Can Haematological Parameters Discriminate COVID-19 from Influenza?

Area of Science:

• Hematology
• Infectious Diseases
• Immunology

Background:

• COVID-19 and influenza share similar symptoms, necessitating accurate differentiation for appropriate treatment and prognosis.
• Complete blood count (CBC) parameters are often comparable between these viral infections.
• Novel biomarkers are needed to reliably distinguish between COVID-19 and influenza.

Purpose of the Study:

• To evaluate the utility of complete blood count (CBC) cellular population data (CPD) and automated flow cytometry in differentiating COVID-19 from influenza.
• To identify specific CBC parameters and leukocyte subpopulations that can discriminate between these two diseases.

Main Methods:

• Analysis of CBC, including leukocyte cellular population data (CPD), and automated flow cytometry in 350 COVID-19 and 102 influenza patients.
• Comparison of various hematological parameters and cell counts between the two patient groups.
• Development of a logistic regression model incorporating 17 parameters, including CPD, to discriminate between COVID-19 and influenza.

Main Results:

• Platelet counts were lower in influenza patients compared to COVID-19 patients.
• CD16^pos monocyte counts and the ratio of CD16^pos monocytes to total monocytes were higher in COVID-19 patients.
• A logistic regression model achieved 96.2% sensitivity and 86.6% efficiency in discriminating COVID-19 from influenza, with an AUC of 0.862.

Conclusions:

• While classical CBC parameters are similar, specific cellular population data (CPD), CD16^pos monocyte levels, and B-lymphocyte counts can effectively differentiate COVID-19 from influenza.
• Automated flow cytometry analysis of leukocyte subpopulations offers a valuable tool for distinguishing these viral respiratory infections.
• The developed logistic regression model demonstrates high accuracy in clinical settings for differentiating COVID-19 and influenza.