Sun Zhijian, Wang Feiying, Chen Jie, Liu Xinlei, Sun Jiao, Sui Yameng, Zhang Xiaojie, Shu Qiang
Department of Rheumatology, The Second Hospital of Shandong University, Jinan, China.
Department of Hematology, Women and Children's Hospital, Qingdao, China.
Ann Transl Med. 2022 Dec;10(24):1365. doi: 10.21037/atm-22-5791.
Rheumatoid arthritis (RA) is an autoinflammatory disease, its core treatment principle is to achieve remission as soon as possible. There is no good prediction model that can accurately predict the remission rate of patients to choose a good treatment scheme. Here, we aimed to verify the prognostic value of some inflammatory indicators in RA and establish a prediction model to predict the remission rate after treatment.
A total of 223 patients were enrolled at Qilu Hospital from June 2014 to June 2020. Baseline clinical data were collected and plasma was obtained to detect the inflammatory indicators. All patients were treated with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). All patients were followed up and were recorded the time to reach the disease activity score-28 with erythrocyte sedimentation rate (DAS28-ESR) of <2.6. A total of 156 patients were randomly assigned to the development cohort, and 67 patients were assigned to the validation cohort. Inflammatory indicators in plasma were detected by enzyme-linked immunosorbent assay (ELISA). The predictive factors were screeded by using least absolute shrinkage and selection operator (LASSO) and Cox regression. The model was created and verified by using the standard method. A total of 6 independent risk factors were analyzed to construct a nomogram to predict the remission rate in 3, 6 and 12 months.
The remission rates after treatment in 3, 6 and 12 months were 38.76%, 58.91%, and 81.40%, respectively. Patient age, C-reactive protein (CRP), interleukin (IL)-6, galectin-9 (Gal-9), health assessment questionnaire (HAQ), and DAS28-ESR were included in the prognostic model to predict the remission rate. The resulting model had good discrimination ability in both the development cohort (C-index, 0.729) and the validation cohort (C-index, 0.710). Time-dependent receiver operating characteristic (ROC) curve, calibration analysis, and decision curve analysis (DCA) showed that the model has significant discriminant power and clinical practicability in predicting the remission rate.
We established a new predictive model and validated it. The model can predict the remission rate in 3, 6 and 12 months after receiving csDMARDs treatment. By using this model, we can facilitate the identification of high-risk patients early and intervene with them as soon as possible.
类风湿关节炎（RA）是一种自身炎症性疾病，其核心治疗原则是尽快实现病情缓解。目前尚无能够准确预测患者缓解率以选择合适治疗方案的良好预测模型。在此，我们旨在验证一些炎症指标在RA中的预后价值，并建立一个预测模型来预测治疗后的缓解率。
2014年6月至2020年6月期间，共有223例患者在齐鲁医院入组。收集基线临床数据并获取血浆以检测炎症指标。所有患者均接受传统合成改善病情抗风湿药物（csDMARDs）治疗。对所有患者进行随访，并记录达到疾病活动评分28（DAS28）且红细胞沉降率（ESR）<2.6的时间。共有156例患者被随机分配至开发队列，67例患者被分配至验证队列。采用酶联免疫吸附测定（ELISA）法检测血浆中的炎症指标。通过使用最小绝对收缩和选择算子（LASSO）及Cox回归筛选预测因素。采用标准方法创建并验证模型。分析6个独立危险因素以构建列线图，预测3、6和12个月后的缓解率。
治疗后3、6和12个月的缓解率分别为38.76%、58.91%和81.40%。患者年龄、C反应蛋白（CRP）、白细胞介素（IL）-6、半乳糖凝集素-9（Gal-9）、健康评估问卷（HAQ）和DAS28-ESR被纳入预后模型以预测缓解率。所得模型在开发队列（C指数，0.729）和验证队列（C指数，0.710）中均具有良好的区分能力。时间依赖性受试者工作特征（ROC）曲线、校准分析和决策曲线分析（DCA）表明，该模型在预测缓解率方面具有显著的判别力和临床实用性。
我们建立并验证了一个新的预测模型。该模型可以预测接受csDMARDs治疗后3、6和12个月的缓解率。通过使用该模型，我们可以尽早识别高危患者并尽快对其进行干预。
