Aim: To evaluate feasibility and diagnostic performance of coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of hemodynamically significant coronary artery disease (CAD) in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA).

Methods and results: Patients with severe AS (n=95, 78.6±8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA were included and retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting & Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: (1) all patients with CAD-RADS ≥4 are referred for ICA; (2) patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤0.80; (3) patients with CAD-RADS <2 or CAD-RADS <4 and normal CT-FFR are not referred for ICA. In 12 patients (13%), significant CAD was diagnosed in ICA and treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥50% during ICA. Using the proposed algorithm, hemodynamically significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40% and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%). 

Conclusions: Combination of CT-FFR and CAD-RADS is able to identify hemodynamically significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs.