Autism and Epilepsy Co-Morbidity
Notice bibliographique
Résumé
Source: Clarke DF, Roberts W, Daraksan M, et al. The prevalence of autistic spectrum disorder in children surveyed in a tertiary care epilepsy clinic. Epilepsia. 2005;46:1970–1977.The prevalence of autistic spectrum disorder (ASD) in children aged 2–18 years with epilepsy was evaluated at the Tertiary Care Epilepsy Clinic at the Hospital for Sick Children in Toronto, Ontario. Parents were asked to complete 2 questionnaires based on the DSM-IV diagnostic criteria: an autism screening questionnaire (ASQ) addressing age, social interaction, and language development of the child; and a pediatric sleep questionnaire (PSQ) relating to sleep disorders and behavior. Of 290 questionnaires distributed, 107 were returned, and 97 (33%) subjects were included in the study. The mean age was 12.7 years. A diagnosis of ASD had not been previously suspected in the majority. Patients with scores above the ASQ diagnostic cutoff of 15 (31 patients [32%]) were assigned to the ASD group and those with scores below the ASQ cutoff (66 patients [68%]) were included in the non-ASD group. A comparison of ASD and non-ASD groups showed similar mean age (11 years), body mass indices, male sex predominance (61% and 49%), average seizure frequency (10.5 and 5.38 per month), and number with generalized seizures (12/29 [41%] and 29/61 [47%]). Statistically significant differences included younger mean age at first seizure in the ASD group (21 months vs 55 months; P=.0001) and greater mean number of antiepileptic drugs (AEDs) used in the ASD group (1.77±0.80 vs 1.45±0.91; P=.04). An increase in sleep-related problems in the ASD group included an increased frequency of nocturnal arousals (38% vs 17%, P=.06), difficulty in falling back to sleep after arousal (42% vs 18%, P=.02), early morning awakening (55% vs 26%, P=.01), and more daytime sleepiness reported by teachers (73% vs 45%, P=.01). Behavior scores pertaining to attention, hyperactivity, and impulsiveness were worse in the ASD versus non-ASD groups (3.7 vs 2.2, P=.001). Sleep-disordered breathing was strongly associated with a worse mean behavioral score of 3.45 in the ASD group versus 2.17 for non-ASD subjects.Dr. Millichap has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of a commercial product/device. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.The Committee on Children with Disabilities of the American Academy of Pediatrics (AAP) recommends a prolonged sleep-deprived EEG in autistic children with regression.1 Sleep disorders are reported in children with epilepsy,2,3 and in those with autism.4 Sleep EEGs are abnormal in children with autism and subclinical seizures, and treatment with the anticonvulsant valproate results in improvement in language and social skills,5 an observation confirmed in children with autism and epilepsy.6The present report emphasizes the importance of clinical vigilance for symptoms of autism and regression in language and communication in children with an onset of epilepsy. The authors also demonstrate the frequency of sleep and behavioral disorders in children with co-morbid symptoms of epilepsy and ASD. Other co-morbidities associated with epilepsy include ADHD, developmental disabilities, migraines, depression/anxiety, and accidental injury.7 Children with autism and co-morbid cognitive impairment are at higher risk for epilepsy and abnormal EEGs (P<.05), according to a recent retrospective study of 56 patients with autism referred for routine EEG.8Before seizing these results, the limitations of this study are worth noting. The questionnaires used are intended for screening and not definitive diagnosis.9 In addition, the 33% response rate may reflect some selection bias in that parents of children with ASD may have been more likely to participate. On the other hand, even if we assume the non-respondents were 100% non-autistic, the rate of ASD was greater in this population than in the general population.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».