Development and validation of a cytokine-associated nomogram for predicting severe influenza A virus infection in children.

To investigate the risk factors for severe influenza A virus (IAV) infection in children and construct a nomogram prediction model based on these factors. A retrospective analysis was conducted on the clinical data of 178 children with IAV infection admitted to the Third Affiliated Hospital of Zhengzhou University between January and February 2025. According to disease severity, patients were divided into a mild group (n = 123) and a severe group (n = 55). The severe group was further stratified into a pneumonia subgroup (n = 25) and a non-pneumonia subgroup (n = 30) based on the presence of pneumonia. General clinical characteristics were compared between the mild and severe groups. Variables identified through univariate analysis were entered into multivariate logistic regression to determine independent risk factors for severe IAV infection. A nomogram model was subsequently established, and its performance was evaluated and validated using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Compared with the mild group, children in the severe group were older, had longer hospital stays and prolonged fever duration, and had higher incidences of pneumonia and mental status abnormalities (all P < 0.05). Regarding immune inflammatory indicators, neutrophil percentage, Interleukin-2 (IL-2), tumor necrosis factor-alpha (TNF-α), IL-5, IL-12P70, and immunoglobulin G (IgG) and IgA levels were significantly higher in the severe group than in the mild group (all P < 0.05), whereas Interferon-alpha (IFN-α) levels were significantly lower (P < 0.05). Multivariate logistic regression analysis demonstrated that elevated IL-2 and TNF-α levels, prolonged fever duration, and mental status abnormalities were independently associated with severe IAV infection (all P < 0.05). The calibration curve, ROC curve, and DCA indicated that the nomogram model had good predictive Value and clinical utility. The multidimensional nomogram constructed by integrating IL-2, TNF-α, and fever duration and mental status abnormalities demonstrates high predictive value and clinical utility in forecasting severe IAV infection. This model aids in the early identification of severe IAV infections, thereby reducing the occurrence of long-term complications and enhancing the accuracy and timeliness of clinical decision-making.