  Background: Coronavirus disease 2019 (COVID-19) and Influenza A are common
disease caused by viral infection. The clinical symptoms and transmission
routes of the two diseases are similar. However, there are no relevant studies
on laboratory diagnostic models to discriminate COVID-19 and influenza A. This
study aims at establishing a signature of laboratory findings to tell patients
with COVID-19 apart from those with influenza A perfectly. Materials: In this
study, 56 COVID-19 patients and 54 influenza A patients were included.
Laboratory findings, epidemiological characteristics and demographic data were
obtained from electronic medical record databases. Elastic network models,
followed by a stepwise logistic regression model were implemented to identify
indicators capable of discriminating COVID-19 and influenza A. A nomogram is
diagramed to show the resulting discriminative model. Results: The majority of
hematological and biochemical parameters in COVID-19 patients were
significantly different from those in influenza A patients. In the final model,
albumin/globulin (A/G), total bilirubin (TBIL) and erythrocyte specific volume
(HCT) were selected as predictors. Using an external dataset, the model was
validated to perform well. Conclusion: A diagnostic model of laboratory
findings was established, in which A/G, TBIL and HCT were included as highly
relevant indicators for the segmentation of COVID-19 and influenza A, providing
a complimentary means for the precise diagnosis of these two diseases.
