A Clinical Probability-Based, Stepwise Algorithm for the Diagnosis of Giant Cell Arteritis: Study Protocol and Baseline Characteristics of the First 50 Patients Included in the Prospective Validation Study with Focus on Cranial Symptoms.

Background: Early diagnosis of giant cell arteritis (GCA) is crucial to avoid loss of vision, but detailed headache characteristics of GCA have been poorly studied. Clinical prediction rules have shown promise in guiding management decisions in suspected GCA.

Methods: This is a prospective, monocentric cohort study on patients ≥50 years of age with suspected GCA. The diagnostic efficacy and safety of a previously published prediction rule embedded in a stepwise diagnostic algorithm is compared to the final clinical diagnosis incorporating the results of temporal artery biopsy (TAB). The protocol of the ongoing study is presented in detail. Based on an interim analysis of the first 50 included patients, characteristics of cranial symptoms of patients with positive and negative TAB are compared, and a modification of the original prediction rule is presented.

Results: TAB was positive in 23 and negative in 26 cases. In one patient, the TAB specimen contained no arterial segment, so this patient was excluded from the interim analysis. Headache was more commonly located temporally and bilaterally. Cranial ischemic symptoms and superficial temporal artery-related symptoms were more common in patients with positive TAB. The quality and intensity of headaches did not differ significantly between groups. As the original prediction rule misclassified a single patient who eventually had a positive TAB, the clinical prediction rule was modified.

Conclusions: Given the limited sensitivity and specificity of cranial symptoms, a stepwise diagnostic algorithm based on the modified prediction rule may facilitate clinical decision-making in suspected GCA.