预测COVID-19患者的再入院使用血液生物标志物和机器学习在医院在家计划中的医院
Maria Glòria Bonet-Papell1,2, Georgina Company-Se3, María Delgado-Capel4
1Department of Hospital at Home, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain.
Frontiers in medicine
|April 10, 2025
概括
在家医院 (HaH) 计划有效地管理了COVID-19肺炎. 高Hs-TnT水平预测了从医院转移到医院护理的患者的再入院风险.
科学领域:
- 医疗保健管理的管理
- 传染性疾病 传染性疾病
- 生物标志物研究 生物标志物研究
背景情况:
- 随着COVID-19的爆发,人们越来越需要灵活的医疗服务模式.
- 家庭医院 (HaH) 计划对于管理患者激增和COVID-19肺炎至关重要.
- 了解再接收因子对于优化 HaH 过渡至关重要.
研究的目的:
- 识别导致从哈哈再入院到常规住院的因素.
- 应用分类算法来预测再接收风险.
- 支持从医院到高水平环境中做出明智的出院决定.
主要方法:
- 在871名转移到HaH的COVID-19患者中的血液生物标志物 (IL-6,HS-TnT,CRP,费里丁,D-二次体) 的分析.
- 在成功完成 HaH 的患者和重新入院的患者之间比较生物标志物水平.
- 实施和评估用于再接收预测的分类算法 (包括SVM).
主要成果:
- 在医院入院和HAH入院之间,在未重新入院的患者中发现了显著的生物标志物差异 (IL-6,Hs-TnT,CRP,费里丁).
- 重新入院的患者在 HaH 护理期间显示出更高的CRP和Hs-TnT水平.
- 支持矢量机 (SVM) 在预测再录取的准确率达到86%.
结论:
- Hs-TnT是COVID-19患者重新入院的关键预测因素.
- 分类算法可以帮助临床医生在HaH转移的出院决策中.
- 优化 HaH 过渡可以改善患者的治疗结果和医疗保健能力.
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