirium, acute kidney injury, atrial fibrillation, or ileus). Our secondary outcomes included mean LOS, proportion of patients with ≥2 medical consultations, rapid response team calls, code blues, and top-box patient satisfaction score. Though cost is an important consideration in implementing SCM, limited financial data were available. However, since LOS is a key component in calculating direct costs,⁶ we estimated the cost savings per discharge using mean direct cost per day and the difference in mean LOS between pre- and post-SCM groups.⁵

We defined medical complications using International Classification of Disease (ICD) Codes 9 or 10 that were coded as “not present on admission” (Appendix 1). We used Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey for three questions for patient satisfaction: Did doctors treat with courtesy and respect, listen carefully, and explain things in a way you could understand?

Statistical Analysis

We used regression analysis to assess trends in patient characteristics by year (Appendix 2). Logistic regression with logit link was used to assess the yearly change in our binary outcomes (proportion of patients with ≥1 medical complication, those with ≥2 medical consultations, rapid response team calls, code blue, and top-box patient satisfaction score) and reported odds ratios. Gamma regression with identity link was performed for our continuous outcome (LOS). Beta coefficient was reported to estimate the yearly change in LOS under their original scales. Age, primary insurance, race, Charlson comorbidity score, general or regional anesthesia, surgical service, and duration of surgery were adjusted in the regression analyses for outcomes. SAS 9.4 was used for analysis.

Patient characteristics are shown in Table 1. Overall, 62.8% patients were discharged from Orthopedic surgery service, 72.5% patients underwent elective surgery, and 88.8% received general anesthesia. Between 2012 and 2018, there was a significant increase in the median age of patients (from 60 years to 63 years), mean Charlson comorbidity score increased from 1.07 to 1.46, and median case mix index, a measure of patient acuity, increased from 2.10 to 2.36 (Appendix 2).

Comparing pre-SCM unadjusted rates reported in our prior study (January 2009 to July 2012) to post-SCM (September 2012 to June 2018; Appendix 3), patients with ≥1 medical complication decreased from 10.1% to 6.1%, LOS (mean ± standard deviation) changed from 5.4 ± 2.2 days to 4.6 ± 5.8 days, patients with ≥2 medical consultations decreased from 19.4% to 9.2%, rapid response team calls changed from 1% to 0.9%, code blues changed from 0.3% to 0.2%, and patients with top-box patient satisfaction score increased from 86.4% to 94.2%.⁵

In the adjusted analysis from 2012 to 2018, the odds of patients with ≥1 medical complication decreased by 3.8% per year (P = .01), estimated LOS decreased by 0.3 days per year (P < .0001), and the odds of rapid response team calls decreased by 12.2% per year (P = .001; Table 2). Changes over time in the odds of patients with ≥2 medical consultations, code blues, or top-box patient satisfaction score were not statistically significant (Table 2). Based on the LOS reduction pre- to post-SCM, there were estimated average direct cost savings of $3,424 per discharge between 2012 and 2018.

Since the implementation of SCM on Orthopedic and Neurosurgery services at our institution, there was a decrease in medical complications, LOS, and rapid response team calls. To our knowledge, this is one of the largest studies evaluating the benefits of SCM over 5.8 years. Similar to our prior studies on this SCM model of care,^5,7 other studies have reported a decrease in medical complications,^8-10 LOS,^11-13 and cost of care¹⁴ with SCM.

While the changes in the unadjusted rates of outcomes over the years appeared to be small, while our patient population became older and sicker, there were significant changes in several of our outcomes in the adjusted analysis. We believe that SCM hospitalists have developed a skill set and understanding of these surgical patients over time and can manage more medically complex patients without an increase in medical complications or LOS. We attribute this to our unique SCM model in which the same hospitalists stay year round on the same surgical service. SCM hospitalists have built trusting relationships with the surgical team with greater involvement in decision making, care planning, and patient selection. With minimal turnover in the SCM group and with ongoing learning, SCM hospitalists can anticipate fluid or pain medication requirements after specific surgeries and the surgery-specific medical complications. SCM hospitalists are available on the patient units to provide timely intervention in case of medical deterioration; answer any questions from patients, families, or nursing while the surgical teams may be in the operating room; and coordinate with other medical consultants or outpatient providers as needed.

This study has several limitations. This is a single-center study at an academic institution, limited to two surgical services. We did not have a control group and multiple hospital-­wide interventions may have affected these outcomes. This is an observational study in which unobserved variables may bias the results. We used ICD codes to identify medical complications, which relies on the quality of physician documentation. While our response rate of 21.1% for HCAHPS was comparable to the national average of 26.7%, it may not reliably represent our patient population.¹⁵ Lastly, we had limited financial data.

With the move toward value-based payment and increasing medical complexity of surgical patients, SCM by hospitalists may deliver high-quality care.

Inpatient consultation is an extremely common practice with the potential to improve patient outcomes significantly.^1-3 However, variability in consultation practices may be risky for patients. In addition to underuse when the benefit is clear, the overuse of consultation may lead to additional testing and therapies, increased length of stay (LOS) and costs, conflicting recommendations, and opportunities for communication breakdown.

Consultation use is often at the discretion of individual providers. While this decision is frequently driven by patient needs, significant variation in consultation practices not fully explained by patient factors exists.¹ Prior work has described hospital-level variation¹ and that primary care physicians use more consultation than hospitalists.⁴ However, other factors affecting consultation remain unknown. We sought to explore physician-, patient-, and admission-level factors associated with consultation use on inpatient general medicine services.

Study Design

We conducted a retrospective analysis of data from the University of Chicago Hospitalist Project (UCHP). UCHP is a longstanding study of the care of hospitalized patients admitted to the University of Chicago general medicine services, involving both patient data collection and physician experience surveys.⁵ Data were obtained for enrolled UCHP patients between 2011-2016 from the Center for Research Informatics (CRI). The University of Chicago Institutional Review Board approved this study.

Data Collection

Attendings and patients consented to UCHP participation. Data collection details are described elsewhere.^5,6 Data from EpicCare (EpicSystems Corp, Wisconsin) and Centricity Billing (GE Healthcare, Illinois) were obtained via CRI for all encounters of enrolled UCHP patients during the study period (N = 218,591).

Attending Attribution

We determined attending attribution for admissions as follows: the attending author of the first history and physical (H&P) was assigned. If this was unavailable, the attending author of the first progress note (PN) was assigned. For patients admitted by hospitalists on admitting shifts to nonteaching services (ie, service without residents/students), the author of the first PN was assigned if different from H&P. Where available, attribution was corroborated with call schedules.

Sample and Variables

All encounters containing inpatient admissions to the University of Chicago from May 10, 2011 (Electronic Health Record activation date), through December 31, 2016, were considered for inclusion (N = 51,171, Appendix 1). Admissions including only documentation from ancillary services were excluded (eg, encounters for hemodialysis or physical therapy). Admissions were limited to a length of stay (LOS) ≤ 5 days, corresponding to the average US inpatient LOS of 4.6 days,⁷ to minimize the likelihood of attending handoffs (N = 31,592). If attending attribution was not possible via the above-described methods, the admission was eliminated (N = 3,103; 10.9% of admissions with LOS ≤ 5 days). Finally, the sample was restricted to general medicine service admissions under attendings enrolled in UCHP who completed surveys. After the application of all criteria, 6,153 admissions remained for analysis.

The outcome variable was the number of consultations per admission, determined by counting the unique number of services creating clinical documentation, and subtracting one for the primary team. If the Medical/Surgical intensive care unit (ICU) was a service, then two were subtracted to account for the ICU transfer.

Attending years in practice (ie, years since medical school graduation) and gender were determined from public resources. Practice characteristics were determined from UCHP attending surveys, which address perceptions of workload and satisfaction (Appendix 2).

Patient characteristics (gender, age, Elixhauser Indices) and admission characteristics (LOS, season of admission, payor) were determined from UCHP and CRI data. The Elixhauser Index uses a well-validated system combining the presence/absence of 31 comorbidities to predict mortality and 30-day readmission.⁸ Elixhauser Indices were calculated using the “Creation of Elixhauser Comorbidity Index Scores 1.0” software.⁹ For admissions under hospitalist attendings, teaching/nonteaching team was ascertained via internal teaching service calendars.

Analysis

We used descriptive statistics to examine demographic characteristics. The difference between the lowest and highest quartile consultation use was determined via a two-sample t test. Given the multilevel nature of our count data, we used a mixed-effects Poisson model accounting for within-group variation by clustering on attending and patient (3-level random-effects model). The analysis was done using Stata 15 (StataCorp, Texas).

From 2011 to 2016, 14,848 patients and 88 attendings were enrolled in UCHP; 4,772 patients (32%) and 69 attendings (59.4%) had data available and were included. Mean LOS was 3.0 days (SD = 1.3). Table 1 describes the characteristics of attendings, patients, and admissions.

Seventy-six percent of admissions included at least one consultation. Consultation use varied widely, ranging from 0 to 10 per admission (mean = 1.39, median = 1; standard deviation [SD] = 1.17). The number of consultations per admission in the highest quartile of consultation frequency (mean = 3.47, median = 3) was 5.7-fold that of the lowest quartile (mean = 0.613, median = 1; P <.001).

In multivariable regression, physician-, patient-, and admission-level characteristics were associated with the differential use of consultation (Table 2). On teaching services, consultations called by hospitalist vs nonhospitalist generalists did not differ (P =.361). However, hospitalists on nonteaching services called 8.6% more consultations than hospitalists on teaching services (P =.02). Attending agreement with survey item “The interruption of my personal life by work is a problem” was associated with 8.2% fewer consultations per admission (P =.002).

Patients older than 75 years received 19% fewer consultations compared with patients younger than 49 years (P <.001). Compared with Medicare, Medicaid admissions had 12.2% fewer consultations (P <.001), whereas privately insured admissions had 10.7% more (P =.001). The number of consultations per admission decreased every year, with 45.3% fewer consultations in 2015 than 2011 (P <.001). Consultations increased by each 22% per day increase in LOS (P <.001).

Our analysis described several physician-, patient-, and admission-level characteristics associated with the use of inpatient consultation. Our results strengthen prior work demonstrating that patient-level factors alone are insufficient to explain consultation variability.¹

Hospitalists on nonteaching services called more consultations, which may reflect a higher workload on these services. Busy hospitalists on nonteaching teams may lack time to delve deeply into clinical problems and require more consultations, especially for work with heavy cognitive loads such as diagnosis. “Outsourcing” tasks when workload increases occurs in other cognitive activities such as teaching.¹⁰ The association between work interrupting personal life and fewer consultations may also implicate the effects of time. Attendings who are experiencing work encroaching on their personal lives may be those spending more time with patients and consulting less. This finding merits further study, especially with increasing concern about balancing time spent in meaningful patient care activities with risk of physician burnout.

This finding could also indicate that trainee participation modifies consultation use for hospitalists. Teaching service teams with more individual members may allow a greater pool of collective knowledge, decreasing the need for consultation to answer clinical questions.¹¹ Interestingly, there was no difference in consultation use between generalists or subspecialists and hospitalists on teaching services, possibly suggesting a unique effect in hospitalists who vary clinical practice depending on team structure. These differences deserve further investigation, with implications for education and resource utilization.

We were surprised by the finding that consultations decreased each year, despite increasing patient complexity and availability of consultation services. This could be explained by a growing emphasis on shortening LOS in our institution, thus shifting consultative care to outpatient settings. Understanding these effects is critically important with growing evidence that consultation improves patient outcomes because these external pressures could lead to unintended consequences for quality or access to care.

