title: Validated, electronic health record deployable prediction models for assessing patient risk of 30-day rehospitalization and mortality in older heart failure patients.
abstract: OBJECTIVE
  The study sought to derive and validate risk-prediction tools from a large nationwide registry linked with Medicare claims data.
  
  
  BACKGROUND
  Few clinical models have been developed utilizing data elements readily available in electronic health records (EHRs) to facilitate "real-time" risk estimation.
  
  
  METHODS
  Heart failure (HF) patients ≥ 65 years of age hospitalized in the GWTG-HF (Get With The Guidelines-Heart Failure) program were linked with Medicare claims from January 2005 to December 2009. Multivariable models were developed for 30-day mortality after admission, 30-day rehospitalization after discharge, and 30-day mortality/rehospitalization after discharge. Candidate variables were selected based on availability in EHRs and prognostic value. The models were validated in a 30% random sample and separately in patients with reduced and preserved ejection fraction (EF).
  
  
  RESULTS
  Among 33,349 patients at 160 hospitals, 3,002 (9.1%) died within 30 days of admission, 7,020 (22.8%) were rehospitalized within 30 days of discharge, and 8,374 (27.2%) died or were rehospitalized within 30 days of discharge. Compared with patients classified as low risk, high-risk patients had significantly higher odds of death (odds ratio [OR]: 8.82, 95% confidence interval [CI]: 7.58 to 10.26), rehospitalization (OR: 1.99, 95% CI: 1.86 to 2.13), and death/rehospitalization (OR: 2.65, 95% CI: 2.44 to 2.89). The 30-day mortality model demonstrated good discrimination (c-index 0.75) while the rehospitalization and death/rehospitalization models demonstrated more modest discrimination (c-indices of 0.59 and 0.62), with similar performance in the validation cohort and for patients with preserved and reduced EF.
  
  
  CONCLUSIONS
  These predictive models allow for risk stratification of 30-day outcomes for patients hospitalized with HF and may provide a validated, point-of-care tool for clinical decision making.
authors.affiliation: Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina. Electronic address: zubin.eapen@duke.edu.
authors.forename: Zubin J
authors.initials: ZJ
authors.lastname: Eapen
authors.affiliation: Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
authors.forename: Li
authors.initials: L
authors.lastname: Liang
authors.affiliation: University of California Los Angeles, Los Angeles, California.
authors.forename: Gregg C
authors.initials: GC
authors.lastname: Fonarow
authors.affiliation: Palo Alto VA Medical Center, Palo Alto, California.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Heidenreich
authors.affiliation: Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
authors.forename: Lesley H
authors.initials: LH
authors.lastname: Curtis
authors.affiliation: Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
authors.forename: Eric D
authors.initials: ED
authors.lastname: Peterson
authors.affiliation: Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
authors.forename: Adrian F
authors.initials: AF
authors.lastname: Hernandez
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D057286:Electronic Health Records; D005260:Female; D006333:Heart Failure / Q000401:mortality*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011379:Prognosis; D018570:Risk Assessment; D013997:Time Factors
keywords: electronic health records; heart failure; predictive models; risk stratification