title: Statistical models and patient predictors of readmission for acute myocardial infarction: a systematic review.
abstract: BACKGROUND
  Readmission after acute myocardial infarction (AMI) has been targeted for public reporting because it is a common, costly, and often preventable outcome. To assist in ongoing efforts to risk-stratify patients and profile hospitals through public reporting of performance measures, we conducted a systematic review to identify models designed to compare hospital rates of readmission or predict patients' risk of readmission after AMI and to identify studies evaluating patient characteristics associated with AMI readmission.
  
  
  RESULTS
  We identified relevant English-language studies published between 1950 and 2007 by searching MEDLINE, Scopus, PsycINFO, and all 4 Ovid Evidence-Based Medicine Reviews. Eligible publications reported on readmission up to 1 year after AMI hospitalization among adults. From 751 potentially relevant articles, 35 met our predefined inclusion/exclusion criteria. Overall, none developed models to compare readmission rates among hospitals or models to predict patients' risk of readmission. All 35 examined patient characteristics associated with AMI readmission. However, studies varied in methods for case and outcome identification, used multiple types of data sources, examined differing outcomes (often either readmission alone or a composite outcome of readmission or death) over varying follow-up periods (from 30 days to 1 year), and found few patient characteristics consistently associated with readmission.
  
  
  CONCLUSIONS
  Patient characteristics may be important predictors of AMI readmission; however, few variables were consistently identified. Thus, clinically, patient risk stratification is challenging. From a policy perspective, a validated risk-standardized model to profile hospitals using AMI readmission rates is currently unavailable in the literature.
authors.affiliation: Division of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT 06520-8034, USA. mayur.desai@yale.edu
authors.forename: Mayur M
authors.initials: MM
authors.lastname: Desai
authors.forename: Brett D
authors.initials: BD
authors.lastname: Stauffer
authors.forename: Harm H H
authors.initials: HH
authors.lastname: Feringa
authors.forename: Geoffrey C
authors.initials: GC
authors.lastname: Schreiner
mesh_terms: D019317:Evidence-Based Medicine / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000453:epidemiology*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests