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

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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.

Keywords:
Computed Tomographic AngiographyHigh-Risk PlaquePercutaneous Coronary Intervention

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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.