Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders

NCT05745129 | Enrolling By Invitation

• 1 other identifier: 371282
• Study Type: observational
• Target: 165,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Low Back Pain; Neck Pain; Disc Herniation; Disc Degeneration; Spinal Stenosis; Radiculopathy

Keywords

prognosis; trajectories; risk factors; prognostic factors; prognostic model; prediction model; health outcomes; work outcomes

Outcome Measures

Primary Outcomes (4)

• Patient-reported outcomes

  Patient-reported outcome measures included in clinical registers

  depends upon the registry data, but in general between 2008 and 2022

• Unfavourable outcomes

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

  depends upon the registry data, but in general between 2008 and 2022

• Prescribed medication

  High use of prescribed medication (dispensed drugs)

  depends upon the registry data, but in general between 2008 and 2022

• Sickness absence

  Sickness absence and disability pension

  depends upon the registry data, but in general between 2008 and 2022

Study Arms (1)

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).

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

Sponsors & Collaborators

• Oslo Metropolitan University: lead

Study Sites (1)

Oslo Metropolitan University

Oslo, Norge, 0130, Norway

Location

MeSH Terms

Conditions

Low Back Pain; Neck Pain; Intervertebral Disc Displacement; Intervertebral Disc Degeneration; Spinal Stenosis; Radiculopathy

Condition Hierarchy (Ancestors)

Back Pain; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Spinal Diseases; Bone Diseases; Musculoskeletal Diseases; Hernia; Pathological Conditions, Anatomical; Peripheral Nervous System Diseases; Neuromuscular Diseases; Nervous System Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: February 1, 2023
• First Posted: February 27, 2023
• Study Start: December 1, 2021
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: February 20, 2024

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will not share

Locations

NO (1)