CCTA-Derived Coronary Plaque Burden Offers Enhanced Prognostic Value Over CAC Scoring In Suspected CAD Patients

Authors

Jorge Dahdal, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Ruurt A. Jukema, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Teemu Maaniitty, Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
Nick S. Nurmohamed, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Pieter G. Raijmakers, Radiology, Nuclear Medicine and PET Research, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands.
Roel Hoek, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Roel S. Driessen, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Jos W. Twisk, Department of Epidemiology & Data Science, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Sara Bär, Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
R Nils Planken, Radiology, Nuclear Medicine and PET Research, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands.
Niels van Royen, Department of Cardiology, Radboud University Medical Center, Nijmegen, the Netherlands.
Robin Nijveldt, Department of Cardiology, Radboud University Medical Center, Nijmegen, the Netherlands.
Jeroen J. Bax, Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.
Antti Saraste, Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
Alexander R. van Rosendael, Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.
Paul Knaapen, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Juhani Knuuti, Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
Ibrahim Danad, Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Document Type

Journal Article

Publication Date

3-25-2025

Journal

European heart journal. Cardiovascular Imaging

DOI

10.1093/ehjci/jeaf093

Abstract

AIM: 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. METHODS AND RESULTS: The study enrolled 2,404 patients with suspected CAD but without 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 hazards 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). CONCLUSIONS: 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.

Department

School of Medicine and Health Sciences Resident Works

Share

COinS