Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Low Back Pain, Neck Pain, Disc Herniation, Disc Degeneration. Basic parameters: 18 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Norway
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders (AID-Spine, Part I)
Overview
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.
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.
Primary outcome measures
- Patient-reported outcomes [Time frame: depends upon the registry data, but in general between 2008 and 2022]
- Unfavourable outcomes [Time frame: depends upon the registry data, but in general between 2008 and 2022]
- Prescribed medication [Time frame: depends upon the registry data, but in general between 2008 and 2022]
- Sickness absence [Time frame: depends upon the registry data, but in general between 2008 and 2022]
Eligibility criteria
Inclusion Criteria: patients referred to secondary care for assessment of surgery or not due to disc herniation (lumbar or cervical).
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Exclusion Criteria: ambulant cases who needs immediate treatment
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Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
Norway · 1 center
- Oslo Metropolitan University — Oslo
Publications
- Zouch JH, Berg B, Pripp AH, Storheim K, Ashton-James CE, Ferreira ML, Grotle M, Ferreira PH. Reducing strain on primary healthcare systems through innovative models of care: the impact of direct access physiotherapy for musculoskeletal conditions-an interrupted time series analysis. Fam Med Community Health. 2024 Sep 24;12(3):e002998. doi: 10.1136/fmch-2024-002998. PMID 39317459
Identifiers
NCT: NCT05745129 · 371282