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Rationale and objective <p>This 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.</p> Methods <p>A 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.</p> Results <p>Among 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) &gt; 4.68% emerged as the strongest predictor (HR 5.073, 95% CI 2.930–8.786, <i>P</i> &lt; 0.001). Analyzed continuously, each 1-SD increase in total-NCPAV conferred an HR of 1.82 (95% CI 1.54–2.14, <i>P</i> &lt; 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.</p> Conclusions <p>AI-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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月28日

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  • Xinwei Zhang,
  • Mengyuan Bao,
  • Yongshun Wu,
  • Haicheng Qi,
  • Yan Xing

摘要

Rationale and objective

This 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.

Methods

A 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.

Results

Among 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.

Conclusions

AI-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.