High-sensitivity troponin T, NT-proBNP and glomerular filtration rate: A multimarker strategy for risk stratification in chronic heart failure.
BACKGROUND: In a recent individual patient data meta-analysis, high-sensitivity troponin T (hs-TnT) emerged as robust predictor of prognosis in stable chronic heart failure (HF). In the same population, we compared the relative predictive performances of hs-TnT, N-terminal fraction of pro-B-type natriuretic peptide (NT-proBNP), hs-C-reactive protein (hs-CRP), and estimated glomerular filtration rate (eGFR) for prognosis. METHODS AND RESULTS: 9289 patients (66 ± 12 years, 77% men, 85% LVEF <40%, 60% ischemic HF) were evaluated over a 2.4-year median follow-up. Median eGFR was 58 mL/min/1.73 m2 (interquartile interval 46-70; n = 9220), hs-TnT 16 ng/L (8-20; n = 9289), NT-proBNP 1067 ng/L (433-2470; n = 8845), and hs-CRP 3.3 mg/L (1.4-7.8; n = 7083). In a model including all 3 biomarkers, only hs-TnT and NT-proBNP were independent predictors of all-cause and cardiovascular mortality and cardiovascular hospitalization. hs-TnT was a stronger predictor than NT-proBNP: for example, the risk for all-cause death increased by 54% per doubling of hs-TnT vs. 24% per doubling of NT-proBNP. eGFR showed independent prognostic value from both hs-TnT and NT-proBNP. The best hs-TnT and NT-proBNP cut-offs for the prediction of all-cause death increased progressively with declining renal function (eGFR ≥ 90: hs-TnT 13 ng/L and NT-proBNP 825 ng/L; eGFR < 30: hs-TnT 40 ng/L and NT-proBNP 4608 ng/L). Patient categorization according to these cut-offs effectively stratified patient prognosis across all eGFR classes. CONCLUSIONS: hs-TnT conveys independent prognostic information from NT-proBNP, while hs-CRP does not. Concomitant assessment of eGFR may further refine risk stratification. Patient classification according to hs-TnT and NT-proBNP cut-offs specific for the eGFR classes holds prognostic significance.
Aimo, A; Januzzi, JL; Vergaro, G; Ripoli, A; Latini, R; Masson, S; Magnoli, M; Anand, IS; Cohn, JN; Tavazzi, L; Tognoni, G; Gravning, J; Ueland, T; Nymo, SH; Rocca, H-PB-L; Bayes-Genis, A; Lupón, J; de Boer, RA; Yoshihisa, A; Takeishi, Y; Egstrup, M; Gustafsson, I; Gaggin, HK; Eggers, KM; Huber, K; Tentzeris, I; Wilson Tang, WH; Grodin, JL; Passino, C; Emdin, M
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