Validated, electronic health record deployable prediction models for assessing patient risk of 30-day rehospitalization and mortality in older heart failure patients.

Published

Journal Article

OBJECTIVES: 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.

Full Text

Duke Authors

Cited Authors

  • Eapen, ZJ; Liang, L; Fonarow, GC; Heidenreich, PA; Curtis, LH; Peterson, ED; Hernandez, AF

Published Date

  • June 2013

Published In

Volume / Issue

  • 1 / 3

Start / End Page

  • 245 - 251

PubMed ID

  • 24621877

Pubmed Central ID

  • 24621877

Electronic International Standard Serial Number (EISSN)

  • 2213-1787

Digital Object Identifier (DOI)

  • 10.1016/j.jchf.2013.01.008

Language

  • eng

Conference Location

  • United States