Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study

被引:76
作者
Commandeur, Frederic [1 ]
Slomka, Piotr J. [2 ]
Goeller, Markus [3 ]
Chen, Xi [2 ,3 ,4 ]
Cadet, Sebastien [2 ]
Razipour, Aryabod [1 ]
McElhinney, Priscilla [1 ]
Gransar, Heidi [2 ,4 ]
Cantu, Stephanie [2 ,4 ]
Miller, Robert J. H. [2 ,4 ]
Rozanski, Alan [5 ]
Achenbach, Stephan [3 ]
Tamarappoo, Balaji K. [2 ,4 ]
Berman, Daniel S. [2 ,4 ]
Dey, Damini [1 ]
机构
[1] Cedars Sinai Med Ctr, Biomed Imaging Res Inst, Dept Biomed Sci, 116 N Robertson Blvd, Los Angeles, CA 90048 USA
[2] Cedars Sinai Med Ctr, Dept Imaging & Med, Los Angeles, CA 90048 USA
[3] Friedrich Alexander Univ Erlangen Nurnberg, Dept Cardiol, Erlangen, Germany
[4] Cedars Sinai Med Ctr, Smidt Heart Inst, Los Angeles, CA 90048 USA
[5] Mt Sinai St Lukes Hosp, Div Cardiol, New York, NY USA
关键词
Machine learning; Epicardial adipose tissue; Coronary calcium scoring; Myocardial infarction and cardiac death; EISNER EARLY IDENTIFICATION; C-REACTIVE PROTEIN; ARTERY CALCIUM; COMPUTED-TOMOGRAPHY; SUBCLINICAL ATHEROSCLEROSIS; CARDIOVASCULAR EVENTS; HEART-DISEASE; ARTIFICIAL-INTELLIGENCE; CALCIFICATION; ANGIOGRAPHY;
D O I
10.1093/cvr/cvz321
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Aims Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects. Methods and results Our study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.8 +/- 9.1 years] from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 +/- 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P < 0.05 for all). Subjects with a higher ML score (by Youden's index) had high hazard of suffering events (HR: 10.38, P < 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P = 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men. Conclusions In this prospective study, machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death compared with standard clinical risk assessment. Following further validation, such a personalized paradigm could potentially be used to improve cardiovascular risk assessment. [GRAPHICS] .
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收藏
页码:2216 / 2225
页数:10
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