在巴塞罗那大都市区的四个波段中,COVID-19患者的个人资料:一个集群方法
Daniel Fernández1,2,3, Nuria Perez-Alvarez1,4, Gemma Molist5,6
1Department of Statistics and Operations Research (DEIO), Universitat Politècnica de Catalunya BarcelonaTech (UPC), Barcelona, Spain.
PloS one
|May 7, 2024
概括
住院的COVID-19患者表现出与疾病严重程度相关的不同情况. 识别这些患者群体可以指导量身定制的干预措施,并改善未来疫情的结果.
科学领域:
- 传染性疾病 传染性疾病
- 公共卫生 公共卫生
- 临床医学 临床医学
背景情况:
- 由于COVID-19的流行,需要了解患者的异质性,以优化护理.
- 住院患者表现出不同的反应和结果,需要细微的管理策略.
研究的目的:
- 在住院COVID-19患者中识别不同的患者个人资料.
- 探索这些个人资料与COVID-19严重程度结果之间的关联.
- 为制定有针对性的护理干预措施提供信息.
主要方法:
- 一个前性的,多中心的队列研究在COVID-19的四个浪潮中进行.
- 在使用统计方法的292多个初始变量中选择了36个关键共变量.
- 使用无监督的半参数集群方法 (KAMILA) 来分组患者.
主要成果:
- 集群确定每波2-3个不同的患者个人资料,结果有显著差异,例如住院死亡率.
- 关键的区分因素包括年龄,肥胖,并发病,氧气需求和严重程度得分.
- 在最后一波的疫苗注射中,疫苗注射影响了患者的个人资料.
结论:
- 对住院的COVID-19患者采取一种适合所有人的方法是不够的.
- 聚类促进了患者的差异化,使得量身定制的干预措施成为可能.
- 这种方法可以指导应对当前和未来疾病爆发的反应.
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