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Predicting Major Adverse Cardiac Events Using Deep Learning-based Coronary Artery Disease Analysis at CT Angiography
Jin Young Kim1, Kye Ho Lee2, Ji Won Lee3
1Department of Radiology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Republic of Korea.
Insights
Deep learning analysis of coronary artery disease extent on CT angiography significantly predicts major adverse cardiac events in emergency department patients with chest pain. This AI tool offers superior risk stratification compared to traditional clinical factors.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Radiology
Background:
- Acute chest pain is a common emergency department presentation.
- Accurate risk stratification for major adverse cardiac events (MACEs) is crucial.
- Coronary artery disease (CAD) extent is a key prognostic factor.
Purpose of the Study:
- To assess the predictive capability of deep learning (DL)-based coronary artery disease (CAD) extent analysis.
- To evaluate MACEs in emergency department (ED) patients with acute chest pain.
- To compare DL-based risk stratification with traditional clinical risk factors.
Main Methods:
- Retrospective, multicenter observational study.
- Included patients with acute chest pain undergoing coronary CT angiography (CCTA).
- Utilized a DL model for CAD classification (no CAD, nonobstructive CAD, obstructive CAD) and Cox regression for MACE prediction.
Main Results:
- The DL model identified obstructive CAD in 37.3% of 408 patients.
- Obstructive CAD was significantly more prevalent in patients experiencing MACEs (P < .001).
- DL-based obstructive CAD detection was the strongest independent predictor of MACEs (HR, 88.07; P < .001), enhancing risk stratification (Harrell C: 0.94 vs 0.80).
Conclusions:
- Deep learning-based detection of obstructive CAD offers superior predictive value for MACEs.
- DL analysis improves risk stratification beyond clinical factors in acute chest pain patients.
- This AI approach holds promise for enhanced cardiac risk assessment in the ED.
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. Keywords: Cardiac, CT-Angiography, Outcomes Analysis © RSNA, 2025 See also commentary by Reddy in this issue.
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