BACKGROUND Cardiac troponins are the preferred biomarkers of acute myocardial infarction. Despite superior sensitivity, serial testing of Troponins to identify patients suffering acute coronary syndromes is still required in many cases to overcome limited specificity. Moreover, unstable angina pectoris relies on reported symptoms in the troponin-negative group. In this study, we investigated genome-wide miRNA levels in a prospective cohort of patients with clinically suspected ACS and determined their diagnostic value by applying an in silico neural network. METHODS PAXgene blood and serum samples were drawn and hsTnT was measured in patients at initial presentation to our Chest-Pain Unit. After clinical and diagnostic workup, patients were adjudicated by senior cardiologists in duty to their final diagnosis: STEMI, NSTEMI, unstable angina pectoris and non-ACS patients. ACS patients and a cohort of healthy controls underwent deep transcriptome sequencing. Machine learning was implemented to construct diagnostic miRNA classifiers. RESULTS We developed a neural network model which incorporates 34 validated ACS miRNAs, showing excellent classification results. By further developing additional machine learning models and selecting the best miRNAs, we achieved an accuracy of 0.96 (95% CI 0.96-0.97), sensitivity of 0.95, specificity of 0.96 and AUC of 0.99. The one-point hsTnT value reached an accuracy of 0.89, sensitivity of 0.82, specificity of 0.96, and AUC of 0.96. CONCLUSIONS Here we show the concept of neural network based biomarkers for ACS. This approach also opens the possibility to include multi-modal data points to further increase precision and perform classification of other ACS differential diagnoses.

中文翻译：

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microRNA 神经网络改善了急性冠状动脉综合征 (ACS) 的诊断。

背景心肌肌钙蛋白是急性心肌梗塞的优选生物标志物。尽管具有卓越的敏感性，但在许多情况下仍需要对肌钙蛋白进行连续测试以识别患有急性冠状动脉综合征的患者，以克服有限的特异性。此外，不稳定型心绞痛依赖于肌钙蛋白阴性组报告的症状。在这项研究中，我们调查了临床疑似 ACS 患者的前瞻性队列中的全基因组 miRNA 水平，并通过应用计算机神经网络确定了其诊断价值。方法 抽取 PAXgene 血液和血清样本，并在初次就诊到我们胸痛科的患者中测量 hsTnT。在临床和诊断检查后，患者由负责最终诊断的资深心脏病专家裁定：STEMI、NSTEMI、不稳定型心绞痛和非 ACS 患者。ACS 患者和一组健康对照接受了深度转录组测序。实施机器学习以构建诊断 miRNA 分类器。结果我们开发了一个神经网络模型，该模型包含 34 个经过验证的 ACS miRNA，显示出出色的分类结果。通过进一步开发其他机器学习模型并选择最佳 miRNA，我们实现了 0.96（95% CI 0.96-0.97）的准确度、0.95 的灵敏度、0.96 的特异性和 0.99 的 AUC。单点 hsTnT 值达到了 0.89 的准确度、0.82 的灵敏度、0.96 的特异性和 0.96 的 AUC。结论 在这里，我们展示了基于神经网络的 ACS 生物标志物的概念。