Ing Edsel B, Lahaie Luna Gabriela, Toren Andrew, Ing Royce, Chen John J, Arora Nitika, Torun Nurhan, Jakpor Otana A, Fraser J Alexander, Tyndel Felix J, Sundaram Arun Ne, Liu Xinyang, Lam Cindy Ty, Patel Vivek, Weis Ezekiel, Jordan David, Gilberg Steven, Pagnoux Christian, Ten Hove Martin
Department of Ophthalmology and Vision Sciences, University of Toronto Medical School, Toronto.
Department of Ophthalmology, Queen's University, Kingston, ON.
Clin Ophthalmol. 2017 Nov 22;11:2031-2042. doi: 10.2147/OPTH.S151385. eCollection 2017.
To develop and validate a diagnostic prediction model for patients with suspected giant cell arteritis (GCA).
A retrospective review of records of consecutive adult patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at seven university centers. The pathologic diagnosis was considered the final diagnosis. The predictor variables were age, gender, new onset headache, clinical temporal artery abnormality, jaw claudication, ischemic vision loss (VL), diplopia, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and platelet level. Multiple imputation was performed for missing data. Logistic regression was used to compare our models with the non-histologic American College of Rheumatology (ACR) GCA classification criteria. Internal validation was performed with 10-fold cross validation and bootstrap techniques. External validation was performed by geographic site.
There were 530 complete TABx records: 397 were negative and 133 positive for GCA. Age, jaw claudication, VL, platelets, and log CRP were statistically significant predictors of positive TABx, whereas ESR, gender, headache, and temporal artery abnormality were not. The parsimonious model had a cross-validated bootstrap area under the receiver operating characteristic curve (AUROC) of 0.810 (95% CI =0.766-0.854), geographic external validation AUROC's in the range of 0.75-0.85, calibration p of 0.812, sensitivity of 43.6%, and specificity of 95.2%, which outperformed the ACR criteria.
Our prediction rule with calculator and nomogram aids in the triage of patients with suspected GCA and may decrease the need for TABx in select low-score at-risk subjects. However, misclassification remains a concern.
开发并验证一种针对疑似巨细胞动脉炎（GCA）患者的诊断预测模型。
在七所大学中心对连续接受颞动脉活检（TABx）以诊断疑似GCA的成年患者的记录进行回顾性研究。病理诊断被视为最终诊断。预测变量包括年龄、性别、新发头痛、临床颞动脉异常、颌跛行、缺血性视力丧失（VL）、复视、红细胞沉降率（ESR）、C反应蛋白（CRP）和血小板水平。对缺失数据进行多重填补。使用逻辑回归将我们的模型与非组织学的美国风湿病学会（ACR）GCA分类标准进行比较。采用10倍交叉验证和自助法技术进行内部验证。按地理位置进行外部验证。
有530份完整的TABx记录：397份GCA活检结果为阴性，133份为阳性。年龄、颌跛行、VL、血小板和log CRP是TABx阳性的统计学显著预测因素，而ESR、性别、头痛和颞动脉异常则不是。简约模型在受试者工作特征曲线（AUROC）下的交叉验证自助法面积为0.810（95%CI =0.766 - 0.854），地理外部验证的AUROC范围为0.75 - 0.85，校准p值为0.812，灵敏度为43.6%，特异性为95.2%，优于ACR标准。
我们带有计算器和列线图的预测规则有助于对疑似GCA患者进行分诊，并可能减少部分低评分高危受试者进行TABx的需求。然而，错误分类仍是一个问题。
