title: Risk prediction in patients with heart failure: a systematic review and analysis.
abstract: OBJECTIVE
  This study sought to review the literature for risk prediction models in patients with heart failure and to identify the most consistently reported independent predictors of risk across models.
  
  
  BACKGROUND
  Risk assessment provides information about patient prognosis, guides decision making about the type and intensity of care, and enables better understanding of provider performance.
  
  
  METHODS
  MEDLINE and EMBASE were searched from January 1995 to March 2013, followed by hand searches of the retrieved reference lists. Studies were eligible if they reported at least 1 multivariable model for risk prediction of death, hospitalization, or both in patients with heart failure and reported model performance. We ranked reported individual risk predictors by their strength of association with the outcome and assessed the association of model performance with study characteristics.
  
  
  RESULTS
  Sixty-four main models and 50 modifications from 48 studies met the inclusion criteria. Of the 64 main models, 43 models predicted death, 10 hospitalization, and 11 death or hospitalization. The discriminatory ability of the models for prediction of death appeared to be higher than that for prediction of death or hospitalization or prediction of hospitalization alone (p = 0.0003). A wide variation between studies in clinical settings, population characteristics, sample size, and variables used for model development was observed, but these features were not significantly associated with the discriminatory performance of the models. A few strong predictors emerged for prediction of death; the most consistently reported predictors were age, renal function, blood pressure, blood sodium level, left ventricular ejection fraction, sex, brain natriuretic peptide level, New York Heart Association functional class, diabetes, weight or body mass index, and exercise capacity.
  
  
  CONCLUSIONS
  There are several clinically useful and well-validated death prediction models in patients with heart failure. Although the studies differed in many respects, the models largely included a few common markers of risk.
authors.affiliation: George Institute for Global Health, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom; Department of Cardiology, Oxford University Hospitals NHS Trust, Oxford, United Kingdom. Electronic address: kazem.rahimi@georgeinstitute.ox.ac.uk.
authors.forename: Kazem
authors.initials: K
authors.lastname: Rahimi
authors.affiliation: Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, United Kingdom.
authors.forename: Derrick
authors.initials: D
authors.lastname: Bennett
authors.affiliation: George Institute for Global Health, University of Oxford, Oxford, United Kingdom; IBM, Global Business Services, Business Analytics & Optimization, Zurich, Switzerland.
authors.forename: Nathalie
authors.initials: N
authors.lastname: Conrad
authors.affiliation: George Institute for Global Health, University of Oxford, Oxford, United Kingdom.
authors.forename: Timothy M
authors.initials: TM
authors.lastname: Williams
authors.affiliation: George Institute for Global Health, University of Oxford, Oxford, United Kingdom.
authors.forename: Joyee
authors.initials: J
authors.lastname: Basu
authors.affiliation: Department of Cardiology, Oxford University Hospitals NHS Trust, Oxford, United Kingdom.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Dwight
authors.affiliation: George Institute for Global Health, University of Oxford, Oxford, United Kingdom; The George Institute for Global Health, Sydney, Australia.
authors.forename: Mark
authors.initials: M
authors.lastname: Woodward
authors.affiliation: The George Institute for Global Health, Sydney, Australia; The George Institute for Global Health, Hyderabad, India.
authors.forename: Anushka
authors.initials: A
authors.lastname: Patel
authors.affiliation: BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom.
authors.forename: John
authors.initials: J
authors.lastname: McMurray
authors.affiliation: George Institute for Global Health, University of Oxford, Oxford, United Kingdom; The George Institute for Global Health, Sydney, Australia.
authors.forename: Stephen
authors.initials: S
authors.lastname: MacMahon
mesh_terms: D000367:Age Factors; D001794:Blood Pressure; D015992:Body Mass Index; D015897:Comorbidity; D003661:Decision Support Techniques; D003920:Diabetes Mellitus / Q000453:epidemiology; D017079:Exercise Tolerance; D005260:Female; D006333:Heart Failure / Q000097:blood / Q000401:mortality* / Q000503:physiopathology; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D020097:Natriuretic Peptide, Brain / Q000097:blood; D011379:Prognosis; D051436:Renal Insufficiency, Chronic / Q000453:epidemiology; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D012737:Sex Factors; D012964:Sodium / Q000097:blood
chemical_list: D020097:Natriuretic Peptide, Brain; D012964:Sodium
keywords: death; heart failure; hospitalization; multivariable model; risk prediction; systematic review