Development of a MACE risk prediction model based on CCTA-derived quantitative parameters: a proof-of-concept study

Area of Science:

• Cardiology
• Radiology
• Medical Imaging

Background:

• Coronary artery disease (CAD) poses a significant risk for major adverse cardiovascular events (MACE).
• Identifying reliable predictors of MACE in CAD patients is crucial for effective risk management.
• Quantitative parameters from coronary computed tomography angiography (CCTA) offer potential insights into CAD progression and MACE risk.

Purpose of the Study:

• To identify quantitative CCTA-derived parameters associated with MACE in CAD patients.
• To develop and validate a nomogram-based risk prediction model for MACE using CCTA findings.

Main Methods:

• Retrospective analysis of 280 CAD patients with 1-year follow-up.
• Comparison of baseline characteristics and CCTA parameters between MACE and non-MACE groups.
• Multivariable logistic regression to identify independent predictors, followed by nomogram construction and validation (internal and external).

Main Results:

• Severe stenosis, longer plaque length, increased fibrous plaque volume, plaque burden (PB), coronary artery calcium score (CACS), perivascular fat attenuation index (FAI), and myocardial mass/volume ratio (MAS) were independent MACE risk factors.
• Larger minimal lumen area (MLA) was found to be protective against MACE.
• The combined nomogram model demonstrated high predictive accuracy (AUC=0.936 internally, AUC=0.932 externally), significantly outperforming individual parameters.

Conclusions:

• A nomogram incorporating quantitative CCTA parameters provides a highly accurate tool for predicting MACE in CAD patients.
• This CCTA-based nomogram can serve as a valuable clinical screening tool for risk stratification.
• Further long-term prospective studies are needed to validate these findings for hard cardiovascular outcomes.