Several findings related to patient factors additionally emerged, including age and insurance status. Although related to medical complexity, these effects persist despite adjustment, which raises the question of whether they contribute to the decision to seek consultation. Older patients received fewer consultations, which could reflect the use of more conservative practice models in the elderly,¹² or ageism, which is associated with undertreatment.¹³ With respect to insurance status, Medicaid patients were associated with fewer consultations. This finding is consistent with previous work showing the decreased intensity of hospital services used for Medicaid patients.¹⁴Our study has limitations. Our data were from one large urban academic center that limits generalizability. Although systematic and redundant, attending attribution may have been flawed: incomplete or erroneous documentation could have led to attribution error, and we cannot rule out the possibility of service handoffs. We used a LOS ≤ 5 days to minimize this possibility, but this limits the applicability of our findings to longer admissions. Unsurprisingly, longer LOS correlated with the increased use of consultation even within our restricted sample, and future work should examine the effects of prolonged LOS. As a retrospective analysis, unmeasured confounders due to our limited adjustment will likely explain some findings, although we took steps to address this in our statistical design. Finally, we could not measure patient outcomes and, therefore, cannot determine the value of more or fewer consultations for specific patients or illnesses. Positive and negative outcomes of increased consultation are described, and understanding the impact of consultation is critical for further study.^2,3

We found that the use of consultation on general medicine services varies widely between admissions, with large differences between the highest and lowest frequencies of use. This variation can be partially explained by several physician-, patient-, and admission-level characteristics. Our work may help identify patient and attending groups at high risk for under- or overuse of consultation and guide the subsequent development of interventions to improve value in consultation. One additional consultation over the average LOS of 4.6 days adds $420 per admission or $4.8 billion to the 11.5 million annual Medicare admissions.¹⁵ Increasing research, guidelines, and education on the judicious use of inpatient consultation will be key in maximizing high-value care and improving patient outcomes.

Acknowledgments

The authors would like to acknowledge the invaluable support and assistance of the University of Chicago Hospitalist Project, the Pritzker School of Medicine Summer Research Program, the University of Chicago Center for Quality, and the University of Chicago Center for Health and the Social Sciences (CHeSS). The authors would additionally like to thank John Cursio, PhD, for his support and guidance in statistical analysis for this project.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; or the decision to approve publication of the finished manuscript. Preliminary results of this analysis were presented at the 2018 Society of Hospital Medicine Annual Meeting in Orlando, Florida. All coauthors have seen and agree with the contents of the manuscript. The submission is not under review by any other publication.

Inpatient consultation is an extremely common practice with the potential to improve patient outcomes significantly.^1-3 However, variability in consultation practices may be risky for patients. In addition to underuse when the benefit is clear, the overuse of consultation may lead to additional testing and therapies, increased length of stay (LOS) and costs, conflicting recommendations, and opportunities for communication breakdown.

Consultation use is often at the discretion of individual providers. While this decision is frequently driven by patient needs, significant variation in consultation practices not fully explained by patient factors exists.¹ Prior work has described hospital-level variation¹ and that primary care physicians use more consultation than hospitalists.⁴ However, other factors affecting consultation remain unknown. We sought to explore physician-, patient-, and admission-level factors associated with consultation use on inpatient general medicine services.

Study Design

We conducted a retrospective analysis of data from the University of Chicago Hospitalist Project (UCHP). UCHP is a longstanding study of the care of hospitalized patients admitted to the University of Chicago general medicine services, involving both patient data collection and physician experience surveys.⁵ Data were obtained for enrolled UCHP patients between 2011-2016 from the Center for Research Informatics (CRI). The University of Chicago Institutional Review Board approved this study.

Data Collection

Attendings and patients consented to UCHP participation. Data collection details are described elsewhere.^5,6 Data from EpicCare (EpicSystems Corp, Wisconsin) and Centricity Billing (GE Healthcare, Illinois) were obtained via CRI for all encounters of enrolled UCHP patients during the study period (N = 218,591).

Attending Attribution

We determined attending attribution for admissions as follows: the attending author of the first history and physical (H&P) was assigned. If this was unavailable, the attending author of the first progress note (PN) was assigned. For patients admitted by hospitalists on admitting shifts to nonteaching services (ie, service without residents/students), the author of the first PN was assigned if different from H&P. Where available, attribution was corroborated with call schedules.

Sample and Variables

All encounters containing inpatient admissions to the University of Chicago from May 10, 2011 (Electronic Health Record activation date), through December 31, 2016, were considered for inclusion (N = 51,171, Appendix 1). Admissions including only documentation from ancillary services were excluded (eg, encounters for hemodialysis or physical therapy). Admissions were limited to a length of stay (LOS) ≤ 5 days, corresponding to the average US inpatient LOS of 4.6 days,⁷ to minimize the likelihood of attending handoffs (N = 31,592). If attending attribution was not possible via the above-described methods, the admission was eliminated (N = 3,103; 10.9% of admissions with LOS ≤ 5 days). Finally, the sample was restricted to general medicine service admissions under attendings enrolled in UCHP who completed surveys. After the application of all criteria, 6,153 admissions remained for analysis.

The outcome variable was the number of consultations per admission, determined by counting the unique number of services creating clinical documentation, and subtracting one for the primary team. If the Medical/Surgical intensive care unit (ICU) was a service, then two were subtracted to account for the ICU transfer.

Attending years in practice (ie, years since medical school graduation) and gender were determined from public resources. Practice characteristics were determined from UCHP attending surveys, which address perceptions of workload and satisfaction (Appendix 2).

Patient characteristics (gender, age, Elixhauser Indices) and admission characteristics (LOS, season of admission, payor) were determined from UCHP and CRI data. The Elixhauser Index uses a well-validated system combining the presence/absence of 31 comorbidities to predict mortality and 30-day readmission.⁸ Elixhauser Indices were calculated using the “Creation of Elixhauser Comorbidity Index Scores 1.0” software.⁹ For admissions under hospitalist attendings, teaching/nonteaching team was ascertained via internal teaching service calendars.

Analysis

We used descriptive statistics to examine demographic characteristics. The difference between the lowest and highest quartile consultation use was determined via a two-sample t test. Given the multilevel nature of our count data, we used a mixed-effects Poisson model accounting for within-group variation by clustering on attending and patient (3-level random-effects model). The analysis was done using Stata 15 (StataCorp, Texas).

From 2011 to 2016, 14,848 patients and 88 attendings were enrolled in UCHP; 4,772 patients (32%) and 69 attendings (59.4%) had data available and were included. Mean LOS was 3.0 days (SD = 1.3). Table 1 describes the characteristics of attendings, patients, and admissions.

Seventy-six percent of admissions included at least one consultation. Consultation use varied widely, ranging from 0 to 10 per admission (mean = 1.39, median = 1; standard deviation [SD] = 1.17). The number of consultations per admission in the highest quartile of consultation frequency (mean = 3.47, median = 3) was 5.7-fold that of the lowest quartile (mean = 0.613, median = 1; P <.001).

In multivariable regression, physician-, patient-, and admission-level characteristics were associated with the differential use of consultation (Table 2). On teaching services, consultations called by hospitalist vs nonhospitalist generalists did not differ (P =.361). However, hospitalists on nonteaching services called 8.6% more consultations than hospitalists on teaching services (P =.02). Attending agreement with survey item “The interruption of my personal life by work is a problem” was associated with 8.2% fewer consultations per admission (P =.002).

Patients older than 75 years received 19% fewer consultations compared with patients younger than 49 years (P <.001). Compared with Medicare, Medicaid admissions had 12.2% fewer consultations (P <.001), whereas privately insured admissions had 10.7% more (P =.001). The number of consultations per admission decreased every year, with 45.3% fewer consultations in 2015 than 2011 (P <.001). Consultations increased by each 22% per day increase in LOS (P <.001).

Our analysis described several physician-, patient-, and admission-level characteristics associated with the use of inpatient consultation. Our results strengthen prior work demonstrating that patient-level factors alone are insufficient to explain consultation variability.¹

Hospitalists on nonteaching services called more consultations, which may reflect a higher workload on these services. Busy hospitalists on nonteaching teams may lack time to delve deeply into clinical problems and require more consultations, especially for work with heavy cognitive loads such as diagnosis. “Outsourcing” tasks when workload increases occurs in other cognitive activities such as teaching.¹⁰ The association between work interrupting personal life and fewer consultations may also implicate the effects of time. Attendings who are experiencing work encroaching on their personal lives may be those spending more time with patients and consulting less. This finding merits further study, especially with increasing concern about balancing time spent in meaningful patient care activities with risk of physician burnout.

This finding could also indicate that trainee participation modifies consultation use for hospitalists. Teaching service teams with more individual members may allow a greater pool of collective knowledge, decreasing the need for consultation to answer clinical questions.¹¹ Interestingly, there was no difference in consultation use between generalists or subspecialists and hospitalists on teaching services, possibly suggesting a unique effect in hospitalists who vary clinical practice depending on team structure. These differences deserve further investigation, with implications for education and resource utilization.

We were surprised by the finding that consultations decreased each year, despite increasing patient complexity and availability of consultation services. This could be explained by a growing emphasis on shortening LOS in our institution, thus shifting consultative care to outpatient settings. Understanding these effects is critically important with growing evidence that consultation improves patient outcomes because these external pressures could lead to unintended consequences for quality or access to care.

Several findings related to patient factors additionally emerged, including age and insurance status. Although related to medical complexity, these effects persist despite adjustment, which raises the question of whether they contribute to the decision to seek consultation. Older patients received fewer consultations, which could reflect the use of more conservative practice models in the elderly,¹² or ageism, which is associated with undertreatment.¹³ With respect to insurance status, Medicaid patients were associated with fewer consultations. This finding is consistent with previous work showing the decreased intensity of hospital services used for Medicaid patients.¹⁴Our study has limitations. Our data were from one large urban academic center that limits generalizability. Although systematic and redundant, attending attribution may have been flawed: incomplete or erroneous documentation could have led to attribution error, and we cannot rule out the possibility of service handoffs. We used a LOS ≤ 5 days to minimize this possibility, but this limits the applicability of our findings to longer admissions. Unsurprisingly, longer LOS correlated with the increased use of consultation even within our restricted sample, and future work should examine the effects of prolonged LOS. As a retrospective analysis, unmeasured confounders due to our limited adjustment will likely explain some findings, although we took steps to address this in our statistical design. Finally, we could not measure patient outcomes and, therefore, cannot determine the value of more or fewer consultations for specific patients or illnesses. Positive and negative outcomes of increased consultation are described, and understanding the impact of consultation is critical for further study.^2,3

We found that the use of consultation on general medicine services varies widely between admissions, with large differences between the highest and lowest frequencies of use. This variation can be partially explained by several physician-, patient-, and admission-level characteristics. Our work may help identify patient and attending groups at high risk for under- or overuse of consultation and guide the subsequent development of interventions to improve value in consultation. One additional consultation over the average LOS of 4.6 days adds $420 per admission or $4.8 billion to the 11.5 million annual Medicare admissions.¹⁵ Increasing research, guidelines, and education on the judicious use of inpatient consultation will be key in maximizing high-value care and improving patient outcomes.

Acknowledgments

The authors would like to acknowledge the invaluable support and assistance of the University of Chicago Hospitalist Project, the Pritzker School of Medicine Summer Research Program, the University of Chicago Center for Quality, and the University of Chicago Center for Health and the Social Sciences (CHeSS). The authors would additionally like to thank John Cursio, PhD, for his support and guidance in statistical analysis for this project.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; or the decision to approve publication of the finished manuscript. Preliminary results of this analysis were presented at the 2018 Society of Hospital Medicine Annual Meeting in Orlando, Florida. All coauthors have seen and agree with the contents of the manuscript. The submission is not under review by any other publication.

Inpatient consultation is an extremely common practice with the potential to improve patient outcomes significantly.^1-3 However, variability in consultation practices may be risky for patients. In addition to underuse when the benefit is clear, the overuse of consultation may lead to additional testing and therapies, increased length of stay (LOS) and costs, conflicting recommendations, and opportunities for communication breakdown.

Consultation use is often at the discretion of individual providers. While this decision is frequently driven by patient needs, significant variation in consultation practices not fully explained by patient factors exists.¹ Prior work has described hospital-level variation¹ and that primary care physicians use more consultation than hospitalists.⁴ However, other factors affecting consultation remain unknown. We sought to explore physician-, patient-, and admission-level factors associated with consultation use on inpatient general medicine services.

