AI-guided quantitative CCTA added to a clinical likelihood model significantly improved risk discrimination for MACE (AUC 0.76, 95% CI 0.77-0.80 vs AUC 0.63; P<0.001).
Cohort (n=3,551)
Yes
Does AI-guided quantitative coronary CT angiography (AI-QCT) improve prognostic risk prediction for major adverse cardiovascular events compared to traditional clinical likelihood models in patients with suspected coronary artery disease?
AI-driven quantitative coronary CT angiography significantly improves the prediction of major adverse cardiovascular events over traditional clinical risk models in patients with suspected coronary artery disease.
Effect estimate: AUC 0.76 (95% CI 0.77-0.80)
p-value: p=<0.001
Background Plaque assessment by quantitative coronary CT angiography has demonstrated to correlate highly with intravascular ultrasound and optical coherence tomography, and these modalities have shown strong prognostic value. Objectives The purpose of this study was to identify the prognostic value of artificial intelligence-guided quantitative CCTA (AI-QCT) for major adverse cardiovascular events (MACE) against the risk factor-weighted clinical likelihood model. Methods The CONFIRM2 (COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is a multicenter, international, observational cohort study that included patients with clinically indicated CCTA and follow-up for MACE. Patients without cardiac symptoms and prior coronary artery disease (CAD) were excluded. Across the entire coronary artery tree, the presence, extent, and composition of CAD were analyzed by an AI-QCT software, and 24 variables at a patient, vessel, and plaque level were derived, including percent luminal narrowing, remodeling index, plaque volumes (total, calcified, noncalcified, low attenuation), and plaque composition. The primary MACE endpoint was defined as a composite of all-cause death, myocardial infarction (MI), stroke, congestive heart failure, late revascularizations, and hospitalization for unstable angina. The secondary MACE endpoint was defined as all-cause death and MI. Results A total of 3,551 patients (age 58.8 ± 12.5 years, 50.5% male) were followed for a median of 4.27 (IQR: 3.47-5.08) years during which 167 (4.7%) events occurred. After excluding collinear variables, diameter stenosis (HR: 1.25 95% CI: 1.18-1.32) per 10% increase and noncalcified plaque volume (HR: 1.07 95% CI: 1.03-1.11) per 50 mm3 increase were the only independent predictors for MACE. In multivariable modeling, the discriminatory value defined by area under the curve (AUC) improved from 0.63 (95% CI: 0.58-0.67) based on the risk factor-weighted clinical likelihood model to 0.76 (95% CI: 0.77-0.80), P < 0.001 when adding AI-QCT-based diameter stenosis and noncalcified plaque volume. A similar improvement in risk prediction was seen when adding AI-QCT (AUC 0.77; P < 0.001) to a model with traditional risk factors, age, and sex (AUC: 0.67). In addition, AI-QCT significantly improved discrimination compared to the atherosclerotic cardiovascular disease risk score (AUC: 0.63; 95% CI: 0.58-0.68) to 0.75 (95% CI: 0.69-0.80; P < 0.001). Similar results were seen for the secondary MACE endpoint of death/MI. Conclusions This first multicenter global registry with AI-guided quantitative CT identified noncalcified plaque burden and increment in stenosis severity as the most powerful predictors of MACE, demonstrating the interplay between traditional and novel measures of the severity of CAD. Standardized and rapid quantitative assessment of CAD may improve clinical implementation of multidimensional assessment of CAD as a cornerstone for risk assessment.
Rosendael et al. (Thu,) conducted a cohort in Suspected Coronary Artery Disease (n=3,551). AI-guided quantitative coronary CT angiography (AI-QCT) vs. Risk factor-weighted clinical likelihood model was evaluated on Composite of all-cause death, myocardial infarction, stroke, congestive heart failure, late revascularizations, and hospitalization for unstable angina (AUC 0.76, 95% CI 0.77-0.80, p=<0.001). AI-guided quantitative CCTA added to a clinical likelihood model significantly improved risk discrimination for MACE (AUC 0.76, 95% CI 0.77-0.80 vs AUC 0.63; P<0.001).