Ignite Creation Date:
2025-12-25 @ 4:30 AM
Ignite Modification Date:
2025-12-26 @ 3:33 AM
Study NCT ID:
NCT07038018
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-06-26
First Post:
2025-06-16
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
External Validation of Artificial Intelligence-enabled Electrocardiography (AI-ECG) for the Detection of Left Ventricular Dysfunction (LVD)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D018487', 'term': 'Ventricular Dysfunction, Left'}], 'ancestors': [{'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D018754', 'term': 'Ventricular Dysfunction'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 12500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-08-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-06', 'completionDateStruct': {'date': '2025-09-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-06-25', 'studyFirstSubmitDate': '2025-06-16', 'studyFirstSubmitQcDate': '2025-06-25', 'lastUpdatePostDateStruct': {'date': '2025-06-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-06-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-08-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The Sensitivity and specificity of AI-ECG model for left ventricular ejection fraction ≤ 40%', 'timeFrame': 'within 7 days', 'description': 'The primary objective of the study was to evaluate the sensitivity and specificity of the artificial intelligence-enabled electrocardiography (AI-ECG) model in detecting left ventricular dysfunction, defined as left ventricular ejection fraction (LVEF) ≤ 40% as confirmed by transthoracic echocardiography.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['left ventricular dysfunction', 'artificial intelligence'], 'conditions': ['Cardiac Disease']}, 'referencesModule': {'references': [{'pmid': '35330455', 'type': 'BACKGROUND', 'citation': 'Chen HY, Lin CS, Fang WH, Lou YS, Cheng CC, Lee CC, Lin C. Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis. J Pers Med. 2022 Mar 13;12(3):455. doi: 10.3390/jpm12030455.'}]}, 'descriptionModule': {'briefSummary': 'This is a multi-center, retrospective study evaluating the performance of an artificial intelligence-enabled electrocardiography (AI-ECG) algorithm in detecting reduced left ventricular ejection fraction (LVEF ≤ 40%). All included patients from participating hospitals must have undergone a digital 12-lead electrocardiogram (ECG) and an echocardiogram with assessment of LVEF within seven days. The AI-ECG algorithm will be applied to evaluate its diagnostic performance, which will be further assessed across subgroups stratified by demographic characteristics and clinical factors.', 'detailedDescription': 'Data were collected from 13 hospitals, excluding the medical center that developed the artificial intelligence-enabled electrocardiography (AI-ECG) algorithm. The primary objective of the study was to evaluate the sensitivity and specificity of the AI-ECG model in detecting left ventricular dysfunction, defined as left ventricular ejection fraction (LVEF) ≤ 40%. To ensure clinical applicability, predefined thresholds required both sensitivity and specificity to exceed 0.80 in external validation cohorts. Sample size calculations were based on testing the null hypothesis that sensitivity equals 0.80. In the development hospital cohort, the model demonstrated a sensitivity of 0.869 and a specificity of 0.896. With a two-sided significance level (α) of 0.05 and a power of 90%, an estimated 310 cases of LVEF ≤ 40% were required.