Study Design

We conducted a retrospective analysis of data from the University of Chicago Hospitalist Project (UCHP). UCHP is a longstanding study of the care of hospitalized patients admitted to the University of Chicago general medicine services, involving both patient data collection and physician experience surveys.⁵ Data were obtained for enrolled UCHP patients between 2011-2016 from the Center for Research Informatics (CRI). The University of Chicago Institutional Review Board approved this study.

Data Collection

Attendings and patients consented to UCHP participation. Data collection details are described elsewhere.^5,6 Data from EpicCare (EpicSystems Corp, Wisconsin) and Centricity Billing (GE Healthcare, Illinois) were obtained via CRI for all encounters of enrolled UCHP patients during the study period (N = 218,591).

Attending Attribution

We determined attending attribution for admissions as follows: the attending author of the first history and physical (H&P) was assigned. If this was unavailable, the attending author of the first progress note (PN) was assigned. For patients admitted by hospitalists on admitting shifts to nonteaching services (ie, service without residents/students), the author of the first PN was assigned if different from H&P. Where available, attribution was corroborated with call schedules.

Sample and Variables

All encounters containing inpatient admissions to the University of Chicago from May 10, 2011 (Electronic Health Record activation date), through December 31, 2016, were considered for inclusion (N = 51,171, Appendix 1). Admissions including only documentation from ancillary services were excluded (eg, encounters for hemodialysis or physical therapy). Admissions were limited to a length of stay (LOS) ≤ 5 days, corresponding to the average US inpatient LOS of 4.6 days,⁷ to minimize the likelihood of attending handoffs (N = 31,592). If attending attribution was not possible via the above-described methods, the admission was eliminated (N = 3,103; 10.9% of admissions with LOS ≤ 5 days). Finally, the sample was restricted to general medicine service admissions under attendings enrolled in UCHP who completed surveys. After the application of all criteria, 6,153 admissions remained for analysis.

The outcome variable was the number of consultations per admission, determined by counting the unique number of services creating clinical documentation, and subtracting one for the primary team. If the Medical/Surgical intensive care unit (ICU) was a service, then two were subtracted to account for the ICU transfer.

Attending years in practice (ie, years since medical school graduation) and gender were determined from public resources. Practice characteristics were determined from UCHP attending surveys, which address perceptions of workload and satisfaction (Appendix 2).

Patient characteristics (gender, age, Elixhauser Indices) and admission characteristics (LOS, season of admission, payor) were determined from UCHP and CRI data. The Elixhauser Index uses a well-validated system combining the presence/absence of 31 comorbidities to predict mortality and 30-day readmission.⁸ Elixhauser Indices were calculated using the “Creation of Elixhauser Comorbidity Index Scores 1.0” software.⁹ For admissions under hospitalist attendings, teaching/nonteaching team was ascertained via internal teaching service calendars.

Analysis

We used descriptive statistics to examine demographic characteristics. The difference between the lowest and highest quartile consultation use was determined via a two-sample t test. Given the multilevel nature of our count data, we used a mixed-effects Poisson model accounting for within-group variation by clustering on attending and patient (3-level random-effects model). The analysis was done using Stata 15 (StataCorp, Texas).

From 2011 to 2016, 14,848 patients and 88 attendings were enrolled in UCHP; 4,772 patients (32%) and 69 attendings (59.4%) had data available and were included. Mean LOS was 3.0 days (SD = 1.3). Table 1 describes the characteristics of attendings, patients, and admissions.

Seventy-six percent of admissions included at least one consultation. Consultation use varied widely, ranging from 0 to 10 per admission (mean = 1.39, median = 1; standard deviation [SD] = 1.17). The number of consultations per admission in the highest quartile of consultation frequency (mean = 3.47, median = 3) was 5.7-fold that of the lowest quartile (mean = 0.613, median = 1; P <.001).

In multivariable regression, physician-, patient-, and admission-level characteristics were associated with the differential use of consultation (Table 2). On teaching services, consultations called by hospitalist vs nonhospitalist generalists did not differ (P =.361). However, hospitalists on nonteaching services called 8.6% more consultations than hospitalists on teaching services (P =.02). Attending agreement with survey item “The interruption of my personal life by work is a problem” was associated with 8.2% fewer consultations per admission (P =.002).

Patients older than 75 years received 19% fewer consultations compared with patients younger than 49 years (P <.001). Compared with Medicare, Medicaid admissions had 12.2% fewer consultations (P <.001), whereas privately insured admissions had 10.7% more (P =.001). The number of consultations per admission decreased every year, with 45.3% fewer consultations in 2015 than 2011 (P <.001). Consultations increased by each 22% per day increase in LOS (P <.001).

Our analysis described several physician-, patient-, and admission-level characteristics associated with the use of inpatient consultation. Our results strengthen prior work demonstrating that patient-level factors alone are insufficient to explain consultation variability.¹

Hospitalists on nonteaching services called more consultations, which may reflect a higher workload on these services. Busy hospitalists on nonteaching teams may lack time to delve deeply into clinical problems and require more consultations, especially for work with heavy cognitive loads such as diagnosis. “Outsourcing” tasks when workload increases occurs in other cognitive activities such as teaching.¹⁰ The association between work interrupting personal life and fewer consultations may also implicate the effects of time. Attendings who are experiencing work encroaching on their personal lives may be those spending more time with patients and consulting less. This finding merits further study, especially with increasing concern about balancing time spent in meaningful patient care activities with risk of physician burnout.

This finding could also indicate that trainee participation modifies consultation use for hospitalists. Teaching service teams with more individual members may allow a greater pool of collective knowledge, decreasing the need for consultation to answer clinical questions.¹¹ Interestingly, there was no difference in consultation use between generalists or subspecialists and hospitalists on teaching services, possibly suggesting a unique effect in hospitalists who vary clinical practice depending on team structure. These differences deserve further investigation, with implications for education and resource utilization.

We were surprised by the finding that consultations decreased each year, despite increasing patient complexity and availability of consultation services. This could be explained by a growing emphasis on shortening LOS in our institution, thus shifting consultative care to outpatient settings. Understanding these effects is critically important with growing evidence that consultation improves patient outcomes because these external pressures could lead to unintended consequences for quality or access to care.

Several findings related to patient factors additionally emerged, including age and insurance status. Although related to medical complexity, these effects persist despite adjustment, which raises the question of whether they contribute to the decision to seek consultation. Older patients received fewer consultations, which could reflect the use of more conservative practice models in the elderly,¹² or ageism, which is associated with undertreatment.¹³ With respect to insurance status, Medicaid patients were associated with fewer consultations. This finding is consistent with previous work showing the decreased intensity of hospital services used for Medicaid patients.¹⁴Our study has limitations. Our data were from one large urban academic center that limits generalizability. Although systematic and redundant, attending attribution may have been flawed: incomplete or erroneous documentation could have led to attribution error, and we cannot rule out the possibility of service handoffs. We used a LOS ≤ 5 days to minimize this possibility, but this limits the applicability of our findings to longer admissions. Unsurprisingly, longer LOS correlated with the increased use of consultation even within our restricted sample, and future work should examine the effects of prolonged LOS. As a retrospective analysis, unmeasured confounders due to our limited adjustment will likely explain some findings, although we took steps to address this in our statistical design. Finally, we could not measure patient outcomes and, therefore, cannot determine the value of more or fewer consultations for specific patients or illnesses. Positive and negative outcomes of increased consultation are described, and understanding the impact of consultation is critical for further study.^2,3

We found that the use of consultation on general medicine services varies widely between admissions, with large differences between the highest and lowest frequencies of use. This variation can be partially explained by several physician-, patient-, and admission-level characteristics. Our work may help identify patient and attending groups at high risk for under- or overuse of consultation and guide the subsequent development of interventions to improve value in consultation. One additional consultation over the average LOS of 4.6 days adds $420 per admission or $4.8 billion to the 11.5 million annual Medicare admissions.¹⁵ Increasing research, guidelines, and education on the judicious use of inpatient consultation will be key in maximizing high-value care and improving patient outcomes.

Acknowledgments

The authors would like to acknowledge the invaluable support and assistance of the University of Chicago Hospitalist Project, the Pritzker School of Medicine Summer Research Program, the University of Chicago Center for Quality, and the University of Chicago Center for Health and the Social Sciences (CHeSS). The authors would additionally like to thank John Cursio, PhD, for his support and guidance in statistical analysis for this project.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; or the decision to approve publication of the finished manuscript. Preliminary results of this analysis were presented at the 2018 Society of Hospital Medicine Annual Meeting in Orlando, Florida. All coauthors have seen and agree with the contents of the manuscript. The submission is not under review by any other publication.

Many provisions of the Affordable Care Act (ACA) have served to support the hospitalized patient. The expansion of Medicaid and the creation of state and federal insurance exchanges for the individual insurance market both significantly lessened the financial burden of hospital care for millions of Americans. Other aspects have proven more controversial, as many of the ACA’s health policy interventions linked to cost and quality in new ways, implementing untested concepts derived from healthcare services research on a national scale.

The Hospital Readmissions Reduction Program (HRRP) was no exception. Based on early research examining readmissions,¹ the ACA included a mandate for the Centers for Medicare and Medicaid Services (CMS) to establish the HRRP. Beginning in Fiscal Year 2013, the HRRP reduced payments for excessive, 30-day, risk-standardized readmissions covering six conditions and procedures. As the third leading cause of 30-day readmissions, chronic obstructive pulmonary disease (COPD) was included in the list of designated HRRP conditions.

This inclusion of COPD in HRRP was not without controversy; analysis of Medicare data from before the ACA’s implementation demonstrated that only half of all readmissions for acute exacerbations of COPD were respiratory-related and only a third were directly related to COPD.² Unsurprisingly, the high proportion of readmissions due to non-COPD-related causes is considered to be one of the leading factors for the failure of COPD readmission reduction programs to find significant reductions in readmissions.³ In this month’s issue of the Journal of Hospital Medicine, Buhr and colleagues explore differential readmission diagnoses following acute exacerbations of COPD using a validated, national, all-payer database.⁴

Like many analyses of payer datasets, this study has several limitations. First, although a large area of the US was included, the data did not include all US states. Further, as the study used multiple cross-sectional data using pooling techniques, it was not truly a longitudinal study. It was additionally limited to 10 months out of the calendar year, missing December and January, which have a high seasonal prevalence of viral respiratory illness. Finally, due to the nature of the data, COPD diagnoses were identified through administrative data known to be highly unreliable for fully capturing admissions for acute exacerbation of COPD.

Despite these limitations, the analysis by Buhr and colleagues provides additional value. They found an overall readmission rate of 17%, with just under half (7.69%) due to recurrent COPD. Patients with COPD-related readmissions were younger, had a higher proportion with Medicaid as the payer, were more frequently discharged home without services, had a shorter length of stay, and had fewer comorbidities.

Most critically, Buhr and colleagues—with a multipayer database—confirmed what researchers found in uni-payer⁵ and site-specific⁶ datasets: over half of readmissions are due to diagnoses other than COPD or respiratory-related causes. Patients readmitted due to other, unrelated diagnoses had a higher mean Elixhauser Comorbidity Index score along with higher rates of congestive heart failure and renal failure. To the practicing hospitalist, this finding supports what our internal clinical voice tells us: sicker patients are readmitted more often and more frequently with conditions unrelated to their index admission diagnosis.

The reaffirmation of the finding that the majority of readmissions are due to nonrespiratory-related causes suggests that perhaps we have a different problem than physicians and policymakers originally thought when adding COPD to the HRRP. Many COPD patients suffer from a polychronic disease, requiring a more holistic approach rather than a traditional, disease-driven, siloed approach focused solely on improving COPD-related care. It may also be true that for other subpopulations of patients with COPD, additional in-hospital and transition of care interventions are required to address patients’ multimorbidity and social determinants of health.

As physicians on the front lines of the readmitted patient, hospitalists are uniquely situated to see the challenges of populations with increasing disease complexity and disease combinations.⁷ The HRRP policy remains controversial. This is due in large part to recent work suggesting that while the HRRP may have helped reduce readmissions, its implementation may have driven the unintended consequence of increased mortality.⁸ Thus, our profession faces an existential challenge to traditional care delivery models targeting diseases. What has not been well parsed by the hospital industry or policymakers is what to do about it.

