title: An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure.
abstract: BACKGROUND
  Readmission soon after hospital discharge is an expensive and often preventable event for patients with heart failure. We present a model approved by the National Quality Forum for the purpose of public reporting of hospital-level readmission rates by the Centers for Medicare & Medicaid Services.
  
  
  RESULTS
  We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with heart failure. The model was derived with the use of Medicare claims data for a 2004 cohort and validated with the use of claims and medical record data. The unadjusted readmission rate was 23.6%. The final model included 37 variables, had discrimination ranging from 15% observed 30-day readmission rate in the lowest predictive decile to 37% in the upper decile, and had a c statistic of 0.60. The 25th and 75th percentiles of the risk-standardized readmission rates across 4669 hospitals were 23.1% and 24.0%, with 5th and 95th percentiles of 22.2% and 25.1%, respectively. The odds of all-cause readmission for a hospital 1 standard deviation above average was 1.30 times that of a hospital 1 standard deviation below average. State-level adjusted readmission rates developed with the use of the claims model are similar to rates produced for the same cohort with the use of a medical record model (correlation, 0.97; median difference, 0.06 percentage points).
  
  
  CONCLUSIONS
  This claims-based model of hospital risk-standardized readmission rates for heart failure patients produces estimates that may serve as surrogates for those derived from a medical record model.
authors.affiliation: Section of Health Policy and Administration, School of Public Health, Yale University School of Medicine, New Haven, CT 06520-8088, USA.
authors.forename: Patricia S
authors.initials: PS
authors.lastname: Keenan
authors.forename: Sharon-Lise T
authors.initials: SL
authors.lastname: Normand
authors.forename: Zhenqiu
authors.initials: Z
authors.lastname: Lin
authors.forename: Elizabeth E
authors.initials: EE
authors.lastname: Drye
authors.forename: Kanchana R
authors.initials: KR
authors.lastname: Bhat
authors.forename: Joseph S
authors.initials: JS
authors.lastname: Ross
authors.forename: Jeremiah D
authors.initials: JD
authors.lastname: Schuur
authors.forename: Brett D
authors.initials: BD
authors.lastname: Stauffer
authors.forename: Susannah M
authors.initials: SM
authors.lastname: Bernheim
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Epstein
authors.forename: Yongfei
authors.initials: Y
authors.lastname: Wang
authors.forename: Jeph
authors.initials: J
authors.lastname: Herrin
authors.forename: Jersey
authors.initials: J
authors.lastname: Chen
authors.forename: Jessica J
authors.initials: JJ
authors.lastname: Federer
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Mattera
authors.forename: Yun
authors.initials: Y
authors.lastname: Wang
authors.forename: Harlan M
authors.initials: HM
authors.lastname: Krumholz
mesh_terms: D005260:Female; D006333:Heart Failure / Q000191:economics; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data*; D006278:Medicare; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D010359:Patient Readmission / Q000191:economics / Q000706:statistics & numerical data*; D012986:Software Validation; D014481:United States