Predictive models of radiographic progression and pain progression in patients with knee osteoarthritis: data from the FNIH osteoarthritis (OA) biomarkers consortium project
Authors' conclusion
We identified risk factors for imaging progression and pain progression in patients with knee OA over a 2- to 4-year period, and provided effective predictive models, which could help identify patients at high risk of progression.
Study context
- Population
- Subjects with mild to moderate knee OA (Kellgren-Lawrence grade 1, 2 or 3) in one target knee, FNIH OA Biomarkers Consortium project376 candidate variables
- Participants
- 600 subjects
- Comparison
- Radiographic progressors vs Non-radiographic progressors vs Pain progressors vs Non-pain progressors
- Radiographic progressorsn = 297
- Non-radiographic progressorsn = 303reference
- Pain progressorsn = 297
- Non-pain progressorsn = 303reference
- Outcome
- Prediction of radiographic progression and pain progression
- Follow-up
- 24-48 months
Methodological detailsnested case-control, multivariate logistic regression, nomograms
- Nested case-control
- Multivariate logistic regression
- Nomograms
Main finding
As reported in the abstract
Predictive model performance
Radiographic progression model (8 variables), AUC
0.77
Pain progression model (10 variables), AUC
0.76
Observational data: associations, not effects.
Further findings
Pain progression model with change in WOMAC pain score from baseline to 24 months added
- AUC (significantly improved predictive effectiveness)
- 0.86
Original publication
Predictive models of radiographic progression and pain progression in patients with knee osteoarthritis: data from the FNIH OA biomarkers consortium project. Arthritis Res Ther. Published 30 May 2024. doi:10.
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