A diagnostic model for assessing the risk of osteoporosis in patients with rheumatoid arthritis based on bone turnover markers
Authors' conclusion
Our model is user-friendly and has shown good predictive performance in both internal and internal hold-out validation
Study context
- Population
- Rheumatoid Arthritis (RA) patients who underwent BMD assessments at Longhua Hospital, Shanghai University of Traditional Chinese Medicine, January 2018 to December 2022
- Participants
- 500 patients
- Exposure
- Training set vs Validation set
- Outcome
- Discrimination (AUC) of a model for RA combined with osteoporosis (OP) (RA-OP)
Methodological detailsdiagnostic prediction model, training set (first 70%) and validation set (remaining 30%), bootstrap internal validation (1,000 resamples), internal hold-out validation, single-centre
- Diagnostic prediction model
- Training set (first 70%) and validation set (remaining 30%)
- Bootstrap internal validation (1,000 resamples)
- Internal hold-out validation
- Single-centre
Main finding
As reported in the abstract
Discrimination of the RA-OP diagnostic model (AUC)
- Model AUC (development, training set)
- 0.819 (95% CI: 0.775-0.863)
- Model AUC, internal validation (bootstrap)
- 0.814 (95% CI: 0.772-0.864)
- Model AUC, internal hold-out validation
- 0.772 (95% CI: 0.697-0.847)
- Calibration
- good
Observational data: associations, not effects.
Original publication
A diagnostic model for assessing the risk of osteoporosis in patients with rheumatoid arthritis based on bone turnover markers. Arthritis Res Ther. Published 1 April 2025. doi:10.
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