Troponin T and N-Terminal Pro-B-Type Natriuretic Peptide: A Biomarker Approach to Predict Heart Failure Risk-The Atherosclerosis Risk in Communities Study
Troponin T and N-Terminal Pro-B-Type Natriuretic Peptide: A Biomarker Approach to Predict Heart Failure Risk-The Atherosclerosis Risk in Communities Study
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DOI：
10.1373/clinchem.2013.203638
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发表时间：
2013-12-01
影响因子：
9.3
通讯作者：
Ballantyne, Christie M.
中科院分区：
文献类型：
--
作者：
Nambi, Vijay;Liu, Xiaoxi;Ballantyne, Christie M.
背景：在各种心血管疾病中，心力衰竭（HF）预计在未来几十年的发病率增幅最大；因此，改进HF预测具有重要的价值。在社区动脉粥样硬化风险（ARIC）研究中，我们评估了用高灵敏度测定法和N-末端前B型利钠肽（NT- proBNP）测量心肌肌钙蛋白T （cTnT）是否能改善HF风险预测，这些生物标志物与HF事件密切相关。方法：使用性别特异性模型，我们将cTnT和NT- proBNP添加到9868名没有流行HF的参与者的年龄和种族（“实验室报告”模型）和ARIC HF模型（包括年龄、种族、收缩压、抗高血压药物使用、当前/以前吸烟、糖尿病、体重指数、流行冠心病和心率）中；描述了受试者工作特征曲线下面积（AUC）、综合判别改进、净重分类改进（NRI）和模型拟合。结果：在平均10.4年的随访中，970名参与者发生了心衰。在ARIC HF模型中加入cTnT和NT- proBNP显著改善了所有统计参数（auc分别增加0.040和0.057；女性和男性的连续NRIs分别为50.7%和54.7%）。有趣的是，简单的实验室报告模型与ARIC HF模型在统计学上没有差异。结论：cTnT和NT- proBNP在心衰风险预测中具有重要价值。一个简单的性别特异性模型，包括年龄、种族、cTnT和NT- proBNP（可纳入实验室报告）提供了一个很好的模型，而将cTnT和NTproBNP添加到临床特征中会产生一个很好的心衰预测模型。c 2013美国临床化学协会
BACKGROUND: Among the various cardiovascular diseases, heart failure (HF) is projected to have the largest increases in incidence over the coming decades; therefore, improving HF prediction is of significant value. Weevaluated whether cardiac troponin T (cTnT) measured with a high- sensitivity assay and N- terminal pro- B- type natriuretic peptide (NT- proBNP), biomarkers strongly associated with incident HF, improve HF risk prediction in the Atherosclerosis Risk in Communities (ARIC) study.METHODS: Using sex- specific models, we added 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 9868 participants without prevalent HF; area under the receiver operating characteristic curve (AUC), integrated discrimination improvement, net reclassification improvement (NRI), and model fit were described.RESULTS: Over a mean follow- up of 10.4 years, 970 participants developed incident HF. Adding 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). Interestingly, the simpler laboratory report model was statistically no different than the ARIC HF model.CONCLUSIONS: cTnT and NT- proBNP have significant value in HF risk prediction. A simple sex- 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 NTproBNP to clinical characteristics results in an excellent HF prediction model. c 2013 American Association for Clinical Chemistry