Ignite Creation Date:
2025-12-25 @ 2:16 AM
Ignite Modification Date:
2025-12-26 @ 12:46 AM
Study NCT ID:
NCT07139860
Status:
RECRUITING
Last Update Posted:
2025-08-24
First Post:
2025-08-22
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Artificial Intelligence System for Early Warning of Adverse Events in Acute Myocardial Infarction
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000072657', 'term': 'ST Elevation Myocardial Infarction'}], 'ancestors': [{'id': 'D009203', 'term': 'Myocardial Infarction'}, {'id': 'D017202', 'term': 'Myocardial Ischemia'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D007238', 'term': 'Infarction'}, {'id': 'D007511', 'term': 'Ischemia'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D009336', 'term': 'Necrosis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1400}, 'targetDuration': '1 Year', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2022-11-26', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-22', 'studyFirstSubmitDate': '2025-08-22', 'studyFirstSubmitQcDate': '2025-08-22', 'lastUpdatePostDateStruct': {'date': '2025-08-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-08-24', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'MACCE', 'timeFrame': '1-3 years', 'description': 'Cardiac death\n\nAll-cause mortality\n\nMalignant arrhythmia\n\nNon-fatal recurrent myocardial infarction\n\nNon-fatal stroke\n\nUnplanned repeat revascularization\n\nRehospitalization for heart failure'}]}, 'oversightModule': {'oversightHasDmc': False}, 'conditionsModule': {'conditions': ['Acute Myocardial Infarction With ST Elevation', 'Intelligent Management Platform', 'Early Warning']}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to learn about the effectiveness of an artificial intelligence-based early warning system for predicting adverse events in patients with acute myocardial infarction (AMI). The main question it aims to answer is:\n\nDoes an AI-based early warning system improve the assessment and prediction of adverse events across the full course of AMI care (from prevention to diagnosis, treatment, and rehabilitation)?\n\nParticipants who are receiving routine medical care for AMI in tertiary hospitals will have their multimodal medical data (clinical records, diagnostic tests, imaging, treatment pathways) collected and analyzed. Data will be integrated using innovative cross-modal representation methods and predictive models. The study will follow patients during their hospital stay and subsequent clinical follow-up to evaluate the feasibility, accuracy, and clinical value of the AI-based early warning system.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '1. Retrospective Study Population:\n\n Data were obtained from the Cardiovascular Center Database (CBDBank) of Beijing Friendship Hospital. CBDBank is a continuously collected sample dataset of patients with acute coronary syndrome since January 2013. A total of 1,000 patients with acute myocardial infarction admitted to Beijing Friendship Hospital, Capital Medical University, from January 2017 to December 2019 were included in the retrospective cohort. After discharge, all patients received scheduled telephone follow-ups at 1 month, 3 months, 6 months, and annually thereafter, with continuous follow-up for 3 years.\n2. Prospective Study Population:\n\nA total of 800 patients with acute myocardial infarction admitted to the Cardiovascular Center of Beijing Friendship Hospital from July 2023 to July 2026 will be enrolled in the prospective cohort. Randomization will be performed using random codes, and prognosis prediction will be conducted using either the predictive system or traditional met', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* 1\\. Hospitalized patients who meet the diagnostic criteria for acute myocardial infarction. 2. Patients who agree to participate and sign the informed consent form.\n\nExclusion Criteria:\n\n* 1\\. Patients with terminal malignant tumors and an expected survival time of less than 3 months. 2. Patients with complete disability and inability to communicate. 3. Patients unable to comply with follow-up.'}, 'identificationModule': {'nctId': 'NCT07139860', 'acronym': 'AIEWAEAMI', 'briefTitle': 'Artificial Intelligence System for Early Warning of Adverse Events in Acute Myocardial Infarction', 'organization': {'class': 'OTHER', 'fullName': 'Beijing Friendship Hospital'}, 'officialTitle': 'Artificial Intelligence System for Early Warning of Adverse Events in Acute Myocardial Infarction', 'orgStudyIdInfo': {'id': 'BFH2023063001'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'BFH', 'description': 'group1'}, {'label': 'AZH', 'description': 'group2'}]}, 'contactsLocationsModule': {'locations': [{'zip': '100050', 'city': 'Beijing', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Hui Chen', 'role': 'CONTACT', 'email': '13910710028@163.com', 'phone': '+86 13910710028'}], 'facility': 'Beijing Friendship Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'centralContacts': [{'name': 'Hui Chen', 'role': 'CONTACT', 'email': '13910710028@163.com', 'phone': '+86 13910710028'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Hui Chen', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'PHD', 'investigatorFullName': 'Hui Chen', 'investigatorAffiliation': 'Beijing Friendship Hospital'}}}}