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The ADAPT Study: Assessment of the DiAgnostic Performance of DeepVessel FFR in SuspecTed Coronary Artery Disease — ADAPT  

Coronary Artery Disease Clinical Trial

Official title:
Assessment of the DiAgnostic Performance of DeepVessel FFR in SuspecTed Coronary Artery Disease

Clinical Trial Summary

DEEPVESSEL FFR is a medical device that is designed to extract three- dimensional coronary tree structures and generate computed tomography-derived fraction flow reserve (FFR) values from coronary CT angiogram images. The primary objective of this multi-center clinical validation study is to validate the clinical performance of DEEPVESSEL FFR in identifying patients with myocardial ischemia due to significant obstructive coronary artery diseases.

Clinical Trial Description

Coronary artery disease (CAD) is the most common type of heart disease, and it is the leading cause of death worldwide in both men and women. CAD happens when the coronary arteries become hardened and narrowed, which is due to the buildup of cholesterol-containing deposits-plaque on the inner vessel wall. As the plaque grows, less blood can flow through the arteries due to the vessel narrowing. Decreased blood flow can then lead to chest pain (angina), shortness of breath, or even a heart attack. Fractional flow reserve (FFR), a measure of blood flow reduction caused by vessel narrowing, is accepted as gold standard for assessing the functional significance of stenotic lesions. Multiple randomized trials have demonstrated that FFR has excellent diagnostic value in identifying functionally significant lesions and guiding coronary revascularization procedures. However, FFR is measured invasively through a pressure wire-based cardiac catheter procedure in the catheterization lab. Current guidelines recommend assessing myocardial ischemia of stable patients with CAD through non-invasive functional testing before considering invasive coronary angiography (ICA) or conducting myocardial revascularization. DEEPVESSEL FFR (DVFFR) is a software medical device that is designed to extract three- dimensional coronary tree structures and generate computed tomography -derived FFR values from coronary CT angiogram (CTA) images. It uses deep learning neural networks that encode imaging, structural, and functional characteristics of coronary arteries and learn complex mapping between FFR values and the encoded information. The quantitative FFR analysis based on the coronary CTA images can help clinicians assess the physiological function in patients with CAD non-invasively. The primary objective of this study is to evaluate the diagnostic performance of DVFFR software in identifying patients with significant obstructive CAD causing myocardial ischemia, using invasively measured ICA FFR as the reference standard. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT04828590
Study type Observational
Source Keya Medical
Contact
Status Completed
Phase
Start date August 10, 2020
Completion date December 31, 2021
View Details
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