Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance.
BACKGROUND
Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models.


RESULTS
EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was < 10 in 13 models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P < .001) and sample size (P = .007).


CONCLUSIONS
There is an abundance of HF risk prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution.
Centre of Cardiovascular Research and Education in Therapeutics, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; School of Public Health, Mekelle University, Mekelle, Ethiopia.
Berhe W
BW
Sahle
Centre of Cardiovascular Research and Education in Therapeutics, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia. Electronic address: alice.owen@monash.edu.
Alice J
AJ
Owen
Centre of Cardiovascular Research and Education in Therapeutics, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
Ken Lee
KL
Chin
Centre of Cardiovascular Research and Education in Therapeutics, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; School of Public Health, Curtin University, Perth, Australia.
Christopher M
CM
Reid
D006333:Heart Failure / Q000175:diagnosis* / Q000453:epidemiology* / Q000503:physiopathology; D006801:Humans; D015994:Incidence; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors
Risk prediction model; heart failure; model performance; risk predictors