A serious neuroinflammatory condition in pediatric influenza is Influenza-associated encephalopathy syndrome (IAES). It is marked by focal deficits, recurrent convulsions or seizures, and altered consciousness. This syndrome is classified into five different types, such as Reye’s syndrome, acute encephalopathy with biphasic seizures, late diffusion (ASED), hemorrhagic shock, encephalopathy syndrome (HSES), acute necrotizing encephalopathy (ANE), and clinically mild encephalopathy with reversible splenial syndrome (MERS). Incidences vary globally, with the highest cases reported in Japan (8.7 to 12.4 cases/100,000 children) due to genetic susceptibility. Biomarkers such as interleukin 6 (IL-6), procalcitonin (PCT), immune cell ratios, and D-dimer indicate poor clinical outcomes; however, predictive accuracy remains limited. A recent study published in BMC Infectious Diseases aimed to identify early risk factors and established a predictive model for diagnosis and management of IAES.
In this single-center, retrospective case-control study, a total of 623 pediatric influenza cases with convulsions and fever were enrolled between January 2020 and January 2025 at Wuhan Children’s Hospital, China. All these were divided into two groups IAES group (n = 55, female = 27, age = 37.60 [22.06,60.76] months, body weight = 17.72 ± 8.91 kg) and non-IAES group (n = 568, female = 202, age = 53.85 [23.80,78.03], body weight = 16.78 ± 7.52 kg). Patients with metabolic disorders, brain trauma, post-viral encephalitis, and neurological/encephalopathy complications developing ≥7 days post-admission, as well as cases with more than 30% missing information, were excluded from the study. Statistical analysis was conducted using R 4.4.3 and SPSS 25.0. Appropriate nonparametric or parametric tests identified significant variables for logistic regression and nomogram construction. The predictive model was assessed using calibration, receiver operating characteristic curve (ROC), and decision curve analysis.
Immune dysfunction was observed in IAES children compared to non-IAES children. This correlated with elevated levels of inflammatory biomarkers like C-reactive protein (CRP [15.18 ± 22.70 vs. 10.10 ± 17.08]), IL-6 (763.68 ± 4364.13 vs. 48.11 ± 193.76), PCT (5.07 vs. 0.34), and IL-10 (8.85 vs. 6.92) in IAES. Similarly, reduced levels of albumin (40.5 vs. 44) and hemoglobin (116 vs. 120.5), along with multi-organ impairments including renal, coagulation, hepatic, cardiac, and liver functions, were observed in IAES compared to non-IAES.
Stepwise regression analysis identified independent IAES risk factors (P < 0.2). Each additional seizure (odds ratio [OR] = 1.467, 95% confidence interval [CI]:1.238, 1.738), day from fever too first seizure (OR = 1.385, 95%CI:1.099,1.745), higher respiratory rate (RR [OR = 1.099, 95%CI:1.042,1.160]), and elevated procalcitonin (OR = 1.144, 95%CI:1.094,1.196) increased risk, whereas higher albumin (OR = 0.789, 95%CI:0.716,0.889) was protective. A decreased ratio of CD4+/CD8+ markedly elevated risk (OR = 4.69, 95%CI:0.098,0.466).
A nomogram predictive model for IAES in children with convulsions and fever was developed using R based on multivariate logistic regression results. Six indicators, including days from onset to convulsion (DFOC), albumin, CD4+/CD8+ ratio, number of convulsions, RR, and PCT, were incorporated in this model. This model showed good discrimination with an area under the curve (AUC) of 0.926 (95%CI: 0.902,0.945), high calibration (MSE = 0.00049). Decision curve analysis confirmed more clinical utility, which indicated the model’s potential value in clinical applications.
This study’s limitations include its single-center retrospective design, limited sample size with combined IAES subtypes, potential information bias, lack of causal inference, non-standardized biomarker timing, and the need for prospective multicenter validation.
In conclusion, this study identified key IAES risk factors in children, such as seizure frequency, fever-to-seizure timing, RR, albumin, CD4+/CD8+ ratio, and PCT. A predictive model was developed, offering a potential clinical tool for assessing and intervening early, which requires further validation of this tool.
Reference: Zhang D, Yu X, Sun D. Risk factors and predictive model for pediatric influenza-associated encephalopathy symptoms: a retrospective study. BMC Infect Dis. 2025;25:1228. doi:10.1186/s12879-025-11681-0
