Diagnostic Algorithm for Patients With Suspected Giant Cell Arteritis.

Published

Journal Article

To identify clinical and laboratory factors contributing to the diagnosis of giant cell arteritis (GCA) and develop a diagnostic algorithm for the evaluation of GCA.Retrospective review of 213 consecutive cases of temporal artery biopsy (TAB) seen at a single academic center over a 10-year period (2000-2009). Pathologic specimens were re-reviewed and agreement between the original and second readings was assessed. A composite clinical suspicion score was created by adding 1 point for each of the following criteria: anterior extracranial circulation ischemia, new onset headache, abnormal laboratory results (erythrocyte sedimentation rate, C-reactive protein (CRP), or platelet count), jaw claudication, abnormal or tender superficial temporal artery, constitutional symptoms, and polymyalgia rheumatica; one point was subtracted if a comorbid condition could explain a criterion.Of the 204 TABs analyzed, pathologic findings were confirmatory in 49 (24.0%) and suggestive in 12 (5.9%). TAB-positive patients were more likely to be older (age 75.2 ± 7.8 vs 69.7 ± 11.0 years, P = 0.0002), complain of jaw claudication (relative-risk = 3.26, P = 0.0014), and have thrombocytosis (relative-risk = 3.3, P = 0.0072) and elevated CRP (relative-risk = 1.8, P = 0.037). None of the patients with a clinical score less than 2 had a positive TAB. Diabetes mellitus and kidney disease were often the explanation for the symptoms and abnormal clinical finding(s) that led to a negative TAB.We propose a clinical algorithm that is highly predictive for a positive TAB and can be valuable in the evaluation process of suspected cases of GCA.

Full Text

Duke Authors

Cited Authors

  • El-Dairi, MA; Chang, L; Proia, AD; Cummings, TJ; Stinnett, SS; Bhatti, MT

Published Date

  • September 2015

Published In

Volume / Issue

  • 35 / 3

Start / End Page

  • 246 - 253

PubMed ID

  • 25802967

Pubmed Central ID

  • 25802967

Electronic International Standard Serial Number (EISSN)

  • 1536-5166

International Standard Serial Number (ISSN)

  • 1070-8022

Digital Object Identifier (DOI)

  • 10.1097/wno.0000000000000234

Language

  • eng