Modeling Seizure Self-Prediction: An E-Diary Study
Published Date:Sep 20 2013
Pubmed Central ID:PMC3833277
Funding:1R01DA031275-01A1/DA/NIDA NIH HHS/United States
5P60AA003510-33/AA/NIAAA NIH HHS/United States
5R01AA016599-03/AA/NIAAA NIH HHS/United States
5R01AA12827-07/AA/NIAAA NIH HHS/United States
K23AG030857/AG/NIA NIH HHS/United States
K23NS05140901A1/NS/NINDS NIH HHS/United States
K23NS47256/NS/NINDS NIH HHS/United States
P01 AG003949/AG/NIA NIH HHS/United States
P01 AG027734/AG/NIA NIH HHS/United States
P01 AG03949/AG/NIA NIH HHS/United States
P30 CA13330-35/CA/NCI NIH HHS/United States
R01 AG022092/AG/NIA NIH HHS/United States
R01 AG034087/AG/NIA NIH HHS/United States
R01 NS053998/NS/NINDS NIH HHS/United States
R01AG022374-06A2/AG/NIA NIH HHS/United States
R01AG025119/AG/NIA NIH HHS/United States
R01AG034119/AG/NIA NIH HHS/United States
R01AG12101/AG/NIA NIH HHS/United States
R21 AG036935/AG/NIA NIH HHS/United States
U01-OH10411/OH/NIOSH CDC HHS/United States
U01-OH10412/OH/NIOSH CDC HHS/United States
UL1-RR025750-01/RR/NCRR NIH HHS/United States
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 LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM 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 (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 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 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; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 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-hour prediction window is suitable for the development of pre-emptive therapy.
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