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Modeling seizure self-prediction: an e-diary study.

Haut-SR; Hall-CB; Borkowski-T; Tennen-H; Lipton-RB
Epilepsia 2013 Nov; 54(11):1960-1967
PURPOSE: A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. METHODS: Subjects 18 or older with localization-related epilepsy (LRE) and =3 seizures per month maintained an e-diary, reporting a.m./p.m. data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, "How likely are you to experience a seizure (time frame)?" Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (odds ratio, OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. KEY FINDINGS: Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6 h was as high as 9.31 (CI 1.92-45.23) for "almost certain." Prediction was most robust within 6 h of diary entry, and remained significant up to 12 h. For nine best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; CI 1.68-4.81), favorable change in mood (0.82; CI 0.67-0.99), and number of premonitory symptoms (1.11; CI 1.00-1.24) were significant. SIGNIFICANCE: Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self-awareness of mood and premonitory features. The 6-h prediction window is suitable for the development of preemptive therapy.
Humans; Adolescents; Men; Women; Physiology; Psychology; Statistical-analysis; Central-nervous-system-disorders; Author Keywords: Seizure prediction; Self-prediction; Localization-related epilepsy; Seizure diary; Electronic diary; Premonitory symptoms; Seizure precipitants
Sheryl R. Haut, Epilepsy Management Center, Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467-2490
Publication Date
Document Type
Journal Article
Email Address
Funding Type
Cooperative Agreement
Fiscal Year
NTIS Accession No.
NTIS Price
Identifying No.
Cooperative-Agreement-Number-U01-OH-010411; Cooperative-Agreement-Number-U01-OH-010412
Issue of Publication
Source Name
Performing Organization
Albert Einstein College of Medicine, New York