2022 American College of Rheumatology/EULAR Classification Criteria for Giant Cell Arteritis

Objective To develop and validate updated classification criteria for giant cell arteritis (GCA). Methods Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in 6 phases: 1) identification of candidate items, 2) prospective collection of candidate items present at the time of diagnosis, 3) expert panel review of cases, 4) data-driven reduction of candidate items, 5) derivation of a points-based risk classification score in a development data set, and 6) validation in an independent data set. Results The development data set consisted of 518 cases of GCA and 536 comparators. The validation data set consisted of 238 cases of GCA and 213 comparators. Age >= 50 years at diagnosis was an absolute requirement for classification. The final criteria items and weights were as follows: positive temporal artery biopsy or temporal artery halo sign on ultrasound (+5); erythrocyte sedimentation rate >= 50 mm/hour or C-reactive protein >= 10 mg/liter (+3); sudden visual loss (+3); morning stiffness in shoulders or neck, jaw or tongue claudication, new temporal headache, scalp tenderness, temporal artery abnormality on vascular examination, bilateral axillary involvement on imaging, and fluorodeoxyglucose-positron emission tomography activity throughout the aorta (+2 each). A patient could be classified as having GCA with a cumulative score of >= 6 points. When these criteria were tested in the validation data set, the model area under the curve was 0.91 (95% confidence interval [95% CI] 0.88-0.94) with a sensitivity of 87.0% (95% CI 82.0-91.0%) and specificity of 94.8% (95% CI 91.0-97.4%). Conclusion The 2022 American College of Rheumatology/EULAR GCA classification criteria are now validated for use in clinical research.

Automated summary

This study aimed to develop and validate updated classification criteria for giant cell arteritis (GCA). By collecting and analyzing a large number of samples, a set of indicators and weights for the diagnosis and classification of GCA were determined. These classification criteria achieved high sensitivity and specificity in the validation dataset.