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Article

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

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Division of Vascular Medicine, Medical Clinic and Policlinic IV, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany
2
Interdisciplinary Sonography Center, Medical Clinic and Policlinic IV, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany
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Department of Ophthalmology, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany
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Division of Rheumatology and Clinical Immunology, Medical Clinical and Policlinic IV, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany
*
Author to whom correspondence should be addressed.
Shared first authorship.
Academic Editor: George N. Kouvelos
J. Clin. Med. 2021, 10(6), 1163; https://doi.org/10.3390/jcm10061163
Received: 29 January 2021 / Revised: 19 February 2021 / Accepted: 26 February 2021 / Published: 10 March 2021
(This article belongs to the Section Vascular Medicine)
Background: Risk stratification 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 arteritis (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 of a clinical prediction rule, integrated into a stepwise diagnostic algorithm together with C-reactive protein (CRP) values and hrTCS values. Results: The model consisted of four clinical variables (age > 70, headache, jaw claudication, and 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 (area under the curve (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. View Full-Text
Keywords: giant cell arteritis; anterior ischemic optic neuropathy; clinical prediction rule; diagnostic algorithm; C-reactive protein; temporal compression sonography; ultrasound giant cell arteritis; anterior ischemic optic neuropathy; clinical prediction rule; diagnostic algorithm; C-reactive protein; temporal compression sonography; ultrasound
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MDPI and ACS Style

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. https://doi.org/10.3390/jcm10061163

AMA Style

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. Journal of Clinical Medicine. 2021; 10(6):1163. https://doi.org/10.3390/jcm10061163

Chicago/Turabian Style

Czihal, Michael, Christian Lottspeich, Christoph Bernau, Teresa Henke, Ilaria Prearo, Marc Mackert, Siegfried Priglinger, Claudia Dechant, Hendrik Schulze-Koops, and Ulrich Hoffmann. 2021. "A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis" Journal of Clinical Medicine 10, no. 6: 1163. https://doi.org/10.3390/jcm10061163

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