Modeling seizure self‐prediction: An e‐diary study

Summary 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...

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Published inEpilepsia (Copenhagen) Vol. 54; no. 11; pp. 1960 - 1967
Main Authors Haut, Sheryl R., Hall, Charles B., Borkowski, Thomas, Tennen, Howard, Lipton, Richard B.
Format Journal Article
LanguageEnglish
Published United States Wiley Subscription Services, Inc 01.11.2013
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Abstract Summary 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.
AbstractList Summary 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.
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.
Summary 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. [PUBLICATION ABSTRACT]
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 greater than or equal to 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.
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.PURPOSEA 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.METHODSSubjects 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.KEY FINDINGSNineteen 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.SIGNIFICANCESome 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.
Author Hall, Charles B.
Haut, Sheryl R.
Lipton, Richard B.
Borkowski, Thomas
Tennen, Howard
AuthorAffiliation 2 Department of Neurology, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, New York
3 Department of Epidemiology and Population Health, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, New York
1 Montefiore-Einstein Epilepsy Center, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, New York
4 Community Medicine, University of Connecticut School of Medicine
AuthorAffiliation_xml – name: 2 Department of Neurology, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, New York
– name: 1 Montefiore-Einstein Epilepsy Center, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, New York
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– name: 3 Department of Epidemiology and Population Health, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, New York
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ContentType Journal Article
Copyright Wiley Periodicals, Inc. © 2013 International League Against Epilepsy
Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.
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Issue 11
Keywords Localization-related epilepsy
Seizure prediction
Premonitory symptoms
Seizure diary
Seizure precipitants
Electronic diary
Self-prediction
Language English
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Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.
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2010; 18
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2007a; 68
1989; 8
2008; 17
2011; 97
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1992; 13
2000; 1
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2007b; 69
1997; 6
1993; 2
2000; 38
2013; 12
2011; 21
2005; 6
1994; 35
2001; 18
1996; 47
2003; 60
2007; 64
2012; 25
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2009; 18
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22088481 - Epilepsy Res. 2011 Dec;97(3):231-5
2812181 - Neuroepidemiology. 1989;8(5):228-33
23642342 - Lancet Neurol. 2013 Jun;12(6):563-71
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16531010 - Epilepsy Res. 2006 Jul;70(1):83-8
12654956 - Neurology. 2003 Mar 25;60(6):935-40
12609456 - Epilepsy Behav. 2000 Aug;1(4):S9-S14
17998482 - Neurology. 2007 Nov 13;69(20):1905-10
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16861044 - Epilepsy Behav. 2006 Sep;9(2):298-306
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8156954 - Epilepsia. 1994 Mar-Apr;35(2):336-43
17242331 - Neurology. 2007 Jan 23;68(4):262-6
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Snippet Summary 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...
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...
Summary 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...
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StartPage 1960
SubjectTerms Adolescent
Adult
Affect - physiology
Child
Diagnostic Self Evaluation
Diaries
Electroencephalography - methods
Electronic diary
Epilepsy
Female
Humans
Localization‐related epilepsy
Male
Odds Ratio
Premonitory symptoms
Seizure diary
Seizure precipitants
Seizure prediction
Seizures - diagnosis
Seizures - physiopathology
Seizures - psychology
Self‐prediction
Time Factors
Young Adult
Title Modeling seizure self‐prediction: An e‐diary study
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fepi.12355
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Volume 54
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