Background

Patients with severe coronavirus disease 2019 (COVID-19) develop acute respiratory distress and multi-system organ failure and are associated with poor prognosis and high mortality. Thus, there is an urgent need to identify early diagnostic and prognostic biomarkers to determine the risk of developing serious illness.

Methods

We retrospectively analyzed 114 patients with COVID-19 at the Jinyintan Hospital, Wuhan based on their clinical and laboratory data. Patients were categorized into severe and mild to moderate disease groups. We analyzed the potential of serological inflammation indicators in predicting the severity of COVID-19 in patients using univariate and multivariate logistic regression, receiver operating characteristic curves, and nomogram analysis. The Spearman method was used to understand the correlation between the serological biomarkers and duration of hospital stay.

Results

Patients with severe disease had reduced neutrophils and lymphocytes; severe coagulation dysfunction; altered content of biochemical factors (such as urea, lactate dehydrogenase); elevated high sensitivity C-reactive protein levels, neutrophil-lymphocyte, platelet-lymphocyte, and derived neutrophil-lymphocyte ratios, high sensitivity C-reactive protein-prealbumin ratio (HsCPAR), systemic immune-inflammation index, and high sensitivity C-reactive protein-albumin ratio (HsCAR); and low lymphocyte-monocyte ratio, prognostic nutritional index (PNI), and albumin-to-fibrinogen ratio. PNI, HsCAR, and HsCPAR correlated with the risk of severe disease. The nomogram combining the three parameters showed good discrimination with a C-index of 0.873 and reliable calibration. Moreover, HsCAR and HsCPAR correlated with duration of hospital stay.

Conclusion

Taken together, PNI, HsCAR, and HsCPAR may serve as accurate biomarkers for the prediction of disease severity in patients with COVID-19 upon admission/hospitalization.

中文翻译：

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用于预测 COVID-19 患者疾病严重程度的新型炎症血清学生物标志物

 背景

重症 2019 冠状病毒病 (COVID-19) 患者会出现急性呼吸窘迫和多系统器官衰竭，且预后不良和死亡率高。因此，迫切需要确定早期诊断和预后生物标志物，以确定患严重疾病的风险。

 方法

我们根据武汉金银潭医院 114 名 COVID-19 患者的临床和实验室数据进行回顾性分析。患者被分为重度和轻至中度疾病组。我们使用单变量和多变量逻辑回归、受试者工作特征曲线和列线图分析，分析了血清学炎症指标在预测患者 COVID-19 严重程度方面的潜力。 Spearman方法用于了解血清学生物标志物与住院时间之间的相关性。

 结果

病情严重的患者中性粒细胞和淋巴细胞减少；严重凝血功能障碍；生化因子（如尿素、乳酸脱氢酶）含量改变；高敏C反应蛋白水平、中性粒细胞-淋巴细胞、血小板-淋巴细胞和衍生中性粒细胞-淋巴细胞比率、高敏C反应蛋白-前白蛋白比率(HsCPAR)、全身免疫炎症指数和高敏C反应蛋白升高-白蛋白比率(HsCAR)；淋巴细胞-单核细胞比率、预后营养指数 (PNI) 和白蛋白与纤维蛋白原比率低。 PNI、HsCAR 和 HsCPAR 与严重疾病的风险相关。结合三个参数的列线图显示出良好的辨别力，C 指数为 0.873，并且校准可靠。此外，HsCAR 和 HsCPAR 与住院时间相关。

 结论

总而言之，PNI、HsCAR 和 HsCPAR 可以作为准确的生物标志物，用于预测 COVID-19 患者入院/住院时的疾病严重程度。