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

ABSTRACT: 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). 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. 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. A diagnostic algorithm based on a clinical prediction rule improves the diagnostic accuracy of hrTCS.

SUBMITTER: Czihal M

PROVIDER: S-EPMC8001831 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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

Czihal Michael M Lottspeich Christian C Bernau Christoph C Henke Teresa T Prearo Ilaria I Mackert Marc M Priglinger Siegfried S Dechant Claudia C Schulze-Koops Hendrik H Hoffmann Ulrich U

Journal of clinical medicine 20210310 6

<h4>Background</h4>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).<h4>Methods</h4>A logistic regression model with candidate items was derived from a cohort of patients with suspected cGCA (<i>n</i> = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort (<i>n</i> = ...[more]

PMID: 33802092

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Project description:BackgroundA novel algorithm and clinical prediction rule (CPR), with 18 variables, was created in 2014. The CPR generated a bone stress injury (BSI) score, which was used to determine the necessity of imaging in suspected BSI. To date, there are no validated algorithms for imaging selection in patients with suspected BSI.HypothesisA simplified CPR will assist clinicians with diagnosis and decision making in patients with suspected BSI.Study designProspective cohort study.Level of evidenceLevel 3.MethodsA total of 778 military trainees with lower extremity pain were enrolled. All trainees were evaluated for 18 clinical variables suggesting BSI. Participants were monitored via electronic medical record review. Then, a prediction model was developed using logistic regression to identify clinical variables with the greatest predictive value and assigned appropriate weight. Test characteristics for various BSI score thresholds were calculated.ResultsOf the enrolled trainees, 204 had imaging-confirmed BSI in or distal to the femoral condyles. The optimized CPR selected 4 clinical variables (weighted score): bony tenderness (3), prior history of BSI (2), pes cavus (2), and increased walking/running volume (1). The optimized CPR with a score ≥3 yielded 97.5% sensitivity, 54.2% specificity, and 98.2% negative predictive value. An isolated measure, bony tenderness, demonstrated similar statistical performance.ConclusionThe optimized CPR, which uses bony tenderness, prior history of BSI, pes cavus, and increased walking/running volume, is valid for detecting BSI in or distal to the femoral condyles. However, bony tenderness alone provides a simpler criterion with an equally strong negative predictive value for BSI decision making.Clinical relevanceFor suspected BSI in or distal to the femoral condyles, imaging can be deferred when there is no bony tenderness. When bony tenderness is present in the setting of 1 or more proven risk factors and no clinical evidence of high-risk bone involvement, presumptive treatment for BSI and serial radiographs may be appropriate.

Dataset Information

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

Publications

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

Similar Datasets

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