Weiss-Tessbach Matthias, Ratzinger Franz, Obermueller Markus, Burgmann Heinz, Staudinger Thomas, Robak Oliver, Schmid Monika, Roessler Bernhard, Jilma Bernd, Kussmann Manuel, Traby Ludwig
Division of Infectious Diseases and Tropical Medicine, Department of Medicine I, Medical University of Vienna, Vienna, Austria.
Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria.
Front Med (Lausanne). 2022 Oct 6;9:917606. doi: 10.3389/fmed.2022.917606. eCollection 2022.
Secondary infections in coronavirus disease 2019 (COVID-19) patients are difficult to distinguish from inflammation associated with COVID-19 and/or extracorporeal membrane oxygenation (ECMO). Therefore, highly specific and sensitive biomarkers are needed to identify patients in whom antimicrobial therapy can be safely withheld. In this prospective monocentric study, 66 COVID-19 patients admitted to the intensive care unit (ICU) for ECMO evaluation were included. A total of 46 (70%) patients with secondary infections were identified by using broad microbiological and virological panels and standardized diagnostic criteria. Various laboratory parameters including C-reactive protein (CRP), interleukin (IL)-6, procalcitonin (PCT), and IL-10 were determined at time of study inclusion. The best test performance for differentiating bacterial/fungal secondary infections and COVID-19 and/or ECMO associated inflammation was achieved by IL-10 (ROC-AUC 0.84) and a multivariant step-wise regression model including CRP, IL-6, PCT, and IL-10 (ROC-AUC 0.93). Data obtained in the present study highlights the use of IL-10 to differentiate secondary bacterial/fungal infections from COVID-19 and/or ECMO associated inflammation in severely ill COVID-19 patients.
2019冠状病毒病(COVID-19)患者的继发感染很难与COVID-19和/或体外膜肺氧合(ECMO)相关的炎症区分开来。因此,需要高度特异性和敏感性的生物标志物来识别那些可以安全停用抗菌治疗的患者。在这项前瞻性单中心研究中,纳入了66名因ECMO评估而入住重症监护病房(ICU)的COVID-19患者。通过使用广泛的微生物学和病毒学检测组以及标准化诊断标准,共识别出46名(70%)继发感染患者。在纳入研究时测定了包括C反应蛋白(CRP)、白细胞介素(IL)-6、降钙素原(PCT)和IL-10在内的各种实验室参数。IL-10(ROC曲线下面积0.84)以及包含CRP、IL-6、PCT和IL-10的多变量逐步回归模型(ROC曲线下面积0.93)在区分细菌/真菌继发感染与COVID-19和/或ECMO相关炎症方面表现出最佳的检测性能。本研究获得的数据突出了IL-10在区分重症COVID-19患者的继发细菌/真菌感染与COVID-19和/或ECMO相关炎症中的作用。