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COVID-19患者受益于雷姆迪西维尔改善生存:基于神经网络的方法

Carolina Garcia-Vidal^1,2,3, Christian Teijón-Lumbreras¹, Tommaso Francesco Aiello^1,2

¹Infectious Disease Department, Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona, Barcelona, Spain.

Journal of medical virology

|March 10, 2025

概括

这项研究开发了一个神经网络,以识别COVID-19患者最有可能从remdesivir治疗中受益. 该模型准确地预测了哪些患者在没有雷梅西维尔的情况下有更高的死亡风险,改善了治疗决策.

科学领域:

医疗人工智能医疗人工智能
传染病研究传染病研究
临床试验分析

背景情况:

雷梅西维尔对COVID-19的疗效在随机试验中显示了相互矛盾的结果.
确定最受益于雷梅西维尔的特定患者亚组对于有效治疗至关重要.

研究的目的:

开发和验证神经网络 (NN) 模型,以识别COVID-19患者,他们可以从remdesivir中获得最大的生存益处.
根据预测的生存益处对患者进行分层,以指导雷梅西维尔治疗决策.

主要方法:

一项多中心观察性研究,涉及住院患有COVID-19的成年人.
使用Ct值,淋巴细胞计数和症状持续时间从衍生队列中开发一个NN.
使用独立患者队列对NN模型进行内部和外部验证.

主要成果:

该报告确定了一小组患者 (33-41.5%) 受益于雷梅西维尔,其特点是较低的CT值,淋巴细胞数量减少和症状持续时间缩短.
在衍生队列中,在确定的高益小组内接受雷梅西维尔治疗的患者中,死亡率明显较低 (7.2%对28.8%).
外部验证证实,在高效益组内 (11%对22%) 接受雷梅西维尔治疗的患者的死亡率降低.

结论:

一个经过验证的神经网络可以有效地识别高死亡风险的COVID-19患者,而不需要remdesivir.
该模型有助于通过选择可能经历生存益处的患者来个性化remdesivir治疗.
这种方法解决了在管理COVID-19患者时需要精准医学的需求.

关键词:

根据COVID-19的情况,神经网络, remdesivir, 人工智能现象类型现象类型

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