Ignite Creation Date:
2025-12-25 @ 12:21 AM
Ignite Modification Date:
2026-02-17 @ 4:52 PM
Study NCT ID:
NCT06580158
Status:
RECRUITING
Last Update Posted:
2024-12-31
First Post:
2024-08-29
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001024', 'term': 'Aortic Valve Stenosis'}], 'ancestors': [{'id': 'D000082862', 'term': 'Aortic Valve Disease'}, {'id': 'D006349', 'term': 'Heart Valve Diseases'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D014694', 'term': 'Ventricular Outflow Obstruction'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-11-08', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-12', 'completionDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-12-30', 'studyFirstSubmitDate': '2024-08-29', 'studyFirstSubmitQcDate': '2024-08-29', 'lastUpdatePostDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-08-30', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Number of patients with positive AI-ECG', 'timeFrame': 'Baseline', 'description': 'Positive AI-ECG will be determined by the sensitivity, specificity, positive predictive value, and negative predictive value.'}, {'measure': 'Number of studies with reasonable image quality in patients with positive AI-ECG', 'timeFrame': 'Baseline', 'description': 'Image quality will be determined by sonographers at the time of imaging and will be scored on a scale from 1-4:\n\n1. Excellent , sufficient for publication\n2. Good, sufficient for data analysis\n3. Fair, just enough for data analysis without complete views\n4. Poor, not usable for data analysis'}], 'secondaryOutcomes': [{'measure': 'Number of times the AI ECG and TTE (transthoracic echocardiogram) are statistically comparative', 'timeFrame': 'Baseline', 'description': 'Will be compared using parametric (2-sample t-test) and non-parametric tests (Wilcoxon rank sum test) for continuous variables, and the χ2 test or Fisher exact test for nominal variables. A p-value of \\< 0.05 will be categorized as significant for the statistical analysis'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'conditions': ['Aortic Stenosis', 'Diastolic Dysfunction']}, 'descriptionModule': {'briefSummary': 'Two recently developed artificial intelligence-enabled electrocardiogram (AI-ECG) models have been developed to detect aortic stenosis (AS) and diastolic dysfunction (DD). AI-ECG for AS has a sensitivity of 78% and specificity of 74%, and AI-ECG for DD has a sensitivity of 83% and specificity of 80%. However, these models have never been prospectively applied to diagnose AS or DD, which may be useful for patients and providers from a diagnostic and prognostic perspective and especially in settings where access to higher- level medical care is limited. In this study, we aim to determine the clinical utility of these AI-ECG models by prospectively applying them to an outpatient cohort and then completing a focused point-of-care ultrasound to evaluate those who are AI-ECG positive for AS and DD.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '60 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* ≥ 60 years of age must have a clinical scheduled ECG performed.\n\nExclusion Criteria:\n\n* \\< 59 years of age\n* Is not scheduled for a clinical ECG\n* Unable to provide consent.'}, 'identificationModule': {'nctId': 'NCT06580158', 'briefTitle': 'AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction', 'organization': {'class': 'OTHER', 'fullName': 'Mayo Clinic'}, 'officialTitle': 'The Clinical Utility of Artificial Intelligence-enabled Electrocardiograms in the Outpatient Practice - Diagnosing Aortic Stenosis and Diastolic Dysfunction', 'orgStudyIdInfo': {'id': '24-000100'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.', 'interventionNames': ['Device: AI-ECG Dashboard', 'Diagnostic Test: Point of care ultrasound (POCUS)']}], 'interventions': [{'name': 'AI-ECG Dashboard', 'type': 'DEVICE', 'description': "Patients standard of care ECG's will be processed through the AI-ECG Dashboard", 'armGroupLabels': ['Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.']}, {'name': 'Point of care ultrasound (POCUS)', 'type': 'DIAGNOSTIC_TEST', 'description': 'Patients will undergo a ultrasound to confirm diagnosis of atrial stenosis or diastolic dysfunction.', 'armGroupLabels': ['Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.']}]}, 'contactsLocationsModule': {'locations': [{'zip': '55905', 'city': 'Rochester', 'state': 'Minnesota', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Jae Oh, M.D.', 'role': 'CONTACT', 'email': 'oh.jae@mayo.edu'}], 'facility': 'Mayo Clinic', 'geoPoint': {'lat': 44.02163, 'lon': -92.4699}}], 'centralContacts': [{'name': 'Levi Disrud', 'role': 'CONTACT', 'email': 'Disrud.Levi@mayo.edu', 'phone': '507-422-5241'}, {'name': 'Jae Oh, M.D.', 'role': 'CONTACT', 'email': 'oh.jae@mayo.edu'}], 'overallOfficials': [{'name': 'Jae Oh, M.D.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Mayo Clinic'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Mayo Clinic', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Jae K. Oh, M.D.', 'investigatorAffiliation': 'Mayo Clinic'}}}}