Clinical prediction models for medication adverse events in patients with rheumatic and musculoskeletal conditions: A systematic literature review
Authors' conclusion
It highlights that existing CPMs are affected by methodological pitfalls, including inappropriate variable selection and lack of clear sample size justification.
Study context
- Population
- Studies developing multivariable clinical prediction models (CPMs) predicting adverse events (AEs) in adult patients using RMD medications; PubMed, Embase and Medline up to March 2024
- Participants
- 12 participants17 CPMs
- Outcome
- Existing CPMs for AEs of RMD medications
Methodological detailsCHARMS data extraction, PROBAST risk of bias assessment
- CHARMS data extraction
- PROBAST risk of bias assessment
Main finding
As reported in the abstract
- Studies identified
- 2406
- Titles/abstracts screened
- 1734
- Reviewed in full
- 38
- Studies meeting eligibility criteria
- 12
- CPMs reported
- 17
- CPMs focused on RA
- 76.4 %
- CPMs on disease modifying anti-rheumatic drugs (DMARDs): MTX
- 69.2 %
- CPMs on DMARDs: biologic drugs
- 15.3 %
- Models with high overall risk of bias (ROB) (inappropriate variable selection and sample size)
- 12 (70.5 %)
Original publication
Clinical prediction models for medication adverse events in patients with rheumatic and musculoskeletal conditions: A systematic literature review. Semin Arthritis Rheum. Published 11 April 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →