Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation, an Open Label Randomized Controlled Trial — HEART-AI  

Structural Heart Abnormality Clinical Trial

Official title: Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation, an Open Label Randomized Controlled Trial

Status Not yet recruiting

Clinical Trial Summary

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 TTEs (Supplemental Table 2, Figure 1). 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 (Supplemental Table 1). The performance for both of these models is presented in supplemental appendix (Supplemental Table 1, Supplemental Table 2, Supplemental Figure 1).

Our primary objective is to assess the impact of displaying the ECHONeXT interpretation on 12-lead ECGs, specifically focusing on the detection rates of Structural Heart Disease (SHD) on TTE among newly referred patients at MHI and on the delay between the time of the first ECG opened in the platform and the TTE evaluation among newly referred patients at MHI at high or intermediate risk of SHD, by comparing patients in the intervention (ECHONeXT prediction of SHD displayed and recommendation on the priority to assign to the TTE) arm and patients in the control (ECHONeXT prediction and recommendation hidden) arm. The main secondary objective is to evaluate the proportion of ECGs where the users agree with the DeepECG diagnosis, across all ECGs accessed by the user of the platform. 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.

Clinical Trial Description

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 rate of Structural Heart Disease (SHD) diagnosis on Transthoracic Echocardiograms (TTEs) and the time between a first ECG and TTE in newly referred patients to MHI. We will achieve this by comparing the diagnostic outcomes and time between the first ECG and TTE 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 (Table 1).

For the purpose of the study, SHD will be defined as presence of any of the following on TTE:

• 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. ;

Related Conditions & MeSH terms

• Congenital Abnormalities
• Structural Heart Abnormality

• NCT number: NCT06462989
• Study type: Interventional
• Source: Montreal Heart Institute
• Status: Not yet recruiting
• Phase: N/A
• Start date: July 1, 2024
• Completion date: January 31, 2027