Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation, an Open Label Randomized Controlled Trial
NCT Number: NCT06462989
Acronym: HEART-AI
Status: NOT_YET_RECRUITING
Study Type: INTERVENTIONAL
Phases: NA
Conditions: Structural Heart Abnormality
Interventions: DEVICE: ECHONEXT
Sponsor: Montreal Heart Institute
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 10200
Study Design: Allocation: RANDOMIZED|Intervention Model: PARALLEL|Masking: NONE|Primary Purpose: DIAGNOSTIC
Start Date: 2024-07-01
Completion Date: 2027-01-31

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.

Primary Outcome Measures: Assess the effect of displaying the ECHONeXT interpretation on the rate of SHD diagnosis on TTE, Diagnosis of SHD (Yes/No) on TTE-To measure the impact of AI-assisted interpretation on the detection of SHD, comparing AI-driven decision-making to standard clinical assessments., 18 months|Evaluate the effect of displaying the ECHONeXT interpretation on the delay between the ECG and the TTE evaluation for patients at high or intermediate risk of SHD, Delay between the time of the first ECG opened in the platform and the TTE calculated as:

Date of TTE evaluation - Date of access of the first ECG where an ECHONeXT interpretation was available and a user consulted the ECG, 18 months