Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
Arthritis Res Ther. 2022 Jul 1;24(1):162. doi: 10.1186/s13075-022-02851-5.
Methotrexate is the preferred initial disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, clinically useful tools for individualized prediction of response to methotrexate treatment in patients with RA are lacking. We aimed to identify clinical predictors of response to methotrexate in patients with rheumatoid arthritis (RA) using machine learning methods.
Randomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus methotrexate were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline and 12 and 24 weeks were included. Latent class modeling of methotrexate response was performed. The least absolute shrinkage and selection operator (LASSO) and random forests methods were used to identify predictors of response.
A total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: "good responders" and "poor responders." Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA), and Health Assessment Questionnaire (HAQ) score were the top predictors of good response using LASSO (area under the curve [AUC] 0.79) and random forests (AUC 0.68) in the external validation set. DAS28-ESR ≤ 7.4, ACPA positive, and HAQ ≤ 2 provided the highest likelihood of response. Among patients with 12-week DAS28-ESR > 3.2, ≥ 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR ≤ 3.2 at 24 weeks.
We have developed and externally validated a prediction model for response to methotrexate within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cutoffs for clinical decision-making.
甲氨蝶呤是类风湿关节炎（RA）首选的初始疾病修饰抗风湿药物（DMARD）。然而，临床上缺乏用于预测 RA 患者对甲氨蝶呤治疗反应的有用工具。我们旨在使用机器学习方法确定类风湿关节炎（RA）患者对甲氨蝶呤治疗反应的临床预测因素。
通过临床研究数据请求联盟和 Vivli 全球临床研究数据中心，确定并获取了 DMARD 初治且随机分配至安慰剂加甲氨蝶呤的 RA 患者的随机对照试验（RCT）。纳入了基线和 12 周、24 周时均有可获得的 28 个关节疾病活动评分和红细胞沉降率（DAS28-ESR）的研究。对甲氨蝶呤反应进行潜在类别建模。使用最小绝对收缩和选择算子（LASSO）和随机森林方法来识别反应的预测因素。
共纳入了 4 项 RCT 的 775 名患者（平均年龄 50 岁，80%为女性）。根据 24 周时 DAS28-ESR 的变化，确定了两种不同类别的患者：“良好反应者”和“不良反应者”。使用 LASSO（曲线下面积 [AUC] 0.79）和随机森林（AUC 0.68）在外部验证集中，基线 DAS28-ESR、抗瓜氨酸化蛋白抗体（ACPA）和健康评估问卷（HAQ）评分是良好反应的最佳预测因素。DAS28-ESR≤7.4、ACPA 阳性和 HAQ≤2 提供了反应的最大可能性。在 12 周 DAS28-ESR＞3.2 的患者中，基线至 12 周时 DAS28-ESR 改善≥1 分，可预测 24 周时达到 DAS28-ESR≤3.2。
我们已经开发并在 DMARD 初治的 RA 患者中外部验证了一个在 24 周内对甲氨蝶呤反应的预测模型，为临床决策提供了加权不同的临床特征和定义的截止值。
