van den Dikkenberg M, Kuijper T M, Kok M R, Lopes Barreto D, Weel-Koenders Aeam
Department of Rheumatology, Maasstad Hospital, Rotterdam, The Netherlands.
Erasmus School of Health Policy and Management, Erasmus University, Rotterdam, The Netherlands.
Scand J Rheumatol. 2025 Mar;54(2):98-105. doi: 10.1080/03009742.2024.2406611. Epub 2024 Oct 30.
Currently, expedited by the coronavirus disease 2019 pandemic, there is high demand for allocating patients in a state of low disease activity to telehealth, ideally based on remote measurements. This cross-sectional study assesses the discriminative accuracy of the Rheumatoid Arthritis Impact of Disease (RAID) questionnaire regarding high and low disease activity. Furthermore, we aimed to optimize this classification, developing a remote triage score based on RAID and other patient-reported outcome measures (PROMs).
Data were acquired from an outpatient clinic cohort of chronic rheumatoid arthritis patients at a large trainee hospital in the Netherlands. Patients were divided into high and low disease categories, based on 28-joint Disease Activity Score-C-reactive protein. Least absolute shrinkage and selection operator logistic regression were performed, including RAID item scores and other PROMs. Receiver operating characteristics curves and areas under the curve (AUCs) were obtained, and cut-off scores were based on predefined criteria of 90% and 95% sensitivity.
In total, 278 patients were analysed, of whom 77.2% were identified as having low disease activity. RAID results correlated with DAS28-CRP, showing good performance. The regression model included the RAID items pain and functional disability assessment, and the self-reported swollen joint count (SR-SJC). With an AUC of 0.88 (95% confidence interval 0.84-0.92), this model performed better than the RAID total score.
A remote triage score based on a composite score of pain, functional disability assessment, and SR-SJC can detect a sufficient proportion of patients with low disease activity who can be allocated to remote consultations.
目前，在2019年冠状病毒病大流行的推动下，对于将疾病活动度低的患者分配到远程医疗服务中的需求很高，理想情况下是基于远程测量。这项横断面研究评估了类风湿关节炎疾病影响（RAID）问卷对于高疾病活动度和低疾病活动度的判别准确性。此外，我们旨在优化这种分类，基于RAID和其他患者报告结局测量指标（PROMs）制定一个远程分诊评分。
数据来自荷兰一家大型实习医院的慢性类风湿关节炎门诊队列患者。根据28个关节疾病活动评分 - C反应蛋白，将患者分为高疾病活动度组和低疾病活动度组。进行了最小绝对收缩和选择算子逻辑回归分析，纳入了RAID项目得分和其他PROMs。获得了受试者工作特征曲线和曲线下面积（AUCs），并根据90%和95%敏感性的预定义标准确定了临界值。
总共分析了278例患者，其中77.2%被确定为疾病活动度低。RAID结果与DAS28 - CRP相关，表现良好。回归模型纳入了RAID项目中的疼痛和功能残疾评估以及自我报告的肿胀关节计数（SR - SJC）。该模型的AUC为0.88（95%置信区间0.84 - 0.92），其表现优于RAID总分。
基于疼痛、功能残疾评估和SR - SJC综合评分的远程分诊评分能够检测出足够比例的疾病活动度低的患者，这些患者可被分配到远程会诊中。