Readmission of the multimorbid patient, coupled with the challenges of the HRRP, focuses our attention on the need to transition care delivery to a model that is better suited to our patients’ needs: mass-customized, mass-produced service delivery. As physicians, we know that care delivery must be oriented around patients who have many diseases and unique life circumstances. It is our profession’s greatest challenge to collaborate with researchers and administrators to help do this with scale.

Acknowledgments

The authors thank Mary Akel for her assistance with manuscript submission.

Many provisions of the Affordable Care Act (ACA) have served to support the hospitalized patient. The expansion of Medicaid and the creation of state and federal insurance exchanges for the individual insurance market both significantly lessened the financial burden of hospital care for millions of Americans. Other aspects have proven more controversial, as many of the ACA’s health policy interventions linked to cost and quality in new ways, implementing untested concepts derived from healthcare services research on a national scale.

The Hospital Readmissions Reduction Program (HRRP) was no exception. Based on early research examining readmissions,¹ the ACA included a mandate for the Centers for Medicare and Medicaid Services (CMS) to establish the HRRP. Beginning in Fiscal Year 2013, the HRRP reduced payments for excessive, 30-day, risk-standardized readmissions covering six conditions and procedures. As the third leading cause of 30-day readmissions, chronic obstructive pulmonary disease (COPD) was included in the list of designated HRRP conditions.

This inclusion of COPD in HRRP was not without controversy; analysis of Medicare data from before the ACA’s implementation demonstrated that only half of all readmissions for acute exacerbations of COPD were respiratory-related and only a third were directly related to COPD.² Unsurprisingly, the high proportion of readmissions due to non-COPD-related causes is considered to be one of the leading factors for the failure of COPD readmission reduction programs to find significant reductions in readmissions.³ In this month’s issue of the Journal of Hospital Medicine, Buhr and colleagues explore differential readmission diagnoses following acute exacerbations of COPD using a validated, national, all-payer database.⁴

Like many analyses of payer datasets, this study has several limitations. First, although a large area of the US was included, the data did not include all US states. Further, as the study used multiple cross-sectional data using pooling techniques, it was not truly a longitudinal study. It was additionally limited to 10 months out of the calendar year, missing December and January, which have a high seasonal prevalence of viral respiratory illness. Finally, due to the nature of the data, COPD diagnoses were identified through administrative data known to be highly unreliable for fully capturing admissions for acute exacerbation of COPD.

Despite these limitations, the analysis by Buhr and colleagues provides additional value. They found an overall readmission rate of 17%, with just under half (7.69%) due to recurrent COPD. Patients with COPD-related readmissions were younger, had a higher proportion with Medicaid as the payer, were more frequently discharged home without services, had a shorter length of stay, and had fewer comorbidities.

Most critically, Buhr and colleagues—with a multipayer database—confirmed what researchers found in uni-payer⁵ and site-specific⁶ datasets: over half of readmissions are due to diagnoses other than COPD or respiratory-related causes. Patients readmitted due to other, unrelated diagnoses had a higher mean Elixhauser Comorbidity Index score along with higher rates of congestive heart failure and renal failure. To the practicing hospitalist, this finding supports what our internal clinical voice tells us: sicker patients are readmitted more often and more frequently with conditions unrelated to their index admission diagnosis.

The reaffirmation of the finding that the majority of readmissions are due to nonrespiratory-related causes suggests that perhaps we have a different problem than physicians and policymakers originally thought when adding COPD to the HRRP. Many COPD patients suffer from a polychronic disease, requiring a more holistic approach rather than a traditional, disease-driven, siloed approach focused solely on improving COPD-related care. It may also be true that for other subpopulations of patients with COPD, additional in-hospital and transition of care interventions are required to address patients’ multimorbidity and social determinants of health.

As physicians on the front lines of the readmitted patient, hospitalists are uniquely situated to see the challenges of populations with increasing disease complexity and disease combinations.⁷ The HRRP policy remains controversial. This is due in large part to recent work suggesting that while the HRRP may have helped reduce readmissions, its implementation may have driven the unintended consequence of increased mortality.⁸ Thus, our profession faces an existential challenge to traditional care delivery models targeting diseases. What has not been well parsed by the hospital industry or policymakers is what to do about it.

Readmission of the multimorbid patient, coupled with the challenges of the HRRP, focuses our attention on the need to transition care delivery to a model that is better suited to our patients’ needs: mass-customized, mass-produced service delivery. As physicians, we know that care delivery must be oriented around patients who have many diseases and unique life circumstances. It is our profession’s greatest challenge to collaborate with researchers and administrators to help do this with scale.

Acknowledgments

The authors thank Mary Akel for her assistance with manuscript submission.

Many provisions of the Affordable Care Act (ACA) have served to support the hospitalized patient. The expansion of Medicaid and the creation of state and federal insurance exchanges for the individual insurance market both significantly lessened the financial burden of hospital care for millions of Americans. Other aspects have proven more controversial, as many of the ACA’s health policy interventions linked to cost and quality in new ways, implementing untested concepts derived from healthcare services research on a national scale.

The Hospital Readmissions Reduction Program (HRRP) was no exception. Based on early research examining readmissions,¹ the ACA included a mandate for the Centers for Medicare and Medicaid Services (CMS) to establish the HRRP. Beginning in Fiscal Year 2013, the HRRP reduced payments for excessive, 30-day, risk-standardized readmissions covering six conditions and procedures. As the third leading cause of 30-day readmissions, chronic obstructive pulmonary disease (COPD) was included in the list of designated HRRP conditions.

This inclusion of COPD in HRRP was not without controversy; analysis of Medicare data from before the ACA’s implementation demonstrated that only half of all readmissions for acute exacerbations of COPD were respiratory-related and only a third were directly related to COPD.² Unsurprisingly, the high proportion of readmissions due to non-COPD-related causes is considered to be one of the leading factors for the failure of COPD readmission reduction programs to find significant reductions in readmissions.³ In this month’s issue of the Journal of Hospital Medicine, Buhr and colleagues explore differential readmission diagnoses following acute exacerbations of COPD using a validated, national, all-payer database.⁴

Like many analyses of payer datasets, this study has several limitations. First, although a large area of the US was included, the data did not include all US states. Further, as the study used multiple cross-sectional data using pooling techniques, it was not truly a longitudinal study. It was additionally limited to 10 months out of the calendar year, missing December and January, which have a high seasonal prevalence of viral respiratory illness. Finally, due to the nature of the data, COPD diagnoses were identified through administrative data known to be highly unreliable for fully capturing admissions for acute exacerbation of COPD.

Despite these limitations, the analysis by Buhr and colleagues provides additional value. They found an overall readmission rate of 17%, with just under half (7.69%) due to recurrent COPD. Patients with COPD-related readmissions were younger, had a higher proportion with Medicaid as the payer, were more frequently discharged home without services, had a shorter length of stay, and had fewer comorbidities.

Most critically, Buhr and colleagues—with a multipayer database—confirmed what researchers found in uni-payer⁵ and site-specific⁶ datasets: over half of readmissions are due to diagnoses other than COPD or respiratory-related causes. Patients readmitted due to other, unrelated diagnoses had a higher mean Elixhauser Comorbidity Index score along with higher rates of congestive heart failure and renal failure. To the practicing hospitalist, this finding supports what our internal clinical voice tells us: sicker patients are readmitted more often and more frequently with conditions unrelated to their index admission diagnosis.

The reaffirmation of the finding that the majority of readmissions are due to nonrespiratory-related causes suggests that perhaps we have a different problem than physicians and policymakers originally thought when adding COPD to the HRRP. Many COPD patients suffer from a polychronic disease, requiring a more holistic approach rather than a traditional, disease-driven, siloed approach focused solely on improving COPD-related care. It may also be true that for other subpopulations of patients with COPD, additional in-hospital and transition of care interventions are required to address patients’ multimorbidity and social determinants of health.

As physicians on the front lines of the readmitted patient, hospitalists are uniquely situated to see the challenges of populations with increasing disease complexity and disease combinations.⁷ The HRRP policy remains controversial. This is due in large part to recent work suggesting that while the HRRP may have helped reduce readmissions, its implementation may have driven the unintended consequence of increased mortality.⁸ Thus, our profession faces an existential challenge to traditional care delivery models targeting diseases. What has not been well parsed by the hospital industry or policymakers is what to do about it.

Readmission of the multimorbid patient, coupled with the challenges of the HRRP, focuses our attention on the need to transition care delivery to a model that is better suited to our patients’ needs: mass-customized, mass-produced service delivery. As physicians, we know that care delivery must be oriented around patients who have many diseases and unique life circumstances. It is our profession’s greatest challenge to collaborate with researchers and administrators to help do this with scale.

Acknowledgments

The authors thank Mary Akel for her assistance with manuscript submission.

SAN FRANCISCO – In men with localized prostate cancer, a hypofractionated radiotherapy regimen that cuts treatment time in half continues to have noninferior efficacy long-term relative to a conventional radiotherapy regimen, an update of the CHHiP trial shows.

Susan London/MDedge News

The 3,216 men in the phase 3 trial had node-negative T1b-T3a prostate cancer and were evenly assigned to a conventional regimen of 74 Gy delivered in 37 fractions, a hypofractionated regimen of 60 Gy in 20 fractions, or a hypofractionated regimen of 57 Gy in 19 fractions. All regimens were delivered with intensity-modulated techniques.

The trial’s 5-year results, previously reported, showed noninferiority of the 60-Gy regimen, compared with the 74-Gy regimen on risk of biochemical or clinical failure (hazard ratio, 0.84), prompting recommendation of the former as a new standard of care for localized prostate cancer (Lancet Oncol. 2016;17:1047-60). Noninferiority could not be established for the 57-Gy regimen.

The 8-year results were essentially the same, confirming noninferiority of the 60-Gy regimen (HR, 0.85) but not the 57-Gy regimen. Meanwhile, bowel and bladder toxicity continued to be low across regimens.

David P. Dearnaley, MB BCh, MD, of the Royal Marsden NHS Foundation Trust, London, reported the 8-year results at the 2020 Genitourinary Cancers Symposium, sponsored by the American Society for Clinical Oncology, ASTRO, and the Society of Urologic Oncology.

Study details

At a median follow-up of 9.3 years, the 8-year rate of freedom from biochemical failure (defined by Phoenix consensus guidelines) or clinical failure (cancer recurrence) was 80.6% with 74 Gy, 83.7% with 60 Gy, and 78.5% with 57 Gy, Dr. Dearnaley reported.

Analyses confirmed noninferiority of the 60-Gy regimen (HR, 0.85; 95% confidence interval, 0.72-1.01; P = .11), but not the 57-Gy regimen (HR, 1.17; 95% CI, 1.00-1.36; P = .10), as the upper bound of the confidence interval crossed the predefined 1.21 boundary for noninferiority.

In an unplanned analysis, the pattern among men younger than 75 years was similar to that in the entire trial population. But among men 75 years of age and older, the 57-Gy arm is actually outperforming the 74-Gy arm (HR, 0.77).

The three regimens yielded a similarly high rate of freedom from metastases, at about 95% in each arm. The 60-Gy regimen had an edge in overall survival relative to the 74-Gy regimen (88.6% vs. 85.9%; HR, 0.84) that is hard to explain, according to Dr. Dearnaley.

“Because there is an 8:1 ratio of non–prostate cancer deaths to prostate cancer deaths, you would have to postulate something other than prostate cancer being affected by the radiotherapy fractionation,” he said. “The answers on a postcard, because I can’t think of one.”

On central pathology review, nearly a fifth of evaluated trial patients had high-risk disease. “I know everybody wants to know about high-risk patients, but I’d rather take the trial results as a whole and look to see if there is any heterogeneity between those groups rather than perform a specific high-risk subgroup analysis,” Dr. Dearnaley said, expressing concern about performing too many subgroup analyses.

That said, older patients on the trial tended to have higher risk. “It does seem hypofractionation was particularly useful in those patients,” he noted. “Now, whether that’s anything to do with their pathology or whether it’s due to their age per se, I really don’t know.”

