Overnight Electroencephalogram to Forecast Epilepsy Development in Children with Autism Spectrum Disorders
Atsuro Daida1, Shingo Oana1, Divya Nadkarni1
1Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA.
Insights
Long-term electroencephalograms (EEGs) can predict epilepsy in children with autism spectrum disorder (ASD). Abnormal EEG findings like epileptiform discharges or slowing significantly increase the risk of future seizures.
Area of Science:
- Neurology
- Pediatrics
- Neurodevelopmental Disorders
Background:
- Autism spectrum disorder (ASD) is associated with a higher prevalence of epilepsy.
- Predicting epilepsy onset in children with ASD is crucial for timely intervention.
Purpose of the Study:
- To determine the predictive utility of long-term electroencephalogram (EEG) for epilepsy onset in children with autism spectrum disorder (ASD).
Main Methods:
- Retrospective analysis of 151 children with ASD undergoing long-term overnight EEG over 15 years.
- Evaluation of clinical EEG findings, demographics, medical history, and Autism Diagnostic Observation Schedule data.
- Survival analysis and Cox regression used to identify predictors of epilepsy onset.
Main Results:
- 17.2% of children with ASD developed epilepsy.
- Interictal epileptiform discharges (IEDs) and slowing on initial EEGs were significantly more common in children who developed seizures.
- Presence of IEDs (HR 3.83) or slowing (HR 2.78) independently predicted an increased risk of unprovoked seizures.
Conclusions:
- Long-term EEGs are valuable tools for forecasting epilepsy in children with ASD.
- Findings support early identification and potential interventions for epilepsy prevention in this population.
Objective:
To establish the utility of long-term electroencephalogram (EEG) in forecasting epilepsy onset in children with autism spectrum disorder (ASD).
Study Design:
A single-institution, retrospective analysis of children with ASD, examining long-term overnight EEG recordings collected over a period of 15 years, was conducted. Clinical EEG findings, patient demographics, medical histories, and additional Autism Diagnostic Observation Schedule data were examined. Predictors for the timing of epilepsy onset were evaluated using survival analysis and Cox regression.
Results:
Among 151 patients, 17.2% (n = 26) developed unprovoked seizures (Sz group), while 82.8% (n = 125) did not (non-Sz group). The Sz group displayed a higher percentage of interictal epileptiform discharges (IEDs) in their initial EEGs compared with the non-Sz group (46.2% vs 20.0%, P = .01). The Sz group also exhibited a greater frequency of slowing (42.3% vs 13.6%, P < .01). The presence of IEDs or slowing predicted an earlier seizure onset, based on survival analysis. Multivariate Cox proportional hazards regression revealed that the presence of any IEDs (HR 3.83, 95% CI 1.38-10.65, P = .01) or any slowing (HR 2.78, 95% CI 1.02-7.58, P = .046 significantly increased the risk of developing unprovoked seizures.
Conclusion:
Long-term EEGs are valuable for predicting future epilepsy in children with ASD. These findings can guide clinicians in early education and potential interventions for epilepsy prevention.
