Troponin T and N-Terminal Pro–B-Type Natriuretic Peptide: A Biomarker Approach to Predict Heart Failure Risk—The Atherosclerosis Risk in Communities Study

Apr 04, 2014
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Authors:
Nambi V, Liu X, Chambless LE, et al.
Citation:
Clin Chem 2013;59:12:1802-1810.
Summary By:
Ragavendra R. Baliga, MBBS, FACC

Study Questions:

Do high-sensitivity cardiac troponin T (cTnT) and N-terminal pro–B-type natriuretic peptide (NT-proBNP) improve prediction of heart failure (HF)?

Methods:

The study cohort was comprised of 9,868 participants without HF from the ARIC (Atherosclerosis Risk in Communities) study. The investigators utilized gender-specific models, to add cTnT and NT-proBNP to age and race (“laboratory report” model) and to the ARIC HF model (includes age, race, systolic blood pressure, antihypertensive medication use, current/former smoking, diabetes, body mass index, prevalent coronary heart disease, and heart rate) in this cohort. Statistical methods described in this paper include area under the receiver operating characteristic curve (AUC), integrated discrimination improvement, net reclassification improvement (NRI), and model fit.

Results:

A total of 970 participants developed incident HF during a mean follow-up of 10.4 years. The investigators found that the addition of cTnT and NT-proBNP to the ARIC HF model significantly improved all statistical parameters (AUCs increased by 0.040 and 0.057; the continuous NRIs were 50.7% and 54.7% in women and men, respectively). Also, the simpler “laboratory report” model was statistically no different than the ARIC HF model.

Conclusions:

The authors concluded that cTnT and NT-proBNP have significant value in HF risk prediction. A simple gender-specific model that includes age, race, cTnT, and NT-proBNP (which can be incorporated in a laboratory report) provides a good model, whereas adding cTnT and NT-proBNP to clinical characteristics results in an excellent HF prediction model.

Perspective:

This study supports the findings of earlier studies on the utility of biomarkers in predicting heart failure. The next steps would be to determine the cost-effectiveness of this approach in reducing the burden of HF.

Keywords: Follow-Up Studies, Atherosclerosis, Area Under Curve, Troponin T, Coronary Disease, Blood Pressure, Heart Rate, Smoking, Heart Diseases, Body Mass Index, Biomarkers, Heart Failure, Peptide Fragments, ROC Curve, Diabetes Mellitus, Natriuretic Peptide, Brain


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