后COVID-19条件中的症状轨迹和临床亚型:系统性审查和集群分析分析
Mingzhi Hu1, Tian Song1, Zhaoyuan Gong1
1Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, China, 86 18515189525.
JMIR public health and surveillance
|July 18, 2025
概括
后COVID-19病情涉及不断演变的症状,神经问题如头随着时间的推移变得突出. 了解这些症状轨迹是有效的患者管理和未来疫情准备的关键.
科学领域:
- 传染病的流行病学传染病的流行病学.
- 长度健康研究是指长度健康研究.
- 公共卫生监督是对公共卫生的监督.
背景情况:
- 后COVID-19状态呈现复杂的,不太了解的症状影响患者的生活质量.
- 关于疾病的发展轨迹和症状的演变,存在很大的知识差距.
研究的目的:
- 为了全面描述COVID-19后疾病症状的时间演变.
- 为了更好地了解疾病轨迹,识别核心症状集群和临床表型.
主要方法:
- 从2019年12月到2024年3月进行系统的文献搜索 (PubMed,科学网,Embase).
- 对症状发病率的元分析和对症状相互关系的网络分析.
- 聚类分析以确定不同的临床亚型.
主要成果:
- 分析了108项研究 (63,771名患者);疲劳是最普遍的.
- 随着时间的推移,呼吸障碍等呼吸系统症状下降;神经系统症状,特别是头,成为主要的.
- 观察到区域差异;不同的亚型汇聚成一种慢性模式.
结论:
- 后COVID-19症状动态演变,神经问题持续存在.
- 这些发现支持了针对后感染后遗症的特定区域的监测和预警系统.
- 监测症状轨迹有助于确定未来流行病的干预点.
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