the adapt study: assessment of the diagnostic performance of deepvessel ffr in suspected coronary artery disease

Conditions: Coronary Artery Disease; Myocardial Ischemia

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.

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.

Eligibility criteria:
inclusion criteria: 1. patients' age ≥18 years; 2. has coronary cta images acquired by ≥64 multidetector row ct scanner, no earlier than 2016 and within 60 days of the ica-ffr procedure; 3. coronary cta image shows at least one vessel segment (≥2mm diameter) with a diameter stenosis of 30%-90%; exclusion criteria: patients with any of the following conditions at the time of cta imaging: 1. acute myocardial infarction; 2. unstable angina; 3. pulmonary edema; 4. heart function classification level iii and iv (nyha heart function classification); 5. implantable cardioverter defibrillator (icd); 6. prior percutaneous coronary intervention (pci) or pacemaker surgery; 7. prior coronary artery bypass grafting (cabg) surgery; 8. prior heart valve replacement; 9. prior history of complex congenital heart disease; 10. prior history of cardiomyopathy; 11. bmi \>35; 12. coronary total occlusion.