Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis

Michael Czihal
* ,
Christian Lottspeich
ORCID logo ,
Christoph Bernau
,
Theresa Henke
,
Ilaria Prearo
,
Marc Mackert
,
Siegfried Priglinger
,
Claudia Dechant
,
Hendrik Schulze-Koops
,
Ulrich Hoffmann
Version 1 : Received: 29 January 2021 / Approved: 1 February 2021 / Online: 1 February 2021 (08:44:33 CET)

A peer-reviewed article of this Preprint also exists.

Czihal, M.; Lottspeich, C.; Bernau, C.; Henke, T.; Prearo, I.; Mackert, M.; Priglinger, S.; Dechant, C.; Schulze-Koops, H.; Hoffmann, U. A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis. J. Clin. Med. 2021, 10, 1163. Czihal, M.; Lottspeich, C.; Bernau, C.; Henke, T.; Prearo, I.; Mackert, M.; Priglinger, S.; Dechant, C.; Schulze-Koops, H.; Hoffmann, U. A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis. J. Clin. Med. 2021, 10, 1163.

Abstract

Background: Risk tratification based on pre-test probability may improve the diagnostic accuracy of temporal artery high-resolution compression sonography (hrTCS) in the diagnostic workup of cranial giant cell arteriitis (cGCA). Methods: A logistic regression model with candidate items was derived from a cohort of patients with suspected cGCA (n = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort (n = 114) by receiver operator characteristics (ROC)-analysis. The clinical items were composed to a clinical prediction rule, integrated into a stepwise diagnostic algorithm together with CRP-values and hrTCS-values. Results: The model consisted of 4 clinical variables (age > 70, headache, jaw claudication, anterior ischemic optic neuropathy). The diagnostic accuracy of the model for discrimination of patients with and without a final clinical diagnosis of cGCA was excellent in both cohorts (AUC 0.96 and AUC 0.92, respectively). The diagnostic algorithm improved the positive predictive value of hrCTS substantially. Within the algorithm, 32.8% of patients (derivation cohort) and 49.1% (validation cohort) would not have been tested by hrtCS. None of these patients had a final diagnosis of cGCA. Conclusion: A diagnostic algorithm based on a clinical prediction rule improves the diagnostic accuracy of hrTCS.

Keywords

Giant cell arteritis; anterior ischemic optic neuropathy; clinical prediction rule; diagnostic algorithm; C-reactive protein; temporal compression sonography; ultrasound

Subject

Medicine and Pharmacology, Immunology and Allergy

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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