开发一种机器学习预测模型,用于克罗恩氏病中因弗力西马布反应:整合临床特征和纵向实验室趋势
Yun Qiu1, Shixian Hu2, Kang Chao3
1Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
这项研究开发了一种机器学习模型,用于预测克罗恩病 (CD) 患者的长期因弗利西马布 (IFX) 反应. 该模型识别了关键的临床预测因子和患者群,从而实现个性化治疗策略,以获得更好的缓解结果.
科学领域:
- 胃肠病学 胃肠病学
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 使用抗瘤坏死因子α (抗TNF-α) 药物治疗克罗恩病 (CD) 的长期临床缓解仍然是一个重大的临床挑战.
- 预测患者对抗TNF-α治疗的反应对于优化治疗策略和改善治疗结果至关重要.
研究的目的:
- 开发一种基于机器学习的预测模型,用于CD患者的长期infliximab (IFX) 疗效.
- 确定预测治疗反应的关键临床特征.
- 根据纵向实验室数据将患者分为不同的集群,以进行量身定制的治疗.
主要方法:
- 利用了来自3个IBD中心的746名CD患者的数据 (2013年6月 - 2022年1月).
- 采用了三种机器学习方法与23个基线预测器来构建预测模型.
- 应用了SHapley添加式扩展 (SHAP) 进行预测重要性和隐性类混合模型 (LCMM) 进行纵向轨迹分析.
主要成果:
- XGBoost模型显示出强大的预测性能,AUC为0.91 (训练) 和0.68 (外部验证).
- 关键预测因素包括血红蛋白,白细胞,ESR,白蛋白,血小板,诊断时的年龄和蒙特利尔分类.
- 根据动态实验室测试轨迹确定了两个不同的患者群.
结论:
- 开发的预测模型有效地区分了长期响应者与非响应者.
- 确定的患者集群突出显示了在CD管理中个性化治疗方法的必要性.
- 这种方法可以帮助临床医生优化克罗恩病的IFX治疗策略.
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