There were no differences between groups on rates of Radiation Therapy Oncology Group toxicity at 5 years, with grade 2 or worse bowel toxicity and bladder toxicity each seen in about 2% of patients.

There were no significant differences in rates of patient-reported “moderate or big” bowel bother (roughly 5%-8%) and urinary bother (roughly 7%-9%). For all regimens, bowel and urinary symptoms remained stable from 2-5 years.

Reassuring for practice

These updated findings “support the continued use of 60 Gy in 20 fractions as the standard of care,” Dr. Dearnaley said.

When the math is run to permit comparison, efficacy findings of the CHHiP trial show “amazing agreement” with those of the similar multinational PROFIT trial, he noted (J Clin Oncol. 2017 Jun 10;35(17):1884-90).

The absolute advantage in the failure-free rate of 3.1% and the overall survival rate of 2.7% for the 60-Gy regimen in CHHiP generated interest among symposium attendees about its possible superiority. “I think the 60 Gy is marginally more effective than the 74 Gy,” Dr. Dearnaley said, but he acknowledged that there are no statistics to prove that.

Susan London/MDedge News

“This CHHiP update is fantastic,” said session cochair Paul L. Nguyen, MD, of the Dana-Farber Cancer Institute in Boston. “It is very reassuring that the initial results the investigators presented several years ago still hold up in the long term. It’s even more reassuring for the use of hypofractionation, and it’s great to know that we can use it across the age spectrum and it works well.”

This trial is the only noninferiority hypofractionation trial in prostate cancer that includes a sizable share of patients at high risk for poor outcomes, a population for whom efficacy of this strategy is of particular interest, Dr. Nguyen noted.

“That’s always been a question,” he said. “The majority of the data from the noninferiority trials is for the low- and intermediate-risk patients. So it really would be interesting to learn whatever we can about high-risk patients from this trial.”

The trial was funded by Cancer Research UK, Department of Health (UK), and the National Institute for Health Research Cancer Research Network. Dr. Dearnaley and Dr. Nguyen disclosed relationships with a range of pharmaceutical companies.
 

SOURCE: Dearnaley DP et al. GUCS 2020. Abstract 325.

SAN FRANCISCO – In men with localized prostate cancer, a hypofractionated radiotherapy regimen that cuts treatment time in half continues to have noninferior efficacy long-term relative to a conventional radiotherapy regimen, an update of the CHHiP trial shows.

Susan London/MDedge News

The 3,216 men in the phase 3 trial had node-negative T1b-T3a prostate cancer and were evenly assigned to a conventional regimen of 74 Gy delivered in 37 fractions, a hypofractionated regimen of 60 Gy in 20 fractions, or a hypofractionated regimen of 57 Gy in 19 fractions. All regimens were delivered with intensity-modulated techniques.

The trial’s 5-year results, previously reported, showed noninferiority of the 60-Gy regimen, compared with the 74-Gy regimen on risk of biochemical or clinical failure (hazard ratio, 0.84), prompting recommendation of the former as a new standard of care for localized prostate cancer (Lancet Oncol. 2016;17:1047-60). Noninferiority could not be established for the 57-Gy regimen.

The 8-year results were essentially the same, confirming noninferiority of the 60-Gy regimen (HR, 0.85) but not the 57-Gy regimen. Meanwhile, bowel and bladder toxicity continued to be low across regimens.

David P. Dearnaley, MB BCh, MD, of the Royal Marsden NHS Foundation Trust, London, reported the 8-year results at the 2020 Genitourinary Cancers Symposium, sponsored by the American Society for Clinical Oncology, ASTRO, and the Society of Urologic Oncology.

Study details

At a median follow-up of 9.3 years, the 8-year rate of freedom from biochemical failure (defined by Phoenix consensus guidelines) or clinical failure (cancer recurrence) was 80.6% with 74 Gy, 83.7% with 60 Gy, and 78.5% with 57 Gy, Dr. Dearnaley reported.

Analyses confirmed noninferiority of the 60-Gy regimen (HR, 0.85; 95% confidence interval, 0.72-1.01; P = .11), but not the 57-Gy regimen (HR, 1.17; 95% CI, 1.00-1.36; P = .10), as the upper bound of the confidence interval crossed the predefined 1.21 boundary for noninferiority.

In an unplanned analysis, the pattern among men younger than 75 years was similar to that in the entire trial population. But among men 75 years of age and older, the 57-Gy arm is actually outperforming the 74-Gy arm (HR, 0.77).

The three regimens yielded a similarly high rate of freedom from metastases, at about 95% in each arm. The 60-Gy regimen had an edge in overall survival relative to the 74-Gy regimen (88.6% vs. 85.9%; HR, 0.84) that is hard to explain, according to Dr. Dearnaley.

“Because there is an 8:1 ratio of non–prostate cancer deaths to prostate cancer deaths, you would have to postulate something other than prostate cancer being affected by the radiotherapy fractionation,” he said. “The answers on a postcard, because I can’t think of one.”

On central pathology review, nearly a fifth of evaluated trial patients had high-risk disease. “I know everybody wants to know about high-risk patients, but I’d rather take the trial results as a whole and look to see if there is any heterogeneity between those groups rather than perform a specific high-risk subgroup analysis,” Dr. Dearnaley said, expressing concern about performing too many subgroup analyses.

That said, older patients on the trial tended to have higher risk. “It does seem hypofractionation was particularly useful in those patients,” he noted. “Now, whether that’s anything to do with their pathology or whether it’s due to their age per se, I really don’t know.”

There were no differences between groups on rates of Radiation Therapy Oncology Group toxicity at 5 years, with grade 2 or worse bowel toxicity and bladder toxicity each seen in about 2% of patients.

There were no significant differences in rates of patient-reported “moderate or big” bowel bother (roughly 5%-8%) and urinary bother (roughly 7%-9%). For all regimens, bowel and urinary symptoms remained stable from 2-5 years.

Reassuring for practice

These updated findings “support the continued use of 60 Gy in 20 fractions as the standard of care,” Dr. Dearnaley said.

When the math is run to permit comparison, efficacy findings of the CHHiP trial show “amazing agreement” with those of the similar multinational PROFIT trial, he noted (J Clin Oncol. 2017 Jun 10;35(17):1884-90).

The absolute advantage in the failure-free rate of 3.1% and the overall survival rate of 2.7% for the 60-Gy regimen in CHHiP generated interest among symposium attendees about its possible superiority. “I think the 60 Gy is marginally more effective than the 74 Gy,” Dr. Dearnaley said, but he acknowledged that there are no statistics to prove that.

Susan London/MDedge News

“This CHHiP update is fantastic,” said session cochair Paul L. Nguyen, MD, of the Dana-Farber Cancer Institute in Boston. “It is very reassuring that the initial results the investigators presented several years ago still hold up in the long term. It’s even more reassuring for the use of hypofractionation, and it’s great to know that we can use it across the age spectrum and it works well.”

This trial is the only noninferiority hypofractionation trial in prostate cancer that includes a sizable share of patients at high risk for poor outcomes, a population for whom efficacy of this strategy is of particular interest, Dr. Nguyen noted.

“That’s always been a question,” he said. “The majority of the data from the noninferiority trials is for the low- and intermediate-risk patients. So it really would be interesting to learn whatever we can about high-risk patients from this trial.”

The trial was funded by Cancer Research UK, Department of Health (UK), and the National Institute for Health Research Cancer Research Network. Dr. Dearnaley and Dr. Nguyen disclosed relationships with a range of pharmaceutical companies.
 

SOURCE: Dearnaley DP et al. GUCS 2020. Abstract 325.

SAN FRANCISCO – In men with localized prostate cancer, a hypofractionated radiotherapy regimen that cuts treatment time in half continues to have noninferior efficacy long-term relative to a conventional radiotherapy regimen, an update of the CHHiP trial shows.

Susan London/MDedge News

The 3,216 men in the phase 3 trial had node-negative T1b-T3a prostate cancer and were evenly assigned to a conventional regimen of 74 Gy delivered in 37 fractions, a hypofractionated regimen of 60 Gy in 20 fractions, or a hypofractionated regimen of 57 Gy in 19 fractions. All regimens were delivered with intensity-modulated techniques.

The trial’s 5-year results, previously reported, showed noninferiority of the 60-Gy regimen, compared with the 74-Gy regimen on risk of biochemical or clinical failure (hazard ratio, 0.84), prompting recommendation of the former as a new standard of care for localized prostate cancer (Lancet Oncol. 2016;17:1047-60). Noninferiority could not be established for the 57-Gy regimen.

The 8-year results were essentially the same, confirming noninferiority of the 60-Gy regimen (HR, 0.85) but not the 57-Gy regimen. Meanwhile, bowel and bladder toxicity continued to be low across regimens.

David P. Dearnaley, MB BCh, MD, of the Royal Marsden NHS Foundation Trust, London, reported the 8-year results at the 2020 Genitourinary Cancers Symposium, sponsored by the American Society for Clinical Oncology, ASTRO, and the Society of Urologic Oncology.

Study details

At a median follow-up of 9.3 years, the 8-year rate of freedom from biochemical failure (defined by Phoenix consensus guidelines) or clinical failure (cancer recurrence) was 80.6% with 74 Gy, 83.7% with 60 Gy, and 78.5% with 57 Gy, Dr. Dearnaley reported.

Analyses confirmed noninferiority of the 60-Gy regimen (HR, 0.85; 95% confidence interval, 0.72-1.01; P = .11), but not the 57-Gy regimen (HR, 1.17; 95% CI, 1.00-1.36; P = .10), as the upper bound of the confidence interval crossed the predefined 1.21 boundary for noninferiority.

In an unplanned analysis, the pattern among men younger than 75 years was similar to that in the entire trial population. But among men 75 years of age and older, the 57-Gy arm is actually outperforming the 74-Gy arm (HR, 0.77).

The three regimens yielded a similarly high rate of freedom from metastases, at about 95% in each arm. The 60-Gy regimen had an edge in overall survival relative to the 74-Gy regimen (88.6% vs. 85.9%; HR, 0.84) that is hard to explain, according to Dr. Dearnaley.

“Because there is an 8:1 ratio of non–prostate cancer deaths to prostate cancer deaths, you would have to postulate something other than prostate cancer being affected by the radiotherapy fractionation,” he said. “The answers on a postcard, because I can’t think of one.”

On central pathology review, nearly a fifth of evaluated trial patients had high-risk disease. “I know everybody wants to know about high-risk patients, but I’d rather take the trial results as a whole and look to see if there is any heterogeneity between those groups rather than perform a specific high-risk subgroup analysis,” Dr. Dearnaley said, expressing concern about performing too many subgroup analyses.

That said, older patients on the trial tended to have higher risk. “It does seem hypofractionation was particularly useful in those patients,” he noted. “Now, whether that’s anything to do with their pathology or whether it’s due to their age per se, I really don’t know.”

There were no differences between groups on rates of Radiation Therapy Oncology Group toxicity at 5 years, with grade 2 or worse bowel toxicity and bladder toxicity each seen in about 2% of patients.

There were no significant differences in rates of patient-reported “moderate or big” bowel bother (roughly 5%-8%) and urinary bother (roughly 7%-9%). For all regimens, bowel and urinary symptoms remained stable from 2-5 years.

Reassuring for practice

These updated findings “support the continued use of 60 Gy in 20 fractions as the standard of care,” Dr. Dearnaley said.

When the math is run to permit comparison, efficacy findings of the CHHiP trial show “amazing agreement” with those of the similar multinational PROFIT trial, he noted (J Clin Oncol. 2017 Jun 10;35(17):1884-90).

The absolute advantage in the failure-free rate of 3.1% and the overall survival rate of 2.7% for the 60-Gy regimen in CHHiP generated interest among symposium attendees about its possible superiority. “I think the 60 Gy is marginally more effective than the 74 Gy,” Dr. Dearnaley said, but he acknowledged that there are no statistics to prove that.

Susan London/MDedge News

“This CHHiP update is fantastic,” said session cochair Paul L. Nguyen, MD, of the Dana-Farber Cancer Institute in Boston. “It is very reassuring that the initial results the investigators presented several years ago still hold up in the long term. It’s even more reassuring for the use of hypofractionation, and it’s great to know that we can use it across the age spectrum and it works well.”

