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代谢组学方法诊断 COVID-19 继发感染(MOSAIC):一项研究方案。

A Metabolomics approach for the diagnosis Of SecondAry InfeCtions in COVID-19 (MOSAIC): a study protocol.

机构信息

Institute of Infection, Immunity and Inflammation, Sir Graeme Davis Building, University Place, Glasgow, Scotland.

Glasgow Polyomics, University of Glasgow, Garscube Campus, Glasgow, Scotland.

出版信息

BMC Infect Dis. 2021 Dec 2;21(1):1204. doi: 10.1186/s12879-021-06832-y.

Abstract

BACKGROUND

Critically ill patients with COVID-19 are at an increased risk of developing secondary bacterial infections. These are both difficult to diagnose and are associated with an increased mortality. Metabolomics may aid clinicians in diagnosing secondary bacterial infections in COVID-19 through identification and quantification of disease specific biomarkers, with the aim of identifying underlying causative microorganisms and directing antimicrobial therapy.

METHODS

This is a multi-centre prospective diagnostic observational study. Patients with COVID-19 will be recruited from critical care units in three Scottish hospitals. Three serial blood samples will be taken from patients, and an additional sample taken if a patient shows clinical or microbiological evidence of secondary infection. Samples will be analysed using LC-MS and subjected to bioinformatic processing and statistical analysis to explore the metabolite changes associated with bacterial infections in COVID-19 patients. Comparisons of the data sets will be made with standard microbiological and biochemical methods of diagnosing infection.

DISCUSSION

Metabolomics analyses may provide additional strategies for identifying secondary infections, which might permit faster initiation of specific tailored antimicrobial therapy to critically ill patients with COVID-19.

摘要

背景

COVID-19 重症患者发生继发性细菌感染的风险增加。这些感染既难以诊断,又与死亡率增加有关。代谢组学可以通过识别和量化疾病特异性生物标志物,帮助临床医生诊断 COVID-19 中的继发性细菌感染,从而确定潜在的致病微生物并指导抗菌治疗。

方法

这是一项多中心前瞻性诊断观察性研究。将从苏格兰三家医院的重症监护病房招募 COVID-19 患者。将从患者身上采集三份连续的血液样本,如果患者出现继发性感染的临床或微生物学证据,将额外采集一份样本。将使用 LC-MS 对样本进行分析,并进行生物信息处理和统计分析,以探讨与 COVID-19 患者细菌感染相关的代谢物变化。将对数据集进行比较,以与感染的标准微生物学和生化方法进行比较。

讨论

代谢组学分析可能为识别继发性感染提供额外的策略,这可能使 COVID-19 重症患者更快地开始接受有针对性的特定抗菌治疗。

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本文引用的文献

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Co-infections: potentially lethal and unexplored in COVID-19.合并感染:在新冠病毒病中可能致命且未被探索。
Lancet Microbe. 2020 May;1(1):e11. doi: 10.1016/S2666-5247(20)30009-4. Epub 2020 Apr 24.

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