CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients.
Authors
Affiliations (13)
Affiliations (13)
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Departamento de Enfermedades Cardiovasculares, Clínica Alemana de Santiago, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile.
- Department of Radiology, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada.
- Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
- Clinical Physiology, Nuclear Medicine and PET, Turku University Hospital and University of Turku, Turku, Finland.
- Department of Vascular Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, the Netherlands.
- Division of Cardiology, The George Washington University School of Medicine, Washington DC, USA.
- Radiology, Nuclear Medicine and PET Research, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Department of Epidemiology & Data Science, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Department of Cardiology, Bern Unviersity Hospital, Bern, Switzerland.
- Department of Cardiology, Radboud University Medical Center, Nijmegen, the Netherlands.
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.
- Heart Center, Turku University Hospital, Turku, Finland.
Abstract
To assess the prognostic utility of coronary artery calcium (CAC) scoring and coronary computed tomography angiography (CCTA)-derived quantitative plaque metrics for predicting adverse cardiovascular outcomes. The study enrolled 2404 patients with suspected coronary artery disease (CAD) but without a prior history of CAD. All participants underwent CAC scoring and CCTA, with plaque metrics quantified using an artificial intelligence (AI)-based tool (Cleerly, Inc). Percent atheroma volume (PAV) and non-calcified plaque volume percentage (NCPV%), reflecting total plaque burden and the proportion of non-calcified plaque volume normalized to vessel volume, were evaluated. The primary endpoint was a composite of all-cause mortality and non-fatal myocardial infarction (MI). Cox proportional hazard models, adjusted for clinical risk factors and early revascularization, were employed for analysis. During a median follow-up of 7.0 years, 208 patients (8.7%) experienced the primary endpoint, including 73 cases of MI (3%). The model incorporating PAV demonstrated superior discriminatory power for the composite endpoint (AUC = 0.729) compared to CAC scoring (AUC = 0.706, P = 0.016). In MI prediction, PAV (AUC = 0.791) significantly outperformed CAC (AUC = 0.699, P < 0.001), with NCPV% showing the highest prognostic accuracy (AUC = 0.814, P < 0.001). 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.