Predicting Major Adverse Cardiac Events Using Deep Learning-based Coronary Artery Disease Analysis at CT Angiography

Abstract

Purpose To evaluate the predictive value of deep learning (DL)-based coronary artery disease (CAD) extent analysis for major adverse cardiac events (MACEs) in patients with acute chest pain presenting to the emergency department (ED). Materials and Methods This retrospective multicenter observational study included consecutive patients with acute chest pain who underwent coronary CT angiography (CCTA) at three institutional EDs from January 2018 to December 2022. Patients were classified as having no CAD, nonobstructive CAD, or obstructive CAD using a DL model. The primary outcome was MACEs during follow-up, defined as a composite of cardiac death, nonfatal myocardial infarction, and hospitalization for unstable angina. Cox proportional hazards regression models were used to evaluate the predictors of MACEs. Results The study included 408 patients (224 male; mean age, 59.4 years ± 14.6 [SD]). The DL model classified 162 (39.7%) patients as having no CAD, 94 (23%) as having nonobstructive CAD, and 152 (37.3%) as having obstructive CAD. Sixty-three (15.4%) patients experienced MACEs during follow-up. Patients with MACEs had a higher prevalence of obstructive CAD than those without (P < .001). In the multivariate analysis of model 1 (clinical risk factors), dyslipidemia (hazard ratio [HR], 2.15) and elevated troponin T levels (HR, 2.13) were predictive of MACEs (all P < .05). In model 2 (clinical risk factors plus DL-based CAD extent), obstructive CAD detected by the DL model was the most significant independent predictor of MACEs (HR, 88.07; P < .001). Harrell C statistic showed that DL-based CAD extent enhanced the risk stratification beyond clinical risk factors (Harrell C statistics: 0.94 vs 0.80, P < .001). Conclusion DL-based detection of obstructive CAD demonstrated stronger predictive value than clinical risk factors for MACEs in patients with acute chest pain presenting to the ED.