Seizure self-prediction in a randomized controlled trial of stress management. (26th November 2019) - Privitera, Michael; Haut, Sheryl R.; Lipton, Richard B.; McGinley, James S.; Cornes, Susannah
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. Clinicaltrials.gov identifier: NCT01444183.