title: An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction.
abstract: BACKGROUND
  National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction.
  
  
  RESULTS
  We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points).
  
  
  CONCLUSIONS
  This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.
authors.affiliation: Section of Cardiovascular Medicine and Robert Wood Johnson Clinical Scholars Program, Department of Internal Medicine, and School of Public Health, Yale University School of Medicine, New Haven, CT 06510, USA. harlan.krumholz@yale.edu
authors.forename: Harlan M
authors.initials: HM
authors.lastname: Krumholz
authors.forename: Zhenqiu
authors.initials: Z
authors.lastname: Lin
authors.forename: Elizabeth E
authors.initials: EE
authors.lastname: Drye
authors.forename: Mayur M
authors.initials: MM
authors.lastname: Desai
authors.forename: Lein F
authors.initials: LF
authors.lastname: Han
authors.forename: Michael T
authors.initials: MT
authors.lastname: Rapp
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Mattera
authors.forename: Sharon-Lise T
authors.initials: SL
authors.lastname: Normand
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000628:therapy*; D017063:Outcome Assessment, Health Care; D010043:Outcome and Process Assessment, Health Care / Q000592:standards*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000592:standards*; D015203:Reproducibility of Results; D012307:Risk Factors; D013997:Time Factors; D014481:United States