\n\nGiven that the prevalence of left ventricular dysfunction was 4% in the development hospital cohort but expected to be lower-between 2.5% and 3%-in external validation settings (i.e., regional and local hospitals), the total sample size needed to accrue the target number of cases was estimated to range between 10,333 and 12,400 patients. To achieve this, six regional hospitals and seven local hospitals were selected as external validation sites. Because both electrocardiography and echocardiography were required within a seven-day interval-leading to anticipated exclusions-approximately 1,500 patients were targeted from each regional hospital and 500 from each local hospital, resulting in a final target sample size of approximately 12,500 patients.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'All patients with ECGs and an echocardiogram within 7 days', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* patients with ECGs and an echocardiogram within 7 days\n\nExclusion Criteria:\n\n* Missing ECG signals\n* Missing LVEF assessment in echocardiograms'}, 'identificationModule': {'nctId': 'NCT07038018', 'briefTitle': 'External Validation of Artificial Intelligence-enabled Electrocardiography (AI-ECG) for the Detection of Left Ventricular Dysfunction (LVD)', 'organization': {'class': 'OTHER', 'fullName': 'Tri-Service General Hospital'}, 'officialTitle': 'External Validation of Artificial Intelligence-Enabled Electrocardiograms for the Detection of Reduced Left Ventricular Ejection Fraction', 'orgStudyIdInfo': {'id': 'B202405084'}}, 'armsInterventionsModule': {'interventions': [{'name': 'AI-ECG Algorithm', 'type': 'DIAGNOSTIC_TEST', 'description': 'AI-ECG Algorithm to detect LVEF\\<=40%'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Hualien City', 'country': 'Taiwan', 'facility': 'Hualien Armed Forces General Hospital', 'geoPoint': {'lat': 23.97694, 'lon': 121.60444}}, {'city': 'Kaohsiung City', 'country': 'Taiwan', 'facility': 'Kaohsiung Armed Forces General Hospital Gangshan Branch', 'geoPoint': {'lat': 22.61626, 'lon': 120.31333}}, {'city': 'Kaohsiung City', 'country': 'Taiwan', 'facility': 'Kaohsiung Armed Forces General Hospital', 'geoPoint': {'lat': 22.61626, 'lon': 120.31333}}, {'city': 'Kaohsiung City', 'country': 'Taiwan', 'facility': 'Zuoying Armed Forces General Hospital', 'geoPoint': {'lat': 22.61626, 'lon': 120.31333}}, {'city': 'Keelung', 'country': 'Taiwan', 'facility': 'Tri-Service General Hospital Keelung Branch', 'geoPoint': {'lat': 25.13089, 'lon': 121.74094}}, {'city': 'Pengfu', 'country': 'Taiwan', 'facility': 'Tri-Service General Hospital Penghu Branch', 'geoPoint': {'lat': 24.98333, 'lon': 121.41667}}, {'city': 'Pingtung City', 'country': 'Taiwan', 'facility': 'Kaohsiung Armed Forces General Hospital Pingtung Branch', 'geoPoint': {'lat': 22.67135, 'lon': 120.48814}}, {'city': 'Taichung', 'country': 'Taiwan', 'facility': 'Taichung Armed Forces General Hospital Zhongqing Branch', 'geoPoint': {'lat': 24.1469, 'lon': 120.6839}}, {'city': 'Taichung', 'country': 'Taiwan', 'facility': 'Taichung Armed Forces General Hospital', 'geoPoint': {'lat': 24.1469, 'lon': 120.6839}}, {'city': 'Taipei', 'country': 'Taiwan', 'facility': 'Tri-Service General Hospital Beitou Branch', 'geoPoint': {'lat': 25.05306, 'lon': 121.52639}}, {'city': 'Taipei', 'country': 'Taiwan', 'facility': 'Tri-Service General Hospital Songshan Branch', 'geoPoint': {'lat': 25.05306, 'lon': 121.52639}}, {'city': 'Taoyuan District', 'country': 'Taiwan', 'facility': 'Taoyuan Armed Forces General Hospital Hsinchu Branch', 'geoPoint': {'lat': 24.9896, 'lon': 121.3187}}, {'city': 'Taoyuan District', 'country': 'Taiwan', 'facility': 'Taoyuan Armed Forces General Hospital', 'geoPoint': {'lat': 24.9896, 'lon': 121.3187}}], 'centralContacts': [{'name': 'Wei-Ting Liu, M.D.', 'role': 'CONTACT', 'email': 'wtliucv@gmail.com', 'phone': '+886287923311', 'phoneExt': '15809'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Tri-Service General Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Clinical Doctor, Principal Investigator', 'investigatorFullName': 'Wei-Ting Liu', 'investigatorAffiliation': 'Tri-Service General Hospital'}}}}