Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Identification of Structural Heart Disease

Overview

The HEART-AI (Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation) is an open-label, single-center, randomized controlled trial, that aims to deploy a platform called DeepECG at point-of-care for AI-analysis of 12-lead ECGs. The platform will be tested among healthcare professionals (medical students, residents, doctors, nurse practitioners) who read 12-lead ECGs. In the intervention group, the platform will display the ECHONeXT structural heart disease (SHD) scores in randomized patients to help doctors prioritize transthoracic echocardiography (TTEs) or magnetic resonance imaging (MRI) and reduce the time to diagnosis of structural heart disease. Also, this platform will display the DeepECG-AI interpretation which detects problems such as ischemic conditions, arrhythmias or chamber enlargements and acts an improved alternative to commercially available ECG interpretation systems such as MUSE. Our primary objective is to assess the impact of displaying the ECHONeXT interpretation on 12-lead ECGs on the time to diagnosis of Structural Heart Disease (SHD) among newly referred patients at MHI. We will compare the time interval from the initial ECG to SHD diagnosis by transthoracic echocardiogram (TTE) or magnetic resonance imaging (MRI) between patients in the intervention arm (where ECHONeXT prediction of SHD and TTE priority recommendation are displayed) and patients in the control arm (where ECHONeXT prediction and recommendation are hidden). The main secondary objective is to evaluate the rate of SHD detection on TTE or MRI among newly referred patients. We also aim to assess the delay between the time of the first ECG opened in the platform and the TTE or MRI evaluation among newly referred patients at high or intermediate risk of SHD. By integrating an AI-analysis platform at the point of care and evaluating its impact on ECG interpretation accuracy and prioritization of incremental tests, the HEART-AI study aims to provide valuable insights into the potential of AI in improving cardiac care and patient outcomes.

The HEART-AI (Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation) study primarily aims to assess the effect of displaying the ECHONeXT interpretation on the time interval from the initial ECG to the rate of Structural Heart Disease (SHD) diagnosis on transthoracic echocardiograms or magnetic resonance imaging. We will achieve this by comparing the time between the first ECG and diagnosis of SHD on TTE or MRI between the intervention group, where the ECHONeXT interpretation is displayed to users, and the control group, where it is not displayed, thereby quantifying the influence of AI-supported diagnostics on clinical decision-making and patient management strategies. For the purpose of the study, SHD will be defined as presence of any of the following on TTE or MRI: * LVEF ≤ 45% * Mild, moderate or severe RV Dysfunction * The presence of one or multiple valvulopathies in this list: * Moderate-to-severe pulmonary regurgitation * Moderate-to-severe tricuspid regurgitation * Moderate-to-severe mitral regurgitation * Moderate-to-severe aortic regurgitation * Moderate-to-severe aortic stenosis * Moderate or severe pericardial effusion (Tamponade or any effusion \> 1 cm) * LV wall thickness ≥ 1.3 cm * Apical cardiomyopathy * Pulmonary hypertension as defined using the systolic pressure of the pulmonary artery greater or equal to 25 mm Hg on TTE.