Study Status: RECRUITING
Recruit Status: RECRUITING
Study Type: OBSERVATIONAL
Official Title: Prospective Validation of a Deep Learning Algorithm for Prediction of the Left Ventricular Ejection Fraction From Coronary Angiogram Videos in Patients With Acute Coronary Syndrome
Brief Summary: Left ventricular ejection fraction (LVEF) is one of the strongest predictors of mortality and morbidity in patients with acute coronary syndrome (ACS).
Transthoracic echocardiography (TTE) remains the gold standard for LVEF measurement.
Currently, LVEF can be estimated at the time of the coronary angiogram but requires a ventriculography.
This latter is performed at the price of an increased amount of contrast media injected and puts the patients at risk for mechanical complications, ventricular arrhythmia or atrio-ventricular blocks.
Artificial intelligence (AI) has previously been shown to be an accurate method for determining LVEF using different data sources.
Fur the purpose of this study, we aim at validating prospectively an AI algorithm, called CathEF, for the prediction of real-time LVEF (AI-LVEF) compared to TTE-LVEF and ventriculography in patients undergoing coronary angiogram for ACS.