30Machine 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
Authors: Commandeur F C , Slomka P J , Goeller M , Chen X , Cadet S , Razipour A , Gransar H , Cantu S , Miller R , Rozanski A , Achenbaclh S , Tamarappoo B , Berman D , Dey D 
Published: October 2019

Background/Introduction

Machine learning (ML) allows objective integration of clinical and imaging data for the prediction of events. ML prediction of cardiovascular events in asymptomatic subjects over long-term follow-up, utilizing quantitative CT measures of coronary artery calcium (CAC) and epicardial adipose tissue (EAT) have not yet been evaluated.

Purpose

To analyze the ability of machine learning to integrate clinical parameters with coronary calcium and EAT quantification in order to improve prediction of myocardial infarction (MI) and cardiac death in asymptomatic subjects.

Methods

We assessed 2071 consecutive subjects [1230 (59%) male, age: 56.049.03] from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial with long-term follow-up after non-enhanced cardiac CT. CAC (Agatston) score, age-and-gender-adjusted CAC percentile, and aortic calcium scores were obtained. EAT volume and density were quantified using a fully automated deep learning method. Extreme gradient boosting, a ML algorithm, was trained using demographic variables, plasma lipid panel measurements, risk factors as well as CAC, aortic calcium and EAT measures from CAC CT scans. ML was validated using 10-fold cross validation; event prediction was evaluated using area-under-receiver operating characteristic curve (AUC) analysis and Cox proportional hazards regression. Optimal ML cut-point for risk of MI and cardiac death was determined by highest Youden's index (sensitivity + specificity – 1).

Results

At 152 years' follow-up, 76 events of MI and/or cardiac death had occurred. ML obtained a significantly higher AUC than the ASCVD risk and CAC score in predicting events (ML: 0.81; ASCVD: 0.76, p<0.05; CAC: 0.75, p<0.01, Figure A). ML performance was mostly driven by age, ASCVD risk and calcium as shown by the variable importance (Figure B); however, all variables with non-zero gain contributed to the ML performance. ML achieved a sensitivity and specificity of 77.6% and 73.5%, respectively. For an equal specificity, ASCVD and CAC scores obtained a sensitivity of 61.8% and 67.1%, respectively. High ML risk was associated with a high risk of suffering an event by Cox regression (HR: 9.25 [95% CI: 5.39–15.87], p<0.001; survival curves in Figure C). The relationships persisted when adjusted for age, gender, CAC, CAC percentile, aortic calcium score, and ASCVD risk score; with a hazard ratio of 3.42 for high ML risk (HR: 3.42 [95% CI: 1.54–7.57], p=0.002).

Conclusion(s)

Machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death in asymptomatic subjects undergoing CAC assessment, compared to standard risk assessment methods.

Acknowledgement/Funding

NHLBI 1R01HL13361, Bundesministerium für Bildung und Forschung (01EX1012B), Dr. Miriam and Sheldon G. Adelson Medical Research Foundation