LAUSR

BACKGROUND It is uncertain how much candidate biomarkers improve risk prediction when added to comprehensive models including routinely collected clinical and laboratory variables in heart failure. METHODS Aldosterone, cystatin C, high sensitivity-troponin T (hs-TnT), galectin-3, growth differentiation factor-15 (GDF-15), kidney injury molecule-1 (KIM-1), matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), soluble suppression of tumorigenicity-2 (ST2), tissue inhibitor of metalloproteinase-1 (TIMP-1) and urinary albumin to creatinine ratio (UACR) were measured in 1559 of PARADIGM-HF participants.We tested whether these biomarkers, individually or collectively, improved the performance of the PREDICT-HF prognostic model, which includes clinical, routine laboratory, and natriuretic peptide data, for the primary endpoint and cardiovascular and all-cause mortality. RESULTS The mean age of participants was 67.3 ± 9.9 years, 1254 (80.4%) were men and 1103 (71%) were NYHA class II. During a mean follow-up of 30.7 months, 300 patients experienced the primary outcome and 197 died. Added individually, only 4 biomarkers were independently associated with all outcomes: hs-TnT, GDF-15, cystatin C and TIMP-1. When all biomarkers were added simultaneously to the PREDICT-HF models, only hs-TnT remained an independent predictor of all three endpoints. GDF-15 also remained predictive of the primary endpoint; TIMP-1 was the only other predictor of both cardiovascular and all-cause mortality. Individually or in combination, these biomarkers did not lead to statistically significant improvements in discrimination or reclassification. CONCLUSIONS None of the biomarkers studied individually or collectively led to a meaningful improvement in the prediction of outcomes over what is provided by clinical, routine laboratory, and natriuretic peptide variables. This article is protected by copyright. All rights reserved.