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一个通过原型 PCR 面板从全血中测量的 9-mRNA 标志物可预测重症 COVID-19 患者入院 28 天的死亡率。

A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients.

机构信息

Joint Research Unit HCL-bioMérieux, Equipe d'Accueil (EA) 7426 "Pathophysiology of Injury-Induced Immunosuppression" (Université Claude Bernard Lyon 1 - Hospices Civils de Lyon, bioMérieux), Lyon, France.

Open Innovation and Partnerships (OIP), bioMérieux Société Anonyme (S.A.), Lyon, France.

出版信息

Front Immunol. 2022 Nov 1;13:1022750. doi: 10.3389/fimmu.2022.1022750. eCollection 2022.

DOI:10.3389/fimmu.2022.1022750
PMID:36389738
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9665875/
Abstract

Immune responses affiliated with COVID-19 severity have been characterized and associated with deleterious outcomes. These approaches were mainly based on research tools not usable in routine clinical practice at the bedside. We observed that a multiplex transcriptomic panel prototype termed Immune Profiling Panel (IPP) could capture the dysregulation of immune responses of ICU COVID-19 patients at admission. Nine transcripts were associated with mortality in univariate analysis and this 9-mRNA signature remained significantly associated with mortality in a multivariate analysis that included age, SOFA and Charlson scores. Using a machine learning model with these 9 mRNA, we could predict the 28-day survival status with an Area Under the Receiver Operating Curve (AUROC) of 0.764. Interestingly, adding patients' age to the model resulted in increased performance to predict the 28-day mortality (AUROC reaching 0.839). This prototype IPP demonstrated that such a tool, upon clinical/analytical validation and clearance by regulatory agencies could be used in clinical routine settings to quickly identify patients with higher risk of death requiring thus early aggressive intensive care.

摘要

与 COVID-19 严重程度相关的免疫反应已被描述,并与不良结局相关。这些方法主要基于在床边常规临床实践中不可用的研究工具。我们观察到,一种称为免疫分析面板 (IPP) 的多重转录组学原型可以捕捉到 ICU COVID-19 患者入院时免疫反应的失调。在单变量分析中,有 9 个转录本与死亡率相关,这个 9-mRNA 特征在包括年龄、SOFA 和 Charlson 评分在内的多变量分析中仍然与死亡率显著相关。使用包含这 9 个 mRNA 的机器学习模型,我们可以预测 28 天的生存状态,接收器操作特征曲线 (AUROC) 的面积为 0.764。有趣的是,将患者年龄添加到模型中可以提高预测 28 天死亡率的性能(AUROC 达到 0.839)。该原型 IPP 表明,这种工具在经过临床/分析验证并获得监管机构批准后,可用于临床常规环境中,以快速识别死亡风险较高的患者,从而需要早期积极的重症监护。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/a48082244aa6/fimmu-13-1022750-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/70fd63a792cf/fimmu-13-1022750-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/566a5706b43d/fimmu-13-1022750-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/498f2e94ac00/fimmu-13-1022750-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/a48082244aa6/fimmu-13-1022750-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/70fd63a792cf/fimmu-13-1022750-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/566a5706b43d/fimmu-13-1022750-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/498f2e94ac00/fimmu-13-1022750-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30ac/9665875/a48082244aa6/fimmu-13-1022750-g004.jpg

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Cell. 2022 Mar 3;185(5):916-938.e58. doi: 10.1016/j.cell.2022.01.012. Epub 2022 Jan 21.
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