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.

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类风湿关节炎患者对甲氨蝶呤反应的临床预测因子：使用临床试验数据的机器学习方法

甲氨蝶呤是类风湿关节炎 (RA) 首选的初始疾病缓解抗风湿药 (DMARD)。然而，缺乏用于个体化预测 RA 患者对甲氨蝶呤治疗反应的临床有用工具。我们旨在使用机器学习方法确定类风湿关节炎 (RA) 患者对甲氨蝶呤反应的临床预测因子。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访问了未接受 DMARD 并随机接受安慰剂加甲氨蝶呤的 RA 患者的随机临床试验 (RCT)。包括在基线和 12 周和 24 周时具有 28 关节计数和红细胞沉降率 (DAS28-ESR) 的可用疾病活动评分的研究。进行了甲氨蝶呤反应的潜在类别建模。使用最小绝对收缩和选择算子 (LASSO) 和随机森林方法来确定响应的预测因子。共纳入来自 4 项 RCT 的 775 名患者（平均年龄 50 岁，80% 为女性）。根据 24 周内 DAS28-ESR 的变化确定了两类不同的患者：“反应良好”和“反应不佳”。基线 DAS28-ESR、抗瓜氨酸蛋白抗体 (ACPA) 和健康评估问卷 (HAQ) 评分是使用 LASSO（曲线下面积 [AUC] 0.79）和随机森林 (AUC 0.68) 获得良好反应的主要预测因素。外部验证集。DAS28-ESR ≤ 7.4、ACPA 阳性和 HAQ ≤ 2 提供了最高的响应可能性。在 12 周 DAS28-ESR > 3.2 的患者中，DAS28-ESR 基线到 12 周的 ≥ 1 点改善预示着在 24 周达到 DAS28-ESR ≤ 3.2。我们已经开发并外部验证了一个预测模型，用于预测未接受 DMARD 的 RA 患者在 24 周内对甲氨蝶呤的反应，为临床决策提供不同权重的临床特征和定义的临界值。