Purpose: It is difficult to predict the prognosis of COVID-19 patients at the disease onset. This study was designed to add new biomarkers into conventional inflammatory panels to build an optimal combination panel, to better triage patients and predict their outcomes.
Patients and Methods: Biochemical parameters representing multi-organ functions, cytokines, acute-phase proteins, and other inflammatory markers were measured in COVID-19 patients on hospital admission. Receiver operating characteristic (ROC) curves, logistic regression, event-free survival (EFS), and Cox analyses were performed to screen and compare the predictive capabilities of the new panel in patients with different illness severity and outcome.
Results: This study included 120 patients with COVID-19, consisting of 32 critical, 28 severe, and 60 mild/moderate patients. Initial levels of the selected biomarkers showed a significant difference in the three groups, all of which influenced patient outcome and EFS to varying degrees. Cox proportional hazard model revealed that procalcitonin (PCT) and interleukin 10 (IL-10) were independent risk factors, while superoxide dismutase (SOD) was an independent protective factor influencing EFS. In discriminating the critical and mild patients, a panel combining PCT, IL-6, and neutrophil (NEUT) yielded the best diagnostic performance with an AUC of 0.99, the sensitivity of 90.60% and specificity of 100%. In distinguishing between severe and mild patients, SOD’s AUC of 0.89 was higher than any other single biomarker. In differentiating the critical and severe patients, the combination of white blood cell count (WBC), PCT, IL-6, IL-10, and SOD achieved the highest AUC of 0.95 with a sensitivity of 75.00% and specificity of 100%.
Conclusion: The optimal combination panel has a substantial potential to better triage COVID-19 patients on admission. Better triage of patients will benefit the rational use of medical resources.

Keywords: COVID-19, diagnostic panel, cytokine, acute-phase protein


中文翻译：

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选择最佳组合小组以更好地分流 COVID-19 住院患者

目的：难以预测 COVID-19 患者发病时的预后。本研究旨在将新的生物标志物添加到传统炎症面板中，以构建最佳组合面板，以更好地对患者进行分类并预测其结果。
患者和方法：在 COVID-19 患者入院时测量了代表多器官功能、细胞因子、急性期蛋白和其他炎症标志物的生化参数。接受者操作特征 (ROC) 曲线、逻辑回归、无事件生存 (EFS) 和 Cox 分析用于筛选和比较新面板对不同疾病严重程度和结果患者的预测能力。
结果：这项研究包括 120 名 COVID-19 患者，其中包括 32 名危重患者、28 名重症患者和 60 名轻度/中度患者。所选生物标志物的初始水平在三组中显示出显着差异，所有这些都不同程度地影响了患者的预后和 EFS。Cox比例风险模型显示，降钙素原（PCT）和白细胞介素10（IL-10）是独立的危险因素，而超氧化物歧化酶（SOD）是影响EFS的独立保护因素。在区分重症和轻度患者时，PCT、IL-6 和中性粒细胞 (NEUT) 的组合产生了最佳诊断性能，AUC 为 0.99，敏感性为 90.60%，特异性为 100%。在区分重症和轻度患者时，SOD 的 AUC 为 0.89，高于任何其他单一生物标志物。
结论：最佳组合面板具有在入院时更好地对 COVID-19 患者进行分类的巨大潜力。更好地对患者进行分诊，有利于医疗资源的合理利用。

关键词： COVID-19，诊断组，细胞因子，急性期蛋白