基于病毒载量和高分辨率微生物组概况的综合性病毒-细菌签名预测了COVID-19死亡率
Zi-Lun Lai1, Yang-Di Su1, Yi-Yao Hsu1
1Department of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
概括
将鼻 (NP) 微生物群与病毒载荷和临床数据相结合,可以改善COVID-19死亡率预测. 这种综合性方法为患者风险分层提供了一个有希望的,非侵入性的工具.
科学领域:
- 微生物学 微生物学
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 鼻 (NP) 微生物群在SARS-CoV-2感染和预测COVID-19结果中的作用需要进一步定义.
- 在SARS-CoV-2感染期间研究NP微生物群在调节宿主免疫反应方面的潜力至关重要.
研究的目的:
- 确定将NP微生物资料与病毒学和临床数据相结合是否可以提高COVID-19患者的死亡率预测.
- 开发一个强大的框架,以使用多omics方法预测COVID-19死亡率.
主要方法:
- 分析了81名COVID-19患者和70名对照患者的鼻拭子,使用16S rRNA测序进行微生物组概况.
- 开发机器学习模型,整合微生物种群,病毒载量和临床元数据,用于死亡率预测.
主要成果:
- 高病毒载量是死亡率最强的独立预测因素 (OR:3.80).
- SARS-CoV-2 感染显著降低了微生物社区的均性.
- 一个集成的机器学习模型 (病毒载量,年龄,微生物种群) 实现了高预测死亡率的准确性 (AUC = 0.9046),单独超过了临床数据.
结论:
- 结合NP微生物群和病毒载量的综合方法为预测COVID-19死亡率提供了强大的框架.
- 该策略为改善COVID-19患者临床风险分层提供了一个有希望的非侵入性工具.
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