Multivariable models to predict a diagnosis of Giant Cell Arteritis: systematic review and meta-analysis.

Mats L Junek,Iva Okaj,Sagar Patel,Angela Hu,Matthew A Jessome,Deborah Koh,Seungwon Choi,Sukhreet Atwal,John Koussiouris,Johan Pushani,Colin Stark,Farid Foroutan,Stephanie Garner,Nader Khalidi
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Abstract

OBJECTIVE Multiple models to predict a diagnosis of Giant cell arteritis (GCA) have been developed to assist clinicians. We conducted a systematic review and meta-analysis of model variables and model performance. METHODS We searched Pubmed, Embase, and the Cochrane Library from January 1990 to April 2024 for studies that used multivariable models to diagnose GCA. Study characteristics, patient characteristics, method of and criteria for diagnosis, and model details were extracted. Metaanalysis of individual signs and symptoms was performed using generic inverse variance. The Prediction model Risk of Bias Assessment tool was used to assess individual model risk of bias. Certainty of the effect estimate for each predictor was assessed using Grading of Recommendations, Assessment, Development and Evaluations framework. RESULTS We screened 2 254 abstracts and included 44 studies. A total of 15 409 patients and 4 340 diagnoses of GCA were included. Predictors with high certainty of effect and large effect size included jaw claudication, C-reactive protein elevation above 24.5 mg/dL, platelets above 400x109/L, positive temporal artery ultrasound, and presence of synovitis (predictive of a non-GCA diagnosis). Other factors classically associated with GCA including vision loss, symptoms of polymyalgia rheumatica, and headache were found to be predictive with lower certainty of effect. Models included were predominantly found to be at high risk of bias. CONCLUSION Predictors of GCA were consistent across models, however, models were of poor methodologic quality. Future models to predict a diagnosis of GCA should be constructed with improved methodologic rigor.
预测巨细胞动脉炎诊断的多变量模型:系统回顾和荟萃分析。
目的建立多种模型来预测巨细胞动脉炎(GCA)的诊断,以帮助临床医生。我们对模型变量和模型性能进行了系统回顾和元分析。方法检索Pubmed、Embase和Cochrane图书馆1990年1月至2024年4月间使用多变量模型诊断GCA的研究。提取研究特征、患者特征、诊断方法和诊断标准以及模型细节。使用通用逆方差对个体体征和症状进行荟萃分析。预测模型偏倚风险评估工具用于评估单个模型的偏倚风险。使用推荐分级、评估、发展和评估框架评估每个预测因子效果估计的确定性。结果共筛选摘要2 254篇,纳入研究44篇。共纳入15 409例患者和4 340例确诊的GCA。具有高确定性效果和大效应量的预测因子包括下颌跛行、c反应蛋白升高高于24.5 mg/dL、血小板高于400x109/L、颞动脉超声阳性和滑膜炎(非gca诊断的预测因素)。与GCA典型相关的其他因素包括视力丧失、风湿性多肌痛症状和头痛,被发现具有较低确定性的预测效果。纳入的模型主要存在高偏倚风险。结论不同模型的GCA预测因子一致,但模型的方法学质量较差。未来预测GCA诊断的模型应该以改进的方法严谨性来构建。
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