Aims

To assess the prognostic utility of coronary artery calcium (CAC) scoring and coronary computed tomography angiography (CCTA)-derived quantitative plaque metrics for predicting adverse cardiovascular outcomes.

Methods and results

The study enrolled 2404 patients with suspected coronary artery disease (CAD) but without a prior history of CAD. All  participants underwent CAC scoring and CCTA, with plaque metrics quantified using an artificial intelligence (AI)-based  tool (Cleerly, Inc). Percent atheroma volume (PAV) and non-calcified plaque volume percentage (NCPV%), reflecting total plaque burden and the proportion of non-calcified plaque volume normalized to vessel volume, were evaluated. The primary endpoint was a composite of all-cause mortality and non-fatal myocardial infarction (MI). Cox proportional hazard models, adjusted for clinical risk factors and early revascularization, were employed for analysis. During a median follow-up of 7.0 years, 208 patients (8.7%) experienced the primary endpoint, including 73 cases of MI (3%). The model incorporating PAV demonstrated superior discriminatory power for the composite endpoint (AUC = 0.729) compared to CAC scoring (AUC = 0.706, P = 0.016). In MI prediction, PAV (AUC = 0.791) significantly outperformed CAC (AUC = 0.699, P < 0.001), with NCPV% showing the highest prognostic accuracy (AUC = 0.814, P < 0.001).

Conclusion 

​​​​​​​AI-driven assessment of coronary plaque burden enhances prognostic accuracy for future adverse cardiovascular events, highlighting the critical role of comprehensive plaque characterization in refining risk stratification strategies