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A Reproducible Cartilage Impact Model to Generate Post-Traumatic Osteoarthritis in the Rabbit
Published on: November 21, 2023
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Developing and internally validating a prediction model for total knee replacement surgery in patients with
Sharmala Thuraisingam1,2, Patty Chondros2, Jo-Anne Manski-Nankervis2
1Department of Surgery, Melbourne Medical School, University of Melbourne, 29 Regent Street, Fitzroy, Victoria, 3065, Australia.
Osteoarthritis and Cartilage Open
|December 7, 2022
Summary
A new clinical algorithm predicts total knee replacement (TKR) risk in osteoarthritis patients within five years. This tool aims to promote early treatment and identify candidates for interventions to delay TKR surgery.
Area of Science:
- Orthopedics
- Health Informatics
- Epidemiology
Background:
- Osteoarthritis affects millions, often leading to total knee replacement (TKR).
- Predicting TKR risk is crucial for timely intervention and resource allocation.
- Current methods may not adequately identify patients progressing towards TKR.
Purpose of the Study:
- To develop and validate a clinical algorithm predicting TKR probability in osteoarthritis patients.
- To facilitate early adoption of first-line treatments for those at high risk of TKR.
- To identify a cohort for testing interventions that prevent or delay TKR.
Main Methods:
- Utilized electronic health records (EHRs) from 201,462 Australian patients with osteoarthritis (aged ≥45).
- Linked EHRs with the Australian Orthopaedic Association National Joint Replacement Registry and National Death Index.
- Developed a Fine and Gray competing risk prediction model.
Main Results:
- Over 5 years, 7.9% of patients underwent TKR and 6.9% died.
- Key predictors included age, prior knee surgery, osteoarthritis medication, comorbidity count, and mental health diagnosis.
- Model discrimination was 0.67 (95% CI: 0.66–0.67), with acceptable calibration.
Conclusions:
- The developed algorithm shows potential for reducing the economic burden of TKR in Australia.
- External validation and further optimization are planned before general practice EHR implementation.
- This predictive model can aid in personalized management of osteoarthritis patients.
