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离子动态轨迹与败血症患者的预后有关:一种潜在的类混合模型研究

Wanling Xu¹, Shurui Ren¹, Zheng Li¹

¹Department of Emergency Medicine, the First Hospital of Jilin University, 1 Xinmin Street, Changchun, Jilin 130021, China.

International immunopharmacology

|July 24, 2025

概括

败血症患者的水平变化预测死亡率. 使用动态的新早期预警系统可以识别高风险个体,以更好地管理败血症.

科学领域:

关键护理医学关键护理医学
生物化学生物化学
医疗信息学医疗信息学

背景情况:

扰乱平衡与败血症预后不佳有关.
在败血症中变化的动态模式和临床意义尚不清楚.

研究的目的:

开发一种早期预警系统,用于在败血症中发现平衡.
分析离子动态与败血症患者的临床结果之间的关联.

主要方法:

对949名败血症患者进行了回顾性队列研究.
隐性类混合模型 (LCMM) 用于在服用后5天内分析纵向血清水平.
XGBoost机器学习模型用于高风险患者识别.

主要成果:

确定了三种轨迹子类:稳定正常,持久低,快速下降.
离子动态轨迹独立预测了住院死亡率;快速下降的组具有最高的风险.
观察到度和死亡风险之间存在U形关系,安全区间为1.75-2.25毫摩尔/升.

结论:

使用LCMM对浸中动态代谢的新见解.
动态为预后分层和精确干预败血症提供了基础.
在XGBoost模型有效地识别了高风险患者的早期干预.

关键词:

的恒温稳定的恒温稳定动态模式的动态模式隐性类混合物模型模型有限制的立方线.败血症这是一种败血症.机器学习模型XGBoost机器学习模型

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