CCTA-Derived coronary plaque burden offers enhanced prognostic value over coronary artery calcium (CAC) scoring in suspected coronary artery disease (CAD) patients
Authors' conclusion
AI-driven assessment of coronary plaque burden enhances prognostic accuracy for future adverse cardiovascular events, highlighting the critical role of comprehensive plaque characterization in refining risk stratification strategies.
Study context
- Population
- Patients with suspected coronary artery disease (CAD) but without a prior history of CADenrolled, all underwent CAC scoring and CCTA
- Participants
- 2,404 patientsEvents: 208 patients (8.7%)
- Predictors
- Coronary artery calcium (CAC) scoring; CCTA-derived percent atheroma volume (PAV) and non-calcified plaque volume percentage (NCPV%)
- Predicted outcome
- Composite of:
- All-cause mortality
- Non-fatal myocardial infarction (MI)
Methodological detailsCox proportional hazard models adjusted for clinical risk factors and early revascularization, plaque metrics quantified using an artificial intelligence (AI)-based tool (Cleerly, Inc)
- Cox proportional hazard models adjusted for clinical risk factors and early revascularization
- Plaque metrics quantified using an AI-based tool (Cleerly, Inc)
Model performance
As reported in the abstract
AUC
Model incorporating PAV
0.729
CAC scoring
0.706
PAV
0.791
CAC
0.699
NCPV%
0.814
- Model incorporating PAV vs CAC scoring: P = 0.016
- Median follow-up of 7.0 years
Further findings
| Outcome | P value |
|---|---|
| MI predictionPAV vs CAC | P < 0.001 |
| MI predictionNCPV% | P < 0.001 |
- Myocardial infarction (MI) prediction: comparator not clearly stated; NCPV% showed the highest prognostic accuracy
- MI prediction: 73 cases of MI (3%)
Original publication
CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients. Eur Heart J Cardiovasc Imaging. Published 25 March 2025. doi:10.
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