Abstracts

Rationale: Physiological variation in secretion of estrogen and progesterone during the menstrual cycle can influence the occurrence of seizures in some women with epilepsy, resulting in seizure exacerbations around specific days of the cycle. This is known as catamenial epilepsy, which affects up to 40% of women with epilepsy. Different patterns exist depending on the phase of the menstrual cycle associated with the exacerbation: catamenial pattern 1 (C1) when the exacerbation is perimenstrual, C2 when it is periovulatory, and C3 when it occurs during the luteal phase, which is the entire second half of the menstrual cycle. Understanding these patterns can aid in predicting seizures for these women. The aim of this study is to test the feasibility of using our mobile application (app), Seizure Cycle, for seizure prediction in women with catamenial epilepsy. To our knowledge, catamenial seizure prediction using a mobile application has never been done, and previous studies of catamenial seizures were done using paper diaries for up to 3 months.

Methods: With that aim in mind, we developed the Seizure Cycle app, in which women can log their menstrual cycle and seizure-related data. The built-in algorithm in the app can reliably predict the date of next ovulation and menstruation based on variables entered by the participants. Female patients between the ages of 18 and 45 years, with history of documented epilepsy with at least 3 seizures within the prior 3 months, and with regular menstrual cycles (defined as 21-35 days), were approached in the epilepsy clinic at the George Washington University. Once consented and enrolled, participants were asked to provide baseline demographic information including epilepsy history, antiseizure medication history, menarche, and use and type of contraception. They were also asked to send data regarding seizure occurrence and menstrual cycle dates to the research team monthly for 6 months. The research team extracted the information from the electronic diaries and entered the information into a secure Excel spreadsheet for analysis.

Results: At the time of submission, eight participants were enrolled in the study. Five participants completed 6 months of use of the app; two participants were actively using the app but have not yet documented seizures; and one participant withdrew from the study after two months. Figure 1 demonstrates seizure data from participants 1 through 6. C1 and C2 pattern seizures comprised 50% of the seizures logged for participant 1; 0% of the seizures logged for participant 2; 57.1% of the seizures logged for participant 3; 61.1% of the seizures logged for participant 4; 40.5% of the seizures logged for participant 5; and 52.5% of the seizures logged for participant 6.

Conclusions: Based on our results, our participants did not meet criteria for catamenial epilepsy. But, despite our limited data, one could hypothesize that continued use of the app for a longer duration may be beneficial in predicting catamenial seizure exacerbations in a larger cohort of participants.

Funding: Award number UL1TR001876 - NIH National Center for Advancing Translational Sciences