一种基于简单临床预测规则的诊断算法，用于诊断颅巨细胞动脉炎。
A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis.
作者信息
Czihal Michael, Lottspeich Christian, Bernau Christoph, Henke Teresa, Prearo Ilaria, Mackert Marc, Priglinger Siegfried, Dechant Claudia, Schulze-Koops Hendrik, Hoffmann Ulrich
机构信息
Division of Vascular Medicine, Medical Clinic and Policlinic IV, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany.
Interdisciplinary Sonography Center, Medical Clinic and Policlinic IV, Hospital of the Ludwig-Maximilians-University, 80336 Munich, Germany.
出版信息
J Clin Med. 2021 Mar 10;10(6):1163. doi: 10.3390/jcm10061163.
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 ( = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort ( = 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.
背景
基于检测前概率的风险分层可能会提高颞动脉高分辨率压迫超声检查（hrTCS）在颅巨细胞动脉炎（cGCA）诊断检查中的诊断准确性。
方法
从一组疑似cGCA患者（n = 87）中得出一个包含候选项目的逻辑回归模型。通过受试者操作特征（ROC）分析在推导队列和独立验证队列（n = 114）中测试该模型的诊断准确性。临床项目由一个临床预测规则组成，该规则与C反应蛋白（CRP）值和hrTCS值一起被整合到一个逐步诊断算法中。
结果
该模型由四个临床变量组成（年龄>70岁、头痛、颌跛行和前部缺血性视神经病变）。在两个队列中，该模型区分最终临床诊断为cGCA和未诊断为cGCA患者的诊断准确性都非常出色（曲线下面积（AUC）分别为0.96和0.92）。该诊断算法显著提高了hrCTS的阳性预测值。在该算法中，32.8%的患者（推导队列）和49.1%（验证队列）本不会接受hrTCS检查。这些患者均未最终诊断为cGCA。
结论
基于临床预测规则的诊断算法提高了hrTCS的诊断准确性。
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