Interrater Variability of ML-Based CT-FFR in Patients without Obstructive CAD before TAVR: Influence of Image Quality, Coronary Artery Calcifications, and Location of Measurement

Abstract

Objectives: CT-derived fractional flow reserve (CT-FFR) can improve the specificity of coronary CT-angiography (cCTA) for ruling out relevant coronary artery disease (CAD) prior to transcatheter aortic valve replacement (TAVR). However, little is known about the reproducibility of CT-FFR and the influence of diffuse coronary artery calcifications or segment location. The objective was to assess the reliability of machine-learning (ML)-based CT-FFR prior to TAVR in patients without obstructive CAD and to assess the influence of image quality, coronary artery calcium score (CAC), and the location of measurement within the coronary tree. Methods: Patients assessed for TAVR, without obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers with differing experience. Differences in absolute values and categorization into hemodynamically relevant CAD (CT-FFR ≤ 0.80) were compared. Results in regard to CAD were also compared against invasive coronary angiography. The influence of segment location, image quality, and CAC was evaluated. Results: Of the screened patients, 109/388 patients did not have obstructive CAD on cCTA and were included. The median (interquartile range) difference of CT-FFR values was -0.005 (-0.09 to 0.04) (p = 0.47). Differences were smaller with high values. Recategorizations were more frequent in distal segments. Diagnostic accuracy of CT-FFR between both observers was comparable (proximal: Δ0.2%; distal: Δ0.5%) but was lower in distal segments (proximal: 98.9%/99.1%; distal: 81.1%/81.6%). Image quality and CAC had no clinically relevant influence on CT-FFR. Conclusions: ML-based CT-FFR evaluation of proximal segments was more reliable. Distal segments with CT-FFR values close to the given threshold were prone to recategorization, even if absolute differences between observers were minimal and independent of image quality or CAC.

Keywords: aortic stenosis; computed tomography coronary angiography; coronary angiography; coronary artery disease; diagnostic accuracy; machine learning; transcatheter aortic valve implantation.

Figure 1

Diagram of the 18-segment model of the coronary tree. Diagram showing the coronary tree with its division into 18 segments according to the SCCT guidelines [16]. Shaded segments were defined as proximal in this study. L = left; LAD = left anterior descending artery; PDA = posterior descending artery; PLB = posterior-lateral branch; R = right; RCA = right coronary artery; SCCT = Society of Cardiovascular Computed Tomography.

Figure 2

CT-FFR values at patient level. Bland-Altman plot showing the distribution of differences in CT-FFR values between both observers. Differences between both observers are overall very small. The plot shows no indication of a systematic bias. Lower mean CT-FFR values show one outlier (CT-FFR difference of −0.45 at a CT-FFR mean value of 0.58), which has been omitted from the plot for clarity. Lower mean CT-FFR values also show a larger heterogeneity of CT-FFR differences. CT-FFR = CT-derived fractional flow reserve. Dashed horizontal lines represent the mean difference (middle line) and the upper and lower limits of agreement (±1.96 SD, upper and lower lines).