使用机器学习在现实世界ICU队列中对急性和慢性肝功能衰竭患者进行无偏的集群
Mengyi Zhang1,2, Fanpu Ji2,3,4,5,6, Jian Zu7
1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
Nature communications
|January 20, 2026
概括
无监督集群确定了两个不同的急性和慢性肝衰竭患者组,死亡风险明显不同. 酸平衡变量是关键驱动因素,突出了代谢调节的重要因素.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 计算生物学 计算生物学
- 临界护理医学 临界护理医学
背景情况:
- 急性至慢性肝衰竭 (ACLF) 缺乏标准化的定义,阻碍了有效的风险分层和管理.
- 现有的分类方法可能无法完全捕捉ACLF患者群体内的异质性.
研究的目的:
- 根据不偏见的数据分析,应用无监督的集群来分层ACLF患者.
- 确定驱动患者分层的关键因素及其临床影响,特别是死亡风险.
- 在独立的队列中验证已识别的集群和驱动程序.
主要方法:
- 在1,256名ACLF患者的队列中,利用了无监督的集群,包括围绕Medoids进行分区与模两可的集群和非负矩阵因子化 (Lee的算法).
- 评估了多个算法模型,以确定最佳的集群方法.
- 确定了关键的集群驱动因素,并评估了在确定集群中的30天死亡率.
- 在外部,不补偿性肝硬化和EASL-CLIF联盟ACLF队列中验证的发现.
主要成果:
- 确定了两个不同的ACLF患者群,30天死亡率显著不同 (70.35%与26.06%).
- 酸平衡变量 (二碳酸盐,pH,过多,乳酸盐,离子间隙) 成为集群差异化的主要驱动因素.
- 外部验证队列证实了一致的集群分布和死亡率差异,强调了代谢障碍的作用.
结论:
- 无偏见的集群成功地将ACLF患者分为不同的群体,具有不同的死亡风险.
- 酸平衡和代谢调节是ACLF结果的关键决定因素.
- 这种方法提供了更细致的理解和在ACLF管理中改善风险分层的潜力.
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