Incremental value of biomarkers to clinical variables for mortality prediction in acutely decompensated heart failure: the Multinational Observational Cohort on Acute Heart Failure (MOCA) study.
AIM: This study aims to evaluate the incremental value of plasma biomarkers to traditional clinical variables for risk stratification of 30-day and one-year mortality in acutely decompensated heart failure (ADHF). METHODS AND RESULTS: Through an international collaborative network, individual patient data on 5306 patients hospitalized for ADHF were collected. The all-cause mortality rate was 11.7% at 30 days and 32.9% at one year. The clinical prediction model (age, gender, blood pressure on admission, estimated glomerular filtration rate <60 mL/min/1.73 m(2), sodium and hemoglobin levels, and heart rate) had a c-statistic of 0.74 for 30-day mortality and 0.73 for one-year mortality. Several biomarkers measured at presentation improved risk stratification when added to the clinical model. At 30 days, the net reclassification improvement (NRI) was 28.7% for mid-regional adrenomedullin (MR-proADM; p<0.001) and 25.5% for soluble (s)ST2 (p<0.001). At one year, sST2 (NRI 10.3%), MR-proADM (NRI 9.1%), amino-terminal pro-B-type natriuretic peptide (NT-proBNP; NRI 9.1%), mid-regional proatrial natriuretic peptide (MR-proANP; NRI 7.4%), B-type natriuretic peptide (NRI 5.5%) and C-reactive protein (CRP; NRI 5.3%) reclassified patients with ADHF (p<0.05 for all). CRP also markedly improved risk stratification of patients with ADHF as a dual biomarker combination with MR-proADM (NRI 36.8% [p<0.001] for death at 30 days) or with sST2 (NRI 20.3%; [p<0.001] for one-year mortality). CONCLUSION: In this study, biomarkers provided incremental value for risk stratification of ADHF patients. Biomarkers such as sST2, MR-proADM, natriuretic peptides and CRP, reflecting different pathophysiologic pathways, add prognostic value to clinical risk factors for predicting both short-term and one-year mortality in ADHF.
Lassus, J; Gayat, E; Mueller, C; Peacock, WF; Spinar, J; Harjola, V-P; van Kimmenade, R; Pathak, A; Mueller, T; Disomma, S; Metra, M; Pascual-Figal, D; Laribi, S; Logeart, D; Nouira, S; Sato, N; Potocki, M; Parenica, J; Collet, C; Cohen-Solal, A; Januzzi, JL; Mebazaa, A; GREAT-Network,
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