Predicting major adverse cardiac events based on multi-parameter coronary computed tomography angiography

Area of Science:

• Cardiovascular Imaging
• Radiology
• Biostatistics

Background:

• Major adverse cardiac events (MACE) prediction is crucial for cardiovascular disease management.
• Coronary computed tomography angiography (CCTA) offers detailed coronary artery visualization.
• Integrating multi-parameter CCTA data may enhance MACE prediction accuracy.

Purpose of the Study:

• To develop and validate a nomogram model for improved MACE prediction.
• To assess the incremental value of combining CCTA stenosis, plaque characteristics, and FFRct.

Main Methods:

• Retrospective enrollment of patients with and without MACE.
• Propensity score matching for control group selection.
• Analysis of CCTA stenosis, plaque features, and FFRct.
• Development of multi-parameter models and a final nomogram using regression analysis.

Main Results:

• The combined model of CCTA stenosis, plaque characteristics, and FFRct demonstrated superior predictive performance (AUC=0.922).
• Key independent risk factors identified include CCTA stenosis ≥50%, low attenuation plaque, positive remodeling, napkin ring sign, lipid plaque volume, and FFRct.
• A nomogram was successfully constructed using these identified risk factors.

Conclusions:

• Multi-parameter CCTA analysis significantly enhances MACE prediction.
• The developed nomogram provides a practical and user-friendly tool for clinical risk stratification.