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.

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

Coronary Artery Disease

Myocardial Ischemia

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.

No intervention

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

On the vessel level, if a vessel with at least one stenosis lesion with a FFR measurement less or equal to 0.80, this vessel is considered to be ischemic. A true positive on the vessel level is defined as a vessel containing at least one stenosis with DVFFR value ≤0.80 and its corresponding reference ICA-FFR value is also ≤0.80.

On the vessel lever, if a vessel with at least one stenosis lesion with a FFR measurement less or equal to 0.80, this vessel is considered to be ischemic. A true positive on the vessel level is defined as a vessel containing at least one stenosis with DVFFR value ≤0.80 and its corresponding reference ICA-FFR value is also ≤0.80.

At the patient level, if a patient has at least one vessel that has been identified as causing ischemia, this patient is considered a patient positive for ischemia. A true positive on the patient level is defined as when a patient has at least one lesion with a DVFFR value ≤0.80 and its corresponding reference ICA-FFR is also ≤0.80.

Correlation between CT-derived DVFFR values and wire-measured ICA-FFR values will be evaluated at the vessel level.

For coronary CTA, hemodynamically significant obstruction of a coronary artery is defined as a stenosis ≥50%. The per-patient stenosis degree will be specified as the most severe stenosis among the major epicardial artery vessels presented in coronary CTA.

Stratified analyses on different subgroups of subjects' data, ranging from patient demographics and disease conditions.

Patients' datasets with suspected CAD containing at least one 30%-90% coronary CTA stenosis; and ICA-FFR was measured on vessels with diameters greater than 2 mm will be analyzed. Diagnostic performance based on CT-derived FFR using DVFFR software will be compared with the diagnostic performance from ICA-FFR measurements.

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

About this trial

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Type

Placebo

Accepting

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