Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio.

Background: The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFR) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFR) as a novel invasive resting pressure-wire index for detecting hemodynamically significant coronary artery stenosis.

Methods: In our single center study, patients with coronary artery disease (CAD) who had a clinically indicated coronary computed tomography angiography (cCTA) and subsequent invasive coronary angiography (ICA) with pressure wire-measurement were included. On-site prototype CT-FFR software and on-site CT-plaque software were used to calculate the hemodynamic relevance of coronary stenosis.

Results: We enrolled 33 patients (70% male, mean age 68 ± 12 years). On a per-lesion basis, the area under the receiver operating characteristic curve (AUC) of CT-FFR (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter (LL/MLD; 0.80), minimal luminal diameter (MLD; 0.77), remodeling index (RI; 0.76), degree of luminal diameter stenosis (0.75), and minimal luminal area (MLA; 0.75).

Conclusion: CT-FFR and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFR had the best discriminatory power, using RFR as the reference standard.