title: Comparing AMI mortality among hospitals in patients 65 years of age and older: evaluating methods of risk adjustment.
abstract: BACKGROUND
  Interest in the reporting of risk-adjusted outcomes for patients with acute myocardial infarction is growing. A useful risk-adjustment model must balance parsimony and ease of data collection with predictive ability.
  
  
  RESULTS
  From our analysis of 82 359 patients >/=65 years of age admitted with acute myocardial infarction to 2401 hospitals, we derived a parsimonious model that predicts 30-day mortality. The model was validated on a similar group of 78 699 patients from 2386 hospitals. Of the 73 candidate predictor variables examined, 7 variables describing patient characteristics on arrival were selected for inclusion in the final model: age, cardiac arrest, anterior or lateral location of myocardial infarction, systolic blood pressure, white blood cell count, serum creatinine, and congestive heart failure. The area under the receiver-operating characteristic curve for the final model was 0.77 in the derivation cohort and 0.77 in the validation cohort. The rankings of hospitals by performance (in deciles) with this model were most similar to a comprehensive 27-variable model based on medical chart review and least similar to models based on administrative billing codes.
  
  
  CONCLUSIONS
  A simple 7-variable risk model performs as well as more complex models in comparing hospital outcomes for acute myocardial infarction. Although there is a continuing need to improve methods of risk adjustment, our results provide a basis for hospitals to develop a simple approach to compare outcomes.
authors.affiliation: Sections of Cardiovascular Medicine, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA. harlan.krumholz@yale.edu
authors.forename: H M
authors.initials: HM
authors.lastname: Krumholz
authors.forename: J
authors.initials: J
authors.lastname: Chen
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: M J
authors.initials: MJ
authors.lastname: Radford
authors.forename: Y T
authors.initials: YT
authors.lastname: Chen
authors.forename: T A
authors.initials: TA
authors.lastname: Marciniak
mesh_terms: D000367:Age Factors; D000368:Aged; D015331:Cohort Studies; D003404:Creatinine / Q000097:blood; D005260:Female; D006323:Heart Arrest; D006333:Heart Failure; D006801:Humans; D007958:Leukocyte Count; D008297:Male; D006278:Medicare; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000401:mortality* / Q000503:physiopathology; D015203:Reproducibility of Results; D020379:Risk Adjustment; D013599:Systole; D014481:United States
chemical_list: D003404:Creatinine