Optimizing clinical use of biomarkers in high-risk acute heart failure patients.

Aim: The clinical value of single biomarkers at single time-points to predict outcomes in patients with acute heart failure (AHF) is limited. We performed a multimarker, multi-time-point analysis of biomarkers for the prediction of post-discharge clinical outcomes in high-risk AHF patients.

Methods And Results: A set of 48 circulating biomarkers were measured in the PROTECT trial which enrolled 2033 patients with AHF. Associations between baseline levels of biomarkers and outcomes (30-day all-cause mortality, 30-day death or rehospitalization for renal/cardiovascular causes and 180-day all-cause mortality) were evaluated. Prognostic accuracies of baseline, days 2 or 3, 7, and 14 biomarker measurements were estimated and compared utilizing a time-dependent area under the curve (AUC) analysis. Forty-four biomarkers were significantly associated with outcomes, but 42 had limited prognostic value (C-index < 0.70). However, multimarker models combining best-performing biomarkers from different clusters had a much stronger prognostic value. Combining blood urea nitrogen (BUN), chloride, interleukin (IL)-6, cTnI, sST-2 and VEGFR-1 into a clinical model yielded a 11% increase in C-index to 0.84 and 0.78 for 30-day and 180-day all-cause mortality, respectively, and cNRI of 0.86 95% CI [0.55-1.11] and 0.76 95% CI [0.57-0.87]. Prognostic gain was modest for the 30-day death/rehospitalization for cardiovascular or renal causes endpoint. Comparative time-dependent AUC analysis indicated that late measurements provided superior accuracy for the prediction of all-cause mortality over 180 days, with few exceptions including BUN and galectin-3. However, the predictive value of most biomarkers showed a diminishing pattern over time irrespective of moment of measurement.

Conclusions: Multimarker models significantly improve risk prediction. Subsequent measurements, beyond admission, are needed for majority of biomarkers to maximize prognostic value over time, particularly in the long term.