Combined Coronary CT-angiography And TAVI-planning For Ruling-out Significant Coronary Artery Disease: Added Value Of Machine-Learning Based CT-FFR

Abstract

Introduction: Coronary artery disease (CAD) is a frequent co-morbidity in patients undergoing transcatheter aortic valve implantation (TAVI). Current guidelines recommend its assessment prior to TAVI. Invasive coronary angiography (ICA) may be omitted, if significant CAD can be excluded on coronary CT-angiography (cCTA). Though, being a very sensitive test, cCTA is limited by relatively low specificity and positive predictive value, particularly in high-risk patients.To analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to further increase the diagnostic performance of cCTA for ruling-out significant CAD during pre-TAVI evaluation in patients with high pre-test probability for CAD.