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AI-enabled Quantitative High-Risk Plaque Attributes for Predicting Coronary Events in Nonculprit Vessels
Qian Chen1, Xiaofei Gao2, Ling Lin2
1Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Insights
Artificial intelligence (AI) analysis of coronary CT angiography (CCTA) identifies high-risk features in nonculprit vessels. These AI-derived features predict major adverse cardiac events (MACE) after percutaneous coronary intervention.
Area of Science:
- Cardiovascular Imaging and Intervention
- Artificial Intelligence in Medicine
- Radiology and Cardiac Diagnostics
Background:
- Percutaneous coronary intervention (PCI) aims to revascularize culprit lesions.
- Nonculprit coronary artery disease significantly impacts long-term outcomes post-PCI.
- Accurate risk stratification of nonculprit vessels is crucial for personalized patient management.
Purpose of the Study:
- To evaluate the prognostic capability of artificial intelligence (AI)-derived high-risk features from coronary CT angiography (CCTA).
- To assess these features in nonculprit vessels among patients who have undergone PCI.
- To determine if AI-derived features offer incremental value beyond traditional risk factors.
Main Methods:
- Retrospective analysis of 1495 patients undergoing CCTA and subsequent PCI.
- AI algorithms assessed high-risk CCTA features: significant stenosis, high-risk plaque, high plaque volume, low CT-FFR, and high pericoronary adipose tissue attenuation.
- Multivariable Cox regression analyzed the association between high-risk features and nonculprit vessel-related major adverse cardiac events (MACE) over a median follow-up of 3.3 years.
Main Results:
- A total of 2014 nonculprit vessels were analyzed; 5.0% experienced MACE.
- Vessel-specific CT-FFR (aHR=0.14, P=.02) and necrotic core volume (aHR=1.30, P=.01) were independent predictors of MACE.
- AI-derived features demonstrated incremental prognostic value when added to clinical risk factors (AUC increased from 0.60 to 0.67, P<.001).
Conclusions:
- AI-derived high-risk features from CCTA provide significant independent prognostic information for nonculprit vessels.
- These features enhance risk prediction for major adverse cardiac events in patients post-PCI.
- AI analysis of CCTA represents a promising tool for improving risk stratification in coronary artery disease.
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 < .001). Conclusion AI-derived high-risk features at CCTA provided independent and incremental prognostic values for nonculprit vessel-related MACE. Keywords: Computed Tomographic Angiography, High-Risk Plaque, Percutaneous Coronary Intervention Supplemental material is available for this article. © RSNA, 2026.
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