School of Medicine, Inha University, Incheon, Korea.
Department of Cardiology, Inha University Hospital, School of Medicine, Inha University, Incheon, Korea.
Sci Rep. 2023 Aug 3;13(1):12635. doi: 10.1038/s41598-023-39911-y.
Pretest probability (PTP) for assessing obstructive coronary artery disease (ObCAD) was updated to reduce overestimation. However, standard laboratory findings and electrocardiogram (ECG) raw data as first-line tests have not been evaluated for integration into the PTP estimation. Therefore, this study developed an ensemble model by adopting machine learning (ML) and deep learning (DL) algorithms with clinical, laboratory, and ECG data for the assessment of ObCAD. Data were extracted from the electronic medical records of patients with suspected ObCAD who underwent coronary angiography. With the ML algorithm, 27 clinical and laboratory data were included to identify ObCAD, whereas ECG waveform data were utilized with the DL algorithm. The ensemble method combined the clinical-laboratory and ECG models. We included 7907 patients between 2008 and 2020. The clinical and laboratory model showed an area under the curve (AUC) of 0.747; the ECG model had an AUC of 0.685. The ensemble model demonstrated the highest AUC of 0.767. The sensitivity, specificity, and F1 score of the ensemble model ObCAD were 0.761, 0.625, and 0.696, respectively. It demonstrated good performance and superior prediction over traditional PTP models. This may facilitate personalized decisions for ObCAD assessment and reduce PTP overestimation.
用于评估阻塞性冠状动脉疾病（ObCAD）的术前概率（PTP）已得到更新，以减少高估。然而，标准的实验室检查结果和心电图（ECG）原始数据作为一线检查尚未经过评估，以纳入 PTP 评估。因此，本研究采用机器学习（ML）和深度学习（DL）算法，结合临床、实验室和心电图数据，开发了一种用于评估 ObCAD 的集成模型。从疑似 ObCAD 患者的电子病历中提取数据，这些患者接受了冠状动脉造影。采用 ML 算法，纳入了 27 项临床和实验室数据来识别 ObCAD，而采用 DL 算法则利用了 ECG 波形数据。集成方法将临床-实验室和 ECG 模型相结合。我们纳入了 2008 年至 2020 年期间的 7907 名患者。临床和实验室模型的 AUC 为 0.747；ECG 模型的 AUC 为 0.685。集成模型表现出最高的 AUC 为 0.767。集成模型 ObCAD 的灵敏度、特异性和 F1 评分分别为 0.761、0.625 和 0.696。它表现出良好的性能和优于传统 PTP 模型的预测能力。这可能有助于 ObCAD 评估的个性化决策，并减少 PTP 的高估。
