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

Michael Czihal; Christian Lottspeich; Christoph Bernau; Teresa Henke; Ilaria Prearo; Marc Mackert; Siegfried Priglinger; Claudia Dechant; Hendrik Schulze-Koops; Ulrich Hoffmann

doi:10.3390/jcm10061163

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

J Clin Med. 2021 Mar 10;10(6):1163. doi: 10.3390/jcm10061163.

Authors

Affiliations

¹ 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.
³ Department of Ophthalmology, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany.
⁴ Division of Rheumatology and Clinical Immunology, Medical Clinical and Policlinic IV, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany.

Abstract

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.

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