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Fig. 3 | BMC Pediatrics

Fig. 3

From: Evaluation of disease severity and prediction of severe cases in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era: a multicenter retrospective study

Fig. 3

Construction and performance evaluation of the prediction model for severe pediatric H1N1 infection in post-COVID-19 era. A The result of logistic regression analysis. Age, BMI, fever duration, leucocyte count, lymphocyte proportion, proportion of CD3+ T cells, TNF-α, and IL-10 were independently associated with severe H1N1 infection in the post-COVID-19 group. B and C ROC curve analysis showed that the AUC for the training set was 0.973, with a sensitivity of 93.1% and a specificity of 93.6%, and the AUC was 0.949 for the validation set, with a sensitivity of 90.5% and a specificity of 88.6%. D and E Calibration curve analysis indicated favorable agreement between the predicted probability and the observed probability in both training and internal validation sets. F and G DCA identified good clinical utility of this prediction model in both training and internal validation sets. AUC, area under the curve; BMI, body mass index; DCA, decision curve analysis; IL-10, interleukin 10; ROC, receiver operating characteristic; TNF-α, tumor necrosis factor α

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BMC Pediatrics

ISSN: 1471-2431

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