TY - JOUR
T1 - Multilevel Analysis of Readmissions After Radical Cystectomy for Bladder Cancer in the USA
T2 - Does the Hospital Make a Difference?
AU - Cole, Alexander P.
AU - Ramaswamy, Ashwin
AU - Harmouch, Sabrina
AU - Fletcher, Sean A.
AU - Gild, Philipp
AU - Sun, Maxine
AU - Lipsitz, Stuart R.
AU - Chiang, H. Abraham
AU - Haider, Adil H.
AU - Preston, Mark A.
AU - Kibel, Adam S.
AU - Trinh, Quoc Dien
N1 - Publisher Copyright:
© 2018 European Association of Urology
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Cystectomy
KW - Healthcare quality, access, and evaluation
KW - Multilevel analysis
KW - Patient readmission
KW - Quality of health care
KW - Reimbursement incentive
KW - Urinary bladder neoplasms
KW - Urological surgical procedures
UR - http://www.scopus.com/inward/record.url?scp=85068175472&partnerID=8YFLogxK
U2 - 10.1016/j.euo.2018.08.027
DO - 10.1016/j.euo.2018.08.027
M3 - Article
C2 - 31277772
AN - SCOPUS:85068175472
SN - 2588-9311
VL - 2
SP - 349
EP - 354
JO - European urology oncology
JF - European urology oncology
IS - 4
ER -