Factors influencing the predictive power of models for predicting mortality and/or heart failure hospitalization in patients with heart failure.

The present paper systematically reviews and compares existing prediction models in order to establish the strongest variables, models, and model characteristics in patients with heart failure predicting outcome. To improve decision making accurately predicting mortality and heart-failure hospitalization in patients with heart failure can be important for selecting patients with a poorer prognosis or nonresponders to current therapy, to improve decision making. MEDLINE/PubMed was searched for papers dealing with heart failure prediction models. To identify similar models on the basis of their variables hierarchical cluster analysis was performed. Meta-analysis was used to estimate the mean predictive value of the variables and models; meta-regression was used to find characteristics that explain variation in discriminating values between models. We identified 117 models in 55 papers. These models used 249 different variables. The strongest predictors were blood urea nitrogen and sodium. Four subgroups of models were identified. Mortality was most accurately predicted by prospective registry-type studies using a large number of clinical predictor variables. Mean C-statistic of all models was 0.66 ± 0.0005, with 0.71 ± 0.001, 0.68 ± 0.001 and 0.63 ± 0.001 for models predicting mortality, heart failure hospitalization, or both, respectively. There was no significant difference in discriminating value of models between patients with chronic and acute heart failure. Prediction of mortality and in particular heart failure hospitalization in patients with heart failure remains only moderately successful. The strongest predictors were blood urea nitrogen and sodium. The highest C-statistic values were achieved in a clinical setting, predicting short-term mortality with the use of models derived from prospective cohort/registry studies with a large number of predictor variables.