Cytokine-based nomogram for discriminating viral pneumonia from Mycoplasma pneumoniae pneumonia in children

Abstract

For children with pneumonia, differential diagnosis between viral infection and Mycoplasma pneumoniae (MP) infection is difficult. We retrospectively enrolled 336 hospitalized children who were diagnosed with community-acquired pneumonia and whose infection was exclusively viral or MP. We analyzed hematological indicators, biochemical markers, and cytokines. Least absolute shrinkage and selection operator (LASSO) regression analysis and logistic regression analysis were performed to identify and validate the factors that predicted the pathogenic diagnosis. The final predictive model incorporated four factors: tumor necrosis factor-α/interleukin (IL)-10, age, IL-8 and procalcitonin. This predictive model was visualized with a nomogram and had good performance. Using logistic regression analysis, the C-index of this predictive model was 0.878. Using receiver operating characteristic plot, the area under the curve was 0.8785. We established a model with a nomogram to discriminate viral infection from MP infection in hospitalized children with community-acquired pneumonia.