基于机器学习的预测,使用临床数据来预测患有类风湿性关节炎的患者对Janus激酶抑制剂的反应
Yeo-Jin Lee1, Gyucheol Choi2, Joongyeub Yeo3
1Department of Rheumatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Frontiers in immunology
|December 12, 2025
概括
机器学习模型准确地预测了类风湿性关节炎 (RA) 患者对氏激酶 (JAK) 抑制剂的反应. 这种个性化的方法可以改善RA患者的治疗选择和临床结果.
科学领域:
- 类风湿病学 类风湿病学
- 人工智能的人工智能
- 药物基因组学 药物基因组学
背景情况:
- 类风湿性关节炎 (RA) 是一种慢性炎症状况,治疗反应变化.
- 许多中度至重度RA患者在当前的治疗方法下无法实现缓解.
- 需要个性化治疗策略来优化RA患者的治疗结果.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测中度至重度RA患者对Janus激酶 (JAK) 抑制剂的治疗反应.
- 确定基线临床关键因素,预测对特定的JAK抑制剂 (托法西提尼布和巴里西提尼布) 的反应.
- 评估ML引导治疗选择的潜力,以改善患者的治疗结果.
主要方法:
- 从韩国风湿病学院生物治疗药物 (KOBIO) 登记册和阿桑医疗中心回顾性数据收集.
- 使用基线共变量的渐变增强机器学习模型 (XGBoost) 的培训和验证.
- 使用分层分离的独立培训和测试数据集来预测6个月的低疾病活性或缓解.
主要成果:
- 这项研究包括264名中度至重度RA患者;247人接受了托法西提尼布或巴里西提尼布.
- 6个月后,65% (托法西提尼布) 和70% (巴里西提尼布) 的患者实现了低疾病活性或缓解.
- ML模型显示出高预测性能 (托法西替尼:ROC-AUC 0.82,准确率为80%;巴里西替尼:ROC-AUC 0.88,准确率为88%).
- 关键预测因素包括总胆固醇,CRP,关节胀/疼痛,患者的整体评估和氧化联合使用.
结论:
- 机器学习模型可以准确地预测RA中JAK抑制剂的治疗反应.
- 这些模型可以帮助 RA 患者选择个性化的治疗方法.
- 以模型为指导的治疗选择可能会改善显著比例的患者的治疗结果.
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