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

Structural Heart Abnormality Clinical Trial

— HEART-AI  

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06462989
• Other study ID #: HEART-AI-001
• Status: Not yet recruiting
• Phase: N/A
• Start date: July 1, 2024
• Est. completion date: January 31, 2027

Study information

• Verified date: June 2024
• Source: Montreal Heart Institute
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 10200
• Est. completion date: January 31, 2027
• Est. primary completion date: January 31, 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

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

Patients

1. New patients without known cardiac disease or who had previous known cardiac disease without a previous transthoracic echocardiogram in the last 24 months, either performed at MHI or at a different center.

2. 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:

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.

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Related Conditions & MeSH terms

• Congenital Abnormalities
• Structural Heart Abnormality

Intervention

Device:
• ECHONEXT
ECHONEXT Artificial intelligence algorithm

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Montreal Heart Institute

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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