Ignite Creation Date:
2025-12-24 @ 11:10 PM
Ignite Modification Date:
2026-01-02 @ 1:59 AM
Study NCT ID:
NCT06806969
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-02-04
First Post:
2025-01-14
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Artificial Intelligence Enabled Decision Support for Selection of Patients for Lumbar Spine Surgery
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007405', 'term': 'Intervertebral Disc Displacement'}], 'ancestors': [{'id': 'D013122', 'term': 'Spinal Diseases'}, {'id': 'D001847', 'term': 'Bone Diseases'}, {'id': 'D009140', 'term': 'Musculoskeletal Diseases'}, {'id': 'D006547', 'term': 'Hernia'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D003661', 'term': 'Decision Support Techniques'}], 'ancestors': [{'id': 'D008919', 'term': 'Investigative Techniques'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2024-11-05', 'size': 814300, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2024-11-06T04:26', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'OTHER', 'interventionModel': 'SINGLE_GROUP', 'interventionModelDescription': 'Feasibility of software (medical device integrated in electronic health record) and following changed workflow in outpatient clinic (20 patient, 6 surgeons)'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 26}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-02-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-11', 'completionDateStruct': {'date': '2027-06-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-30', 'studyFirstSubmitDate': '2025-01-14', 'studyFirstSubmitQcDate': '2025-01-30', 'lastUpdatePostDateStruct': {'date': '2025-02-04', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-02-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-05-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': "Surgeons' acceptability", 'timeFrame': 'Acceptability will be assessed continuously, but finally evaluated towards the end of the study, after iterative redesign of the DST and the related workflow according to requirements identified with qualitative methods at up to 70 weeks.', 'description': "Surgeons' acceptability of the decision support for a following clinical pilot study (yes/no)"}, {'measure': "Patients' acceptability", 'timeFrame': 'Acceptability will be assessed continuously, but finally evaluated towards the end of the study, after iterative redesign of the DST and the related workflow according to requirements identified with qualitative methods at up to 70 weeks.', 'description': "Patients' acceptability of the decision support for a clinical pilot study (yes/no)"}], 'secondaryOutcomes': [{'measure': "Surgeons' compliance rate", 'timeFrame': 'The rates and the duration will be calculated as averages for the study period, and towards the end of the study, after iterative redesign according to requirements identified with the qualitative methods at up to 70 weeks.', 'description': 'The proportion of consultations in which the surgeon uses the decision support as intended'}, {'measure': "Patients' compliance rate", 'timeFrame': 'The rates and the duration will be calculated as averages for the study period, and towards the end of the study, after iterative redesign according to requirements identified with the qualitative methods at up to 70 weeks.', 'description': 'The proportion of patients who complete the online questionnaire with the required information before the outpatient clinic visit'}, {'measure': 'Time', 'timeFrame': 'The rates and the duration will be calculated as averages for the study period, and towards the end of the study, after iterative redesign according to requirements identified with the qualitative methods at up to 70 weeks.', 'description': 'Duration of the consultation (minutes)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['artificial intelligence', 'decision support', 'surgery selection'], 'conditions': ['Intervertebral Disc Displacement', 'Spinal Stenosis Lumbar', 'Lumbar Disc Herniation', 'Lumbar Spine Degeneration']}, 'descriptionModule': {'briefSummary': 'Background One third of patients operated for lumbar disc herniation (LDH) or spinal stenosis (LSS) do not achieve substantial improvement. Studies indicate that well informed shared decision making (SDM) can improve the selection to surgery, and thus the outcomes. Numerous algorithms for outcome prediction have therefore been developed, and some use artificial intelligence (AI). Most are trained on small datasets, few are accurate, all are stand-alone or web-based applications not integrated in the electronic health record (EHR), and none are implemented in routine clinical practice.\n\nThe Norwegian registry for spine surgery (NORspine) comprises a cohort of more than 69,000 cases. The investigators have used AI to analyze the dataset and predict the outcome, and developed a decision support tool (DST) which is seamlessly integrated in the EHR DIPS Arena®.\n\nThe investigators intend to use the tool to inform the SDM between surgeons and patients about the indication for surgery (yes or no), to increase the proportion with a successful outcome. The aim of the study is to assess the safety and feasibility of the DST for use in a subsequent pilot study.