Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA; Departments of Biomedical Informatics, Biostatistics, Epidemiology, and Cardiology, University of Missouri, Columbia, MO.
Department of Imaging Physics, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, TX, USA.
Cell Rep Med. 2024 Oct 15;5(10):101746. doi: 10.1016/j.xcrm.2024.101746. Epub 2024 Sep 25.
We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET) and 17,649 ECG-single-photon emission computed tomography (SPECT) data pairs, the ML model is trained with a swarm intelligence approach and support vector regression (SVR). The model achieves a receiver-operator curve (ROC) area under the curve (AUC) of 0.83, with a sensitivity and specificity of 0.75. An ECG-MFR value below 2 is significantly associated with MACE, with hazard ratios (HRs) of 3.85 and 3.70 in the discovery and validation phases, respectively. The model's C-statistic is 0.76, with a net reclassification improvement (NRI) of 0.35. Validated in an independent cohort, the ML model using ECG data offers superior MACE prediction compared to baseline clinical models, highlighting its potential for risk stratification in patients with coronary artery disease (CAD) using the accessible 12-lead ECG.
我们开发了一种使用心电图（ECG）预测心肌血流储备（MFR）的机器学习（ML）模型，并评估其对主要不良心血管事件（MACE）的预后价值。使用 3639 对心电图正电子发射断层扫描（PET）和 17649 对心电图单光子发射计算机断层扫描（SPECT）数据对，该 ML 模型采用群体智能方法和支持向量回归（SVR）进行训练。该模型的接收者操作曲线（ROC）下面积（AUC）为 0.83，灵敏度和特异性分别为 0.75。ECG-MFR 值低于 2 与 MACE 显著相关，在发现和验证阶段的危险比（HR）分别为 3.85 和 3.70。该模型的 C 统计量为 0.76，净重新分类改善（NRI）为 0.35。在独立队列中进行验证后，使用 ECG 数据的 ML 模型在预测 MACE 方面优于基线临床模型，突出了其在使用可获得的 12 导联 ECG 对冠心病（CAD）患者进行风险分层的潜力。
