基于机器学习的COVID-19急性呼吸窘迫综合征表型和临床结果:系统性审查
Eric Daniel Tenda1, Joshua Henrina1, Jistrani Samosir2
1Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Heliyon
|June 27, 2023
概括
与COVID-19相关的急性呼吸系统应急综合征 (CARDS) 呈现出不同的亚现型. 这些亚表型显著影响患者的治疗结果和治疗反应,强调在临床试验中需要标准化表型.
科学领域:
- 关键护理医学 关键护理医学
- 肺部病理学 肺部病理学
- 传染性疾病 传染性疾病
背景情况:
- 与COVID-19相关的急性呼吸窘迫综合征 (CARDS) 可能与典型的ARDS不同.
- 存在不同的ARDS表型,但CARDS亚表型及其临床影响尚不清楚.
研究的目的:
- 系统地审查关于CARDS亚型类型的证据.
- 为了确定CARDS亚型和临床结果之间的关联.
主要方法:
- 系统审查PubMed,EBSCO主机和Web of Science的研究 (截至2022年7月1日).
- 包括四项研究,其中包括1776名CARDS患者.
- 分析已识别的CARDS亚表型和相关结果 (死亡率,无呼吸器日等). ) 的情况.
主要成果:
- 三项研究使用隐性类分析 (LCA) 来识别亚表型 (SP).
- SPs与通风参数的变化,炎症水平 (高或低炎症) 和并发症有关.
- 所有研究都确定了与较差结果相关的SP,包括死亡率和器官损伤.
- 在高炎症性和低炎症性SPs之间观察到对皮质类固醇的不同反应.
结论:
- 表型化对于理解CARDS,指导管理和改善预后至关重要.
- 为了一致性和可比性,对CARDS表型的共识方法是必要的.
- 未来的表型分层随机临床试验应该等待关于表型化方法的共识.
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