Published online Dec 26, 2015. doi: 10.4330/wjc.v7.i12.902
Peer-review started: June 27, 2015
First decision: August 3, 2015
Revised: August 28, 2015
Accepted: October 23, 2015
Article in press: October 27, 2015
Published online: December 26, 2015
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.
Core tip: We present a review of the most relevant scores developed for the risk stratification of patients hospitalized for acute heart failure. For each score, the strengths, weaknesses, statistical pertinence and applicability in a real-world situation are evaluated. Furthermore, we revisit the general criteria and statistical metrics that should be considered in the design and analysis of prognostic studies.