Acute heart failure (AHF) is a complex and heterogeneous syndrome not only associated with a concerning rise in incidence, but also with still unacceptably high rates of mortality and morbidity. As this dismal outcome is at least in part due to a mismatch between the severity of AHF and the intensity of its management, both in-hospital and immediately after discharge, early and accurate risk prediction could contribute to more effective, risk-adjusted management.
Biomarkers are noninvasive and highly reproducible quantitative tools that have improved the understanding of AHF pathophysiology. They can help guide the intensity of AHF management. In addition, using a statistical model to estimate risk from a combination of several predictor variables such as vital signs or demographics has gained more and more attention over recent years. In this context, the aim of a statistical model, which gives a so-called risk score, is to help clinicians to make more standardised decisions.
This review highlights recent advances and remaining uncertainties regarding risk stratification in AHF by characterising and comparing the potential of biomarkers and risk scores.