Predictors of mortality and morbidity in patients with chronic heart failure

Aims We aimed to develop prognostic models for patients with chronic heart failure (CHF). Methods and results We evaluated data from 7599 patients in the CHARM programme with CHF with and without left ventricular systolic dysfunction. Multi-variable Cox regression models were developed using baseline candidate variables to predict all-cause mortality (n=1831 deaths) and the composite of cardiovascular (CV) death and heart failure (HF) hospitalization (n=2460 patients with events). Final models included 21 predictor variables for CV death/HF hospitalization and for death. The three most powerful predictors were older age (beginning > 60 years), diabetes, and lower left ventricular ejection fraction (EF) (beginning < 45%). Other independent predictors that increased risk included higher NYHA class, cardiomegaly, prior HF hospitalization, male sex, lower body mass index, and lower diastolic blood pressure. The model accurately stratified actual 2-year mortality from 2.5 to 44% for the lowest to highest deciles of predicted risk. Conclusion In a large contemporary CHF population, including patients with preserved and decreased left ventricular systolic function, routine clinical variables can discriminate risk regardless of EF. Diabetes was found to be a surprisingly strong independent predictor. These models can stratify risk and help define how patient characteristics relate to clinical course.