Diagnostic Assessment Strategies and Disease Subsets in Giant Cell Arteritis: Data From an International Observational Cohort.

Diagnostic assessment in giant cell arteritis (GCA) is rapidly changing as vascular imaging becomes more available. This study was undertaken to determine if clinical GCA subsets have distinct profiles or reflect differential diagnostic assessments. Patients were recruited from an international cohort and divided into 4 subsets based on a temporal artery (TA) abnormality (positive TA biopsy [TAB] or halo sign on TA ultrasound [TA-US]) and/or evidence of large vessel (LV) involvement on imaging: 1) both TA abnormality and LV involvement (TA+/LV+ GCA); 2) TA abnormality without LV involvement (TA+/LV- GCA); 3) LV involvement without TA abnormality (TA-/LV+ GCA); and 4) clinically diagnosed GCA without LV involvement or TA abnormality (TA-/LV- GCA). Nine hundred forty-one patients with GCA were recruited from 72 international study sites. Most patients received multiple forms of diagnostic assessment, including TAB (n = 705 [75%]), TA-US (n = 328 [35%]), and LV imaging (n = 534 [57%]). Assessment using TAB, TA-US, and LV imaging confirmed the diagnosis of GCA in 66%, 79%, and 40% of cases, respectively. GCA subsets had distinct profiles independent of diagnostic assessment strategies. TA+/LV- were the most common subset (51%), with a high burden of cranial ischemia. Those in the TA-/LV- subset (26%) had a high prevalence of cranial ischemia and musculoskeletal symptoms. Patients in the TA-/LV+ subset (12%) had prevalent upper extremity vascular abnormalities and a low prevalence of vision loss, and those in the TA+/LV+ subset (11%) were older and had a high prevalence of cranial ischemia, constitutional symptoms, and elevated acute-phase reactant levels. Vascular imaging is increasingly incorporated into the diagnostic assessment of GCA and identifies clinical subsets of patients based on involvement of temporal and extracranial arteries.

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© 2019, American College of Rheumatology. This article has been contributed to by US Government employees and their work is in the public domain in the USA.