Vasculitis & polymyalgia rheumatica
Development of a diagnostic prediction model for giant cell arteritis by sequential application of Southend Giant Cell Arteritis Probability Score and ultrasonography: a prospective multicentre study
Authors' conclusion
A prediction tool for giant cell arteritis (the HAS-GCA score), combining SGCAPS and the halo count, reliably confirms and excludes giant cell arteritis from giant cell arteritis mimics in fast-track clinics.
Study context
- Population
- Consecutive patients with suspected new onset GCA referred to fast-track clinics, seven centres in Italy, the Netherlands, Spain and UKmean age 71 years
- Participants
- 229 patients
- Exposure
- Prediction models combining Southend Giant Cell Arteritis Probability Score (SGCAPS) with each of three ultrasonographic scores (halo count, halo score, OGUS) for diagnosis of GCA
- GCAn = 84
- Controlsn = 145reference
- Outcome
- Accuracy of prediction models (SGCAPS plus ultrasonographic score) for final clinical diagnosis of GCA
- Follow-up
- 6 months
Methodological detailsmulticentre, inception cohort, consecutive patients, diagnostic prediction model, multivariable logistic regression, Recruited between Oct 1, 2019, and June 30, 2022
- Multicentre
- Inception cohort
- Consecutive patients
- Diagnostic prediction model
- Multivariable logistic regression
- Recruited between Oct 1, 2019, and June 30, 2022
Main finding
As reported in the abstract
Optimism-adjusted C statistic
0.969
95% CI 0.952 to 0.990
Patients classified into low or high probability groups (of 229)
169
74%
Misclassification in low probability group (of 105)
two (2%)
Misclassification in high probability group (of 64)
two (3%)
Observational data: associations, not effects.
Further findings
Discrimination of SGCAPS and the three ultrasonographic scores
- SGCAPS and all three ultrasonographic scores discriminated well between patients with and without GCA
Inter-rater and intra-rater reliability of ultrasonographic scores
- Reliability high for all three ultrasonographic scores
- Separate patient-based reliability exercise with five patients and five observers
Original publication
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