What question did this study set out to answer?

The study aims to evaluate AI-derived high-risk features from coronary CT angiography and their ability to predict major adverse cardiac events in nonculprit vessels.

February 28, 2026

AI-enabled Quantitative High-Risk Plaque Attributes for Predicting Coronary Events in Nonculprit Vessels

Key Result

AI-derived necrotic core volume increased MACE risk by 30%, and vessel-specific CT fractional flow reserve reduced MACE risk (HR 0.14) in nonculprit vessels.

Key Points

The study aims to evaluate AI-derived high-risk features from coronary CT angiography and their ability to predict major adverse cardiac events in nonculprit vessels.
Retrospective analysis of patients undergoing coronary CT angiography followed by percutaneous coronary intervention.
Evaluation of AI-derived high-risk features, including stenosis and plaque characteristics.
Assessment of prognostic value using multivariable Cox regression analyses.
Out of 2014 nonculprit vessels, major adverse cardiac events occurred in 100 vessels (5.0%).
Vessel-specific CT fractional flow reserve was identified as an independent predictor of major adverse cardiac events (adjusted hazard ratio, 0.14).
Necrotic core volume also emerged as a significant predictor (adjusted hazard ratio, 1.30).
Adding high-risk features improved predictive power, increasing area under the curve from 0.60 to 0.67.

Structured PICO

Do AI-derived high-risk CCTA features predict nonculprit vessel-related MACE in patients undergoing percutaneous coronary intervention?

Population

1495 patients (mean age 66 ± 10 years, 1100 male) with 2014 nonculprit vessels who underwent CCTA at a tertiary hospital followed by percutaneous coronary intervention within 3 months.

Intervention

Evaluation of AI-derived high-risk CCTA features (significant stenosis, high-risk plaque, high plaque volume, low CT fractional flow reserve, and high pericoronary adipose tissue attenuation).

Comparator

Clinical risk factors alone (for incremental prognostic value assessment).

Outcome

Nonculprit vessel-related major adverse cardiac events (MACE).composite

AI-derived quantitative plaque features from CCTA, specifically CT-FFR and necrotic core volume, provide independent and incremental prognostic value over clinical risk factors for predicting MACE in nonculprit vessels after PCI.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Purpose To assess the prognostic value of artificial intelligence (AI)-derived high-risk features obtained from coronary CT angiography (CCTA) in nonculprit vessels of patients who have undergone percutaneous coronary intervention. Materials and Methods This retrospective study included patients who underwent CCTA at a tertiary hospital between June 2013 and June 2023 followed by percutaneous coronary intervention within 3 months. AI-derived high-risk CCTA features were evaluated, including significant stenosis, high-risk plaque, high plaque volume, low CT fractional flow reserve, and high pericoronary adipose tissue attenuation. The primary end point was nonculprit vessel-related major adverse cardiac events (MACE). The prognostic value of high-risk CCTA features was assessed using multivariable Cox regression analyses. Results A total of 1495 patients (mean age, 66 years ± 10; 1100 male patients) with 2014 nonculprit vessels were analyzed with a median follow-up of 3.3 years. MACE occurred in 100 vessels (5.0%). In a multivariable Cox analysis adjusted for high-risk features, vessel-specific CT fractional flow reserve (adjusted hazard ratio, 0.14; 95% CI: 0.03, 0.76; P = .02) and necrotic core volume (adjusted hazard ratio, 1.30; 95% CI: 1.06, 1.59; P = .01) were independent predictors of MACE and showed an incremental prognostic value when added to clinical risk factors (area under the receiver operating characteristic curve, 0.60 vs 0.67; P Keywords: Computed Tomographic Angiography, High-Risk Plaque, Percutaneous Coronary Intervention Supplemental material is available for this article. © RSNA, 2026.

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