预测治疗效应的异质性到膝关节骨关节炎疼痛和症状的跨直流刺激:一种机器学习方法
Chiyoung Lee1, Heewon Kim2, Seoyoung Kim2
1The University of Arizona College of Nursing, Tucson, AZ, USA.
The Clinical journal of pain
|February 2, 2026
概括
预测膝关节骨关节炎 (KOA) 对跨直流刺激 (tDCS) 的治疗反应是可能的. 基线疼痛灾难化,有条件疼痛调节和压力疼痛值确定了可能从tDCS中受益的个体.
科学领域:
- 神经学 神经学
- 疼痛管理 疼痛管理
- 生物医学工程 生物医学工程
背景情况:
- 膝关节关节炎 (KOA) 是导致残疾的主要原因.
- 跨直流刺激 (tDCS) 在KOA疼痛管理方面表现有前途.
- 在KOA中对tDCS的治疗反应是高度可变的.
研究的目的:
- 在KOA患者中识别tDCS对各种治疗效果的预测因子.
- 根据对tDCS的预测反应对患者进行分层.
- 优化tDCS处理分配和参数定制.
主要方法:
- 一个随机临床试验 (n=60) 的二次分析,每天进行15次tDCS会议.
- 基于小组的轨迹建模以根据疼痛模式对参与者进行分类.
- 在基线数据上训练的机器学习分类器 (MLP,ElasticNet,RF,GBDT).
- 使用f回归,相互信息和SHAP分析进行特征选择.
主要成果:
- 确定了两个不同的响应组:高响应者 (n=28) 和低响应者 (n=32).
- 响应的关键预测因素包括疼痛灾难化,条件疼痛调制 (CPM) 和压力疼痛值 (PPTh).
- 较高的疼痛灾难化,较低的CPM和较低的PPTh与较差的tDCS反应有关.
结论:
- 基线疼痛灾难化,CPM和PPTh可以预测KOA中的tDCS疗效.
- 这些因素可以指导针对性治疗的患者分层.
- 个性化tDCS协议可以根据个体预测器配置文件开发.
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