Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study.
Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study.
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DOI：
10.2196/48487
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发表时间：
2024-01-03
影响因子：
2.2
通讯作者：
Kuang, Jie
中科院分区：
文献类型：
--
作者：
Zhang, Pin;Wu, Lei;Zou, Ting-Ting;Zou, ZiXuan;Tu, JiaXin;Gong, Ren;Kuang, Jie
急性心肌梗死(AMI)患者接受经皮冠状动脉介入治疗(PCI)后，主要心血管不良事件(MACEs)发生率居高不下，缺乏指导临床治疗的早期预测模型。本研究旨在开发基于机器学习的新诊断急性心肌梗死患者接受经皮冠状动脉介入治疗后急性心肌梗死的早期预测模型。2018年1月至2019年12月接受冠状动脉介入治疗的急性心肌梗死患者共1531例纳入这一连续队列。这些数据包括人口统计特征、临床调查、实验室测试和与疾病相关的事件。建立了人工神经网络、k近邻、支持向量机和随机森林四种机器学习模型，并与Logistic回归模型进行了比较。我们的主要结果是预测MACEs的模型性能，这取决于准确性、受试者操作特征曲线下的面积和F1评分。总共对1362名患者进行了成功的随访。中位随访期25.9个月，MACEs发生率为18.5%(252/1362)。人工神经网络模型、随机森林模型、k近邻模型、支持向量机模型和Logistic回归模型的受试者工作特征曲线下面积分别为80.49%、72.67%、79.80%、77.20%和71.77%。在ANN模型中，排名前5位的预测因素是左心室射血分数、植入支架的数量、年龄、糖尿病和冠状动脉疾病的血管数量。ANN模型对急性心肌梗死患者介入治疗后的MACE有较好的预测作用。在临床实践中，使用基于机器学习的预测模型可能会改善患者管理和结果。
The incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking. This study aimed to develop machine learning–based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI. A total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models—artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest—were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score. In total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease. The ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning–based prediction models may improve patient management and outcomes in clinical practice.