Analysis of risk factors for cluster seizures in children with epilepsy and construction of a Nomogram model
FANG Jie
WANG Xin-ru
LU Yuan-hang
LI Rui
QU Rui
DAI Yuan-yuan
Abstract：Objective To explore the risk factors for cluster seizures in children with epilepsy and to construct a risk prediction Nomogram model based on these factors.Methods Total 200 children with epilepsy who were treated at The Affiliated Hospital of Xuzhou Medical University between March 2022 and September 2023 were enrolled.Based on the presence or absence of cluster seizures,the pediatric patients were divided into cluster seizures group(n=98)and no cluster seizures group(n=102).Univariate and multivariate stepwise Logistic regression analyses were employed to identify risk factors for cluster seizures.A Nomogram model was then constructed based on these factors.The model's performance was evaluated using receiver operating characteristic(ROC)curve analysis,calibration curves,and the Hosmer-Lemeshow goodness-of-fit test to validate its discriminative capacity,calibration accuracy and stability.Results Logistic regression analysis identified the following independent risk factors for cluster seizures:structural etiology(OR=3.403,95%CI:1.442-8.027;P=0.005),infantile-onset seizures(OR=4.720,95%CI:2.150-10.365;P=0.000),multiple seizure types(OR=6.446,95%CI:2.085-19.933;P=0.001),and generalized-multifocal discharges(OR=13.257,95%CI:4.669-37.641;P=0.000).The Nomogram model incorporating these risk factors demonstrated excellent predictive performance,with an area under the curve(AUC)of 0.768(95%CI:0.703-0.834,P=0.000).The optimal cutoff value for predicting cluster seizures was 0.513.Calibration curves showed good agreement between predicted and observed probabilities,and the Hosmer-Lemeshow goodness-of-fit test indicated that the model had good stability(P=0.988).Conclusions Epileptic children exhibiting structural etiology,infantile-onset seizures,multiple seizure types,and generalized-multifocal discharges are more susceptible to cluster seizures.The developed Nomogram model based on these factors provides a valuable clinical tool for predicting the risk of cluster seizures in children with epilepsy.
Keywords：EpilepsyChildCluster seizures(not in MeSH)Risk factorsLogistic modelsNomograms
Publication Date：2025-11-25
Online Publishing Date：2025-12-16(First online date of this platform, not the publication date of the document)
Pages：8( 1004-1011 )