\n\nThe device The DST (the device) is an integrate compound of software-solutions. Baseline data are registered by patients and surgeons on questionnaires integrated in DIPS Arena®, and transferred to NORspine. The data are also transferred (de-identified) to the AI-enabled prediction algorithm which operates in a cloud-based model hosting service. The algorithm has been trained and validated on a dataset from NORspine. The area under the curve for prediction of the main outcome (Oswestry disability index after12 months) in receiver operating characteristic analysis is very high (0.85) for LDH and moderate (0.72) for LSS. The model host also calculates outcomes (proportions with substantial, slight, or no improvement, and worsening) for the 50 cases with baseline variables most similar to the present case ("patients-like-me"). Finally, the individual prediction and the outcomes for the "patients-like-me" are transferred back and displayed in the regular user interface of DIPS Arena® for use in the SDM.\n\nClinical investigations For this feasibility study, the investigators will use convergent qualitative and quantitative mixed methods. The comparator is decision making in routine clinical practice, without use of the DST. The study will include 20 patients with magnetic resonance imaging confirmed LDH or LSS referred for evaluation of the indication for surgery, and six surgeons who do the evaluations. The study will iteratively redesign the user interface of the DST until it is considered safe and feasible for use in a following pilot study.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Patients with MRI-confirmed LDH or LSS referred to University hospital of North Norway Tromsø for assessment of indication for surgery\n* Specialists and physicians in training (for two years or more) in neurosurgery or orthopedic surgery who evaluate such patients at the neurosurgical outpatient clinic at University hospital of North Norway Tromsø\n\nExclusion Criteria:\n\n* Patients unable to consent because of\n\n * Age \\< 18 years\n * Serious drug abuse of severe psychiatric disorders\n * Language barriers (patients who cannot speak or read Norwegian)\n* Patients with a baseline ODI ≤14 (LDH) or ≤22 (LSS)\n* Patients undergoing non-elective/emergency operations\n* Patients with degenerative conditions other that LDH and LSS, fractures, primary infections, or malignant conditions of the spine\n* Physicians in training with less than two years' experience with spine surgery"}, 'identificationModule': {'nctId': 'NCT06806969', 'briefTitle': 'Artificial Intelligence Enabled Decision Support for Selection of Patients for Lumbar Spine Surgery', 'organization': {'class': 'OTHER', 'fullName': 'University Hospital of North Norway'}, 'officialTitle': 'Artificial Intelligence (AI) Enabled Decision Support Tool for Selection of Patients for Lumbar Spine Surgery: a Feasibility Study', 'orgStudyIdInfo': {'id': 'CIV-NO-24-06-047736'}, 'secondaryIdInfos': [{'id': 'CIV-NO-24-06-047736', 'type': 'OTHER', 'domain': 'Norwegian Medical Products Agency'}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Decision support', 'description': 'Patients and surgeons. Patients with lumbar disc herniation or lumbar spinal stenosis who will receive a digital form regarding patient-related outcome measures in advance of outpatient clinic, and will experience the use of the decision support in the consultation with the spine surgeon. Spine surgeons who will use the decision support in outpatient clinic to decide whether to perform spinal surgery.', 'interventionNames': ['Device: Decision support']}], 'interventions': [{'name': 'Decision support', 'type': 'DEVICE', 'description': 'Patients will digitally fill out forms, which will go into the decision support tool integrated in the electronic health record journal, which predicts outcome of surgery for the patient, to inform shared decision making.', 'armGroupLabels': ['Decision support']}]}, 'contactsLocationsModule': {'locations': [{'zip': '9010', 'city': 'Tromsø', 'state': 'Troms', 'country': 'Norway', 'contacts': [{'name': 'Tor Ingebrigtsen, Professor and consultant neurosurgeon', 'role': 'CONTACT', 'email': 'tor.ingebrigtsen@unn.no', 'phone': '+47 911 99843'}], 'facility': 'University Hospital of North Norway', 'geoPoint': {'lat': 69.6489, 'lon': 18.95508}}], 'centralContacts': [{'name': 'Tor Ingebrigtsen, Professor and consultant neurosurgeon', 'role': 'CONTACT', 'email': 'tor.ingebrigtsen@unn.no', 'phone': '+47 911 99843'}, {'name': 'Tore Solberg, Professor and consultant neurosurgeon', 'role': 'CONTACT', 'email': 'tore.solberg@unn.no', 'phone': '+47 913 64531'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Due to data privacy concerns, individual data from interviews will not be available to others in other forms than the refined analyses and descriptions in open-access publications.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Hospital of North Norway', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}