Constructing a predictive model based on peripheral blood signs to differentiate infectious mononucleosis from chronic active EBV infection.

Abstract

Objective: To develop a prediction model based on peripheral blood signs to distinguish between infectious mononucleosis and chronic active EBV infection.

Methods: Retrospective data was collected for 60 patients with IM (IM group) and 20 patients with CAEBV infection (CAEBV group) who were hospitalized and diagnosed at the General Hospital of Tianjin Medical University between December 2018 and September 2022. The analyses used were univariate and LASSO (least absolute shrinkage and selection operator) logistic regression.

Results: Univariate analyses revealed that both IM and CAEBV-infected patients displayed overlapping and intersecting clinical manifestations, such as fever, sore throat, enlarged lymph nodes, and enlargement of the liver and spleen, and that in contrast to inflammatory responses in peripheral blood, CAEBV-infected patients had more severe inflammatory responses. Nine biomarkers-HGB, lymphocyte count, percentage of lymphocytes, ALB, fibrinogen, CRP, IFN-, IL-6, and EBV-DNA load-were subsequently selected by LASSO logistic regression modeling to serve as discriminatory models.

Conclusions: Our investigation offers a solid foundation for diagnosing IM and CAEBV infection using the LASSO logistic regression model based on the significance and availability of peripheral blood indicators. Infected patients with CAEBV require early medical attention.