Predictors of Epilepsy in Children with ASD from a Large National US Sample

Saturday, May 16, 2015: 3:04 PM
Grand Ballroom A (Grand America Hotel)
J. B. Ewen1, A. R. Marvin2, J. K. Law2, P. A. Law3 and P. H. Lipkin4, (1)Neurology and Developmental Medicine, Kennedy Krieger Institute/Johns Hopkins School of Medicine, Baltimore, MD, (2)Medical Informatics, Kennedy Krieger Institute, Baltimore, MD, (3)Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, (4)Pediatrics/Neurology and Developmental Medicine, Kennedy Krieger Institute/Johns Hopkins School of Medicine, Baltimore, MD
Background:  The relationship between ASD and epilepsy is complex but has the potential to yield important mechanistic and therapeutic insights into both disorders. Nevertheless, there is considerable disagreement among epidemiological data that suggest which features of ASD predict the occurrence of epilepsy.

Objectives: To identify predictors of epilepsy in ASD from a large US autism registry.

Methods:   The Interactive Autism Network (IAN) is a questionnaire-based autism registry that contains parent-provided clinical data on over 16,000 unique children with ASD. Data from IAN included diagnosis of epilepsy (“Has your child ever had a seizure or been diagnosed with epilepsy?”), intellectual disability (ID) (past diagnosis of ID or currently non-verbal), anomalous motor development/cerebral palsy, sex, autism severity (Social Responsiveness Scale [SRS] total score), history of regression and maternal history of epilepsy. Number of subjects (n) varied by analysis depending on completeness of data. Linear regression was used to characterize the relationship between ASD severity and the diagnosis of epilepsy; generalized linear models were used to characterize the other relationships between potential predictors and the odds of epilepsy diagnosis.

Results: Predictors of epilepsy in children with ASD included ID (odds ratio = 3.3, n = 8557; t = 12.2; p < 0.0001), anomalous motor development (OR = 2.5, n = 8458; t = 11.5; p < 0.0001) and maternal epilepsy (OR = 2.5; n = 7688; t = 3.9, p < 0.0001). The presence of epilepsy increased with? SRS total score of severity by 5.5 points, on average (n = 4803). Female sex was associated with an increase in odds of being diagnosed with epilepsy (OR = 1.44; n = 8557; t = 3.5; p = 0.004), but this relationship disappeared when SRS score was controlled for. Contrary to prior findings (Viscidi et al, PLoS ONE 2014), controlling for ID did not obliterate the relationship between sex and the presence of epilepsy. A history of regression did not increase the incidence of epilepsy diagnosis.

Conclusions: Consistent with other large studies of ASD, the IAN data suggest a link between ID and increased risk for epilepsy. Contrary to other studies (Viscidi et al), intelligence did not mediate other relationships of patient characteristics to epilepsy, perhaps because intelligence in our sample was ascertained by parental report rather than measured IQ used in other studies. Autism severity, anomalous motor development and maternal history of epilepsy were all independent predictors of increased epilepsy risk. The increased risk associated with female sex disappeared once ASD severity (but not presence of ID) was controlled for.