Diagnostic Accuracy and Robustness of AI-based Fully Automated CT-FFR for the Detection of Significant CAD in Patients With Transcatheter Aortic Valve Replacement

OBJECTIVES: To explore the diagnostic accuracy and robustness of artificial intelligence (AI)-based fully automated CT-derived fractional flow reserve (CT-FFR) in detecting significant coronary artery disease (CAD) in patients with transcatheter aortic valve replacement (TAVR). METHODS: This single-center retrospective study included consecutive patients who underwent TAVR between January 2020 and June 2023. All patients received preoperative coronary CT angiography (CCTA) and invasive coronary angiography (ICA). CT-FFR was evaluated with a fully automated AI-based software. The diagnostic performance of CCTA and CT-FFR for the identification of significant CAD was compared using ICA (≥70% diameter stenosis) as the reference standard. Patients who underwent post-TAVR CCTA within 3 months were used to calculate CT-FFR values. The post-TAVR CT-FFR calculations were compared with pre-TAVR CT-FFR to evaluate the robustness of the AI-based software. RESULTS: A total of 77 pre-TAVR patients and 164 vessels were included. Significant CAD was identified by ICA in 18 patients (23.4%). In per-patient analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 44.4%, 91.5%, 61.5%, 84.4%, and 80.5% for CCTA and 94.4%, 83.1%, 64.0%, 98.0%, and 85.7% for CT-FFR. The area under the receiver operating characteristic curve of CT-FFR was superior to CCTA (0.83 vs. 0.63, P = 0.001). Thirty-five (45.5%) patients underwent CT-FFR calculations before and after TAVR. There was good agreement between pre- and post-TAVR of CT-FFR values (intraclass correlation coefficient 0.85). CONCLUSIONS: AI-based fully automated CT-FFR enables to improve the diagnostic performance of CCTA for the detection of significant CAD pre-TAVR and demonstrates robust stability post-TAVR.