在克罗恩病患者中监测度和优化剂量:基于机器学习的共变组合模型
Yuewen Chen1,2, Shoutian Zhang1,2, Si Chen3
1Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
机器学习可以准确地预测克罗恩病患者的Infliximab水平,从而实现个性化的剂量. 这种方法通过实时优化药物度来提高治疗效率和安全性.
科学领域:
- 药理学和治疗学 药理学和治疗学
- 生物医学数据科学 生物医学数据科学
- 免疫学 免疫学 免疫学
背景情况:
- 伊弗利克西马布 (IFX) 的最低度与克罗恩病 (CD) 的疗效和毒性有关.
- 目前的治疗药物监测导致剂量调整延迟,结果不理想.
- 需要对IFX水平进行实时预测,以实现个性化剂量.
研究的目的:
- 开发和验证一个机器学习 (ML) 框架,用于预测IFX低谷度.
- 为了实现IFX水平的实时预测,在注射前的治疗点预测IFX水平.
- 为CD患者优化个性化的IFX剂量.
主要方法:
- 分析了274名中国CD患者 (460个样本) 的回顾性队列.
- 评估了9个非线性ML算法,其中4个最佳模型集成到软投票组合分类器中.
- SHAP分析确定了关键预测因素,剂量调整策略进行了潜在的外部验证.
主要成果:
- 总体模型在测试组中实现了强大的区分 (AUC=0.829,精度=0.826) 和强大的通用性 (外部验证AUC=0.800).
- 关键预测因素包括抗药抗体 (ADA,22.8%) 和纤维素原 (Fg,21.4%).
- 该模型建议采取谨慎的剂量策略,优先考虑治疗窗口内的最小有效剂量.
结论:
- 为动态IFX度监测开发并验证了一种可解释的整体ML模型.
- 这种基于ML的方法优化了CD患者个性化的IFX剂量.
- 该框架有可能提高IFX治疗的治疗效率和安全性.
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