深度学习衍生的生存败血症运动风险组中的死亡率和抗生素时间:一个多中心研究
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Ben J Gross1, Allison Donahue2, James S Ford2
1Division of Biomedical Informatics, University of California , La Jolla, San Diego, USA.
Critical care (London, England)
|July 14, 2025
概括
深度学习模型对败血症患者进行了分层分层,揭示了低风险个体的死亡率与抗生素时间不相关. 然而,可能患有败血症的患者在一小时内从抗生素中受益.
科学领域:
- 医学研究 医学研究
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 目前的生存性败血症运动 (SSC) 准则建议根据败血症概率和休克存在的抗生素时间.
- 缺乏基于这些建议的客观患者分层和结果分析.
研究的目的:
- 使用深度学习模型客观地分层败血症患者.
- 在风险分层组内根据抗生素时间分析患者的结果.
主要方法:
- 观察性队列研究,使用两个卫生系统的2016-2024年的数据.
- 两种深度学习模型根据败血症概率和休克可能性将患者分为四个风险组.
- 主要结局是短期死亡率 (住院死亡率或住院过渡).
主要成果:
- 分析了34,087名可能患有败血症的成年患者.
- 风险组的死亡率在不同风险组之间有很大差异.
- 患有可能的败血症和低冲击风险的患者在1或3小时内服用抗生素时与晚些时候服用抗生素时的死亡率相似.
- 患有可能败血症的患者,当抗生素在选后1小时内被给予时,死亡率较低.
结论:
- 对于低风险败血症患者 (可能的败血症,不太可能的休克),可能可以接受更温和的抗生素给药时间.
- 及时 (在1小时内) 服用抗生素与可能患有败血症的患者的死亡率降低有关,不论是否有冲击风险.
- 需要进一步的前性研究来证实这些发现,并完善抗生素时间指南.
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