Prognostic value of AI-enabled quantitative coronary CT angiography for major adverse cardiovascular events: A systematic review and meta-analysis

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; I 2 = 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.