基于生物标志物的血症分类算法的时间稳定性
Emma Rademaker1,2, Rombout B E van Amstel3, Said El Bouhaddani4,5
1Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. e.rademaker-2@umcutrecht.nl.
Intensive care medicine
|December 1, 2025
概括
败血症患者的免疫特征随着时间的推移而变化不稳定,子组之间经常发生变化. 这种免疫内型不稳定性挑战了它们用于指导败血症治疗的临床应用.
科学领域:
- 关键护理医学 关键护理医学
- 免疫学 免疫学 免疫学
- 计算生物学是一种计算生物学.
背景情况:
- 败血症治疗受到宿主反应异质性的阻碍.
- 数据驱动的分析在单个时间点确定了不同的败血症亚表型 (末型).
- 这些内型的时间稳定性在很大程度上是未知的.
研究的目的:
- 评估败血症患者免疫特征的时间稳定性.
- 为了确定已识别的败血症内型是否会随着时间的推移保持一致.
- 评估动态生物标志物衍生的内型的临床实用性.
主要方法:
- 在两个ICU队列中分析了345名败血症患者的免疫生物标记数据.
- 每8小时测量30种免疫生物标志物,长达7天.
- 在初始分类和重新分类中应用隐性配置分析,通过过渡率和兰德指数 (RI) 评估时间稳定性.
主要成果:
- 在ICU入院时确定了三种不同的免疫特征:自适应性免疫激活 (A),超炎症 (B) 和减弱的炎症 (C).
- 在48小时内,个人资料患病率发生显著变化,个人资料C从39%增加到56%.
- 较差的类内凝聚力 (中位数RI65%) 表明患者没有始终保持其入院概况,频繁的类间过渡.
结论:
- 败血症患者的免疫特征在短时间内是动态和不稳定的.
- 大约三分之一的患者在每个时间点都转移了个人资料,挑战了内型的一致性.
- 观察到的不稳定性质疑目前用于败血症管理的生物标志物衍生的内型化策略的临床实用性.
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