Abstract
BACKGROUND: Artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCCTA) offers automated assessment of coronary plaque burden and morphology. Although AI-QCCTA has improved diagnostic consistency and downstream testing efficiency, its prognostic value for major adverse cardiovascular events (MACE) has not been comprehensively quantified.
METHODS: We systematically searched PubMed, Embase, and Cochrane through October 2025 for studies evaluating AI-based plaque analysis in patients without prior MACE undergoing CCTA. Outcomes of interest were pooled using random-effects GLMM models, and prognostic associations were synthesized using inverse-variance random-effects meta-analysis of hazard ratios (HRs). The primary endpoint was MACE; secondary outcomes included myocardial infarction (MI), revascularization, angina, stroke, and mortality. Subgroup analysis was done to identify the association of different plaque characteristics in predicting MACE/MI/Death.
RESULTS: Ten studies (n = 20,195) were included. Across six cohorts (n = 18,804), pooled rates were: all-cause mortality 1.20% (95% CI 0.38-3.77%), cardiovascular mortality 0.32% (0.21-0.48%), MACE 5.07% (1.25-18.46%), MI 1.30% (0.41-3.99%), and revascularization 13.09% (6.57-24.40%). AI-enabled plaque burden predicted MACE (HR 1.95, 95% CI 1.29-2.94; I2 = 99%), consistent in sensitivity analysis as per same AI platform use (HR 1.88, 95% CI 1.15-3.07). Low-attenuation plaque showed the strongest association (HR 2.95, 95% CI 1.95-4.45).
CONCLUSIONS: AI-QCCTA provides prognostic value beyond stenosis severity, with vulnerable plaque characteristics-particularly low-attenuation and non-calcified plaque most strongly predicting adverse cardiovascular outcomes. These findings support the integration of AI-enabled plaque analysis into contemporary risk stratification.