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用因深度学习来个性化医疗:哪些患有败血症的重症监护病患可以从皮质类固醇治疗中受益?
Ameet Jagesar1, Louk Smalbil2, Etienne Galea1
1Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam Public Health (APH), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Journal of intensive medicine
|February 25, 2026
概括
深度学习确定了患有败血症的重症监护患者,他们可以从皮质类固醇中受益. 皮质类固醇反应者往往有严重的代谢酸和血液循环受损,不像不太生病的患者.
科学领域:
- 关键护理医学 关键护理医学
- 人工智能在医学中的应用
- 药物基因组学 药物基因组学
背景情况:
- 败血症是一种危及生命的器官功能障碍,需要重症监护.
- 皮质类固醇用于败血症的免疫调节,但由于患者异质性,临床试验结果不确切.
- 确定从皮质类固醇中受益的患者子组至关重要.
研究的目的:
- 在因果关系框架内利用深度学习来识别患有败血症的重症监护患者,其中皮质类固醇可以改善生存率.
- 开发一种可预测的模型,用于在败血症中对皮质类固醇治疗的反应.
主要方法:
- 培训了一个治疗不可知代表网络 (TARNet) 来估计皮质类固醇治疗的死亡率降低.
- 使用阿姆斯特丹UMCdb ICU数据库进行模型开发,并使用MIMIC-IV进行外部验证.
- 基于预测28天死亡率减少10%的治疗响应者进行分类.
主要成果:
- 该研究分析了2920例败血症入院病例,将245例归类为反应患者,2098例归类为不反应患者,577例归类为可能受到皮质类固醇的伤害.
- 内部验证 (AUROC 0.79) 和外部验证 (AUROC 0.71) 证明了模型的有效性.
- 响应者通常呈现出严重的代谢酸和血液循环受损.
结论:
- 在败血症中对皮质类固醇反应的特征是严重的代谢酸化和血液循环受损.
- 模型预测和医生治疗之间存在差异,突出需要改进临床评估.
- 计算决策支持系统为败血症治疗优化提供了一个有希望的途径.
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