This trial is the only noninferiority hypofractionation trial in prostate cancer that includes a sizable share of patients at high risk for poor outcomes, a population for whom efficacy of this strategy is of particular interest, Dr. Nguyen noted.

“That’s always been a question,” he said. “The majority of the data from the noninferiority trials is for the low- and intermediate-risk patients. So it really would be interesting to learn whatever we can about high-risk patients from this trial.”

The trial was funded by Cancer Research UK, Department of Health (UK), and the National Institute for Health Research Cancer Research Network. Dr. Dearnaley and Dr. Nguyen disclosed relationships with a range of pharmaceutical companies.
 

SOURCE: Dearnaley DP et al. GUCS 2020. Abstract 325.

Individuals who exhibit antisocial behavior over a lifetime have a thinner cortex and smaller surface area in key brain regions relative to their counterparts who do not engage in antisocial behavior, new research shows.

However, investigators found no widespread structural brain abnormalities in the group of individuals who exhibited antisocial behavior only during adolescence.

These brain differences seem to be “quite specific and unique” to individuals who exhibit persistent antisocial behavior over their life, lead researcher Christina O. Carlisi, PhD, of University College London, said during a press briefing.

“Critically, the findings don’t directly link brain structure abnormalities to antisocial behavior,” she said. Nor do they mean that anyone with a smaller brain or brain area is destined to be antisocial or to commit a crime.

“Our findings support the idea that, for the small proportion of individuals with life-course–persistent antisocial behavior, there may be differences in their brain structure that make it difficult for them to develop social skills that prevent them from engaging in antisocial behavior,” Dr. Carlisi said in a news release. “These people could benefit from more support throughout their lives.”

The study, the investigators noted, provides the first robust evidence to suggest that underlying neuropsychological differences are primarily associated with life-course-persistent persistent antisocial behavior. It was published online Feb. 17 in the Lancet Psychiatry (doi: 10.1016/S2215-0366[20]30002-X).

Support for second chances

Speaking at the press briefing, coauthor Terrie E. Moffitt, PhD, of Duke University, Durham, N.C., said it’s well known that most young criminals are between the ages of 16 and 25.

Breaking the law is not at all rare in this age group, but not all of these young offenders are alike, she noted. Only a few become persistent repeat offenders.

“They start as a young child with aggressive conduct problems and eventually sink into a long-term lifestyle of repetitive serious crime that lasts well into adulthood, but this is a small group,” Dr. Moffitt explained. “In contrast, the larger majority of offenders will have only a short-term brush with lawbreaking and then grow up to become law-abiding members of society.”

The current study suggests that what makes short-term offenders behave differently from long-term offenders might involve some vulnerability at the level of the structure of the brain, Dr. Moffitt said.

The findings stem from 672 individuals in the Dunedin Multidisciplinary Health and Development Study, a population-representative, longitudinal birth cohort that assesses health and behavior.

On the basis of reports from parents, care givers, and teachers, as well as self-reports of conduct problems in persons aged 7-26 years, 80 participants (12%) had “life-course–persistent” antisocial behavior, 151 (23%) had adolescent-only antisocial behavior, and 441 (66%) had “low” antisocial behavior (control group, whose members never had a pervasive or persistent pattern of antisocial behavior).

Brain MRI obtained at age 45 years showed that, among individuals with persistent antisocial behavior, mean surface area was smaller (95% confidence interval, –0.24 to –0.11; P less than .0001) and mean cortical thickness was lower (95% CI, –0.19 to –0.02; P = .020) than was those of their peers in the control group.

For those in the life-course–persistent group, surface area was reduced in 282 of 360 anatomically defined brain parcels, and cortex was thinner in 11 of 360 parcels encompassing frontal and temporal regions (which were associated with executive function, emotion regulation, and motivation), compared with the control group.

Widespread differences in brain surface morphometry were not found in those who exhibited antisocial behavior during adolescence only. Such behavior was likely the result of their having to navigate through socially tough years.

“These findings underscore prior research that really highlights that there are different types of young offenders. They are not all the same; they should not all be treated the same,” coauthor Essi Viding, PhD, who also is affiliated with University College London, told reporters.

The findings support current strategies aimed at giving young offenders “a second chance” as opposed to enforcing harsher policies that prioritize incarceration for all young offenders, Dr. Viding added.

Important contribution

The authors of an accompanying commentary noted that, despite “remarkable progress in the past 3 decades, the etiology of antisocial behavior remains elusive” (Lancet Psychiatry. 2020 Feb 17. doi: 10.1016/S2215-0366[20]30035-3).

This study makes “an important contribution by identifying structural brain correlates of antisocial behavior that could be used to differentiate among individuals with life-course-persistent antisocial behavior, those with adolescence-limited antisocial behavior, and non-antisocial controls,” write Inti A. Brazil, PhD, of the Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands, and Macià Buades-Rotger, PhD, of the Institute of Psychology II, University of Lübeck, Germany.

They noted that the findings might help to move the field closer to achieving the long-standing goal of incorporating neural data into assessment protocols for antisocial behavior.

The discovery of “meaningful morphologic differences between individuals with life-course–persistent and adolescence-limited antisocial behavior offers an important advance in the use of brain metrics for differentiating among individuals with antisocial dispositions.

“Importantly, however, it remains to be determined whether and how measuring the brain can be used to bridge the different taxometric views and theories on the etiology of antisocial behavior,” Dr. Brazil and Dr. Buades-Rotger concluded.

The study was funded by the U.S. National Institute on Aging; the Health Research Council of New Zealand; the New Zealand Ministry of Business, Innovation and Employment; the U.K. Medical Research Council; the Avielle Foundation; and the Wellcome Trust. The study authors and the authors of the commentary disclosed no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

Individuals who exhibit antisocial behavior over a lifetime have a thinner cortex and smaller surface area in key brain regions relative to their counterparts who do not engage in antisocial behavior, new research shows.

However, investigators found no widespread structural brain abnormalities in the group of individuals who exhibited antisocial behavior only during adolescence.

These brain differences seem to be “quite specific and unique” to individuals who exhibit persistent antisocial behavior over their life, lead researcher Christina O. Carlisi, PhD, of University College London, said during a press briefing.

“Critically, the findings don’t directly link brain structure abnormalities to antisocial behavior,” she said. Nor do they mean that anyone with a smaller brain or brain area is destined to be antisocial or to commit a crime.

“Our findings support the idea that, for the small proportion of individuals with life-course–persistent antisocial behavior, there may be differences in their brain structure that make it difficult for them to develop social skills that prevent them from engaging in antisocial behavior,” Dr. Carlisi said in a news release. “These people could benefit from more support throughout their lives.”

The study, the investigators noted, provides the first robust evidence to suggest that underlying neuropsychological differences are primarily associated with life-course-persistent persistent antisocial behavior. It was published online Feb. 17 in the Lancet Psychiatry (doi: 10.1016/S2215-0366[20]30002-X).

Support for second chances

Speaking at the press briefing, coauthor Terrie E. Moffitt, PhD, of Duke University, Durham, N.C., said it’s well known that most young criminals are between the ages of 16 and 25.

Breaking the law is not at all rare in this age group, but not all of these young offenders are alike, she noted. Only a few become persistent repeat offenders.

“They start as a young child with aggressive conduct problems and eventually sink into a long-term lifestyle of repetitive serious crime that lasts well into adulthood, but this is a small group,” Dr. Moffitt explained. “In contrast, the larger majority of offenders will have only a short-term brush with lawbreaking and then grow up to become law-abiding members of society.”

The current study suggests that what makes short-term offenders behave differently from long-term offenders might involve some vulnerability at the level of the structure of the brain, Dr. Moffitt said.

The findings stem from 672 individuals in the Dunedin Multidisciplinary Health and Development Study, a population-representative, longitudinal birth cohort that assesses health and behavior.

On the basis of reports from parents, care givers, and teachers, as well as self-reports of conduct problems in persons aged 7-26 years, 80 participants (12%) had “life-course–persistent” antisocial behavior, 151 (23%) had adolescent-only antisocial behavior, and 441 (66%) had “low” antisocial behavior (control group, whose members never had a pervasive or persistent pattern of antisocial behavior).

Brain MRI obtained at age 45 years showed that, among individuals with persistent antisocial behavior, mean surface area was smaller (95% confidence interval, –0.24 to –0.11; P less than .0001) and mean cortical thickness was lower (95% CI, –0.19 to –0.02; P = .020) than was those of their peers in the control group.

For those in the life-course–persistent group, surface area was reduced in 282 of 360 anatomically defined brain parcels, and cortex was thinner in 11 of 360 parcels encompassing frontal and temporal regions (which were associated with executive function, emotion regulation, and motivation), compared with the control group.

Widespread differences in brain surface morphometry were not found in those who exhibited antisocial behavior during adolescence only. Such behavior was likely the result of their having to navigate through socially tough years.

“These findings underscore prior research that really highlights that there are different types of young offenders. They are not all the same; they should not all be treated the same,” coauthor Essi Viding, PhD, who also is affiliated with University College London, told reporters.

The findings support current strategies aimed at giving young offenders “a second chance” as opposed to enforcing harsher policies that prioritize incarceration for all young offenders, Dr. Viding added.

Important contribution

The authors of an accompanying commentary noted that, despite “remarkable progress in the past 3 decades, the etiology of antisocial behavior remains elusive” (Lancet Psychiatry. 2020 Feb 17. doi: 10.1016/S2215-0366[20]30035-3).

This study makes “an important contribution by identifying structural brain correlates of antisocial behavior that could be used to differentiate among individuals with life-course-persistent antisocial behavior, those with adolescence-limited antisocial behavior, and non-antisocial controls,” write Inti A. Brazil, PhD, of the Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands, and Macià Buades-Rotger, PhD, of the Institute of Psychology II, University of Lübeck, Germany.

They noted that the findings might help to move the field closer to achieving the long-standing goal of incorporating neural data into assessment protocols for antisocial behavior.

The discovery of “meaningful morphologic differences between individuals with life-course–persistent and adolescence-limited antisocial behavior offers an important advance in the use of brain metrics for differentiating among individuals with antisocial dispositions.

“Importantly, however, it remains to be determined whether and how measuring the brain can be used to bridge the different taxometric views and theories on the etiology of antisocial behavior,” Dr. Brazil and Dr. Buades-Rotger concluded.

The study was funded by the U.S. National Institute on Aging; the Health Research Council of New Zealand; the New Zealand Ministry of Business, Innovation and Employment; the U.K. Medical Research Council; the Avielle Foundation; and the Wellcome Trust. The study authors and the authors of the commentary disclosed no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

Individuals who exhibit antisocial behavior over a lifetime have a thinner cortex and smaller surface area in key brain regions relative to their counterparts who do not engage in antisocial behavior, new research shows.

However, investigators found no widespread structural brain abnormalities in the group of individuals who exhibited antisocial behavior only during adolescence.

These brain differences seem to be “quite specific and unique” to individuals who exhibit persistent antisocial behavior over their life, lead researcher Christina O. Carlisi, PhD, of University College London, said during a press briefing.

“Critically, the findings don’t directly link brain structure abnormalities to antisocial behavior,” she said. Nor do they mean that anyone with a smaller brain or brain area is destined to be antisocial or to commit a crime.

“Our findings support the idea that, for the small proportion of individuals with life-course–persistent antisocial behavior, there may be differences in their brain structure that make it difficult for them to develop social skills that prevent them from engaging in antisocial behavior,” Dr. Carlisi said in a news release. “These people could benefit from more support throughout their lives.”

The study, the investigators noted, provides the first robust evidence to suggest that underlying neuropsychological differences are primarily associated with life-course-persistent persistent antisocial behavior. It was published online Feb. 17 in the Lancet Psychiatry (doi: 10.1016/S2215-0366[20]30002-X).

Support for second chances

Speaking at the press briefing, coauthor Terrie E. Moffitt, PhD, of Duke University, Durham, N.C., said it’s well known that most young criminals are between the ages of 16 and 25.

