Clinical characteristics analysis and prediction model construction for pediatric influenza virus pneumonia complicated by bacterial infection.

Background: Bacterial co-infection substantially worsens the clinical course and outcomes of pediatric influenza virus pneumonia, yet early recognition is difficult at presentation. We aimed to (I) characterize the clinical features associated with bacterial co-infection and (II) develop and validate a pragmatic prediction model for bedside risk estimation in children with influenza virus pneumonia. Methods: A retrospective cohort study was conducted on pediatric patients diagnosed with influenza virus pneumonia. Patients were divided into bacterial co-infection and non-co-infection groups. Clinical characteristics were compared, and logistic regression analysis was performed to identify independent predictors. The dataset was randomly split into a training set (n=1,607) and an independent test set (n=359). A predictive model was constructed and validated using the receiver operating characteristic (ROC) curve, calibration plot, Brier score, and decision curve analysis (DCA). A nomogram was built to facilitate individualized risk assessment. Results: Nine variables-age, temperature, white blood cell count (WBC), C-reactive protein (CRP), platelet count (PLT), procalcitonin (PCT), lactate dehydrogenase (LDH), underlying diseases, and gasping-were independently associated with bacterial co-infection and used to build the model. Discrimination was high (AUC 0.971 in the training set and 0.953 in the test set) with satisfactory overall accuracy (Brier scores 0.053 and 0.098, respectively). Calibration curves indicated good agreement between predicted and observed risks, and DCA showed net clinical benefit across a broad range of decision thresholds. The nomogram translates the model into a user-friendly tool for individualized risk estimation at the point of care. Conclusions: This model effectively predicts bacterial co-infection in children with influenza virus pneumonia and may support early diagnosis and risk stratification in clinical practice. External, multicenter validation is warranted prior to widespread implementation.