上海交通大学学报（医学版）2025，Vol.45Issue(12)：1589-1597,9.DOI:10.3969/j.issn.1674-8115.2025.12.004
急性心肌梗死介入治疗并发症风险预测模型构建
Intraoperative complications in percutaneous coronary intervention for acute myocardial infarction:development of a risk prediction model
摘要
Abstract
Objective·Patients with acute myocardial infarction(AMI)undergoing percutaneous coronary intervention(PCI)are at risk of severe complications,such as hypotension and malignant arrhythmias,which directly affect procedural success and patient prognosis.Current clinical practice lacks targeted assessment tools,and traditional assessment tools have limitations in predictive efficacy.This study innovatively applies machine learning methods to construct a precise intraoperative complication prediction model,addressing the insufficient non-linear relationship capture in existing scoring systems and providing an early risk warning tool for surgeons.Methods·The study included 811 emergency PCI patients who were treated at Shanghai Fengxian Central Hospital from 2019 to 2022,and defined 53 candidate variables.A multi-stage feature engineering framework was employed,including univariate screening,Spearman rank correlation analysis verification,and SHapley Additive exPlanations(SHAP)-based stability optimization.The prediction model was ultimately constructed using the eXtreme Gradient Boosting(XGBoost)algorithm.The primary endpoint was a composite of intraoperative complications,including hypotension,malignant arrhythmias,and severe bradyarrhythmias.Results·The model identified six core predictive factors:lowest left ventricular ejection fraction(LVEF),culprit vessel in the right coronary artery(CVRCA),culprit vessel presence(CVP),culprit vessel in the left anterior descending artery(CVADA),B-type natriuretic peptide(BNP),and heart rate(HR).The XGBoost model achieved areas under the curve(AUCs)of 0.88 and 0.84 in the training and validation sets,respectively.Conclusion·This study successfully constructed,for the first time,an intraoperative complication prediction model for AMI patients undergoing PCI based on the XGBoost algorithm.Its predictive performance significantly outperforms that of traditional scoring systems.By automatically selecting key clinical features and effectively capturing complex interactions between variables,the model provides individualized risk assessment for surgeons,thereby supporting clinical decision-making and potentially improving procedural success rates and patient prognosis.关键词
急性心肌梗死/经皮冠状动脉介入治疗/机器学习/XGBoost/术中并发症Key words
acute myocardial infarction/percutaneous coronary intervention/machine learning/XGBoost/intraoperative complications分类
医药卫生引用本文复制引用
阮青青,苏树智,李延婷,任渊,戴勇,乔增勇..急性心肌梗死介入治疗并发症风险预测模型构建[J].上海交通大学学报（医学版）,2025,45(12):1589-1597,9.基金项目
安徽省高校自然科学基金(2022AH040113) （2022AH040113）
安徽理工大学医学专项培育项目(YZ2023H2A007) （YZ2023H2A007）
安徽理工大学职业医学与健康联合研究中心研究基金(OMH-2023-05). Natural Science Research Project of Universities in Anhui Province(2022AH040113) （OMH-2023-05）
Medical Special Cultivation Project of Anhui University of Science and Technology(YZ2023H2A007) （YZ2023H2A007）
Research Fund of Joint Research Center of Occupational Medicine and Health,Anhui University of Science and Technology(OMH-2023-05). （OMH-2023-05）
