Literature DB >> 31645468

Seizure self-prediction in a randomized controlled trial of stress management.

Michael Privitera1, Sheryl R Haut2, Richard B Lipton2, James S McGinley2, Susannah Cornes2.   

Abstract

OBJECTIVE: Using electronic diaries as part of a randomized controlled trial of stress reduction for epilepsy, we evaluated factors associated with successful seizure self-prediction.
METHODS: Adults with medication-resistant focal epilepsy were recruited from 3 centers and randomized to treatment with progressive muscle relaxation or control focused attention. An 8-week baseline was followed by 12 weeks of double-blind treatment. Twice daily, participants rated the likelihood of a seizure in the next 24 hours on a 5-point scale from very unlikely to almost certain, along with mood, premonitory symptoms, stress ratings, and seizure counts. We analyzed the association of mood, premonitory symptoms, stress, and circadian influences on seizure self-prediction.
RESULTS: Sixty-four participants completed the trial (3,126 seizures). Diary entry adherence was >82%. Participant self-prediction was associated with seizure occurrence at 6, 12, and 24 hours (p < 0.0001). Odds ratio (OR) of seizure prediction increased systematically with participants' prediction of seizure likelihood (p < 0.0001, all levels of prediction and all time intervals). For the 12-hour prediction window, median specificity for seizure prediction was 0.94 and negative predictive value 0.94; median sensitivity was 0.10 and positive predictive value 0.13. A subset of 13 participants (20% of sample) met criteria for good predictors (median OR for seizure prediction 5.25). Mood, stress, premonitory symptoms, seizure time, and randomized group were not associated with seizure occurrence.
CONCLUSION: In this prospective study, participants' prediction of a high probability of seizure was significantly associated with subsequent seizure occurrence within 24 hours. Future studies should focus on understanding factors that drive self-prediction. CLINICALTRIALSGOV IDENTIFIER: NCT01444183.
© 2019 American Academy of Neurology.

Entities:  

Year:  2019        PMID: 31645468     DOI: 10.1212/WNL.0000000000008539

Source DB:  PubMed          Journal:  Neurology        ISSN: 0028-3878            Impact factor:   9.910


  6 in total

1.  Development and Validation of Forecasting Next Reported Seizure Using e-Diaries.

Authors:  Daniel M Goldenholz; Shira R Goldenholz; Juan Romero; Rob Moss; Haoqi Sun; Brandon Westover
Journal:  Ann Neurol       Date:  2020-07-09       Impact factor: 10.422

2.  Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study.

Authors:  Andrea Biondi; Petroula Laiou; Elisa Bruno; Pedro F Viana; Martijn Schreuder; William Hart; Ewan Nurse; Deb K Pal; Mark P Richardson
Journal:  JMIR Res Protoc       Date:  2021-03-19

3.  A Patient Perspective on Seizure Detection and Forecasting.

Authors:  Aria Moss; Evan Moss; Robert Moss; Lisa Moss; Sharon Chiang; Peter Crino
Journal:  Front Neurol       Date:  2022-02-11       Impact factor: 4.003

Review 4.  Future opportunities for research in rescue treatments.

Authors:  James W Wheless; Daniel Friedman; Gregory L Krauss; Vikram R Rao; Michael R Sperling; Enrique Carrazana; Adrian L Rabinowicz
Journal:  Epilepsia       Date:  2022-09       Impact factor: 6.740

Review 5.  Cycles in epilepsy.

Authors:  Philippa J Karoly; Vikram R Rao; Maxime O Baud; Nicholas M Gregg; Gregory A Worrell; Christophe Bernard; Mark J Cook
Journal:  Nat Rev Neurol       Date:  2021-03-15       Impact factor: 42.937

Review 6.  Noninvasive detection of focal seizures in ambulatory patients.

Authors:  Philippe Ryvlin; Leila Cammoun; Ilona Hubbard; France Ravey; Sandor Beniczky; David Atienza
Journal:  Epilepsia       Date:  2020-06-02       Impact factor: 5.864

  6 in total

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