长期COVID纵向症状在国家社区基队列中的负担集群
Yanhan Shen1,2, Zach Shahn1,2, McKaylee M Robertson1,2
1Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, USA.
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
长期COVID呈现出多样化的症状模式,使分类复杂化. 这项研究使用社区数据确定了不同的集群和表型,突出了个性化长期COVID管理策略的必要性.
科学领域:
- 纵向队列研究是指纵向队列研究.
- 临床流行病学临床流行病学
- 在健康领域的数据科学.
背景情况:
- 长期COVID在临床上是异质的,症状轨迹使分类复杂化.
- 以前的研究经常使用电子健康记录,冒着不充分报告症状的风险.
- 这项研究利用了纵向的,基于社区的数据来进行强有力的症状集群特征.
研究的目的:
- 描述症状集群,并确定长期COVID患者的危险因素.
- 为了解决依赖电子健康记录数据的先前研究的局限性.
- 为量身定制的长期COVID管理和风险分层提供基础.
主要方法:
- 分析了从2020年12月到2022年12月的CHASING COVID队列数据,随访时间≥12个月.
- 在多个归算数据集中使用纵向K-means对自我报告的症状进行集群.
- 使用多变量后勤回归来评估人口统计,临床和社会因素与集群成员的关联.
主要成果:
- 确定了三个长期COVID集群:最高,中等和最低的症状负担.
- 负担最高的群体 (中位数为6个症状) 包括疲劳,注意力集中困难和炼后的不适.
- 年龄较大,女性性别,心理健康障碍和免疫缺陷与更高的症状负担有关.
结论:
- 独特的长期COVID集群和表型突出显著的异质性.
- 这些发现支持针对长期COVID的量身定制管理和风险分层方法的开发.
- 基于社区的纵向数据为长期COVID症状学提供了有价值的视角.
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