Full-Length Original Research
Modeling seizure self-prediction: An e-diary study
Version of Record online: 20 SEP 2013
Wiley Periodicals, Inc. © 2013 International League Against Epilepsy
Volume 54, Issue 11, pages 1960–1967, November 2013
How to Cite
Epilepsia, 54(11):1960–1967, 2013
- Issue online: 6 NOV 2013
- Version of Record online: 20 SEP 2013
- Manuscript Accepted: 3 AUG 2013
- National Institute of Health. Grant Numbers: RO1 NS053998, 5P60AA003510-33, 5R01AA016599-03, 5R01AA12827-07, 1R01DA031275-01A1, PO1 AG03949, RO1AG025119, RO1AG022374-06A2, RO1AG034119, RO1AG12101, K23AG030857, K23NS05140901A1, K23NS47256
- Shor Foundation for Epilepsy Research
- National Institute of Aging. Grant Numbers: P01 AG03949, P01 AG027734, R01 AG022092, R01 AG034087, R21 AG036935
- National Center for Research Resources. Grant Number: UL1-RR025750-01
- National Cancer Institute. Grant Number: P30 CA13330-35
- National Institute of Occupational Safety and Health. Grant Numbers: 200-2011-39372, 200-2011-39489 39379, U01-OH10411, U01-OH10412
- National Headache Foundation
- Migraine Research Fund
- National Institute of Neurological Disorders and Stroke
- Seizure prediction;
- Localization-related epilepsy;
- Seizure diary;
- Electronic diary;
- Premonitory symptoms;
- Seizure precipitants
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.
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.
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.
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.