Cao Chuangjie, Xie Haitao, Guo Ruohan, Dou Chengyun, Tang Jian
Department of Pathology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, P.R. China.
Department of Clinical Laboratory Medicine, Institution of Microbiology and Infectious Diseases, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, P.R. China.
Exp Ther Med. 2025 Jan 22;29(3):56. doi: 10.3892/etm.2025.12806. eCollection 2025 Mar.
Coronavirus disease 2019 (COVID-19) and influenza A outbreaks have spread rapidly in China. It is difficult to accurately differentiate these two different respiratory tract infections on the basis of their similar early-stage symptoms and lymphocytopenia. In the present study, the age, sex and white blood cell, neutrophil, lymphocyte, monocyte and eosinophil counts, as well as the neutrophil-to-lymphocyte ratio (NLR) of 201 outpatients with confirmed COVID-19 and 246 outpatients with influenza A were investigated and compared. A receiver operating characteristic curve was drawn to determine the thresholds in distinguishing COVID-19 from influenza A Our study found that the monocyte count and NLR were significantly elevated, while the eosinophil count/percentage was higher in outpatients with COVID-19 than in those with influenza A (0.06±0.07 vs. 0.04±0.09, P=0.002; 0.95±1.12 vs. 0.56±0.95, P<0.001, respectively). Logit(P)=-1.11 + 1.29 x eosinophil percentage -12.07 x eosinophil count +1.10 x monocyte count, deduced from the eosinophil count/percentage and monocyte count, had the largest area under the curve at 0.67, with high specificity (80.1%) and a sensitivity of 47.3%. The present study demonstrated that a higher eosinophil count/percentage may be a potential biomarker to significantly differentiate early COVID-19 from influenza A.
2019冠状病毒病（COVID-19）和甲型流感疫情在中国迅速蔓延。基于这两种不同呼吸道感染相似的早期症状和淋巴细胞减少症，很难准确区分它们。在本研究中，对201例确诊COVID-19门诊患者和246例甲型流感门诊患者的年龄、性别、白细胞、中性粒细胞、淋巴细胞、单核细胞和嗜酸性粒细胞计数，以及中性粒细胞与淋巴细胞比值（NLR）进行了调查和比较。绘制了受试者工作特征曲线以确定区分COVID-19和甲型流感的阈值。我们的研究发现，COVID-19门诊患者的单核细胞计数和NLR显著升高，而嗜酸性粒细胞计数/百分比高于甲型流感门诊患者（分别为0.06±0.07对0.04±0.09，P=0.002；0.95±1.12对0.56±0.95，P<0.001）。根据嗜酸性粒细胞计数/百分比和单核细胞计数推导的Logit(P)= -1.11 + 1.29×嗜酸性粒细胞百分比 - 12.07×嗜酸性粒细胞计数 + 1.10×单核细胞计数，曲线下面积最大，为0.67，具有高特异性（80.1%）和47.3%的敏感性。本研究表明，较高的嗜酸性粒细胞计数/百分比可能是将早期COVID-19与甲型流感显著区分开来的潜在生物标志物。
