Ignite Creation Date:
2025-12-24 @ 5:57 PM
Ignite Modification Date:
2026-01-02 @ 4:34 AM
Study NCT ID:
NCT06461468
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2024-08-06
First Post:
2024-06-04
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Hypertrophic Cardiomyopathy Federated Learning Implementation Platform
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002312', 'term': 'Cardiomyopathy, Hypertrophic'}], 'ancestors': [{'id': 'D009202', 'term': 'Cardiomyopathies'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D001020', 'term': 'Aortic Stenosis, Subvalvular'}, {'id': 'D001024', 'term': 'Aortic Valve Stenosis'}, {'id': 'D000082862', 'term': 'Aortic Valve Disease'}, {'id': 'D006349', 'term': 'Heart Valve Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2024-06-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-08', 'completionDateStruct': {'date': '2026-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-08-05', 'studyFirstSubmitDate': '2024-06-04', 'studyFirstSubmitQcDate': '2024-06-13', 'lastUpdatePostDateStruct': {'date': '2024-08-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-06-17', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnosis of HCM', 'timeFrame': 'Through study completion, an average of 2 years', 'description': "The number/instances of HCM diagnoses as identified by the ML model as compared to clinical diagnosis confirmation. Due to model training and efficacy goals, HCM diagnosis determined clinically via EKG/ECHO reading will be compared to the ML model's capacity to identify HCM correctly and efficiently."}], 'secondaryOutcomes': [{'measure': 'Diagnosis of different types of HCM', 'timeFrame': 'Through study completion, an average of 2 years', 'description': "Diagnosis of different types of HCM (i.e., apical, obstructive), HCM without hypertrophy, genetic positive/negative indicators, among others, as identified by the ML model as compared to clinical diagnosis confirmation. Due to model training and efficacy goals, HCM diagnosis determined clinically via EKG/ECHO reading will be compared to the ML model's capacity to identify HCM correctly and efficiently."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['HCM', 'Hypertrophic Cardiomyopathy', 'Federated Learning', 'Federated Learning Platform', 'FLP'], 'conditions': ['Hypertrophic Cardiomyopathy']}, 'descriptionModule': {'briefSummary': 'HCM FLIP study is a two-phase protocol focusing on the detection of Hypertrophic Cardiomyopathy using Electrocardiograms and Echocardiograms through Federated Learning.', 'detailedDescription': "HCM FLIP (Hypertrophic Cardiomyopathy Federated Learning Implementation Platform) aim to build and test a model's system impact to detect hypertrophic cardiomyopathy (HCM) by training a machine learning (ML) model with electrocardiograms (ECGs) and echocardiograms (ECHOs). Approximately 10-1000 HCM cases and 30-10,000 age/sex-matched controls per institution, depending on size, will be included in the study. We hypothesize that a federated ML model will discriminate cases of HCM from those without HCM in a real-world setting."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Enrollment Target:\n\nPhase 1: Approximately 10-100 HCM cases and 30-10,000 age/sex-matched controls per institution, depending on size, will be included in the study. Since a larger sample size benefits training ML models, additional cases could be included based on availability within each institution.\n\nPhase 2: Institution-specific cohort of patients (see Section 7.4.2) to simulate implementation of the algorithm; natural case control ratio and consecutive patients for identified period of time. The numbers of patients will be dependent on the volume of each institution and should reflect the local practice.', 'healthyVolunteers': False, 'eligibilityCriteria': 'HCM-Labeled Case Inclusion Criteria:\n\n* Patients with maximum left ventricular wall thickness exceeding 15 mm (including the right ventricular component of the septum) without any other explanation for ventricular hypertrophy (e.g., severe hypertension, cardiac amyloidosis, severe AS, as determined by local investigators). The measurement could be made in an ECHO or on magnetic resonance imaging (MRI).\n* Patients must have \\> one (1) ECG and/or \\> one (1) ECHO available that meet minimum compatibility requirements. If multiple ECGs and ECHOs are available per patient, then all available data meeting compatibility requirements will be used for model training purposes.\n\nHCM-Labeled Case Exclusion Criteria:\n\n* Any sign of infiltration found in cardiac MRI, if performed.\n\nControl Case (Non-HCM) Inclusion Criteria:\n\n* No diagnosis of HCM\n* Age/sex are matched to HCM cases (+/- 5 years, if possible; +/- 10 years if numbers do not permit).\n* Patient must have \\> one (1) ECG and/or \\> one (1) ECHO available that meet minimum compatibility requirements. If multiple ECGs and ECHOs are available per patient, then all available data meeting compatibility requirements will be used for model training purposes.\n\nControl Case (Non-HCM) Exclusion Criteria\n\n* Suggestion of HCM in a clinically obtained ECHO or cardiac MRI report unless subsequently confirmed no diagnosis of HCM. Any new clinical information discovered during the study will be left to the discretion of the local investigator.'}, 'identificationModule': {'nctId': 'NCT06461468', 'acronym': 'HCM FLIP', 'briefTitle': 'Hypertrophic Cardiomyopathy Federated Learning Implementation Platform', 'organization': {'class': 'OTHER', 'fullName': 'American Heart Association'}, 'officialTitle': 'Detection of Hypertrophic Cardiomyopathy Using Electrocardiograms and Echocardiograms Through Federated Learning', 'orgStudyIdInfo': {'id': 'HCM FLIP'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'HCM-Labeled Cases', 'description': 'Cases with maximum left ventricular wall thickness exceeding 15 mm (including the right ventricular component of the septum) without any other explanation for ventricular hypertrophy (e.g., severe hypertension, cardiac amyloidosis, severe aortic stenosis (AS)). The measurement could be made in an echocardiogram or on magnetic resonance imaging (MRI).'}, {'label': 'Control Cases (Non-HCM)', 'description': 'No diagnosis of HCM.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '36532', 'city': 'Fairhope', 'state': 'Alabama', 'country': 'United States', 'facility': 'Thomas Hospital', 'geoPoint': {'lat': 30.52297, 'lon': -87.90333}}, {'zip': '60901', 'city': 'Kankakee', 'state': 'Illinois', 'country': 'United States', 'facility': 'Riverside Medical Center', 'geoPoint': {'lat': 41.12003, 'lon': -87.86115}}, {'zip': '21287', 'city': 'Baltimore', 'state': 'Maryland', 'country': 'United States', 'facility': 'Johns Hopkins University School of Medicine', 'geoPoint': {'lat': 39.29038, 'lon': -76.61219}}, {'zip': '48109', 'city': 'Ann Arbor', 'state': 'Michigan', 'country': 'United States', 'facility': 'University of Michigan Medical Center', 'geoPoint': {'lat': 42.27756, 'lon': -83.74088}}, {'zip': '44691', 'city': 'Wooster', 'state': 'Ohio', 'country': 'United States', 'facility': 'Wooster Community Hospital', 'geoPoint': {'lat': 40.80517, 'lon': -81.93646}}, {'zip': '75390', 'city': 'Dallas', 'state': 'Texas', 'country': 'United States', 'facility': 'The University of Texas Southwestern Medical Center', 'geoPoint': {'lat': 32.78306, 'lon': -96.80667}}], 'overallOfficials': [{'name': 'Samantha Johnson, MPH', 'role': 'STUDY_DIRECTOR', 'affiliation': 'American Heart Association'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'No participant data is identifiable nor will be shared across participating institutions.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'American Heart Association', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}