Ignite Creation Date:
2025-12-25 @ 1:22 AM
Ignite Modification Date:
2026-01-06 @ 12:37 PM
Study NCT ID:
NCT06637293
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-09-19
First Post:
2024-10-09
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'stepped wedge randomization'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-10-06', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2027-03', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-18', 'studyFirstSubmitDate': '2024-10-09', 'studyFirstSubmitQcDate': '2024-10-09', 'lastUpdatePostDateStruct': {'date': '2025-09-19', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2024-10-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'sensitivity of cardiology referrals', 'timeFrame': '18 months', 'description': 'Compare the sensitivity of cardiology referrals made by family physicians and nurse practitioners before and after the activation of AI-assisted diagnostics and recommendations from the DeepECG platform.'}], 'secondaryOutcomes': [{'measure': 'specificity, negative predictive value, and positive predictive value of cardiology referrals', 'timeFrame': '18 months', 'description': 'Compare the specificity, negative predictive value, and positive predictive value of cardiology referrals made by family physicians and nurse practitioners before and after the activation of AI-assisted diagnostics and recommendations.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Primary Care Provider', 'Structural Heart Disease']}, 'referencesModule': {'seeAlsoLinks': [{'url': 'https://www.heartwise.ai/', 'label': 'Related Info'}]}, 'descriptionModule': {'briefSummary': 'The DAISEA-ECG project aims to improve the diagnosis of heart diseases in primary care through the DeepECG platform, which combines ECG-AI and ECHONeXT algorithms. This study uses a stepped wedge design, where each Family Medicine Group acts as its own control. The FMGs will gradually transition from the control period (without AI recommendations) to the intervention period (with AI recommendations activated) in a randomized sequence.\n\nThe primary objective is to compare the sensitivity of family physicians in detecting cardiac pathologies, with and without the assistance of the DeepECG platform. Sensitivity is defined as the proportion of patients correctly referred to cardiology or for transthoracic echocardiography (TTE) among those who indeed required cardiovascular evaluation, as confirmed by an independent adjudication committee.', 'detailedDescription': 'Mathematically, sensitivity is calculated as True Positive / (True Positive + False Negative), where True Positive represents correctly referred patients and false negatives represents patients who should have been referred but were not.\n\nThe secondary objectives include determining the rate of cardiovascular evaluation referrals before and after the intervention (implementation of the DeepECG platform), the individual characteristics of the intervention (PPV, NPV, and specificity), as well as evaluating the feasibility of implementing AI-based automatic ECG interpretation in primary care through surveys of family physicians and cardiologists.\n\nPPV: Positive predictive value NPV: Negative predictive value'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\nFamily Physicians or Nurse Practitioners\n\nFamily physicians or nurse practitioners (NPs) practicing in one of the participating FMGs.\n\nFamily physicians who have given their free and informed consent. Patients\n\nAdult patients (18 years or older). Patients without follow-up in cardiology or internal medicine for cardiovascular issues (arrhythmia, heart failure, myocardial infarction, atherosclerotic coronary artery disease, valvular heart disease) or those who had a negative investigation in the past with no additional follow-up.\n\nECG\n\nAny 12-lead ECG performed with the MUSE GE 360 machine. ECG of adequate technical quality for interpretation (otherwise, it will be automatically rejected by the platform).\n\n\\-\n\nExclusion Criteria:\n\n* Family Physicians or Nurse Practitioners\n\nFamily physicians practicing exclusively in pediatrics (patients under 18 years old).\n\nFamily physicians unable to follow the project guidelines.'}, 'identificationModule': {'nctId': 'NCT06637293', 'acronym': 'DAISEA-ECG', 'briefTitle': 'Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care', 'organization': {'class': 'OTHER', 'fullName': 'Montreal Heart Institute'}, 'officialTitle': 'Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care', 'orgStudyIdInfo': {'id': 'DAISEA-ECG'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'No DeepECG plateform diagnosis & recommendations'}, {'type': 'EXPERIMENTAL', 'label': 'DeepECG plateform diagnosis & recommendations', 'interventionNames': ['Device: DeepECG plateform diagnosis & recommendations']}], 'interventions': [{'name': 'DeepECG plateform diagnosis & recommendations', 'type': 'DEVICE', 'description': 'EchoNeXT\\& ECG-AI algorithm', 'armGroupLabels': ['DeepECG plateform diagnosis & recommendations']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'H1T1C8', 'city': 'Montreal', 'state': 'Quebec', 'country': 'Canada', 'contacts': [{'name': 'Marie-Gabrielle Lessard, MSc', 'role': 'CONTACT', 'email': 'marie-gabrielle.lessard@icm-mhi.org', 'phone': '5143763330', 'phoneExt': '2094'}, {'name': 'Robert Avram, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Montreal Heart Institute', 'geoPoint': {'lat': 45.50884, 'lon': -73.58781}}], 'centralContacts': [{'name': 'Robert Avram, MD', 'role': 'CONTACT', 'email': 'robert.avram.md@gmail.com', 'phone': '514 376 3330'}, {'name': 'Marie-Gabrielle Lessard, MSc', 'role': 'CONTACT', 'email': 'marie-gabrielle.lessard@icm-mhi.org', 'phone': '514 376 3330', 'phoneExt': '2094'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Montreal Heart Institute', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Interventional cardiologist', 'investigatorFullName': 'Robert Avram', 'investigatorAffiliation': 'Montreal Heart Institute'}}}}