title: Risk prediction models for mortality in ambulatory patients with heart failure: a systematic review.
abstract: BACKGROUND
  Optimal management of heart failure requires accurate assessment of prognosis. Many prognostic models are available. Our objective was to identify studies that evaluate the use of risk prediction models for mortality in ambulatory patients with heart failure and describe their performance and clinical applicability.
  
  
  RESULTS
  We searched for studies in Medline, Embase, and CINAHL in May 2012. Two reviewers selected citations including patients with heart failure and reporting on model performance in derivation or validation cohorts. We abstracted data related to population, outcomes, study quality, model discrimination, and calibration. Of the 9952 studies reviewed, we included 34 studies testing 20 models. Only 5 models were validated in independent cohorts: the Heart Failure Survival Score, the Seattle Heart Failure Model, the PACE (incorporating peripheral vascular disease, age, creatinine, and ejection fraction) risk score, a model by Frankenstein et al, and the SHOCKED predictors. The Heart Failure Survival Score was validated in 8 cohorts (2240 patients), showing poor-to-modest discrimination (c-statistic, 0.56-0.79), being lower in more recent cohorts. The Seattle Heart Failure Model was validated in 14 cohorts (16 057 patients), describing poor-to-acceptable discrimination (0.63-0.81), remaining relatively stable over time. Both models reported adequate calibration, although overestimating survival in specific populations. The other 3 models were validated in a cohort each, reporting poor-to-modest discrimination (0.66-0.74). Among the remaining 15 models, 6 were validated by bootstrapping (c-statistic, 0.74-0.85); the rest were not validated.
  
  
  CONCLUSIONS
  Externally validated heart failure models showed inconsistent performance. The Heart Failure Survival Score and Seattle Heart Failure Model demonstrated modest discrimination and questionable calibration. A new model derived from contemporary patient cohorts may be required for improved prognostic performance.
authors.affiliation: Heart Failure and Transplantation Program, Toronto General Hospital, University Health Network, Ontario, Canada.
authors.forename: Ana C
authors.initials: AC
authors.lastname: Alba
authors.forename: Thomas
authors.initials: T
authors.lastname: Agoritsas
authors.forename: Milosz
authors.initials: M
authors.lastname: Jankowski
authors.forename: Delphine
authors.initials: D
authors.lastname: Courvoisier
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Walter
authors.forename: Gordon H
authors.initials: GH
authors.lastname: Guyatt
authors.forename: Heather J
authors.initials: HJ
authors.lastname: Ross
mesh_terms: D000553:Ambulatory Care; D003661:Decision Support Techniques; D006333:Heart Failure / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology / Q000628:therapy; D006801:Humans; D015233:Models, Statistical; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors
keywords: heart failure; prediction models; prognosis; survival