AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD

Authors

Nick S. Nurmohamed, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA. Electronic address: https://twitter.com/NickNurmohamed.
Michiel J. Bom, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Ruurt A. Jukema, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Robin J. de Groot, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Roel S. Driessen, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Pepijn A. van Diemen, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Ruben W. de Winter, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Emilie L. Gaillard, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands.
Ralf W. Sprengers, Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Erik S. Stroes, Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands.
James K. Min, Cleerly Inc, Denver, Colorado, USA.
James P. Earls, Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA; Cleerly Inc, Denver, Colorado, USA.
Rhanderson Cardoso, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Ron Blankstein, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Ibrahim Danad, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands.
Andrew D. Choi, Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA. Electronic address: adchoi@gwu.edu.
Paul Knaapen, Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

Document Type

Journal Article

Publication Date

7-7-2023

Journal

JACC. Cardiovascular imaging

DOI

10.1016/j.jcmg.2023.05.020

Keywords

AI-QCT; ASCVD; CAD; CCTA; MACE; atherosclerosis

Abstract

BACKGROUND: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography (CCTA) analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed CCTA, coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. RESULTS: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 (IQR: 8.6-11.5) years, 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively). CONCLUSIONS: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.

APA Citation

Nurmohamed, Nick S.; Bom, Michiel J.; Jukema, Ruurt A.; de Groot, Robin J.; Driessen, Roel S.; van Diemen, Pepijn A.; de Winter, Ruben W.; Gaillard, Emilie L.; Sprengers, Ralf W.; Stroes, Erik S.; Min, James K.; Earls, James P.; Cardoso, Rhanderson; Blankstein, Ron; Danad, Ibrahim; Choi, Andrew D.; and Knaapen, Paul, "AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD" (2023). GW Authored Works. Paper 3105.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/3105

Department

Medicine