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Development and validation of a cytokine-associated nomogram for predicting severe influenza A virus infection in
Junxiang Li1, Yuxia Yang2, Ziwei Yue1
1Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Insights
This study identified key risk factors for severe influenza A virus (IAV) infection in children, including elevated Interleukin-2 (IL-2) and tumor necrosis factor-alpha (TNF-α) levels, prolonged fever, and mental status changes. A predictive nomogram model was developed to aid early identification of severe IAV cases.
Area of Science:
- Pediatrics
- Infectious Diseases
- Medical Informatics
Background:
- Influenza A virus (IAV) infection poses a significant health risk to children.
- Identifying risk factors and developing predictive models for severe IAV infection is crucial for timely clinical intervention.
Purpose of the Study:
- To investigate risk factors associated with severe IAV infection in pediatric patients.
- To develop and validate a nomogram prediction model for severe IAV infection in children.
Main Methods:
- Retrospective analysis of clinical data from 178 children with IAV infection.
- Comparison of clinical characteristics and immune inflammatory indicators between mild and severe IAV infection groups.
- Development and validation of a nomogram using logistic regression, calibration curves, ROC curves, and DCA.
Main Results:
- Severe IAV infection was associated with older age, longer hospital stays, prolonged fever, pneumonia, and mental status abnormalities.
- Elevated levels of Interleukin-2 (IL-2), tumor necrosis factor-alpha (TNF-α), IL-5, IL-12P70, IgG, and IgA, along with lower Interferon-alpha (IFN-α), were observed in severe cases.
- Independent risk factors for severe IAV infection included elevated IL-2 and TNF-α, prolonged fever duration, and mental status abnormalities.
Conclusions:
- A nomogram integrating IL-2, TNF-α, fever duration, and mental status abnormalities demonstrates high predictive value for severe IAV infection in children.
- This model can aid in early identification of severe IAV infections, reducing complications and improving clinical decision-making.
Abstract:
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.
