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