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The following is a summary of “Overnight EEG to Forecast Epilepsy Development in Children with Autism Spectrum Disorders,” published in the July 2024 issue of Pediatrics by Daida et al.

This study aims to evaluate the effectiveness of long-term electroencephalogram (EEG) monitoring in predicting the onset of epilepsy in children with autism spectrum disorder (ASD). A single-institution, retrospective analysis was conducted over 15 years, involving an extensive review of overnight EEG recordings from children with ASD. The analysis incorporated clinical EEG findings, patient demographics, medical histories, and Autism Diagnostic Observation Schedule (ADOS) data. Survival analysis and Cox regression models were employed to identify predictors of epilepsy onset.

Out of 151 patients studied, 17.2% (n=26) developed unprovoked seizures (Sz group), while the remaining 82.8% (n=125) did not (non-Sz group). The Sz group demonstrated a significantly higher proportion of interictal epileptiform discharges (IEDs) in their initial EEGs compared to the non-Sz group (46.2% vs. 20.0%, p=0.01). Additionally, the Sz group exhibited a greater incidence of EEG slowing (42.3% vs. 13.6%, p < 0.01). Both IEDs and EEG slowing were found to be significant predictors of earlier seizure onset. Multivariate Cox proportional hazards regression analysis further indicated that the presence of any IEDs (hazard ratio [HR] 3.83, 95% CI 1.38-10.65, p=0.01) or any slowing (HR 2.78, 95% CI 1.02-7.58, p=0.046) notably increased the risk of developing unprovoked seizures.

These findings underscore the utility of long-term EEG monitoring as a predictive tool for epilepsy in children with ASD. By identifying key EEG abnormalities such as IEDs and slowing, clinicians can better anticipate epilepsy development and implement early educational strategies and potential interventions to mitigate the risk.

Source: sciencedirect.com/science/article/abs/pii/S0022347624003202