AUTHOR=Gohmann Robin F. , Schug Adrian , Pawelka Konrad , Seitz Patrick , Majunke Nicolas , El Hadi Hamza , Heiser Linda , Renatus Katharina , Desch Steffen , Leontyev Sergey , Noack Thilo , Kiefer Philipp , Krieghoff Christian , Lücke Christian , Ebel Sebastian , Borger Michael A. , Thiele Holger , Panknin Christoph , Abdel-Wahab Mohamed , Horn Matthias , Gutberlet Matthias TITLE=Interrater variability of ML-based CT-FFR during TAVR-planning: influence of image quality and coronary artery calcifications JOURNAL=Frontiers in Cardiovascular Medicine VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1301619 DOI=10.3389/fcvm.2023.1301619 ISSN=2297-055X ABSTRACT=Objective

To compare machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) in patients before transcatheter aortic valve replacement (TAVR) by observers with differing training and to assess influencing factors.

Background

Coronary computed tomography angiography (cCTA) can effectively exclude CAD, e.g. prior to TAVR, but remains limited by its specificity. CT-FFR may mitigate this limitation also in patients prior to TAVR. While a high reliability of CT-FFR is presumed, little is known about the reproducibility of ML-based CT-FFR.

Methods

Consecutive patients with obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers. Categorization into hemodynamically significant CAD was compared against invasive coronary angiography. The influence of image quality and coronary artery calcium score (CAC) was examined.

Results

CT-FFR was successfully performed on 214/272 examinations by both observers. The median difference of CT-FFR between both observers was −0.05(−0.12-0.02) (p < 0.001). Differences showed an inverse correlation to the absolute CT-FFR values. Categorization into CAD was different in 37/214 examinations, resulting in net recategorization of Δ13 (13/214) examinations and a difference in accuracy of Δ6.1%. On patient level, correlation of absolute and categorized values was substantial (0.567 and 0.570, p < 0.001). Categorization into CAD showed no correlation to image quality or CAC (p > 0.13).

Conclusion

Differences between CT-FFR values increased in values below the cut-off, having little clinical impact. Categorization into CAD differed in several patients, but ultimately only had a moderate influence on diagnostic accuracy. This was independent of image quality or CAC.