Predicting mortality in patients with acute heart failure: Role of risk scores.

Abstract

Acute heart failure is a leading cause of hospitalization and death, and it is an increasing burden on health care systems. The correct risk stratification of patients could improve clinical outcome and resources allocation, avoiding the overtreatment of low-risk subjects or the early, inappropriate discharge of high-risk patients. Many clinical scores have been derived and validated for in-hospital and post-discharge survival; predictive models include demographic, clinical, hemodynamic and laboratory variables. Data sets are derived from public registries, clinical trials, and retrospective data. Most models show a good capacity to discriminate patients who reach major clinical end-points, with C-indices generally higher than 0.70, but their applicability in real-world populations has been seldom evaluated. No study has evaluated if the use of risk score-based stratification might improve patient outcome. Some variables (age, blood pressure, sodium concentration, renal function) recur in most scores and should always be considered when evaluating the risk of an individual patient hospitalized for acute heart failure. Future studies will evaluate the emerging role of plasma biomarkers.