AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD
JACC. Cardiovascular imaging
AI-QCT; ASCVD; CAD; CCTA; MACE; atherosclerosis
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.
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.