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

NCT06462989 | Enrolling By Invitation

• 1 other identifier: HEART-AI-001
• Study Type: interventional
• Target: 16,160
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

Structural Heart Abnormality; Structural Heart Disease

Keywords

artificial intelligence; transthoracic echocardiogram; magnetic resonance imaging

Outcome Measures

Primary Outcomes (1)

• Assess the effect of displaying the ECHONeXT interpretation on the time to diagnosis of Structural Heart Disease (SHD)

  Time interval from the first ECG opened in the platform to SHD diagnosis on TTE or MRI, calculated as: Date of SHD diagnosis on TTE - Date of access of the first ECG where an ECHONeXT interpretation was available and a user consulted the ECG

  18 months

Secondary Outcomes (5)

• Assess the effect of displaying the ECHONeXT interpretation on the rate of SHD diagnosis on TTE

  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

  18 months

• Assess the agreement of the users with the ECG-AI algorithm's interpretations

  18 months

• Determine the acceptability and usability of the DeepECG platform in clinical practice based on the end-of-study survey

  18 months

• Determine the primary endpoint stratified according to the presence of a previous TTE > 24 months or no previous TTE (brand new patients)

  18 months

Other Outcomes (10)

• Describe the engagement of users and the overall utilization of the DeepECG platform algorithm in the clinical setting

  18 months

• Compare the TTE priority classification assigned by the user between the intervention and the control group

  18 months

• Compare the TTE priority classification assigned by the user between the intervention and the control group stratified by location (emergency vs outpatient

  18 months

Study Arms (2)

ECHONEXT interpretation

EXPERIMENTAL

The ECHONeXT algorithm was trained to predict the presence of SHD on TTE using a single 12-lead ECG. It was developed at Columbia hospital, released as open-weights and validated at the MHI. It was trained on 800,000 ECG and TTE pairs.

Other: ECHONEXT

No ECHONEXT interpretation

NO INTERVENTION

Not displaying the ECHONEXT algorithm interpretation.

Interventions

ECHONEXT OTHER

ECHONEXT Artificial intelligence algorithm

ECHONEXT interpretation

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Users
• Users who are providing clinical care and who read ECGs as part of their practice.
• Users who have provided informed consent to participate in the study.
• Users who have completed the required training on the use of the DeepECG platform.
• ECG
• lead ECGs recorded during the study period at the Montreal Heart Institute.
• ECGs of adequate technical quality for interpretation, as determined by the recording software and visual inspection.
• Patients
• \. Patients aged 18 years or older
• Outpatients or patients who presented at the ambulatory emergency department. The location will be determined according to the ECG where it was recorded which is entered by the ECG technician. These locations will be included for the eligibility of the randomization:
• a. locations\_to\_keep = \['21\_URGENCE AMBULATOIRE', '1\_CARDIOLOGIE GENERALE', "17\_CLINIQUE D'ARYTHMIE"\]
• New patients without a prior formal evaluation by a cardiologist or internal medicine specialist for suspected or provisionally identified cardiac conditions, including:
• Arrhythmia
• Heart Failure
• Coronary Artery Disease

You may not qualify if:

• Users
• \. Users who are unable to commit to the duration of the study (approximately 1 month minimum) or adhere to the study protocol.
• \. ECG with too many artefacts or without any QRS visible as interpretated by the MUSE GE algorithm.

Sponsors & Collaborators

• Montreal Heart Institute: lead

Study Sites (1)

Montreal Heart Institute

Montreal, Quebec, H1T1C8, Canada

Location

Related Links

• Related Info

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Interventional Cardiologist

Study Record Dates

• First Submitted: June 12, 2024
• First Posted: June 17, 2024
• Study Start: April 16, 2025
• Primary Completion: January 31, 2026
• Study Completion (Estimated): January 31, 2027
• Last Updated: July 31, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)