Identifying Laboratory Parameters Profiles of COVID-19 and Influenza in Children: A Decision Tree Model

Background: The COVID-19 pandemic has put other infectious diseases, especially in children, into a new perspective. Our study focuses on two important viral infections: COVID-19 and influenza, which often present with similar clinical symptoms. Taking into consideration that the pathophysiology and systemic impact of the two viruses are distinct, which can lead to measurable differences in laboratory values, this study aimed to analyze laboratory features that differentiate between COVID-19 and influenza virus infections in pediatric patients. Methods: We statistically analyzed the routinely available laboratory data of 98 patients with influenza virus and 78 patients with COVID-19. Afterwards, the classification and regression tree (CART) method was performed to identify specific clinical scenarios, based on multilevel interactions of different features that could assist clinicians in evidence-based differentiation. Results: Significant differences between the two groups were observed in ALT, eosinophils, hemoglobin, and creatinine. Influenza-infected infants presented significantly higher leukocyte, neutrophil, and basophil counts compared to infants infected with COVID-19. Regarding children (over 12 months), significantly lower levels of ALT and eosinophil counts were observed in those with influenza compared to those with COVID-19. Furthermore, the CART decision tree model identified distinct profiles based on a combination of features such as age, leukocytes, lymphocytes, platelets, and neutrophils. Conclusions: After further refinement and application, such machine learning-based, evidence-driven models, considering the large scale of clinical and laboratory variables, might help to improve, support, and sustain healthcare practices. The differential decision tree may contribute to enhanced clinical risk assessment and decision making.