Multiple biomarkers for risk prediction in chronic heart failure.
Circ Heart Fail. 2012 Mar 1;5(2):183-90
Authors: Ky B, French B, Levy WC, Sweitzer NK, Fang JC, Wu AH, Goldberg LR, Jessup M, Cappola TP
BACKGROUND: Prior studies have suggested using a panel of biomarkers that measure diverse biological processes as a prognostic tool in chronic heart failure. Whether this approach improves risk prediction beyond clinical evaluation is unknown.
METHODS AND RESULTS: In a multicenter cohort of 1513 chronic systolic heart failure patients, we measured a contemporary biomarker panel consisting of high-sensitivity C-reactive protein, myeloperoxidase, B-type natriuretic peptide, soluble fms-like tyrosine kinase receptor-1, troponin I, soluble toll-like receptor-2, creatinine, and uric acid. From this panel, we calculated a parsimonious multimarker score and assessed its performance in predicting risk of death, cardiac transplantation, or ventricular assist device placement in comparison to an established clinical risk score, the Seattle Heart Failure Model (SHFM). During a median follow-up of 2.5 years, there were 317 outcomes: 187 patients died; 99 were transplanted; and 31 had a ventricular assist device placed. In unadjusted Cox models, patients in the highest tertile of the multimarker score had a 13.7-fold increased risk of adverse outcomes compared with the lowest tertile (95% confidence interval, 8.75-21.5). These effects were independent of the SHFM (adjusted hazard ratio, 6.80; 95% confidence interval, 4.18-11.1). Addition of the multimarker score to the SHFM led to a significantly improved area under the receiver operating characteristic curve of 0.803 versus 0.756 (P=0.003) and appropriately reclassified a significant number of patients who had the outcome into a higher risk category (net reclassification improvement, 25.2%; 95% confidence interval, 14.2-36.2%; P<0.001).
CONCLUSIONS: In ambulatory chronic heart failure patients, a score derived from multiple biomarkers integrating diverse biological pathways substantially improves prediction of adverse events beyond current metrics.
PMID: 22361079 [PubMed - indexed for MEDLINE]Link to Article at PubMed