Factors In fl uencing the Predictive Power of Models for Predicting Mortality and / or Heart Failure Hospitalization in Patients With Heart Failure

Fro Am Ce He gra the pa Ma 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. (J Am Coll Cardiol HF 2014;2:429–36) © 2014 by the American College of Cardiology Foundation. H eart failure (HF) is a major cause of cardiovascular mortality and morbidity (1), and its prevalence and incidence is increasing (2). Despite wider use of evidence-based medical therapy and preventive device therapy, the prognosis remains poor (3,4). m the *Department of Clinical Epidemiology, Biostatistics, and Bioinf sterdam, Amsterdam, the Netherlands; and the yDepartment of Cardio nter, Groningen, the Netherlands. Prof. Voors received consultancy althCare, Cardio3Biosciences, Celladon, Novartis, Servier, Torrent, T nt (#FP7-242209-BIOSTAT-CHF) from the European Commission; and Dutch Heart Foundation. All other authors have reported that they ha per to disclose. nuscript received December 3, 2013; revised manuscript received April 14 Accurately predicting prognosis can be of benefit for patients with heart failure. First, patients with a poorer prognosis might benefit more from aggressive treatment and a closer follow-up (5). Prediction rules like the CHA2DS2-VASc (Congestive heart failure [or left ventricular systolic dysfunction]; Hypertension: ormatics, Academic Medical Center, University of logy, University of Groningen, University Medical fees and/or research grants from Alere, Bayer revena Vifor Pharmaceuticals; is supported by a is a clinical established investigator (2006T37) of ve no relationships relevant to the contents of this , 2014, accepted April 15, 2014. ABBR EV I A T I ON S