AID-Spine: Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders

Sponsor
Oslo Metropolitan University (Other)
Overall Status
Enrolling by invitation
CT.gov ID
NCT05745129
Collaborator
(none)
165,000
1
36
4586.5

Study Details

Study Description

Brief Summary

The primary objective is to use machine learning methods on large survey and health register data to identify participants with different treatment trajectories and health outcomes after surgical and/or conservative treatment for spinal disorders.

Secondary objectives are to 1) conduct external validation of the prediction models, and 2) explore how the prediction models can be implemented into AI-based clinical co-decision tools and interventions.

Condition or Disease Intervention/Treatment Phase

    Detailed Description

    Three work packages are conducted. In the first, the investigators will use data from three general population surveys in Norway (HUNT, Tromsø, and Ullensaker) linked to administrative health registry data (Norwegian Patient Registry (for secondary care) and Norwegian Registry for Primary Health Care) and clinical registers on spinal disorders (the Norwegian registry for spine surgery, NorSpine, and the Norwegian registry for neck and back pain) to explore treatment trajectories and health outcomes following an episode of back and/or neck pain. The investigators will use different combinations of these data sets to assess the impact of a wide range of risk/ prognostic factors and to develop prognostic models for different health and welfare outcomes.

    Four major outcomes will be adressed; a) unfavourable outcomes, b) use of prescribed medication, c) use of sickness absence and other disability benefits, and d) patient-reported outcomes.

    In the second work package, the investigators will conduct external validation studies of the prediction models by using Danish and Swedish data. There is a large overlap and similarities in health and welfare registers across the Nordic countries.

    In the third work package the investigators will first conduct a feasibility study in a secondary care hospital setting in which surgeons examine and assess referred patients with disc herniation and spinal stenosis for surgical treatment (or not). Qualitative interviews will be used to gain a better understanding of today's clinical decision-making process.

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    165000 participants
    Observational Model:
    Other
    Time Perspective:
    Prospective
    Official Title:
    Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders (AID-Spine, Part I)
    Actual Study Start Date :
    Dec 1, 2021
    Anticipated Primary Completion Date :
    Dec 1, 2023
    Anticipated Study Completion Date :
    Nov 30, 2024

    Arms and Interventions

    Arm Intervention/Treatment
    Treatment cohort

    Surgical vs conservative treatment. Surgery procedures are based on the NOMESCO Classification of Surgical Procedures (NCSP). Conservative treatment includes all non-surgical treatment methods such as pharmaceutical treatment, physical medicine and physiotherapy modalities (information, patient education, exercise, manual therapy etc), cognitive-behavioural therapy, multidisciplinary treatment, acupuncture, and others (e.g. chiropractic treatment, homeopathy, naprapathy, osteopathy). The results will be described for important spinal subgroups (specific diagnoses, nerve-root affections, and non-specific conditions).

    Outcome Measures

    Primary Outcome Measures

    1. Patient-reported outcomes [depends upon the registry data, but in general between 2008 and 2022]

      Patient-reported outcome measures included in clinical registers

    2. Unfavourable outcomes [depends upon the registry data, but in general between 2008 and 2022]

      Healthcare utilization, reoperation, infection, or other complications after surgery.

    3. Prescribed medication [depends upon the registry data, but in general between 2008 and 2022]

      High use of prescribed medication (dispensed drugs)

    4. Sickness absence [depends upon the registry data, but in general between 2008 and 2022]

      Sickness absence and disability pension

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 80 Years
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No

    Inclusion Criteria: patients referred to secondary care for assessment of surgery or not due to disc herniation (lumbar or cervical).

    -

    Exclusion Criteria: ambulant cases who needs immediate treatment

    -

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Oslo Metropolitan University Oslo Norge Norway 0130

    Sponsors and Collaborators

    • Oslo Metropolitan University

    Investigators

    None specified.

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Margreth Grotle, Professor, Oslo Metropolitan University
    ClinicalTrials.gov Identifier:
    NCT05745129
    Other Study ID Numbers:
    • 371282
    First Posted:
    Feb 27, 2023
    Last Update Posted:
    Feb 27, 2023
    Last Verified:
    Feb 1, 2023
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Margreth Grotle, Professor, Oslo Metropolitan University
    Additional relevant MeSH terms:

    Study Results

    No Results Posted as of Feb 27, 2023