Until now, few studies have been carried out to compare the laboratory findings and clinical symptoms of COVID-19 and influenza A patients. For instance, Li (12) and Natalie(9) et al. found significant differences in the counts of WBC and NE but no significant differences in AST, ALT, Cr or other indicators. Tang (13)et al. showed no difference in the counts of WBC, NE and LY, but there were significant differences in PLT, ALB and AST.
Significant differences in most laboratory findings between COVID-19 and influenza A patients at the onset of diseases were found. For example, the count of WBC and NE decreased significantly in COVID-19 patients, which is consistent with the findings of Chen’s study (14) and Guan’s study (15). On the other hand, several studies reported that WBC and NE were increased on the first day of infection in influenza A patients such as (16, 17). LY was decreased more significantly in influenza A patients, compared to COVID-19 patients. Chen (18)et al. found that patients infected with H7N9 had lymphopenia. Another study showed (19) that LY had a high specificity in the laboratory diagnosis of influenza A and could improve the detection rate of H1N1 patients relatively. Flick (20)et al. also proposed that fever (body temperature > 38℃) and changes in leukocyte parameters in influenza A patients are diagnostic criteria that can increase the sensitivity of clinical diagnosis to 86.4%. However, several studies (21–24) have suggested that leukocyte parameters may be more helpful in identifying viral or bacterial respiratory infections. In addition, the erythrocyte parameters of patients in both groups were also changed. Specifically, the RBC, HGB and HCT counts of the influenza A patients decreased more significantly than those of the COVID-19 patients, while RDW was higher than that of the COVID-19 patients. Salvagno (25) et al. reported that RDW was an important indicator of red blood cell homeostasis and impaired red blood cell production. In addition, elevated RDW was also a marker of inflammation and oxidation state. In the early stage of infection, H1N1 patients had a high frequency of fever and pneumonia (26), and thus the RDW of the influenza A patients was significantly increased. In addition, the count of PLT in influenza A and COVID-19 patients were significantly reduced. Abelleira (27) et al. found in control study of patients with influenza A that the count of PLT in the case group was lower than that in the control group. Chen (14)et al. and Guan (15) et al. both confirmed that PLT level was reduced in COVID-19 patients.
There were also significant changes in biochemical markers. Some studies have reported that patients with COVID-19(14, 28) and influenza A (29–31) have different degrees of kidney injury and liver injury for unknown reasons. The AST, ALT and GGT levels of most patients in the two groups were all higher than the upper limit of the RIs, while the TP, ALB and GLB levels showed a decreasing trend. These results were consistent with the studies of Romina (8, 27) et al. Carbon dioxide combining power (CO2-CP) represents the level of bicarbonate in plasma. The average measured value in the COVID-19 patients was lower than the lower limit of the RIs. Metabolic acidosis was reported in patients infected with SARS-CoV-2 (32) with severe disease, which directly reduced the CO2-CP. Other studies have also confirmed (8, 29) that patients with H1N1 had clinical symptoms of hypoxemia and reduced partial pressure of carbon dioxide, which were associated with reduced CO2-CP. At the same time, the K+, Na+ and Cl− levels of patients in the two groups were also decreased. Gao (8) and Chen (14) et al. mentioned that both COVID-19 and influenza A patients suffered vomiting and diarrhea, and more than one-half of influenza A patients were reported to have hypokalemia and hyponatremia (29). The specific reasons are not clear, but it is currently believed that electrolyte parameter changes may be related to the above clinical symptoms.
Although some laboratory findings were found to be different in the two diseases, their variation trends were similar. Therefore, to better classify the two diseases, this study aimed to establish a diagnostic model according to laboratory findings for COVID-19 and influenza A and then select the most representative indicators for the clinical identification of the two viral infections. Using machine learning methods, we showed that the three laboratory findings of A/G, TBIL and HCT possess predictive capacity to discriminate the two diseases (P < 0.001, 0.014 and 0.037, respectively). Studies have found that influenza A patients exhibit hypoproteinemia and hypoalbuminemia (29). Tang (33)et al. reported that the ALB level of influenza A patients was significantly lower than that of COVID-19 patients. In this study, the GLB level of influenza A patients was higher than that of COVID-19 patients. These conclusions indirectly proved that the A/G ratio in influenza A patients was decreased significantly, which was consistent with the results of this study. In addition, TBIL level in influenza A patients were significantly higher than those in COVID-19 patients. A cohort study by Zhang(34) and Tang(33) et al. confirmed that the TBIL level in influenza A patients was increased significantly. This may also be due to liver injury caused by clinical drugs, which somehow influence TBIL level. The drug oseltamivir is commonly used for the treatment of influenza A virus, which is metabolized in vitro by liver esterase. The frequent use of oseltamivir reduces the level of liver esterase and leads to drug residues in the body of patients, thereby causing liver damage(35). Hematocrit (HCT) refers to the volume ratio of sinking red blood cells to whole blood measured after centrifugal precipitation of a certain amount of whole blood treated with anticoagulant, which indirectly reflects the number and volume of red blood cells. In this study, the HCT level in the influenza A patients was lower than that in the COVID-19 patients, and the RBC count was significantly lower than that in the COVID-19 patients. It was recently confirmed that influenza viruses have the ability to agglutinate erythrocytes by binding to sialic acid receptors on host cells (36), resulting in decreased RBCs, HGB and HCT in influenza patients. Jarika (37) et al. observed the ability of antibodies against influenza A virus to bind to red blood cells through a hemagglutination inhibition test, and the results showed that a certain number of red blood cells in humans bound to antibodies against influenza A virus, which may explain why the RBC and HCT of influenza patients were lower than those of COVID-19 patients. However, a study reported (38) that COVID-19 patients also suffered from decreased coagulation function and anemia during hospitalization, which may result in a significantly lower number of RBC. Therefore, HCT is an important indicator for differentiating the two diseases.
To reduce the workload of clinicians and improve the rational utilization of medical resources, the establishment of an effective prediction model has important clinical significance. Currently, clinical decision models have been explored to validate the prognosis of SARS-CoV-2. Sun(11) et al. established a prediction model including laboratory blood tests, clinical symptoms and radiology, Fabrizio (39) et al. established a prediction model for diagnosing disease severity, and Ma (40) et al. established a prediction model for patient mortality based on laboratory findings. The model established in this paper has the following two advantages. First, the model is concise and easy to understand and use. Second, this model was mainly used to distinguish COVID-19 from influenza A based on the typical laboratory findings selected comprehensively, including hematological and biochemical parameters, which indirectly provides a good clinical basis for diagnosis.
In summary, this study established a laboratory diagnostic model for COVID-19 and influenza A patients and identified more representative indicators for the segmentation of the two diseases. This model may provide better diagnostic clues and treatment plans for clinical practice that has certain clinical practicality.