Ignite Creation Date:
2025-12-24 @ 10:23 PM
Ignite Modification Date:
2026-01-02 @ 12:03 AM
Study NCT ID:
NCT05118035
Status:
COMPLETED
Last Update Posted:
2023-02-08
First Post:
2021-11-01
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Applying an Artificial Intelligence-Enabled Electrocardiographic System for Reducing Mortality
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002318', 'term': 'Cardiovascular Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['PARTICIPANT']}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'intervention group:8001 control group:7964'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 15965}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2021-12-15', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-02', 'completionDateStruct': {'date': '2022-12-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-02-03', 'studyFirstSubmitDate': '2021-11-01', 'studyFirstSubmitQcDate': '2021-11-01', 'lastUpdatePostDateStruct': {'date': '2023-02-08', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-11-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-04-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'All cause mortality (death)', 'timeFrame': 'Within 90 days', 'description': "After performing an electrocardiogram, the patient's survival is tracked."}], 'secondaryOutcomes': [{'measure': 'Cardiovascular cause mortality (death)', 'timeFrame': 'Within 90 days', 'description': "After performing an electrocardiogram, the patient's survival is tracked."}, {'measure': 'Arrhythmia medication', 'timeFrame': 'Within 12 hours', 'description': 'After performing an electrocardiogram, the patient recieved related intervention.'}, {'measure': 'Electrolyte examination', 'timeFrame': 'Within 3 days', 'description': 'After performing an electrocardiogram, the patient recieved electrolyte examination'}, {'measure': 'Cadiac examination', 'timeFrame': 'Within 3-7 days', 'description': 'After performing an electrocardiogram, the patient recieved cadiac examination'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Cardiovascular Diseases', 'Intensity Care', 'Morality']}, 'referencesModule': {'references': [{'pmid': '40494963', 'type': 'DERIVED', 'citation': 'Hsieh PH, Lin C, Lin CS, Liu WT, Lin TK, Tsai DJ, Hung YJ, Chen YH, Lin CY, Lin SH, Tsai CS. Economic analysis of an AI-enabled ECG alert system: impact on mortality outcomes from a pragmatic randomized trial. NPJ Digit Med. 2025 Jun 11;8(1):348. doi: 10.1038/s41746-025-01735-7.'}, {'pmid': '38684860', 'type': 'DERIVED', 'citation': 'Lin CS, Liu WT, Tsai DJ, Lou YS, Chang CH, Lee CC, Fang WH, Wang CC, Chen YY, Lin WS, Cheng CC, Lee CC, Wang CH, Tsai CS, Lin SH, Lin C. AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial. Nat Med. 2024 May;30(5):1461-1470. doi: 10.1038/s41591-024-02961-4. Epub 2024 Apr 29.'}]}, 'descriptionModule': {'briefSummary': 'This is a randomized controlled trial (RCT) to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for early detection of clinical deterioration for reducing mortality.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients in emergency department or inpatient department.\n* Patients recieved at least 1 ECG examination.\n\nExclusion Criteria:\n\n* The patients recieved ECG at the period of inactive AI-ECG system.'}, 'identificationModule': {'nctId': 'NCT05118035', 'briefTitle': 'Applying an Artificial Intelligence-Enabled Electrocardiographic System for Reducing Mortality', 'organization': {'class': 'OTHER', 'fullName': 'National Defense Medical Center, Taiwan'}, 'officialTitle': 'Applying an Artificial Intelligence-Enabled Electrocardiographic System for Reducing Mortality: a Pragmatic Randomized Clinical Trial', 'orgStudyIdInfo': {'id': 'NDMC2021005'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Intervention', 'description': 'Patients randomized to intervention will have access to the screening tool. Once the AI-ECG indicates high risk of mortality, a warning message would be immediately triggered and sent to the corresponding attending physicians. Notifications appear in the recipient\'s smartphone message system for the prompt attention. The message notified the physician that, "An ECG was received for patient X. An ECG indicates high risk of mortality. Please intensively attend to patient\'s conditions. If the physicians need to further identify the ECG, click on the following link to connect the ECG and the result of AI-ECG prediction." Of note, although we will actively send a warning message for high risk cases, the AI-ECG report for low risk cases still presented the degree of risk. Physicians can check the relative severity by access EHR for patients in the intervention group.', 'interventionNames': ['Other: AI-enabled ECG-based Screening Tool']}, {'type': 'NO_INTERVENTION', 'label': 'Control', 'description': 'Patients will continue routine practice.'}], 'interventions': [{'name': 'AI-enabled ECG-based Screening Tool', 'type': 'OTHER', 'description': 'Primary care clinicians in the intervention group had access to the report, which shows the risk prediction results for each patients. Moreover, the clinicians will recieve a short message when patients with a high risk ECG identified by AI.', 'armGroupLabels': ['Intervention']}]}, 'contactsLocationsModule': {'locations': [{'zip': '114', 'city': 'Taipei', 'country': 'Taiwan', 'facility': 'National Defense Medical Center', 'geoPoint': {'lat': 25.05306, 'lon': 121.52639}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'National Defense Medical Center, Taiwan', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor', 'investigatorFullName': 'Chin Lin', 'investigatorAffiliation': 'National Defense Medical Center, Taiwan'}}}}