Alexander P Cole1, Ashwin Ramaswamy1, Sabrina Harmouch1, Sean A Fletcher1, Philipp Gild2, Maxine Sun3, Stuart R Lipsitz4, H Abraham Chiang5, Adil H Haider6, Mark A Preston5, Adam S Kibel5, Quoc-Dien Trinh7. 1. Division of Urological Surgery and Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 2. Department of Urology, Hamburg University Hospital, Hamburg, Germany. 3. Lank Center for Genitourinary Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. 4. Division of General Internal Medicine and Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 5. Division of Urological Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 6. Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 7. Division of Urological Surgery and Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: trinh.qd@gmail.com.
Abstract
BACKGROUND: Hospitals are increasingly being held responsible for their readmissions rates. The contribution of hospital versus patient factors (eg, case mix) to hospital readmissions is unknown. OBJECTIVE: To estimate the relative contribution of hospital and patient factors to readmissions after radical cystectomy (RC) for bladder cancer. DESIGN, SETTING, AND PARTICIPANTS: We identified individuals who underwent RC in 2014 in the Nationwide Readmissions Database (NRD). The NRD is a nationally representative (USA), all-payer database that includes readmissions at index and nonindex hospitals. Survey weights were used to generate national estimates. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The main outcome was readmission within 30 d after RC. Using a multilevel mixed-effects model, we estimated the statistical association between patient and hospital characteristics and readmission. A hospital-level random-effects term was used to estimate hospital-level readmission rates while holding patient characteristics constant. RESULTS AND LIMITATIONS: We identified a weighted sample of 7095 individuals who underwent RC at 341 hospitals in the USA. The 30-d readmission rate was 29.5% (95% confidence interval [CI] 27.8-31.2%), ranging from 1.4% (95% CI 0.6-2.2%) in the bottom quartile to 73.6% (95% CI 68.4-78.7) in the top. In our multilevel model, female sex and comorbidity score were associated with a higher likelihood of readmission. The hospital random-effects term, encompassing both measured and unmeasured hospital characteristics, contributed minimally to the model for readmission when patient characteristics were held constant at population mean values (pseudo-R2<0.01% for hospital effects). Surgical volume, bed size, hospital ownership, and academic status were not significantly associated with readmission rates when these terms were added to the model. CONCLUSIONS: After adjusting for patient characteristics, hospital-level effects explained little of the large between-hospital variability in readmission rates. These findings underscore the limitations of using 30-d post-discharge readmissions as a hospital quality metric. PATIENT SUMMARY: The chance of being readmitted after radical cystectomy varies substantially between hospitals. Little of this variability can be explained by hospital-level characteristics, while far more can be explained by patient characteristics and random variability.
BACKGROUND: Hospitals are increasingly being held responsible for their readmissions rates. The contribution of hospital versus patient factors (eg, case mix) to hospital readmissions is unknown. OBJECTIVE: To estimate the relative contribution of hospital and patient factors to readmissions after radical cystectomy (RC) for bladder cancer. DESIGN, SETTING, AND PARTICIPANTS: We identified individuals who underwent RC in 2014 in the Nationwide Readmissions Database (NRD). The NRD is a nationally representative (USA), all-payer database that includes readmissions at index and nonindex hospitals. Survey weights were used to generate national estimates. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The main outcome was readmission within 30 d after RC. Using a multilevel mixed-effects model, we estimated the statistical association between patient and hospital characteristics and readmission. A hospital-level random-effects term was used to estimate hospital-level readmission rates while holding patient characteristics constant. RESULTS AND LIMITATIONS: We identified a weighted sample of 7095 individuals who underwent RC at 341 hospitals in the USA. The 30-d readmission rate was 29.5% (95% confidence interval [CI] 27.8-31.2%), ranging from 1.4% (95% CI 0.6-2.2%) in the bottom quartile to 73.6% (95% CI 68.4-78.7) in the top. In our multilevel model, female sex and comorbidity score were associated with a higher likelihood of readmission. The hospital random-effects term, encompassing both measured and unmeasured hospital characteristics, contributed minimally to the model for readmission when patient characteristics were held constant at population mean values (pseudo-R2<0.01% for hospital effects). Surgical volume, bed size, hospital ownership, and academic status were not significantly associated with readmission rates when these terms were added to the model. CONCLUSIONS: After adjusting for patient characteristics, hospital-level effects explained little of the large between-hospital variability in readmission rates. These findings underscore the limitations of using 30-d post-discharge readmissions as a hospital quality metric. PATIENT SUMMARY: The chance of being readmitted after radical cystectomy varies substantially between hospitals. Little of this variability can be explained by hospital-level characteristics, while far more can be explained by patient characteristics and random variability.
Authors: Jacek Kryś; Błażej Łyszczarz; Zofia Wyszkowska; Kornelia Kędziora-Kornatowska Journal: Int J Environ Res Public Health Date: 2019-07-02 Impact factor: 3.390
Authors: Wei Shen Tan; Jeffrey J Leow; Maya Marchese; Ashwin Sridhar; Giles Hellawell; Matthew Mossanen; Jeremy Y C Teoh; Sarah Fowler; Alexandra J Colquhoun; Jo Cresswell; James W F Catto; Quoc-Dien Trinh; John D Kelly Journal: Eur Urol Open Sci Date: 2021-09-20
Authors: Vishnukamal Golla; Yong Shan; Hemalkumar B Mehta; Zachary Klaassen; Douglas S Tyler; Jacques Baillargeon; Ashish M Kamat; Stephen J Freedland; John L Gore; Karim Chamie; Yong-Fang Kuo; Stephen B Williams Journal: Eur Urol Open Sci Date: 2020-06-23