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 in | Epilepsia (Copenhagen) Vol. 54; no. 11; pp. 1960 - 1967 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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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. |
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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 – name: 4 Community Medicine, University of Connecticut School of Medicine – name: 3 Department of Epidemiology and Population Health, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, New York |
Author_xml | – sequence: 1 givenname: Sheryl R. surname: Haut fullname: Haut, Sheryl R. organization: Montefiore Medical Center and the Albert Einstein College of Medicine – sequence: 2 givenname: Charles B. surname: Hall fullname: Hall, Charles B. organization: Montefiore Medical Center and the Albert Einstein College of Medicine – sequence: 3 givenname: Thomas surname: Borkowski fullname: Borkowski, Thomas organization: Montefiore Medical Center and the Albert Einstein College of Medicine – sequence: 4 givenname: Howard surname: Tennen fullname: Tennen, Howard organization: University of Connecticut School of Medicine – sequence: 5 givenname: Richard B. surname: Lipton fullname: Lipton, Richard B. organization: Montefiore Medical Center and the Albert Einstein College of Medicine |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24111898$$D View this record in MEDLINE/PubMed |
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Keywords | Localization-related epilepsy Seizure prediction Premonitory symptoms Seizure diary Seizure precipitants Electronic diary Self-prediction |
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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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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 |
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