Multilevel Analysis of Readmissions After Radical Cystectomy for Bladder Cancer in the USA: Does the Hospital Make a Difference?

  • Alexander P. Cole
  • , Ashwin Ramaswamy
  • , Sabrina Harmouch
  • , Sean A. Fletcher
  • , Philipp Gild
  • , Maxine Sun
  • , Stuart R. Lipsitz
  • , H. Abraham Chiang
  • , Adil H. Haider
  • , Mark A. Preston
  • , Adam S. Kibel
  • , Quoc Dien Trinh

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish (UK)
Pages (from-to)349-354
Number of pages6
JournalEuropean urology oncology
Volume2
Issue number4
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Cystectomy
  • Healthcare quality, access, and evaluation
  • Multilevel analysis
  • Patient readmission
  • Quality of health care
  • Reimbursement incentive
  • Urinary bladder neoplasms
  • Urological surgical procedures

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