Clinical and anamnestic factors of epilepsy development and course in children with cerebral palsy
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background. Cerebral palsy (CP) is a high-priority issue in pediatric neurology that develops in 30–67% of cases compared to 0.4–0.8% in general population. Studying risk factors and determining clinical characteristics of children with CP may aid in early epilepsy prevention and diagnostics in this group. Objective : Studying risk factors and developing a model for predicting development of epilepsy in children with CP. Material and methods. There were examined 128 CP children aged 1 to 6 years divided into two groups: Group 1 included 65 patients with CP and epilepsy (27 of whom (39.7%) had a drug-resistant epilepsy), and Group 2 included 63 children with CP. All patients underwent routine electroencephalography (EEG) and video-EEG monitoring, as well as brain magnetic resonance imaging to verify epilepsy diagnosis. Regression coefficients were presented in a multivariate binary logistic regression model for predicting development of epilepsy in children with CP based on Akaike information criterion magnitude used for predictor selection with exclusion. Results. The study allowed to identify clinical and anamnestic factors predicting development of epilepsy in children with CP (p<0.05): burdened obstetric history (miscarriages, stillbirths), threatened miscarriage, arterial hypertension, grade 3 prematurity, Apgar score ≤2 points at the 1st minute and ≤4 points at the 5th minute, neonatal seizures, spastic CP. Conclusion. The model proposed for predicting development of epilepsy in children with CP demonstrated a high level of predictive power. The model is characterized by an accuracy of 85.2%, a sensitivity of 87.7%, and a specificity of 82.5%; the prognostic value of a positive result comprised 83.8%, which will allow to objectively plan patient monitoring.
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $142
OpenAlex ID: W4410046518