title: Statistical models and patient predictors of readmission for heart failure: a systematic review.
abstract: BACKGROUND
  Readmission after heart failure (HF) hospitalization is an increasing focus for physicians and policy makers, but statistical models are needed to assess patient risk and to compare hospital performance. We performed a systematic review to describe models designed to compare hospital rates of readmission or to predict patients' risk of readmission, as well as to identify studies evaluating patient characteristics associated with hospital readmission, all among patients admitted for HF.
  
  
  METHODS
  We identified relevant studies published between January 1, 1950, and November 19, 2007, by searching MEDLINE, Scopus, PsycINFO, and all 4 Ovid Evidence-Based Medicine Reviews. Eligible English-language publications reported on readmission after HF hospitalization among adult patients. We excluded experimental studies and publications without original data or quantitative outcomes.
  
  
  RESULTS
  From 941 potentially relevant articles, 117 met inclusion criteria: none contained models to compare readmission rates among hospitals, 5 (4.3%) presented models to predict patients' risk of readmission, and 112 (95.7%) examined patient characteristics associated with readmission. Studies varied in case identification, used multiple types of data sources, found few patient characteristics consistently associated with readmission, and examined differing outcomes, often either readmission alone or a combined outcome of readmission or death, measured across varying periods (from 14 days to 4 years). Two articles reported model discriminations of patient readmission risk, both of which were modest (C statistic, 0.60 for both).
  
  
  CONCLUSIONS
  Our systematic review identified no model designed to compare hospital rates of readmission, while models designed to predict patients' readmission risk used heterogeneous approaches and found substantial inconsistencies regarding which patient characteristics were predictive. Clinically, patient risk stratification is challenging. From a policy perspective, a validated risk-standardized statistical model to accurately profile hospitals using readmission rates is unavailable in the published English-language literature to date.
authors.affiliation: Department of Geriatrics and Adult Development, Mount Sinai School of Medicine, One Gustave L Levy Place, Box 1070, New York, NY 10029, USA. joseph.ross@mssm.edu
authors.forename: Joseph S
authors.initials: JS
authors.lastname: Ross
authors.forename: Gregory K
authors.initials: GK
authors.lastname: Mulvey
authors.forename: Brett
authors.initials: B
authors.lastname: Stauffer
authors.forename: Vishnu
authors.initials: V
authors.lastname: Patlolla
authors.forename: Susannah M
authors.initials: SM
authors.lastname: Bernheim
authors.forename: Patricia S
authors.initials: PS
authors.lastname: Keenan
authors.forename: Harlan M
authors.initials: HM
authors.lastname: Krumholz
mesh_terms: D018450:Disease Progression; D019317:Evidence-Based Medicine; D005260:Female; D006333:Heart Failure / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D008297:Male; D015233:Models, Statistical; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D019057:Quality-Adjusted Life Years; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D016019:Survival Analysis; D014481:United States