Comparison of machine learning computed tomography-based fractional flow reserve and coronary CT angiography-derived plaque characteristics with invasive resting full-cycle ratio - Baumann, Stefan; Hirt, Markus; Rott, Christina; Özdemir, Gökce Havva; Tesche, Christian; Becher, Tobias; Weiß, Christel; Hetjens, Svetlana; Akın, Ibrahim; Schönberg, Stefan; Borggrefe, Martin; Janssen, Sonja; et al. (Journal of Clinical Medicine, 2020)
Background: The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFRML) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFRTM) 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-FFRML 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-FFRML (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter4 (LL/MLD4; 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-FFRML and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFRML had the best discriminatory power, using RFRTM as the reference standard.
Gesehen am 26.11.2020