title: The number of seizures needed in the EMU.
abstract: OBJECTIVE
  The purpose of this study was to develop a quantitative framework to estimate the likelihood of multifocal epilepsy based on the number of unifocal seizures observed in the epilepsy monitoring unit (EMU).
  
  
  METHODS
  Patient records from the EMU at Massachusetts General Hospital (MGH) from 2012 to 2014 were assessed for the presence of multifocal seizures as well the presence of multifocal interictal discharges and multifocal structural imaging abnormalities during the course of the EMU admission. Risk factors for multifocal seizures were assessed using sensitivity and specificity analysis. A Kaplan-Meier survival analysis was used to estimate the risk of multifocal epilepsy for a given number of consecutive seizures. To overcome the limits of the Kaplan-Meier analysis, a parametric survival function was fit to the EMU subjects with multifocal seizures and this was used to develop a Bayesian model to estimate the risk of multifocal seizures during an EMU admission.
  
  
  RESULTS
  Multifocal interictal discharges were a significant predictor of multifocal seizures within an EMU admission with a p < 0.01, albeit with only modest sensitivity 0.74 and specificity 0.69. Multifocal potentially epileptogenic lesions on MRI were not a significant predictor p = 0.44. Kaplan-Meier analysis was limited by wide confidence intervals secondary to significant patient dropout and concern for informative censoring. The Bayesian framework provided estimates for the number of unifocal seizures needed to predict absence of multifocal seizures. To achieve 90% confidence for the absence of multifocal seizure, three seizures are needed when the pretest probability for multifocal epilepsy is 20%, seven seizures for a pretest probability of 50%, and nine seizures for a pretest probability of 80%.
  
  
  CONCLUSIONS
  These results provide a framework to assist clinicians in determining the utility of trying to capture a specific number of seizures in EMU evaluations of candidates for epilepsy surgery.
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A.
authors.forename: Aaron F
authors.initials: AF
authors.lastname: Struck
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A.
authors.forename: Andrew J
authors.identifier: http://orcid.org/0000-0002-0828-826X
authors.initials: AJ
authors.lastname: Cole
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A.
authors.forename: Sydney S
authors.initials: SS
authors.lastname: Cash
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A.
authors.forename: M Brandon
authors.initials: MB
authors.lastname: Westover
mesh_terms: D000328:Adult; D015331:Cohort Studies; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*
keywords: Bayesian inference; Epilepsy monitoring unit; Epilepsy surgery; Survival analysis