Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

Version 1 : Received: 29 January 2021 / Approved: 1 February 2021 / Online: 1 February 2021 (08:44:33 CET)

How to cite: 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. Preprints 2021, 2021020001 (doi: 10.20944/preprints202102.0001.v1). 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. Preprints 2021, 2021020001 (doi: 10.20944/preprints202102.0001.v1).

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.

Subject Areas

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

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