Ignite Creation Date:
2025-12-25 @ 2:00 AM
Ignite Modification Date:
2026-03-02 @ 4:11 AM
Study NCT ID:
NCT06724094
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-05-13
First Post:
2024-11-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Artificial Intelligence to Detect Early Total Knee Replacement Implant Failure
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2105}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-08', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-12', 'completionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-05-12', 'studyFirstSubmitDate': '2024-11-06', 'studyFirstSubmitQcDate': '2024-12-06', 'lastUpdatePostDateStruct': {'date': '2025-05-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-12-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Predictive accuracy of machine learning model', 'timeFrame': 'Up to 21 years. Data starts from 2003.', 'description': 'The predictive accuracy of a machine learning algorithm. Using common ML measured, AUROC etc.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Aseptic Loosening of Prosthetic Joint']}, 'descriptionModule': {'briefSummary': "The goal of this trial is to investigate whether Machine Learning (ML) can be used to detect small degrees of loosening, lucent zones, or any other changes on radiographs that might predict early failure following NexGen total knee replacement.\n\nResearchers will identify plain AP and lateral plain film radiographs from two groups of patients. Those who has NexGen total knee replacements (TKRs) that went on to failure, and those who has well performing TKRs. Radiographs from these two groups will be labelled as 'failure' and 'well performing' and will be processed through a machine learning algorithm.\n\nThe algorithm will be successful if it is able to detect a NexGen TKR that went on to failure or went on to perform well. This will be determined by using a test set.\n\nThe population will be adults who had the recalled a NexGen Total Knee Replacement with a standard tibial tray. It will include adults only, who has the TKR at University Hospitals Southampton between 2003 and 2022.\n\nFailure will be defined as revision of tibial or femoral components which is likely due to aspectic loosening. It will exclude washouts, exchange of poly, peri-prosthetic fractures, microbiologically confirmed infection.\n\nWell performing TKRs will be defined as patients who have had their TKR in situ for 10 years and have reported no significant symptoms."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The population will be adults who had the recalled a NexGen Total Knee Replacement with a standard tibial tray. It will include adults only, who has the TKR at University Hospitals Southampton between 2003 and 2022.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Had a NexGen TKR between 2003 and 2022.\n\nExclusion Criteria:\n\n* Below 18 yrs old.\n* Revision surgery for any reason other than aseptic loosening\n* patients who have not had a revision but who do not have a well functioning TKR.'}, 'identificationModule': {'nctId': 'NCT06724094', 'briefTitle': 'Artificial Intelligence to Detect Early Total Knee Replacement Implant Failure', 'organization': {'class': 'OTHER', 'fullName': 'University Hospital Southampton NHS Foundation Trust'}, 'officialTitle': 'Using Machine Learning to Detect and Predict Loosening NexGen Total Knee Replacement', 'orgStudyIdInfo': {'id': 'T&O0238'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Failed', 'description': 'This group will be made up of patient who had revision TKR due to aseptic loosening within 10 years of primary arthroplasty'}, {'label': 'Well performing', 'description': 'This group will be made up of patients who have an asymptomatic TKR 10 years after primary arthroplasty.'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Rory Ormiston', 'role': 'CONTACT', 'email': 'rory.ormiston@uhs.nhs.uk', 'phone': '07443 432819'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Hospital Southampton NHS Foundation Trust', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}