Risk factors and predictive model for pediatric influenza-associated encephalopathy symptoms: a retrospective study.

Abstract

Background: Constructing a predictive model for influenza-associated encephalopathy symptoms (IAES) is crucial for early identification of severe cases among febrile seizure patients with influenza.

Methods: A retrospective analysis was conducted on the clinical data of influenza children with symptoms of fever and convulsions who were hospitalized at Wuhan Children's Hospital from January 2020 to January 2025. Patients were divided into the IAES group and the Non -IAES group for a comparative study based on whether they developed acute encephalopathy syndrome. SPSS and R programming language were used to analyze the risk factors of IAES and build a prediction model.

Results: Among 623 pediatric influenza cases with symptoms of fever and convulsions, there were 55 cases (8.8%) in the IAES group. There were 568 cases (91.2%) of children in the Non-IAES group. Number of convulsions, day of fever course when first convulsion occurred, respiratory rate(RR), procalcitonin (PCT), albumin(Alb), the CD4+/CD8 + ratio are independent risk factors for IAES. This study constructed a Clinical risk scoring tool for IAES accompanied by fever and convulsions and verified internally that the model has good discrimination (AUC = 0.926) and calibration (MSE = 0.00049).

Conclusion: Independent risk factors for IAES children with symptoms of fever and convulsions include an increased number of convulsions, the timing of the first convulsion during the course of the fever, elevated respiratory rate, increased procalcitonin, decreased albumin, and a reduction in the CD4+/CD8 + ratio. The nomogram model developed in this study assists clinicians in assessing the risk of IAES children and provides guidance for early intervention and clinical decision-making in IAES.