Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 1:34 AM
Ignite Modification Date:
2025-12-25 @ 1:34 AM
NCT ID:
NCT06018194
Brief Summary:
The goal of this study is to evaluate the diagnostic accuracy of a novel plaque-based coronary CT angiography (CCTA) fractional flow reserve (FFRct) software device for the estimation of invasive fractional flow reserve (FFR).
Researchers will compare the Elucid plaque-based FFRct analysis to invasively measured FFR in patients who have previously undergone CCTA and invasively assessed FFR.
Detailed Description:
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.
Study:
NCT06018194
Study Brief:
Protocol Section:
NCT06018194