Clinical trial NCT06462989: Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Identification of Structural Heart Disease
Official title: Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation, an Open Label Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: Structural Heart Abnormality
Structural Heart Disease
Keywords: artificial intelligence
Lead sponsor: Montreal Heart Institute
Sponsor class: OTHER
First posted: 2024-06-17
Enrollment: 16160
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: 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

1. 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 = \['21_URGENCE AMBULATOIRE', '1_CARDIOLOGIE GENERALE', "17_CLINIQUE D'ARYTHMIE"\]
2. New patients without a prior formal evaluation by a cardiologist or internal medicine specialist for suspected or provisionally identified cardiac conditions, including:

   1. Arrhythmia
   2. Heart Failure
   3. Coronary Artery Disease
   4. Valvular Heart Disease
   5. Cardiomyopathy
   6. Other cardiac conditions
3. Patients with previous TTE:

   1. Have no documented history of any cardiac condition
   2. No transthoracic echocardiogram in the last 24 months (from any center)

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.

Primary outcomes:
measure: Assess the effect of displaying the ECHONeXT interpretation on the time to diagnosis of Structural Heart Disease (SHD)
timeFrame: 18 months
Secondary outcomes:
measure: Assess the effect of displaying the ECHONeXT interpretation on the rate of SHD diagnosis on TTE
timeFrame: 18 months
measure: 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
timeFrame: 18 months
measure: Assess the agreement of the users with the ECG-AI algorithm's interpretations
timeFrame: 18 months
measure: Determine the acceptability and usability of the DeepECG platform in clinical practice based on the end-of-study survey
timeFrame: 18 months
measure: Determine the primary endpoint stratified according to the presence of a previous TTE > 24 months or no previous TTE (brand new patients)
timeFrame: 18 months
Other outcomes:
measure: Describe the engagement of users and the overall utilization of the DeepECG platform algorithm in the clinical setting
timeFrame: 18 months
measure: Compare the TTE priority classification assigned by the user between the intervention and the control group
timeFrame: 18 months
measure: Compare the TTE priority classification assigned by the user between the intervention and the control group stratified by location (emergency vs outpatient
timeFrame: 18 months
measure: Review additional qualitative feedback and insights captured after reading an ECG
timeFrame: 18 months
measure: Assess the agreement of the user with the ECG-AI algorithm's interpretations according to practice type, number of years in practice, age of user and familiarity with AI tools (based on end of study questionnaire)
timeFrame: 18 months
measure: Describe the agreement of the user with the ECG-AI algorithm's interpretation according to the diagnosis category of the ECG-AI (ischemic, arrythmia, chamber enlargement, structural heart disease, other)
timeFrame: 18 months
measure: Describe the agreement of the user with the ECG-AI algorithm's interpretation in the two subgroups.
timeFrame: 18 months
measure: Evaluate the effect of displaying the ECHONeXT interpretation on the delay between the ECG and the TTE evaluation, by subgroups defined by the risk level of SHD (low/ intermediate/ high)
timeFrame: 18 months
measure: Evaluate the model performance after applying continual learning
timeFrame: 18 months
measure: Sensitivity and specificity of ECHONeXT to detect SHD on TTE
timeFrame: 18 months
Locations:
city: Montreal
country: Canada
facility: Montreal Heart Institute
state: Quebec
Study officials:
References: