AI Article Synopsis
- Researchers studied 336 hospitalized children with community-acquired pneumonia to differentiate between viral infections and Mycoplasma pneumoniae (MP) infections.
- They used various analyses, including LASSO regression and logistic regression, to identify key factors for predicting the type of infection.
- A predictive model was created incorporating factors like tumor necrosis factor-α, age, and procalcitonin, achieving a C-index of 0.878, indicating good accuracy in distinguishing between the two types of infections.
Article 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.
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| http://dx.doi.org/10.1016/j.diagmicrobio.2024.116611 | DOI Listing |
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