使用机器学习算法优化高成本生物药物治疗在全国一群患有炎症性肠道疾病的患者中
Jason K Hou1,2,3, Tiffany M Tang4,5, Shubhada Sansgiry2,3
1Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX 77030, United States.
JAMIA open
|December 8, 2025
概括
这项研究评估了克罗恩病患者的因弗利西马布 (IFX) 生物类似开关.
科学领域:
- 胃肠病学 胃肠病学
- 药理学 药理学是指药理学的学科.
- 医疗信息学 医疗信息学
背景情况:
- 像Infliximab (IFX) 这样的生物疗法对于管理克罗恩病 (CD) 和性结肠炎 (UC) 至关重要.
- 引入IFX生物仿制药需要评估其实际的有效性和安全性.
- 预测模型可以加强临床决策支持,在治疗过渡期间管理炎症性肠道疾病.
研究的目的:
- 在美国退伍军人中评估非医疗性因弗利克西马布 (IFX) 生物类似开关的现实世界的安全性和有效性.
- 开发和比较统计和机器学习 (ML) 模型,用于预测IFX生物类似药切换后的不良临床事件.
- 确定IFX生物类似转换患者疾病爆发的关键预测因素.
主要方法:
- 以退伍军人卫生管理局 (2017-2020) 的数据为依据的回顾性队列研究.
- 包括2529名维持IFX的CD或UC患者,比较发病者与生物类似药切换器.
- 使用传统和ML方法开发和评估分类和生存模型.
主要成果:
- 生物类似开关在生存模型中对疾病爆发的预测价值很小.
- 客观的实验室数据显示了最高的预测验证.
- 随机森林 (RF +) 模型的表现优于其他方法,以先前的爆发和医疗保健遭遇为关键预测因素;血红蛋白是实验室的顶级预测因素.
结论:
- 预测模型,特别是RF+,可以帮助识别IFX生物相似开关后患有爆发风险较高的患者.
- 这些模型可能有助于优化CD和UC患者的生物治疗.
- 客观的实验室数据和患者病史对于预测IFX治疗IBD患者的结果至关重要.
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