Prognostic Value of AI-Based Quantitative Coronary CTA vs Human Reader-Based Visual Assessment: Results From the CONFIRM2 Registry.

Background The severity and extent of whole heart coronary plaque volume and stenosis can be reliably measured by artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT). Limited data are available on the potential incremental prognostic value compared with currently recommended qualitative coronary computed tomography angiography (CTA) reads and the coronary artery calcium score (CACS).Objectives The aim of this study was to evaluate the prognostic value of AI-QCT compared with human coronary CTA reads, including the CAD-RADS (Coronary Artery Disease-Reporting and Data System), CACS, and the modified Duke Index.Methods CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is a multicenter, international, observational cohort study of patients undergoing clinically indicated coronary CTA with follow-up for major adverse cardiac events (MACE). Asymptomatic patients and those with cardiac history were excluded. Coronary artery disease presence, extent, and composition were quantified by AI-QCT across the coronary tree, yielding 24 patient-, vessel-, and plaque-level variables. On the basis of prior analyses, noncalcified plaque burden and diameter stenosis were identified as the strongest predictors and combined for statistical modeling as "AI-QCT." Comparator computed tomography scores included CAD-RADS, CACS, and the modified Duke Index, whereas clinical predictors were summarized in the risk factor-weighted clinical likelihood score. Area under the curve (AUC) and continuous net reclassification index (NRI) were calculated to assess the incremental value. The primary endpoint was MACE (death, myocardial infarction [MI], stroke, heart failure, late revascularization, or hospital stay for unstable angina), and the secondary endpoint was death or MI.Results In 1,916 patients with all risk scores available, 87 (4.5%) MACE and 27 (1.4%) death/MI events occurred during 3 years of follow-up. There was a stepwise risk increase with higher coronary artery disease classifications with CAD-RADS and CACS. The addition of AI-QCT significantly improved risk stratification for MACE compared with CAD-RADS (AUC: 0.81 vs 0.79; P < 0.001 and NRI: 0.47; P < 0.001), CACS (AUC: 0.79 vs 0.70; P < 0.001 and NRI 0.61; P < 0.001), the modified Duke Index (AUC: 0.81 vs 0.76; P < 0.001 and NRI: 0.52; P < 0.001), and CAD-RADS + CACS model (AUC: 0.81 vs 0.79; P = 0.004 and NRI: 0.54; P < 0.001). AI-QCT also improved discrimination when results were adjusted for the risk factor-weighted clinical likelihood and for the prediction of death/MI. Excluding 195 patients with severe stenosis (≥70%), in a multivariable model of CAD-RADS and AI-QCT, only AI-QCT was significantly associated with MACE and death/MI, and AI-QCT significantly improved risk stratification compared with CAD-RADS for MACE (AUC: 0.77 vs 0.72; P < 0.001 and NRI: 0.54; P < 0.001) and death/MI (AUC: 0.81 vs 0.73; P = 0.011 and NRI: 0.69; P = 0.001).Conclusions AI-QCT provided incremental prognostic information compared with CAD-RADS 2.0, CACS, and the modified Duke Index for the prediction of MACE as well as the secondary endpoint of death or nonfatal MI.