Modeling seizure self-prediction: an e-diary study

Details

• Personal Author:
  Borkowski T ; Hall CB ; Haut SR ; Lipton RB ; Tennen H
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Adolescent Men Mental Health Nervous System Nervous System Disorders Occupational Health Safety Women
• Keywords:
  Adolescents Author Keywords: Seizure Prediction Central-nervous-system-disorders Electronic Diary Humans Localization-related Epilepsy Physiology Premonitory Symptoms Psychology Seizure Diary Seizure Precipitants Self-prediction Statistical-analysis

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• ISSN:
  0013-9580
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  54
• Issue:
  11
• NIOSHTIC Number:
  nn:20043564
• Citation:
  Epilepsia 2013 Nov; 54(11):1960-1967
• Contact Point Address:
  Sheryl R. Haut, Epilepsy Management Center, Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467-2490
• Email:
  sheryl.haut@einstein.yu.edu
• Federal Fiscal Year:
  2014
• Performing Organization:
  Albert Einstein College of Medicine, New York
• Peer Reviewed:
  True
• Start Date:
  20120901
• Source Full Name:
  Epilepsia
• End Date:
  20160228
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:6ce08248217875948a226b8a0af9cb521d1d07f8a569073b72f851052a40e2424b2bce8d50a3783af5476b429810f2a59a8ed4aae0127191a19feb5fff7c41cc
• Download URL:
  https://stacks.cdc.gov/view/cdc/195437/cdc_195437_DS1.pdf
• File Type:
  [PDF - 209.48 KB ]