韩国队列中SARS-CoV-2的后急性后果的静态和动态评分系统
Gyeongdeok Seo1, Hyejin Joo2, Kyungwoo Song1
1Department of Statistics and Data Science, Yonsei University, Seoul, 03722, Republic of Korea.
概括
研究人员开发了数据驱动的评分系统,以定义和跟踪SARS-CoV-2感染 (PASC,长期COVID) 后的持续症状. 这些工具有助于识别和监测PASC,帮助临床研究和实践.
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:
- 在SARS-CoV-2感染后持续的症状,称为长期COVID或PASC,代表了重大的公共卫生挑战.
- 准确识别和监测PASC对于有效的临床管理和研究至关重要.
研究的目的:
- 开发一个数据驱动的PASC定义.
- 创建静态和动态的评分系统,用于识别和监测PASC.
- 为PASC评估建立一个定量框架.
主要方法:
- 一个多中心,前性-后性观察队列研究,涉及12个韩国机构.
- 招募了8,761名成年人 (7,208名感染者,1,553名对照),其中4,668人符合分析标准.
- 利用参与者报告的症状,调查和实验室数据来确定关键的PASC指标,特别是厌氧/衰老和疲劳.
主要成果:
- 一个静态的PASC分数,值为13,在24个月内确定了19%的感染参与者和4%的对照人为PASC阳性.
- 一个动态评分,跟踪六个月间隔,揭示了症状负担在感染后0-5个月达到顶峰,随后下降.
- 使用相同的门值,动态得分将33%的感染参与者归类为PASC阳性,突出时间症状波动.
结论:
- 根据确定的症状和开发的评分系统,建立了PASC的定量定义.
- 引入了一个动态评分框架,以随着时间的推移监测PASC,并考虑到症状的变化.
- 开发的工具支持未来的临床研究和实践,用于管理长期的COVID.
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