Artificial intelligence-guided quantitative coronary CT assessment to rule-in or rule-out myocardial ischaemia

AIMS: To evaluate the ability of artificial intelligence-based quantitative CT (AI-QCT) parameters, diameter stenosis, percent atheroma volume (PAV) and average lumen area (ALA) to rule-in or rule-out ischaemia. METHODS AND RESULTS: This post-hoc, vessel-level analysis included patients with suspected coronary artery disease from the computed tomographic evaluation of atherosclerotic determinants of myocardial ischaemia (CREDENCE) (612 patients; 1727 vessels) and PACIFIC-1 (208 patients; 612 vessels) studies who underwent CCTA and invasive fractional flow reserve (FFR). In addition to diameter stenosis, PAV and ALA were evaluated as key predictors of ischaemia. We report abnormal FFR prevalence based on these variables and define rule-out (75% prevalence, ischaemia highly likely; further testing typically unnecessary), and intermediate risk (15-75%, consider additional functional assessment). PAV and ALA were dichotomized using median values derived from the CREDENCE cohort (14.7% and 3.9 mm2) and validated in PACIFIC-1. In CREDENCE, all vessels with 1-24% stenosis were ruled-out. Among vessels with 25-49% stenosis, 74% met rule-out criteria, while 26%, characterized by large PAV and small ALA, were intermediate risk. Within the proposed9723 framework vessels with 50-69% stenosis were classified as intermediate risk. For 70-99% stenosis, 93% met rule-in criteria, except a small subset with small PAV and large ALA. In PACIFIC-1, 86% of vessels with 14.7%), and ALA (70% are reliably ruled-in. This practical approach enhances the diagnostic utility of CCTA and streamlines clinical decision-making.