Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
Division of Nephrology, Department of Internal Medicine, New Taipei City Hospital, Taiwan.
Comput Methods Programs Biomed. 2019 May;173:109-117. doi: 10.1016/j.cmpb.2019.01.013. Epub 2019 Jan 31.
Hospital admission rate for the patients with chest pain has already been increased worldwide but no existing risk score has been designed to stratify non-ST-elevation myocardial infarction (NSTEMI) from non-cardiogenic chest pain. Clinical diagnosis of chest pain in the emergency department is always highly subjective and variable. We, therefore, aimed to develop an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the real clinical setting.
A standard protocol was developed to collect data from chest pain patients who had visited the emergency department between December 2016 and February 2017. All the chest pain patients with aged <20 years were primarily included in this study. However, STEMI, previous history of ACS, and out-of-hospital cardiac arrest were excluded from our study. An artificial neural network (ANN) model was then developed to predict NSTEMI patients. The accuracy, sensitivity, specificity, and receiver operating characteristic curve was used to measure the performance of this model.
A total of 268 chest pain patients were included in this study; of those, 47 (17.5%) was stable NSTEMI, and 221 (82.5%) was unstable angina patients. Serval risk factors such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, glutamic-oxaloacetic transaminase, glutamic pyruvic transaminase and troponin were independently associated with stable NSTEMI. The area under the receiver operating characteristic (AUROC) and accuracy of ANN were 98.4, and 92.86. Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value of the ANN model was 90.91, 93.33, 76.92, and 97.67 respectively.
Our prediction model showed a higher accuracy to predict NSTEMI patients. This model has a potential application in disease detection, monitoring, and prognosis of chest pain at risk of AMI.
全球范围内，因胸痛而住院的患者人数已经增加，但尚未设计出专门的风险评分来区分非 ST 段抬高型心肌梗死（NSTEMI）与非心源性胸痛。急诊科对胸痛的临床诊断始终具有高度主观性和变异性。因此，我们旨在开发一种人工智能方法来预测稳定型 NSTEMI，以便在实际临床环境中提供有价值的见解，减少误诊。
制定了一个标准方案，从 2016 年 12 月至 2017 年 2 月期间在急诊科就诊的胸痛患者中收集数据。本研究主要纳入年龄<20 岁的所有胸痛患者。然而，STEMI、既往 ACS 病史和院外心脏骤停均排除在本研究之外。然后开发了一个人工神经网络（ANN）模型来预测 NSTEMI 患者。使用准确性、敏感性、特异性和受试者工作特征曲线来衡量该模型的性能。
本研究共纳入 268 例胸痛患者；其中，47 例（17.5%）为稳定型 NSTEMI，221 例（82.5%）为不稳定型心绞痛患者。一些风险因素，如心脏风险因素、收缩压、血红蛋白、校正 QT 间期（QTc）、PR 间期、谷草转氨酶、谷丙转氨酶和肌钙蛋白，与稳定型 NSTEMI 独立相关。接受者操作特征（ROC）曲线下面积（AUROC）和 ANN 的准确性分别为 98.4 和 92.86。此外，ANN 模型的敏感性、特异性、阳性预测值和阴性预测值分别为 90.91、93.33、76.92 和 97.67。
我们的预测模型显示出更高的准确性来预测 NSTEMI 患者。该模型在检测、监测和预测有 AMI 风险的胸痛疾病方面具有潜在的应用价值。
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