Vasculitis & polymyalgia rheumatica
Multivariable Models to Predict a Diagnosis of Giant Cell Arteritis: Systematic Review and Metaanalysis
Authors' conclusion
Predictors of GCA were consistent across models; however, models were of poor methodologic quality.
Study context
- Population
- Studies that used multivariable models to diagnose giant cell arteritis (GCA) (PubMed, Embase, Cochrane Library, January 1990 to April 2024)
- Participants
- 15,409 participants44 studies
- Outcome
- Model variables (predictors of a GCA diagnosis) and model performance
Methodological detailsgeneric inverse variance, PROBAST risk of bias assessment, Grading of Recommendations, Assessment, Development and Evaluation (GRADE) certainty assessment
- Generic inverse variance
- PROBAST risk of bias assessment
- GRADE certainty assessment
Pooled result
As reported in the abstract
Predictors of a GCA diagnosis
- Predictors with high certainty of effect and large effect size: jaw claudication, CRP elevation > 24.5 mg/L, platelets > 400 × 109/L, positive temporal artery US, and synovitis (predictive of a non-GCA diagnosis)
- Vision loss, PMR symptoms and headache predictive with lower certainty of effect
- Models predominantly at high risk of bias
- 4340 diagnoses of GCA
Original publication
Multivariable Models to Predict a Diagnosis of Giant Cell Arteritis: Systematic Review and Metaanalysis. J Rheumatol. Published 1 July 2025. doi:10.
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