Ignite Creation Date:
2025-12-26 @ 1:18 PM
Ignite Modification Date:
2025-12-26 @ 1:18 PM
Study NCT ID:
NCT06070506
Status:
COMPLETED
Last Update Posted:
2023-10-11
First Post:
2023-08-30
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Heart Failure With Improved Ejection Fraction and Deep Learning
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D006333', 'term': 'Heart Failure'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 422}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-03-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-10', 'completionDateStruct': {'date': '2023-05-20', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-10-08', 'studyFirstSubmitDate': '2023-08-30', 'studyFirstSubmitQcDate': '2023-09-30', 'lastUpdatePostDateStruct': {'date': '2023-10-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-10-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'change of left ventricular ejection fraction', 'timeFrame': '3 months', 'description': 'left ventricular ejection fraction value in millimeters'}], 'secondaryOutcomes': [{'measure': 'change of clinical predictor of EF improvement', 'timeFrame': '3 months', 'description': 'weight in kilograms, height in meters(weight and height will be combined to report BMI in kg/m\\^2)'}, {'measure': 'the independent clinical predictor of HFimpEF', 'timeFrame': '3 months', 'description': 'prealbumin in mg/L'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Heart failure', 'Left ventricular ejection fraction', 'Heart failure with improved ejection fraction', 'Deep learning'], 'conditions': ['Heart Failure', 'Diagnosis']}, 'descriptionModule': {'briefSummary': 'The aim of this study was to design a deep learning-based trained model to assist in HFimpEF diagnosis.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'The participants in the study are HF patients hospitalized in the Department of Cardiology of the First Affiliated Hospital of Harbin Medical University who had no less than two echocardiograms at baseline and during the follow-up period, between January 2014 to December 2022.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age \\>18 years.\n2. The diagnostic criteria of HF follows the 2018 Chinese Guidelines for the Diagnosis and Treatment of Heart Failure, having symptoms of dyspnea, fatigue or decreased activity tolerance, having signs of fluid retention (such as pulmonary congestion and peripheral edema), having echocardiogram abnormalities in cardiac structure and/or function, showing elevated natriuretic peptide levels (BNP\\>35 ng/L or/and N-terminal pro-BNP \\>125 ng/L).\n3. Have reviewing echocardiography after discharge.\n\nExclusion Criteria:\n\n1. Patients with hypertrophic, restrictive, or invasive cardiomyopathy and congenital or rheumatic heart disease.\n2. Patients with heart transplantation during follow-up.'}, 'identificationModule': {'nctId': 'NCT06070506', 'briefTitle': 'Heart Failure With Improved Ejection Fraction and Deep Learning', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital of Harbin Medical University'}, 'officialTitle': 'Deep Learning-Based Prediction Model of Heart Failure With Improved Ejection Fraction', 'orgStudyIdInfo': {'id': '2020020668'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'HFrEF group', 'description': 'Heart failure patients with LVEF persistently ≤40%.'}, {'label': 'HFimpEF group', 'description': 'Heart failure patients with previous LVEF ≤40% and a follow-up LVEF of more than 40%.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '150000', 'city': 'Harbin', 'state': 'Heilongjiang', 'country': 'China', 'facility': 'Yihui Kong', 'geoPoint': {'lat': 45.75, 'lon': 126.65}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Yihui Kong', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Yihui Kong', 'investigatorAffiliation': 'First Affiliated Hospital of Harbin Medical University'}}}}