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在感染患者中预测tigecycline相关的不良事件:具有临床解释性的机器学习方法

Shiya Wu^1,2, Yuheng Chen³, Wenjie Fan⁴

¹School of Pharmacy, Fujian Medical University, Fuzhou, China.

Frontiers in pharmacology

|December 4, 2025

概括

这项研究创建了一个对tigecycline (TGC) 副作用的预测工具,例如肝损伤和凝血问题. 在线名谱有助于识别有风险的患者,帮助早期干预和个性化治疗策略.

科学领域:

药理学和毒理学药理学和毒理学
医疗信息学医疗信息学
临床医学临床医学

背景情况:

提格环素 (TGC) 对多药耐药性感染至关重要,但与肝毒性和凝血障碍有关.
目前对这些TGC相关不良事件的预测工具是不够的.
开发强大的预测模型对于安全的TGC利用至关重要.

研究的目的:

开发和验证在线动态诺米克图,用于预测接受tigecycline的患者的肝毒性和凝血功能障碍.
确定与这些不良事件相关的关键临床特征.
为了增强个性化tigecycline治疗的临床决策.

主要方法:

对2553名用tigecycline治疗的患者进行了回顾性分析.
使用Boruta和LASSO进行特征选择.
评估了七个具有交叉验证,ROC和校准曲线的机器学习模型.
沙普利添加式解释 (SHAP) 对于在线动态名录的解释性和开发.

主要成果:

后勤回归实现肝毒性AUC为0.800,凝血功能障碍AUC为0.755.
肝损伤的危险因素:长期治疗,高剂量,ICU入院,HBV,升高的LDH和GGT.
凝血功能障碍的危险因素:治疗时间长,ICU入院,Cr升高,败血症,败血性休克.
梅洛西西林和较高的红细胞水平显示有保护作用.

结论:

一个在线动态名录成功开发,具有良好的预测性能.
诺米图有助于早期识别和分层对TGC诱导不良事件敏感的患者的风险分层.
该工具支持个性化治疗计划,以更安全地使用tigecycline.

关键词:

凝血障碍凝血障碍肝脏损伤肝脏损伤是什么?机器学习是机器学习.风险预测模型的风险预测模型这种药物是tigecycline.

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