中国医学装备2024，Vol.21Issue(6)：56-61,6.DOI:10.3969/j.issn.1672-8270.2024.06.011
基于临床指标、CT冠状动脉钙化评分和心外膜脂肪组织的机器学习预测心肌梗死风险研究
Study on the prediction for the risk of myocardial infarction by machine learning based on clinical indicator,CAC CT score and epicardial adipose tissue
摘要
Abstract
Objective:To assess the performance of machine learning(ML),and integrate the clinical parameters with coronary artery calcium(CAC)score of computed tomography(CT)and quantification of automated epicardial adipose tissue(EAT),so as to predict the long-term risk of myocardial infarction(MI)and cardiogenic death in asymptomatic patients.Methods:A total of 1 058 subjects with cardiovascular risk factors and without symptoms of coronary heart disease who underwent physical examination at the Fifth Medical Center of Chinese PLA General Hospital from January 2013 to October 2015 were selected as this study subjects.A long-term follow-up was conducted on them after CAC score.EAT volume and density were quantified using a fully automated deep learning method.ML extreme gradient boosting was trained by using clinical data,risk score of atherosclerotic cardiovascular disease,CAC score and automated EAT measure,and the repeated 10-fold cross validation was used to verify the model.Results:During the 8-year follow-up period,61 cases of 1 058 subjects occurred events of MI and(or)cardiac death.The area under curve(AUC)value of ML was significantly higher than that of the atherosclerotic cardiovascular disease(ASCVD)risk and the predicting events of CAC score(ML:0.82,ASCVD:0.77,CAC:0.77).Compared with ML with only clinical variable,machine learning based on ASCVD,CAC and EAT had more predictive ability for MI and cardiac death[AUC 0.82(95%CI:77-87)vs.0.78(95%CI:0.72-0.84),P=0.02].The survival rate of subjects with high ML scores had a greater decline degree with the increasing of time,therefore,the subjects with higher ML scores were more likely to experience events.Conclusion:ML,which integrated clinical and quantitative imaging variables,can provide long-term risk prediction for patients with cardiovascular risk factors.关键词
CT冠状动脉钙化评分/心外膜脂肪组织量化/心肌梗死(MI)/机器学习(ML)Key words
Computed tomography(CT)score of coronary artery calcification/Quantization of epicardial adipose tissue/Myocardial infarction(MI)/Machine learning(ML)分类
医药卫生引用本文复制引用
苑文雯,高旭东,赵静,李筱涵,刘佳,皋月娟,庞君丽,赵利利,李伯安..基于临床指标、CT冠状动脉钙化评分和心外膜脂肪组织的机器学习预测心肌梗死风险研究[J].中国医学装备,2024,21(6):56-61,6.基金项目
北京市自然科学基金(7222172) Beijing Natural Science Foundation(7222172) （7222172）
