Ignite Creation Date:
2025-12-24 @ 8:00 PM
Ignite Modification Date:
2025-12-25 @ 5:34 PM
Study NCT ID:
NCT07263204
Status:
RECRUITING
Last Update Posted:
2025-12-04
First Post:
2025-11-17
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI-Enabled Diagnosis and Prognosis of Hypertrophic Cardiomyopathy
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002312', 'term': 'Cardiomyopathy, Hypertrophic'}, {'id': 'D017379', 'term': 'Hypertrophy, Left Ventricular'}], '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'}, {'id': 'D006332', 'term': 'Cardiomegaly'}, {'id': 'D006984', 'term': 'Hypertrophy'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 15000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-23', 'studyFirstSubmitDate': '2025-11-17', 'studyFirstSubmitQcDate': '2025-11-23', 'lastUpdatePostDateStruct': {'date': '2025-12-04', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-04', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-06-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'model diagnostic performance', 'timeFrame': 'year 2', 'description': 'Model performance was evaluated using calculated metrics including accuracy, sensitivity, specificity, and the area under the ROC curve (AUC).'}], 'secondaryOutcomes': [{'measure': 'model diagnostic performance', 'timeFrame': 'year 2', 'description': "The accuracy rate of the model's phenotype-specific classification for patients with different patterns of myocardial hypertrophy"}, {'measure': "the model's generalizability", 'timeFrame': 'year 2', 'description': "The model's diagnostic performance on the external, multicentre validation cohort, including overall accuracy, sensitivity, specificity, and area under the ROC curve (AUC)."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Hypertrophic Cardiomyopathy (HCM)', 'Left Ventricular Hypertrophy']}, 'descriptionModule': {'briefSummary': 'By harnessing artificial intelligence to decode the 12-lead electrocardiogram, the project will enable precise ECG-based phenotyping of hypertrophic cardiomyopathy-accurately classifying septal, apical, and other morphologic subtypes-while simultaneously differentiating HCM from hypertensive heart disease, aortic stenosis, and other phenocopy disorders.', 'detailedDescription': 'To overcome the twin bottlenecks of late detection and poor inter-centre reproducibility, the project leverages a large, multicentre historical cohort and anchors its pipeline on the 12-lead ECG-an inexpensive, ubiquitously available signal that can be captured in any department. Using deep-learning architectures augmented with attention mechanisms, we will develop (1) a discriminative model that separates HCM from phenocopies and normal hearts, and (2) an algorithmic framework that remains stable across devices and populations. Model governance will be embedded through version-controlled releases, cloud-edge deployment, and an "offline replay" evaluation loop, producing an end-to-end evidence chain that mirrors real-world clinical workflows.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '1. HCM cohort: Adults diagnosed with hypertrophic cardiomyopathy in accordance with the \\*2023 Chinese Guidelines for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy in Adults\\*.\n2. HCM phenocopy cohort: Adults with an LV wall thickness ≥ 13 mm at any site on echocardiography.\n3. Healthy-control cohort: Adults with no history of cardiac disease and no evidence of myocardial hypertrophy on echocardiography.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Adults aged ≥ 18 years.\n2. HCM cohort: Adults diagnosed with hypertrophic cardiomyopathy in accordance with the \\*2023 Chinese Guidelines for the Diagnosis and Treatment of Hypertrophic Cardiomyopathy in Adults\\*.\n3. HCM phenocopy cohort: Adults with an LV wall thickness ≥ 13 mm at any site on echocardiography.\n4. Healthy-control cohort: Adults with no history of cardiac disease and no evidence of myocardial hypertrophy on echocardiography.\n\nExclusion Criteria:\n\nPatients from whom analyzable ECG data cannot be obtained.'}, 'identificationModule': {'nctId': 'NCT07263204', 'briefTitle': 'AI-Enabled Diagnosis and Prognosis of Hypertrophic Cardiomyopathy', 'organization': {'class': 'OTHER', 'fullName': 'Second Affiliated Hospital, School of Medicine, Zhejiang University'}, 'officialTitle': 'Precision Diagnosis and Prognostic Prediction of Hypertrophic Cardiomyopathy Using Artificial Intelligence: A Multicenter Study', 'orgStudyIdInfo': {'id': '2024-1546'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'HCM', 'description': 'diagnosed with hypertrophic cardiomyopathy by echocardiography and cardiac magnetic resonance imaging'}, {'label': 'phenocopy', 'description': 'patients with left-ventricular hypertrophy attributable to non-hypertrophic cardiomyopathy conditions'}, {'label': 'normal control', 'description': 'healthy individuals without myocardial hypertrophy'}]}, 'contactsLocationsModule': {'locations': [{'zip': '310009', 'city': 'Hangzhou', 'state': 'Zhejiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Xiaojie Xie, MD, PhD', 'role': 'CONTACT', 'email': 'xiexj@zju.edu.cn', 'phone': '(+86)0571-87784700'}], 'facility': 'Second Affiliated Hospital, Zhejiang University School of Medicine', 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}], 'centralContacts': [{'name': 'Xiaojie Xie, MD, PhD', 'role': 'CONTACT', 'email': 'xiexj@zju.edu.cn', 'phone': '(+86)0571-87784700'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Second Affiliated Hospital, School of Medicine, Zhejiang University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}