Abstract
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.
Highlights
Assessment of coronary artery stenosis with coronary computed tomographic angiography and additional computed tomographic morphological plaque characteristics derived from coronary computed tomography angiography (cCTA) datasets leads to a more detailed evaluation of suspicious stenosis [1]
We calculated a pretest probability of 57% ± 19%, which was calculated with the coronary artery disease (CAD) consortium clinical score [18]
In order to compensate for this disadvantage of cCTA, which is caused by its low specificity for the detection of hemodynamically relevant stenosis, techniques have been developed in recent years to improve the quantification of suspicious coronary artery stenosis
Summary
Assessment of coronary artery stenosis with coronary computed tomographic angiography (cCTA) and additional computed tomographic morphological plaque characteristics derived from cCTA datasets leads to a more detailed evaluation of suspicious stenosis [1]. Two studies published recently showed that the joined assessment of some computed tomographic morphological plaque characteristics and cCTA could improve the detection of hemodynamically relevant stenosis [2,3]. The functional relevance of a lesion may be assessed by cCTA-based fractional flow reserve (CT-FFR) [4]. CT-FFRML was compared in previously published studies to the invasive gold standards invasive FFR [8] and instantaneous wave-free ratio (iwFR) [11] and showed promising results. 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. 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
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