Early follow-up data from seizure diaries can be used to predict subsequent seizures in same cohort by borrowing strength across participants

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Accurate prediction of seizures in persons with epilepsy offers opportunities for both precautionary measures and preemptive treatment. Previously identified predictors of seizures include patient-reported seizure anticipation, as well as stress, anxiety, and decreased sleep. In this study, we developed three models using 30 days of nightly seizure diary data in a cohort of 71 individuals with a history of uncontrolled seizures to predict subsequent seizures in the same cohort over a 30-day follow-up period. The best model combined the individual's seizure history with that of the remainder of the cohort, resulting in 72% sensitivity for 80% specificity, and 0.83 area under the receiver operating characteristic curve. The possibility of clinically relevant prediction should be examined through electronic data capture and more specific and more frequent sampling, and with patient training to improve prediction.

Original languageEnglish (US)
Pages (from-to)472-475
Number of pages4
JournalEpilepsy and Behavior
Volume14
Issue number3
DOIs
StatePublished - Mar 2009

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Seizures
ROC Curve
Epilepsy
Sleep
Anxiety
Sensitivity and Specificity

Keywords

  • Epilepsy
  • Generalized linear mixed models
  • Seizure prediction

ASJC Scopus subject areas

  • Clinical Neurology
  • Behavioral Neuroscience
  • Neurology

Cite this

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title = "Early follow-up data from seizure diaries can be used to predict subsequent seizures in same cohort by borrowing strength across participants",
abstract = "Accurate prediction of seizures in persons with epilepsy offers opportunities for both precautionary measures and preemptive treatment. Previously identified predictors of seizures include patient-reported seizure anticipation, as well as stress, anxiety, and decreased sleep. In this study, we developed three models using 30 days of nightly seizure diary data in a cohort of 71 individuals with a history of uncontrolled seizures to predict subsequent seizures in the same cohort over a 30-day follow-up period. The best model combined the individual's seizure history with that of the remainder of the cohort, resulting in 72{\%} sensitivity for 80{\%} specificity, and 0.83 area under the receiver operating characteristic curve. The possibility of clinically relevant prediction should be examined through electronic data capture and more specific and more frequent sampling, and with patient training to improve prediction.",
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AU - Lipton, Richard B.

AU - Tennen, Howard

AU - Haut, Sheryl R.

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AB - Accurate prediction of seizures in persons with epilepsy offers opportunities for both precautionary measures and preemptive treatment. Previously identified predictors of seizures include patient-reported seizure anticipation, as well as stress, anxiety, and decreased sleep. In this study, we developed three models using 30 days of nightly seizure diary data in a cohort of 71 individuals with a history of uncontrolled seizures to predict subsequent seizures in the same cohort over a 30-day follow-up period. The best model combined the individual's seizure history with that of the remainder of the cohort, resulting in 72% sensitivity for 80% specificity, and 0.83 area under the receiver operating characteristic curve. The possibility of clinically relevant prediction should be examined through electronic data capture and more specific and more frequent sampling, and with patient training to improve prediction.

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