Ignite Creation Date:
2025-12-25 @ 2:03 AM
Ignite Modification Date:
2025-12-26 @ 2:28 AM
Study NCT ID:
NCT04941560
Status:
COMPLETED
Last Update Posted:
2023-03-21
First Post:
2021-06-22
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Assessing the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003324', 'term': 'Coronary Artery Disease'}], 'ancestors': [{'id': 'D003327', 'term': 'Coronary Disease'}, {'id': 'D017202', 'term': 'Myocardial Ischemia'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D001161', 'term': 'Arteriosclerosis'}, {'id': 'D001157', 'term': 'Arterial Occlusive Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 460}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2021-09-06', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-03', 'completionDateStruct': {'date': '2023-02-10', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-03-17', 'studyFirstSubmitDate': '2021-06-22', 'studyFirstSubmitQcDate': '2021-06-22', 'lastUpdatePostDateStruct': {'date': '2023-03-21', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-06-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-02-10', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area under receiver operating curve (AUC)', 'timeFrame': 'At the end of enrollment (1 mouth)', 'description': 'Area under receiver operating curve of algorithm assessed in test group'}], 'secondaryOutcomes': [{'measure': 'Sensitivity of algorithm', 'timeFrame': 'At the end of enrollment (1 mouth)', 'description': 'Sensitivity of algorithm assessed in test group'}, {'measure': 'Specificity of algorithm', 'timeFrame': 'At the end of enrollment (1 mouth)', 'description': 'Specificity of algorithm assessed in test group'}, {'measure': 'Positive predictive value (PPV)', 'timeFrame': 'At the end of enrollment (1 mouth)', 'description': 'PPV of algorithm assessed in test group'}, {'measure': 'Negative predictive value (NPV)', 'timeFrame': 'At the end of enrollment (1 mouth)', 'description': 'NPV of algorithm assessed in test group'}, {'measure': 'Diagnostic accuracy rate', 'timeFrame': 'At the end of enrollment (1 mouth)', 'description': 'Diagnostic accuracy rate of algorithm assessed in test group'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial intelligence', 'Facial characteristics'], 'conditions': ['Coronary Artery Disease']}, 'descriptionModule': {'briefSummary': 'The purposes of this study are 1) to explore the association between multi-dimension facial characteristics and the increased risk of coronary artery diseases (CAD); 2) to evaluate the diagnostic efficacy of multi-dimension appearance factors for coronary artery diseases.', 'detailedDescription': "Previous study demonstrated the feasibility of using deep learning to detect coronary artery disease based on facial photos. However, several limitations made the algorithm hard to be utilized in clinical practice, including low specificity and lack of external validation. Adding multi-dimension facial characteristics may further increase the algorithm effect.\n\nThus, the investigators designed a single-center, cross-sectional study to explore the association between multi-dimension facial characteristics and CAD and to evaluate the predictive efficacy of multi-dimension appearance factors for CAD. The investigators will recruit patients undergoing coronary angiography or coronary computer tomography angiography. Patients' baseline information and multi-dimension facial images will be collected. The investigators will train and validate a deep learning algorithm based on multi-dimension facial photos."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients who undergo coronary angiography or coronary computer tomography angiography from both resident patients and outpatient.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Undergoing coronary angiography or coronary computer tomography angiography\n* Written informed consent\n\nExclusion Criteria:\n\n* Prior percutaneous coronary intervention (PCI)\n* Prior coronary artery bypass graft (CABG)\n* Screening coronary artery disease before treating other heart diseases\n* Without blood biochemistry outcome\n* With artificially facial alteration (i.e. cosmetic surgery, facial trauma or make-up)\n* Other situations which make patients fail to be photographed'}, 'identificationModule': {'nctId': 'NCT04941560', 'briefTitle': 'Assessing the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases', 'organization': {'class': 'OTHER_GOV', 'fullName': 'China National Center for Cardiovascular Diseases'}, 'officialTitle': 'Artificial Intelligence to Assess the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases', 'orgStudyIdInfo': {'id': '20210621-2021-1471'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Algorithm training and test group', 'description': 'Patients undergoing coronary angiography or coronary computer tomography angiography will be enrolled. Patients data will be used to training and validate the algorithm for CAD detection based on facial photos.', 'interventionNames': ['Other: No intervention']}], 'interventions': [{'name': 'No intervention', 'type': 'OTHER', 'description': 'No intervention', 'armGroupLabels': ['Algorithm training and test group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '100032', 'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Fuwai hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'China National Center for Cardiovascular Diseases', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'SPONSOR'}}}}