Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-31 @ 7:27 AM
Study NCT ID:
NCT07428694
Status:
RECRUITING
Last Update Posted:
2026-02-24
First Post:
2026-02-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 20}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-10-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2026-10-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-17', 'studyFirstSubmitDate': '2026-02-17', 'studyFirstSubmitQcDate': '2026-02-17', 'lastUpdatePostDateStruct': {'date': '2026-02-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-24', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-10-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Correct interpretation of inspiratory leak by machine learning tool', 'timeFrame': 'one year', 'description': 'Measured in comparison with god standard method of polygraphy'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Chronic Respiratory Failure']}, 'descriptionModule': {'briefSummary': 'Study of the applicability of machine learning tools in detecting inspiratory leakage in longterm non-invasive ventilation. The study was conducted in two stages. Firstly the ML model was trained on both bench model created scenarios and then ten patients. And secondly the success of the model was assessed in a proof of concept pilot study of ten patients.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients undergoing treatment with Lumis 100/150 for type 2 chronic resiratory failure', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* elective hospitalisation for control of non-invasive ventilation\n* use of ResMedLumis 100/150 ventilator\n* treatment for \\>3 months\n\nExclusion Criteria:\n\n* current exacerbation'}, 'identificationModule': {'nctId': 'NCT07428694', 'briefTitle': 'From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak', 'organization': {'class': 'OTHER', 'fullName': 'University of Oslo'}, 'officialTitle': 'From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak', 'orgStudyIdInfo': {'id': '878631'}}, 'contactsLocationsModule': {'locations': [{'city': 'Oslo', 'status': 'RECRUITING', 'country': 'Norway', 'contacts': [{'name': 'Marte Allgot, Cand.med', 'role': 'CONTACT', 'email': 'm.s.allgot@medisin.uio.no', 'phone': '+4799616202'}, {'name': 'S', 'role': 'CONTACT', 'phone': '+4799616202'}], 'facility': 'Oslo University Hospital', 'geoPoint': {'lat': 59.91273, 'lon': 10.74609}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Oslo', 'class': 'OTHER'}, 'collaborators': [{'name': 'Oslo University Hospital', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal investigator', 'investigatorFullName': 'Marte Skogstad Allgot', 'investigatorAffiliation': 'University of Oslo'}}}}