Association of AI-assisted quantitative coronary plaque burden and CT-derived fractional flow reserve with major adverse cardiovascular events.

RATIONALE AND

objectiveThis single-center retrospective study evaluated the associations of AI-quantified coronary plaque parameters and CT-derived fractional flow reserve (CT-FFR) with major adverse cardiovascular events (MACEs) in patients with coronary artery disease, and derived optimal risk cutoff values for plaque burden.

methodsA total of 381 patients who underwent CCTA were consecutively enrolled. MACEs were defined as a composite of all-cause death, myocardial infarction (fatal and nonfatal), heart failure death, malignant arrhythmia, coronary revascularization, and rehospitalization for angina exacerbation. Maximum follow-up was 18 months. Risk cutoff values were derived from receiver operating characteristic analysis. Univariate and multivariate Cox regression, Kaplan-Meier analysis, and five predictive models (plaque model, CT-FFR model, combined model, LASSO-Cox, and Cox survival neural network) were constructed.

resultsAmong 381 patients, 67 (17.6%) developed MACEs. All six total plaque parameters showed significant associations with MACEs. In multivariate Cox regression, total noncalcified percent atheroma volume (NCPAV) > 4.68% emerged as the strongest predictor (HR 5.073, 95% CI 2.930-8.786, P < 0.001). Analyzed continuously, each 1-SD increase in total-NCPAV conferred an HR of 1.82 (95% CI 1.54-2.14, P < 0.001). The combined model C-index was 0.750 (95% CI 0.696-0.804; optimism-corrected 0.708), comparable to the plaque model alone (0.744, 95% CI 0.686-0.801; corrected 0.705). The LASSO-Cox and Cox survival neural network models achieved C-indices of 0.747 (95% CI 0.674-0.816) and 0.730 (95% CI 0.628-0.833), respectively. In landmark sensitivity analyses excluding early events, the combined model C-index rose to 0.792, with the likelihood ratio test P value narrowing from 0.117 to 0.061, suggesting a trend toward incremental value for CT-FFR after accounting for potential incorporation bias.

conclusionsAI-quantified total noncalcified plaque burden was the strongest predictor of MACEs. The addition of CT-FFR to plaque parameters did not provide a clinically meaningful or statistically significant improvement in overall model performance, including discrimination, model fit, reclassification, or discrimination slope. Although landmark analyses suggested a possible trend toward incremental value after exclusion of early revascularization-driven events, this finding should be considered exploratory and requires further validation. Vessel-specific analyses identified RCA plaque burden as having the greatest prognostic weight among the target vessels; however, this exploratory finding also warrants confirmation in independent cohorts.