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

Not Applicable

Not yet recruiting

• Conditions: Structural Heart Abnormality; Structural Heart Disease
• Interventions: Device: ECHONEXT

• Registration Number: NCT06462989
• Lead Sponsor: Montreal Heart Institute

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) 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) 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 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 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.

Detailed 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 time interval from the initial ECG to the rate of Structural Heart Disease (SHD) diagnosis on Transthoracic Echocardiograms.

We will achieve this by comparing the time between the first ECG and diagnosis of SHD on 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.

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.

Study Timeline

• Study First Submitted: 2024-06-12
• Study First Submitted QC: 2024-06-12
• Study First Posted: 2024-06-17
• Status Verified: 2025-01
• Study Start: 2025-01
• Last Update Submitted: 2025-01-29
• Last Update Posted: 2025-01-31
• Primary Completion: 2026-01-31
• Study Completion: 2027-01-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 16160

Inclusion Criteria

• Users

  1. Users who are providing clinical care and who read ECGs as part of their practice.

  2. Users who have provided informed consent to participate in the study.

  3. Users who have completed the required training on the use of the DeepECG platform.

     ECG

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  1. 12-lead ECGs recorded during the study period at the Montreal Heart Institute.
  2. ECGs of adequate technical quality for interpretation, as determined by the recording software and visual inspection.

Patients

1. Patients aged 18 years or older

Additional Inclusion criteria for the randomization part of the study

1. New patients without known cardiac disease, must have no documented history of:

   a. Arrhythmia b. Heart Failure c. Coronary Artery Disease d. Valvular Heart Disease e. Cardiomyopathy f. Other cardiac conditions

2. Patients with previous TTE:

   a. Have no documented history of any cardiac condition b. No transthoracic echocardiogram in the last 24 months (from any center) c. Demonstrate change in clinical status, defined as one or more of: i. New or worsening cardiac symptoms ii. Changes in physical examination

3. 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: Outpatient, General Cardiology Clinic or Ambulatory Emergency Room.

Exclusion Criteria

Users

1. Users who are unable to commit to the duration of the study (approximately 1 month minimum) or adhere to the study protocol.

Additional Exclusion criteria for the randomization part of the study ECG 1. ECG with too many artefacts or without any QRS visible as interpretated by the MUSE GE algorithm.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

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

Primary Outcome Measures

Name	Time	Method
Assess the effect of displaying the ECHONeXT interpretation on the time to diagnosis of Structural Heart Disease (SHD)	18 months	Time interval from the first ECG opened in the platform to SHD diagnosis on TTE, 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

Secondary Outcome Measures

Name	Time	Method
Assess the effect of displaying the ECHONeXT interpretation on the rate of SHD diagnosis on TTE	18 months	Diagnosis of SHD (Yes/No) on TTE
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	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
Assess the agreement of the users with the ECG-AI algorithm's interpretations	18 months	Agreement (Yes/No) of the user with the ECG-AI algorithm's interpretation. Agreement is defined as the user clicking on "thumbs up" on the platform.
Determine the acceptability and usability of the DeepECG platform in clinical practice based on the end-of-study survey	18 months	Questions of the end-of-study survey on the usability and appreciation of the DeepECG platform and the ECHONeXT interpretation
Determine the primary endpoint stratified according to the presence of a previous TTE > 24 months or no previous TTE (brand new patients)	18 months	Questions answer on the pre-ECG questionnaire

Trial Locations

Locations (1)

Montreal Heart Institute; 🇨🇦 Montreal, Quebec, Canada