Breaking the law is not at all rare in this age group, but not all of these young offenders are alike, she noted. Only a few become persistent repeat offenders.

“They start as a young child with aggressive conduct problems and eventually sink into a long-term lifestyle of repetitive serious crime that lasts well into adulthood, but this is a small group,” Dr. Moffitt explained. “In contrast, the larger majority of offenders will have only a short-term brush with lawbreaking and then grow up to become law-abiding members of society.”

The current study suggests that what makes short-term offenders behave differently from long-term offenders might involve some vulnerability at the level of the structure of the brain, Dr. Moffitt said.

The findings stem from 672 individuals in the Dunedin Multidisciplinary Health and Development Study, a population-representative, longitudinal birth cohort that assesses health and behavior.

On the basis of reports from parents, care givers, and teachers, as well as self-reports of conduct problems in persons aged 7-26 years, 80 participants (12%) had “life-course–persistent” antisocial behavior, 151 (23%) had adolescent-only antisocial behavior, and 441 (66%) had “low” antisocial behavior (control group, whose members never had a pervasive or persistent pattern of antisocial behavior).

Brain MRI obtained at age 45 years showed that, among individuals with persistent antisocial behavior, mean surface area was smaller (95% confidence interval, –0.24 to –0.11; P less than .0001) and mean cortical thickness was lower (95% CI, –0.19 to –0.02; P = .020) than was those of their peers in the control group.

For those in the life-course–persistent group, surface area was reduced in 282 of 360 anatomically defined brain parcels, and cortex was thinner in 11 of 360 parcels encompassing frontal and temporal regions (which were associated with executive function, emotion regulation, and motivation), compared with the control group.

Widespread differences in brain surface morphometry were not found in those who exhibited antisocial behavior during adolescence only. Such behavior was likely the result of their having to navigate through socially tough years.

“These findings underscore prior research that really highlights that there are different types of young offenders. They are not all the same; they should not all be treated the same,” coauthor Essi Viding, PhD, who also is affiliated with University College London, told reporters.

The findings support current strategies aimed at giving young offenders “a second chance” as opposed to enforcing harsher policies that prioritize incarceration for all young offenders, Dr. Viding added.

Important contribution

The authors of an accompanying commentary noted that, despite “remarkable progress in the past 3 decades, the etiology of antisocial behavior remains elusive” (Lancet Psychiatry. 2020 Feb 17. doi: 10.1016/S2215-0366[20]30035-3).

This study makes “an important contribution by identifying structural brain correlates of antisocial behavior that could be used to differentiate among individuals with life-course-persistent antisocial behavior, those with adolescence-limited antisocial behavior, and non-antisocial controls,” write Inti A. Brazil, PhD, of the Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands, and Macià Buades-Rotger, PhD, of the Institute of Psychology II, University of Lübeck, Germany.

They noted that the findings might help to move the field closer to achieving the long-standing goal of incorporating neural data into assessment protocols for antisocial behavior.

The discovery of “meaningful morphologic differences between individuals with life-course–persistent and adolescence-limited antisocial behavior offers an important advance in the use of brain metrics for differentiating among individuals with antisocial dispositions.

“Importantly, however, it remains to be determined whether and how measuring the brain can be used to bridge the different taxometric views and theories on the etiology of antisocial behavior,” Dr. Brazil and Dr. Buades-Rotger concluded.

The study was funded by the U.S. National Institute on Aging; the Health Research Council of New Zealand; the New Zealand Ministry of Business, Innovation and Employment; the U.K. Medical Research Council; the Avielle Foundation; and the Wellcome Trust. The study authors and the authors of the commentary disclosed no relevant financial relationships.
 

A version of this article first appeared on Medscape.com.

Kobe Bryant knew me. Not personally, of course. I never received an autograph or shook his hand. But once in a while if I was up early enough, I’d run into Kobe at the gym in Newport Beach where he and I both worked out. As he did for all his fans at the gym, he’d make eye contact with me and nod hello. He was always focused on his workout – working with a trainer, never with headphones on. In person, he appeared enormous. Unlike most retired professional athletes, he still was in great shape. No doubt he could have suited up in purple and gold, and played against the Clippers that night if needed.

Featureflash Photo Agency

Being from New England, I never was a Laker fan. But at Kobe’s peak around 2000, I found him inspiring. I recall watching him play right around the time I was studying for my U.S. medical licensing exams. I thought, if Kobe can head to the gym after midnight and take a 1,000 shots to prepare for a game, then I could set my alarm for 4 a.m. and take a few dozen more questions from my First Aid books. Head down, “Kryptonite” cranked on my iPod, I wasn’t going to let anyone in that test room outwork me. Neither did he. I put in the time and, like Kobe in the 2002 conference finals against Sacramento, I crushed it.*

When we moved to California, I followed Kobe and the Lakers until he retired. To be clear, I didn’t aspire to be like him, firstly because I’m slightly shorter than Michael Bloomberg, but also because although accomplished, Kobe made some poor choices at times. Indeed, it seems he might have been kinder and more considerate when he was at the top. But in his retirement he looked to be toiling to make reparations, refocusing his prodigious energy and talent for the benefit of others rather than for just for scoring 81 points. His Rolls Royce was there before mine at the gym, and I was there early. He was still getting up early and now preparing to be a great venture capitalist, podcaster, author, and father to his girls.

Watching him carry kettle bells across the floor one morning, I wondered, do people like Kobe Bryant look to others for inspiration? Or are they are born with an endless supply of it? For me, I seemed to push harder and faster when watching idols pass by. Whether it was Kobe or Clayton Christensen (author of “The Innovator’s Dilemma”), Joe Jorizzo, or Barack Obama, I found I could do just a bit more if I had them in mind.

On game days, Kobe spoke of arriving at the arena early, long before anyone. He would use the silent, solo time to reflect on what he needed to do perform that night. I tried this last week, arriving at our clinic early, before any patients or staff. I turned the lights on and took a few minutes to think about what we needed to accomplish that day. I previewed patients on my schedule, searched Up to Date for the latest recommendations on a difficult case. I didn’t know Kobe, but I felt like I did.

CC0 1.0 Universal Public Domain Dedication

When I received the text that Kobe Bryant had died, I was actually working on this column. So I decided to change the topic to write about people who inspire me, ironically inspired by him again. May he rest in peace.
 

Dr. Benabio is director of Healthcare Transformation and chief of dermatology at Kaiser Permanente San Diego. The opinions expressed in this column are his own and do not represent those of Kaiser Permanente. Dr. Benabio is @Dermdoc on Twitter. Write to him at dermnews@mdedge.com.

*This article was updated 2/19/2020.

Kobe Bryant knew me. Not personally, of course. I never received an autograph or shook his hand. But once in a while if I was up early enough, I’d run into Kobe at the gym in Newport Beach where he and I both worked out. As he did for all his fans at the gym, he’d make eye contact with me and nod hello. He was always focused on his workout – working with a trainer, never with headphones on. In person, he appeared enormous. Unlike most retired professional athletes, he still was in great shape. No doubt he could have suited up in purple and gold, and played against the Clippers that night if needed.

Featureflash Photo Agency

Being from New England, I never was a Laker fan. But at Kobe’s peak around 2000, I found him inspiring. I recall watching him play right around the time I was studying for my U.S. medical licensing exams. I thought, if Kobe can head to the gym after midnight and take a 1,000 shots to prepare for a game, then I could set my alarm for 4 a.m. and take a few dozen more questions from my First Aid books. Head down, “Kryptonite” cranked on my iPod, I wasn’t going to let anyone in that test room outwork me. Neither did he. I put in the time and, like Kobe in the 2002 conference finals against Sacramento, I crushed it.*

When we moved to California, I followed Kobe and the Lakers until he retired. To be clear, I didn’t aspire to be like him, firstly because I’m slightly shorter than Michael Bloomberg, but also because although accomplished, Kobe made some poor choices at times. Indeed, it seems he might have been kinder and more considerate when he was at the top. But in his retirement he looked to be toiling to make reparations, refocusing his prodigious energy and talent for the benefit of others rather than for just for scoring 81 points. His Rolls Royce was there before mine at the gym, and I was there early. He was still getting up early and now preparing to be a great venture capitalist, podcaster, author, and father to his girls.

Watching him carry kettle bells across the floor one morning, I wondered, do people like Kobe Bryant look to others for inspiration? Or are they are born with an endless supply of it? For me, I seemed to push harder and faster when watching idols pass by. Whether it was Kobe or Clayton Christensen (author of “The Innovator’s Dilemma”), Joe Jorizzo, or Barack Obama, I found I could do just a bit more if I had them in mind.

On game days, Kobe spoke of arriving at the arena early, long before anyone. He would use the silent, solo time to reflect on what he needed to do perform that night. I tried this last week, arriving at our clinic early, before any patients or staff. I turned the lights on and took a few minutes to think about what we needed to accomplish that day. I previewed patients on my schedule, searched Up to Date for the latest recommendations on a difficult case. I didn’t know Kobe, but I felt like I did.

CC0 1.0 Universal Public Domain Dedication

When I received the text that Kobe Bryant had died, I was actually working on this column. So I decided to change the topic to write about people who inspire me, ironically inspired by him again. May he rest in peace.
 

Dr. Benabio is director of Healthcare Transformation and chief of dermatology at Kaiser Permanente San Diego. The opinions expressed in this column are his own and do not represent those of Kaiser Permanente. Dr. Benabio is @Dermdoc on Twitter. Write to him at dermnews@mdedge.com.

*This article was updated 2/19/2020.

Kobe Bryant knew me. Not personally, of course. I never received an autograph or shook his hand. But once in a while if I was up early enough, I’d run into Kobe at the gym in Newport Beach where he and I both worked out. As he did for all his fans at the gym, he’d make eye contact with me and nod hello. He was always focused on his workout – working with a trainer, never with headphones on. In person, he appeared enormous. Unlike most retired professional athletes, he still was in great shape. No doubt he could have suited up in purple and gold, and played against the Clippers that night if needed.

Featureflash Photo Agency

Being from New England, I never was a Laker fan. But at Kobe’s peak around 2000, I found him inspiring. I recall watching him play right around the time I was studying for my U.S. medical licensing exams. I thought, if Kobe can head to the gym after midnight and take a 1,000 shots to prepare for a game, then I could set my alarm for 4 a.m. and take a few dozen more questions from my First Aid books. Head down, “Kryptonite” cranked on my iPod, I wasn’t going to let anyone in that test room outwork me. Neither did he. I put in the time and, like Kobe in the 2002 conference finals against Sacramento, I crushed it.*

When we moved to California, I followed Kobe and the Lakers until he retired. To be clear, I didn’t aspire to be like him, firstly because I’m slightly shorter than Michael Bloomberg, but also because although accomplished, Kobe made some poor choices at times. Indeed, it seems he might have been kinder and more considerate when he was at the top. But in his retirement he looked to be toiling to make reparations, refocusing his prodigious energy and talent for the benefit of others rather than for just for scoring 81 points. His Rolls Royce was there before mine at the gym, and I was there early. He was still getting up early and now preparing to be a great venture capitalist, podcaster, author, and father to his girls.

Watching him carry kettle bells across the floor one morning, I wondered, do people like Kobe Bryant look to others for inspiration? Or are they are born with an endless supply of it? For me, I seemed to push harder and faster when watching idols pass by. Whether it was Kobe or Clayton Christensen (author of “The Innovator’s Dilemma”), Joe Jorizzo, or Barack Obama, I found I could do just a bit more if I had them in mind.

On game days, Kobe spoke of arriving at the arena early, long before anyone. He would use the silent, solo time to reflect on what he needed to do perform that night. I tried this last week, arriving at our clinic early, before any patients or staff. I turned the lights on and took a few minutes to think about what we needed to accomplish that day. I previewed patients on my schedule, searched Up to Date for the latest recommendations on a difficult case. I didn’t know Kobe, but I felt like I did.

CC0 1.0 Universal Public Domain Dedication

When I received the text that Kobe Bryant had died, I was actually working on this column. So I decided to change the topic to write about people who inspire me, ironically inspired by him again. May he rest in peace.
 

