Predictors of mortality in 6975 patients with chronic heart failure in the Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure trial: proposal for a nomogram

Abstract

Background: We developed a prognostic model to assess the risk of all-cause mortality in patients with chronic heart failure.

Methods and results: We examined 6975 patients with chronic heart failure enrolled in the Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure (GISSI-HF) trial (3.9 years follow-up). Multivariable Cox regression models were developed to predict mortality (1969 deaths). By stepwise selection, the full final model included 25 predictors. A reduced model, considering the most significant variables ranked according to the Wald χ(2) (P<0.0001) accounted for most of the prognostic information. Internal validation of the model was performed with bootstrap techniques. The discrimination ability of the reduced model constituted by 12 factors (concordance probability estimate, 0.693) was as good as the full final model (concordance probability estimate, 0.70). Among the first 12 independent risk factors emerging in the reduced model, the 3 most powerful predictors were older age, higher New York Heart Association class, and lower estimated glomerular filtration rate. Other independent predictors that increased risk included lower left ventricular ejection fraction, chronic obstructive pulmonary disease, lower systolic blood pressure, diabetes mellitus, male sex, higher uricemia, lower body mass index, lower hemoglobin, and aortic stenosis. The reduced model was used to build a nomogram to estimate the risk of death in individual patients. In a subgroup of patients, the 2 well-known biomarkers (high-sensitivity cardiac troponin T and N-terminal pro-brain natriuretic peptide) emerged as the most powerful predictors of outcome.

Conclusions: In a large contemporary population with chronic heart failure, this model offers good ability to assess the risk of death, confirming most of the risk factors that have emerged in recent trials.

Clinical trial registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00336336.