PDF(Pubmed)
Abstract：
Background: Disease-modifying antirheumatic drugs (bDMARDs) have shown efficacy in treating Rheumatoid Arthritis (RA). Predicting treatment outcomes for RA is crucial as approximately 30% of patients do not respond to bDMARDs and only half achieve a sustained response. This study aims to leverage machine learning to predict both initial response at 6 months and sustained response at 12 months using baseline clinical data. Methods: Baseline clinical data were collected from 154 RA patients treated at the University Hospital in Erlangen, Germany. Five machine learning models were compared: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), Support Vector Machines (SVM), and Random Forest. Nested cross-validation was employed to ensure robustness and avoid overfitting, integrating hyperparameter tuning within its process. Results: XGBoost achieved the highest accuracy for predicting initial response (AUC-ROC of 0.91), while AdaBoost was the most effective for sustained response (AUC-ROC of 0.84). Key predictors included the Disease Activity Score-28 using erythrocyte sedimentation rate (DAS28-ESR), with higher scores at baseline associated with lower response chances at 6 and 12 months. Shapley additive explanations (SHAP) identified the most important baseline features and visualized their directional effects on treatment response and sustained response. Conclusions: These findings can enhance RA treatment plans and support clinical decision-making, ultimately improving patient outcomes by predicting response before starting medication.
摘要：
背景：改善疾病的抗风湿药（bDMARDs）已显示出治疗类风湿关节炎（RA）的功效。预测RA的治疗结果至关重要，因为大约30％的患者对bDMARD没有反应，只有一半的患者达到持续反应。这项研究旨在利用机器学习来预测6个月时的初始反应和12个月时的持续反应。方法：收集在埃尔兰根大学医院接受治疗的154例RA患者的基线临床资料，德国。比较了五种机器学习模型：极限梯度提升(XGBoost)、自适应提升(AdaBoost)，K-最近邻(KNN),支持向量机(SVM)和随机森林。采用嵌套交叉验证来确保鲁棒性并避免过拟合，在其过程中集成超参数调整。结果：XGBoost预测初始反应的准确性最高（AUC-ROC为0.91），而AdaBoost是最有效的持续反应（AUC-ROC为0.84）。关键预测因子包括使用红细胞沉降率（DAS28-ESR）的疾病活动评分-28，基线评分较高与6个月和12个月时较低的反应机会相关。Shapley加性解释（SHAP）确定了最重要的基线特征，并可视化了它们对治疗反应和持续反应的定向作用。结论：这些发现可以增强RA治疗计划并支持临床决策。通过在开始用药前预测反应，最终改善患者的预后。