Dr. Benabio is director of Healthcare Transformation and chief of dermatology at Kaiser Permanente San Diego. The opinions expressed in this column are his own and do not represent those of Kaiser Permanente. Dr. Benabio is @Dermdoc on Twitter. Write to him at dermnews@mdedge.com.

*This article was updated 2/19/2020.

LAHAINA, HAWAII – Hyperhidrosis affects nearly 5% of the U.S. population, and in a survey of U.S. teenagers, about 17% reported excessive sweating, Jashin Wu, MD, said at the Hawaii Dermatology Seminar provided by the Global Academy for Medical Education/Skin Disease Education Foundation.

In an interview with MDedge reporter Bruce Jancin, Dr. Wu, founder of the Dermatology Research and Education Foundation, Irvine, Calif., discussed the off-label use of oral agents to treat hyperhidrosis. Dr. Wu said he is a fan of oral glycopyrrolate in particular, which he tends to use even earlier than suggested in the International Hyperhidrosis Society guidelines.

Glycopyrrolate is available in 1 mg and 2 mg tablets; Dr. Wu starts patients at a dose of 1 mg twice a day, escalating by 1 mg per week until the “desired effects occur” or the patient has problems tolerating treatment because of side effects.

Other oral options include oxybutynin and propranolol. Sofpironium bromide, an analog of glycopyrrolate, is in the pipeline, he said.

During the interview, Dr. Wu discussed mydriasis, an adverse effect associated with both topical and systemic anticholinergic treatment. In the two pivotal phase 3 randomized trials of prescription glycopyrronium cloth (Qbrexza) for axillary hyperhidrosis, the incidence of mydriasis was 6.8% in 463 patients on active treatment for 4 weeks. Three-quarters of cases were unilateral. The mydriasis resolved without permanent treatment discontinuation in 27 of the 31 patients (J Am Acad Dermatol. 2019 Jan;80[1]:128-138.e2).

“The most important point is that patients need to be educated that they need to wash their hands very well after they apply it to the affected areas” to prevent accidental medication contact with the eyes, he advised.

Alarm bells can go off when a patient with anticholinergic therapy–induced mydriasis presents to an ED without mentioning their treatment status, Dr. Wu observed.

Dr. Wu had no relevant disclosures. SDEF/Global Academy for Medical Education and this news organization are owned by the same parent company.

To listen to the interview, click the play button below.

LAHAINA, HAWAII – Hyperhidrosis affects nearly 5% of the U.S. population, and in a survey of U.S. teenagers, about 17% reported excessive sweating, Jashin Wu, MD, said at the Hawaii Dermatology Seminar provided by the Global Academy for Medical Education/Skin Disease Education Foundation.

In an interview with MDedge reporter Bruce Jancin, Dr. Wu, founder of the Dermatology Research and Education Foundation, Irvine, Calif., discussed the off-label use of oral agents to treat hyperhidrosis. Dr. Wu said he is a fan of oral glycopyrrolate in particular, which he tends to use even earlier than suggested in the International Hyperhidrosis Society guidelines.

Glycopyrrolate is available in 1 mg and 2 mg tablets; Dr. Wu starts patients at a dose of 1 mg twice a day, escalating by 1 mg per week until the “desired effects occur” or the patient has problems tolerating treatment because of side effects.

Other oral options include oxybutynin and propranolol. Sofpironium bromide, an analog of glycopyrrolate, is in the pipeline, he said.

During the interview, Dr. Wu discussed mydriasis, an adverse effect associated with both topical and systemic anticholinergic treatment. In the two pivotal phase 3 randomized trials of prescription glycopyrronium cloth (Qbrexza) for axillary hyperhidrosis, the incidence of mydriasis was 6.8% in 463 patients on active treatment for 4 weeks. Three-quarters of cases were unilateral. The mydriasis resolved without permanent treatment discontinuation in 27 of the 31 patients (J Am Acad Dermatol. 2019 Jan;80[1]:128-138.e2).

“The most important point is that patients need to be educated that they need to wash their hands very well after they apply it to the affected areas” to prevent accidental medication contact with the eyes, he advised.

Alarm bells can go off when a patient with anticholinergic therapy–induced mydriasis presents to an ED without mentioning their treatment status, Dr. Wu observed.

Dr. Wu had no relevant disclosures. SDEF/Global Academy for Medical Education and this news organization are owned by the same parent company.

To listen to the interview, click the play button below.

LAHAINA, HAWAII – Hyperhidrosis affects nearly 5% of the U.S. population, and in a survey of U.S. teenagers, about 17% reported excessive sweating, Jashin Wu, MD, said at the Hawaii Dermatology Seminar provided by the Global Academy for Medical Education/Skin Disease Education Foundation.

In an interview with MDedge reporter Bruce Jancin, Dr. Wu, founder of the Dermatology Research and Education Foundation, Irvine, Calif., discussed the off-label use of oral agents to treat hyperhidrosis. Dr. Wu said he is a fan of oral glycopyrrolate in particular, which he tends to use even earlier than suggested in the International Hyperhidrosis Society guidelines.

Glycopyrrolate is available in 1 mg and 2 mg tablets; Dr. Wu starts patients at a dose of 1 mg twice a day, escalating by 1 mg per week until the “desired effects occur” or the patient has problems tolerating treatment because of side effects.

Other oral options include oxybutynin and propranolol. Sofpironium bromide, an analog of glycopyrrolate, is in the pipeline, he said.

During the interview, Dr. Wu discussed mydriasis, an adverse effect associated with both topical and systemic anticholinergic treatment. In the two pivotal phase 3 randomized trials of prescription glycopyrronium cloth (Qbrexza) for axillary hyperhidrosis, the incidence of mydriasis was 6.8% in 463 patients on active treatment for 4 weeks. Three-quarters of cases were unilateral. The mydriasis resolved without permanent treatment discontinuation in 27 of the 31 patients (J Am Acad Dermatol. 2019 Jan;80[1]:128-138.e2).

“The most important point is that patients need to be educated that they need to wash their hands very well after they apply it to the affected areas” to prevent accidental medication contact with the eyes, he advised.

Alarm bells can go off when a patient with anticholinergic therapy–induced mydriasis presents to an ED without mentioning their treatment status, Dr. Wu observed.

Dr. Wu had no relevant disclosures. SDEF/Global Academy for Medical Education and this news organization are owned by the same parent company.

To listen to the interview, click the play button below.

Key clinical point: Transcutaneous electrical nerve stimulation (TENS) is an effective option for treating migraine attacks in the emergency department.

Major finding: The verum group showed significant improvements on the visual analog scale change from 0 to 120 minutes (P less than .001) and a Likert-type verbal pain scale (P less than .001) compared with the sham group. The need for additional analgesics after 120 minutes was lower in the verum group vs. sham group (2.6% vs. 76.9%).

Study details: A randomized-controlled study evaluated the effectiveness of TENS for emergency treatment of migraine in the verum (n=39) and sham (n=39) groups.

Disclosures: The authors declared no conflicts of interest.

Citation: Hokenek NM et al. Am J Emerg Med. 2020 Jan 15. doi: 10.1016/j.ajem.2020.01.024.

Key clinical point: Transcutaneous electrical nerve stimulation (TENS) is an effective option for treating migraine attacks in the emergency department.

Major finding: The verum group showed significant improvements on the visual analog scale change from 0 to 120 minutes (P less than .001) and a Likert-type verbal pain scale (P less than .001) compared with the sham group. The need for additional analgesics after 120 minutes was lower in the verum group vs. sham group (2.6% vs. 76.9%).

Study details: A randomized-controlled study evaluated the effectiveness of TENS for emergency treatment of migraine in the verum (n=39) and sham (n=39) groups.

Disclosures: The authors declared no conflicts of interest.

Citation: Hokenek NM et al. Am J Emerg Med. 2020 Jan 15. doi: 10.1016/j.ajem.2020.01.024.

Key clinical point: Transcutaneous electrical nerve stimulation (TENS) is an effective option for treating migraine attacks in the emergency department.

Major finding: The verum group showed significant improvements on the visual analog scale change from 0 to 120 minutes (P less than .001) and a Likert-type verbal pain scale (P less than .001) compared with the sham group. The need for additional analgesics after 120 minutes was lower in the verum group vs. sham group (2.6% vs. 76.9%).

Study details: A randomized-controlled study evaluated the effectiveness of TENS for emergency treatment of migraine in the verum (n=39) and sham (n=39) groups.

Disclosures: The authors declared no conflicts of interest.

Citation: Hokenek NM et al. Am J Emerg Med. 2020 Jan 15. doi: 10.1016/j.ajem.2020.01.024.

Key clinical point: Migraine and asthma have a reciprocal association with each other.

Major finding: Patients with asthma had a 47% higher risk for migraine (P less than .001) than control participants, and patients with migraine had a 37% higher risk for asthma (P less than .001).

Study details: The data were obtained from 2 Korean longitudinal follow-up studies (Study 1: 113,059 patients with asthma and 113,059 control participants; Study 2: 36,044 patients with migraine and 114,176 control participants).

Disclosures: This study was partly supported by a grant from the National Research Foundation of Korea. The authors declared no conflicts of interest.

Citation: Kim SY et al. Sci Rep. 2019 Dec 4. doi: 10.1038/s41598-019-54972-8.

Key clinical point: Migraine and asthma have a reciprocal association with each other.

Major finding: Patients with asthma had a 47% higher risk for migraine (P less than .001) than control participants, and patients with migraine had a 37% higher risk for asthma (P less than .001).

Study details: The data were obtained from 2 Korean longitudinal follow-up studies (Study 1: 113,059 patients with asthma and 113,059 control participants; Study 2: 36,044 patients with migraine and 114,176 control participants).

Disclosures: This study was partly supported by a grant from the National Research Foundation of Korea. The authors declared no conflicts of interest.

Citation: Kim SY et al. Sci Rep. 2019 Dec 4. doi: 10.1038/s41598-019-54972-8.

Key clinical point: Migraine and asthma have a reciprocal association with each other.

Major finding: Patients with asthma had a 47% higher risk for migraine (P less than .001) than control participants, and patients with migraine had a 37% higher risk for asthma (P less than .001).

Study details: The data were obtained from 2 Korean longitudinal follow-up studies (Study 1: 113,059 patients with asthma and 113,059 control participants; Study 2: 36,044 patients with migraine and 114,176 control participants).

Disclosures: This study was partly supported by a grant from the National Research Foundation of Korea. The authors declared no conflicts of interest.

Citation: Kim SY et al. Sci Rep. 2019 Dec 4. doi: 10.1038/s41598-019-54972-8.

Key clinical point: Women of reproductive age experiencing migraines should be screened for endometriosis.

Major finding: Migraine headache was more frequent in women with endometriosis than in those without endometriosis (35.2% vs. 17.4%; P = .003).

Study details: The data were obtained from a French case-control study of 314 nonpregnant women younger than 42 years.

Disclosures: The authors declared no conflicts of interest.

Citation: Maitrot-Mantelet L et al. Cephalalgia. 2019 Dec 6. doi: 10.1177/0333102419893965.

Key clinical point: Women of reproductive age experiencing migraines should be screened for endometriosis.

Major finding: Migraine headache was more frequent in women with endometriosis than in those without endometriosis (35.2% vs. 17.4%; P = .003).

Study details: The data were obtained from a French case-control study of 314 nonpregnant women younger than 42 years.

Disclosures: The authors declared no conflicts of interest.

Citation: Maitrot-Mantelet L et al. Cephalalgia. 2019 Dec 6. doi: 10.1177/0333102419893965.

Key clinical point: Women of reproductive age experiencing migraines should be screened for endometriosis.

Major finding: Migraine headache was more frequent in women with endometriosis than in those without endometriosis (35.2% vs. 17.4%; P = .003).

Study details: The data were obtained from a French case-control study of 314 nonpregnant women younger than 42 years.

Disclosures: The authors declared no conflicts of interest.

Citation: Maitrot-Mantelet L et al. Cephalalgia. 2019 Dec 6. doi: 10.1177/0333102419893965.