Invasive fractional flow reserve is a clinically validated measure of lesion-specific ischemia and is preferred over visual estimation of diameter stenosis for clinical decision-making regarding coronary revascularization in patients with stable clinical presentations. Fractional flow reserve derived from coronary computed tomography angiography (FFRct) using computational fluid dynamic (CFD)-based software has been shown to be a reasonably accurate estimate of invasive FFR and is included in contemporary guidelines as a decision-tool for management of patients with intermediate stenosis on CCTA. However, CFD-based FFRct is calculated based predominately on detailed coronary lumen geometry. It is understood that the burden and type of coronary atherosclerosis, in addition to lumen geometry, significantly impacts the vasodilatory capacity of the coronary endothelium.
Preliminary studies suggest that invasive FFR can be accurately estimated based on the quantification of coronary plaque burden and the assessment of plaque composition. Previously, the investigators have demonstrated that a novel plaque-based FFRct approach, using a histologically validated software (ElucidVivoTM) for the measurement of plaque morphology (volume, plaque risk characteristics, and stenosis) to train a deep-learning model, was shown to be accurate and superior to lumen stenosis for predicting invasive FFR in a single-site feasibility study.
In this study, the investigators seek to assess the diagnostic accuracy of the Elucid plaque-based FFRct software to estimate invasive FFR in patients at multiple centers.
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