An Algorithm Based on Combining hs-cTnT and H-FABP for Ruling Out Acute Myocardial Infarction

César Navarro, M. Kurth, M. Ruddock, S. Fishlock, J. Mclaughlin
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引用次数: 1

Abstract

Our previous work demonstrated that algorithms combining high sensitivity cardiac troponin T (hs-cTnT) and heart-type fatty acid-binding protein (H-FABP) may help in ruling out Acute Myocardial Infarction (AMI). For those algorithms, the hs-cTnT thresholds were adopted from the ESC guidelines. This time, we present a data-driven approach that also explores hs-cTnT thresholds.The results show a significant improvement when compared to previous algorithms reported. Using a cohort of n = 360 patients (288 Non-AMI and 72 AMI), a rule-out algorithm used at presentation identified more low-risk patients who presented with chest pain of suspected cardiac origin than the standard ESC algorithm: (199/288 (69.1%) vs. 83/288 (28.8%) (p <0.0005)), respectively.According to our data, our algorithm at the emergency department, would identify additional non-AMI patients in comparison to the ESC algorithm, potentially reducing the number of hospital admissions by 42%.
基于hs-cTnT和H-FABP的急性心肌梗死排除算法
我们之前的工作表明,结合高灵敏度心肌肌钙蛋白T (hs-cTnT)和心脏型脂肪酸结合蛋白(H-FABP)的算法可能有助于排除急性心肌梗死(AMI)。对于这些算法,采用ESC指南中的hs-cTnT阈值。这一次,我们提出了一种数据驱动的方法,也探索了hs-cTnT阈值。结果表明,与以前报道的算法相比,该算法有了显著的改进。在一组n = 360例患者(288例非AMI和72例AMI)中,在就诊时使用的排除算法比标准ESC算法识别出更多疑似心源性胸痛的低风险患者:(199/288 (69.1%)vs. 83/288 (28.8%) (p <0.0005))。根据我们的数据,与ESC算法相比,我们在急诊科的算法将识别出额外的非ami患者,可能将住院人数减少42%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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