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在败血症中基于小组的血小板动态轨迹建模:从表型识别到预后媒介的探索
Xi Zhang1, Jundan Cai2, Chunhui Ni1
1Department of Critical Care Medicine, The affiliated People's Hospital of Jiangsu University, Jiangsu Province, Zhenjiang, China.
Frontiers in medicine
|March 13, 2026
概括
败血症患者的动态血小板计数轨迹预测死亡风险. 识别不同的模式,如持续的低水平,高水平的下降,或反弹上升,有助于个性化治疗和预后.
科学领域:
- 关键护理医学 关键护理医学
- 血液学 血液学 血液学
- 生物统计学 生物统计学
背景情况:
- 败血症是一个重大的全球卫生挑战,原因是死亡率高和住院率高.
- 败血症的异质性使治疗开发复杂化,需要先进的患者分层方法.
- 最近的研究重点是动态生物标志物,以更好地了解败血症异质性.
研究的目的:
- 在入院后的前三天内,在败血症患者中表征血小板计数变化的时间模式.
- 评估已识别的血小板计数轨迹与28天全因死亡率之间的关联.
- 探索潜在的调解途径,包括序列器官衰竭评估 (SOFA) 评分和激活的部分血栓形成时间 (APTT),将血小板动态与结果联系起来.
主要方法:
- 对280名进入江苏大学附属人民医院的败血症患者的回顾性分析.
- 应用基于组的轨迹建模 (GBTM) 来识别不同的血小板计数模式.
- 多变量Cox比例危险回归和调解分析,以评估死亡率关联和潜在机制.
主要成果:
- 确定了三个血小板计数轨迹:持续的低水平 (71.79%),高水平的下降 (20.00%),和反弹的上升 (8.21%).
- 与其他组相比,持久性低水平组的28天死亡率 (71.63%) 显着更高,生存时间更短.
- 与持久的低水平组相比,高水平下降 (HR=0.58) 和反弹上升 (HR=0.07) 轨迹与显著降低的死亡风险有关,部分由SOFA评分和APTT调解.
结论:
- 血小板计数轨迹的动态建模有效地识别出临床相关的败血症亚型.
- 这种方法为预测预后和完善败血症管理中的风险分层提供了一个强大的框架.
- 这些发现为败血症病理生理学提供了新的见解,并支持个性化治疗策略.
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