sST2 Predicts Outcome in Chronic Heart Failure Beyond NT-proBNP and High-Sensitivity Troponin T.
BACKGROUND:Soluble suppression of tumorigenesis-2 (sST2) is a biomarker related to inflammation and fibrosis. OBJECTIVES:This study assessed the independent prognostic value of sST2 in chronic heart failure (HF). METHODS:Individual patient data from studies that assessed sST2 for risk prediction in chronic HF, together with N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity troponin T (hs-TnT), were retrieved. RESULTS:A total of 4,268 patients were evaluated (median age 68 years, 75% males, 65% with ischemic HF, 87% with left ventricular ejection fraction [LVEF] <40%). NT-proBNP, hs-TnT, and sST2 were 1,360 ng/l (interquartile interval: 513 to 3,222 ng/l), 18 ng/l (interquartile interval: 9 to 33 ng/l), and 27 ng/l (interquartile interval: 20 to 39 ng/l), respectively. During a 2.4-year median follow-up, 1,319 patients (31%) experienced all-cause death (n = 932 [22%] for cardiovascular causes). Among the 4,118 patients (96%) with available data, 1,029 (24%) were hospitalized at least once for worsening HF over 2.2 years. The best sST2 cutoff for the prediction of all-cause and cardiovascular death and HF hospitalization was 28 ng/ml, with good performance at Kaplan-Meier analysis (log-rank: 117.6, 61.0, and 88.6, respectively; all p < 0.001). In a model that included age, sex, body mass index, ischemic etiology, LVEF, New York Heart Association functional class, glomerular filtration rate, HF medical therapy, NT-proBNP, and hs-TnT, the risk of all-cause death, cardiovascular death, and HF hospitalization increased by 26%, 25%, and 30%, respectively, per each doubling of sST2. sST2 retained its independent prognostic value across most population subgroups. CONCLUSIONS:sST2 yielded strong, independent predictive value for all-cause and cardiovascular mortality, and HF hospitalization in chronic HF, and deserves consideration to be part of a multimarker panel together with NT-proBNP and hs-TnT.
Emdin, M; Aimo, A; Vergaro, G; Bayes-Genis, A; Lupón, J; Latini, R; Meessen, J; Anand, IS; Cohn, JN; Gravning, J; Gullestad, L; Broch, K; Ueland, T; Nymo, SH; Brunner-La Rocca, H-P; de Boer, RA; Gaggin, HK; Ripoli, A; Passino, C; Januzzi, JL
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