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Will imaging change the diagnosis and management of giant cell arteritis? Cover

Will imaging change the diagnosis and management of giant cell arteritis?

Open Access
|Dec 2019

Abstract

Giant cell arteritis is a common systemic vasculitis affecting the elderly, with maximum prevalence in the 7th decade of age, targeting aortic derived medium and large vessels of the neck and head. Diagnosis is established on a biopsy specimen of the temporal artery wall, through pathological confirmation of panarteritis, typically characterized by mononuclear cell infiltrate, with the 1990 ACR criteria often used in clinical practice.

We present the case of a patient with a new onset headache and systemic inflammation, who did not fulfil the classical diagnostic criteria, nor did the temporal artery biopsy (TAB) provide a positive result. However, the ultrasonographical features, clinical evolution and response to corticosteroid therapy confirmed the diagnosis. This patient had bilateral presence of the halo sign on color duplex ultrasonography (CDUS), cited as a highly specific feature, when compared to the ACR criteria as a standard reference. We employed its positive likelihood-ratio (LR+) of 43 as previously estimated, while considering a low pre-test probability for a positive diagnosis (15%), to calculate a post-test probability of 88%, leading to our decision to treat him as having giant cell arteritis. Remission of the headache and rebound phenomena when tapered off steroid therapy substantially contributed to the positive diagnosis, underlining the importance of future studies needing to use clinical evolution as a reference standard.

DOI: https://doi.org/10.2478/rjim-2019-0013 | Journal eISSN: 2501-062X | Journal ISSN: 1220-4749
Language: English
Page range: 341 - 344
Submitted on: May 4, 2019
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Published on: Dec 16, 2019
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Andra Chiriac, Camelia Badea, Cristian Băicuș, published by N.G. Lupu Internal Medicine Foundation
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.