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This Article Figures Only Full Text Full Text (PDF) CME: Take the course for this article: Volume 69, Number 20, November 13, 2007 Data Supplement Correspondence: Submit a response Alert me when this article is cited Alert me when Correspondence are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Haut, S. R. Articles by Lipton, R. B. Search for Related Content PubMed PubMed Citation Articles by Haut, S. R. Articles by Lipton, R. B. Related Collections Cohort studies All Epilepsy/Seizures Partial seizures Complex partial seizures

Seizure occurrence

Precipitants and prediction

From the Comprehensive Epilepsy Management Center (S.R.H., J.M.) and Departments of Neurology (S.R.H., C.B.H., J.M., R.B.L.) and Epidemiology and Population Health, Montefiore Medical Center and the Albert Einstein College of Medicine (C.B.H., R.B.L.), Bronx, NY.

Address correspondence and reprint requests to Dr. Sheryl R. Haut, Epilepsy Management Center, Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467-2490 haut{at}aecom.yu.edu

Objective: To explore the relationship of seizure occurrence with candidate seizure precipitants in a prospective diary study, and to determine the relationship of precipitants to seizure self-prediction.

Methods: Eligible subjects were 18 or older, had localization-related epilepsy, at least one seizure within 12 months, and were able to maintain a daily diary. Information collected included the occurrence, time and characteristics of all seizures, hours of sleep, medication compliance, stress, anxiety, alcohol use, menstruation, and seizure self-prediction. Each night, subjects reported their estimate of the likelihood of a seizure the next day (self-prediction). Logit-normal models with a random subject-specific intercept were used to estimate an OR for the association of precipitants with seizure occurrence.

Results: Seventy-one subjects returned 15,179 complete diary days. For each hour of increased sleep on the preceding night, the relative odds of a seizure the following day decreased (OR 0.91, 95% CI 0.82, 0.99). One-unit increments of stress and anxiety (on a 10-point scale) were associated with an increased risk of seizure the following day (OR 1.06, 95% CI 1.01, 1.12 and OR 1.07; 95% CI 1.02, 1.12). With self-prediction included in the model, self-prediction (OR 3.7; 95% CI 1.8, 7.2) and hours of sleep for the night prior to the seizure (OR 0.90; 95% CI 0.82, 0.99) remained significant.

Conclusion: Lack of sleep and higher self-reported stress and anxiety levels were associated with seizure occurrence. In a model that included self-prediction, less sleep, and self-prediction had significant effects, whereas stress and anxiety did not. The psychological and biologic mechanisms which link stress and anxiety to self-prediction of seizures requires further exploration. Ultimately, seizure prediction based on precipitants, premonitory features, and self-prediction may provide a foundation for preemptive treatment.

GLOSSARY: CDEM = chronic disorders with episodic manifestations; MMC = Montefiore Medical Center.

Supplemental data at www.neurology.org

Supported by NIH grant K23 NS02192 (PI: Dr. Haut).

Disclosure: The authors report no conflicts of interest.

Presented in part at the annual meeting of the American Epilepsy Society; Washington, DC; December 2–6, 2005.

Received January 23, 2007. Accepted in final form May 21, 2007.