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%. SIGNIFICANCE: 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. Wiley Periodicals, Inc.
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%. SIGNIFICANCE: 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. Wiley Periodicals, Inc.
Authors: L E Jeha; I M Najm; W E Bingaman; F Khandwala; P Widdess-Walsh; H H Morris; D S Dinner; D Nair; N Foldvary-Schaeffer; R A Prayson; Y Comair; R O'Brien; J Bulacio; A Gupta; H O Lüders Journal: Neurology Date: 2006-06-27 Impact factor: 9.910
Authors: Jerome Engel; Michael P McDermott; Samuel Wiebe; John T Langfitt; John M Stern; Sandra Dewar; Michael R Sperling; Irenita Gardiner; Giuseppe Erba; Itzhak Fried; Margaret Jacobs; Harry V Vinters; Scott Mintzer; Karl Kieburtz Journal: JAMA Date: 2012-03-07 Impact factor: 56.272
Authors: B V Savitr Sastri; A Arivazhagan; Sanjib Sinha; Anita Mahadevan; R D Bharath; J Saini; R Jamuna; J Keshav Kumar; S L Rao; B A Chandramouli; S K Shankar; P Satishchandra Journal: J Neurol Sci Date: 2014-03-19 Impact factor: 3.181
Authors: Daniel M Goldenholz; Alexander Jow; Omar I Khan; Anto Bagić; Susumu Sato; Sungyoung Auh; Conrad Kufta; Sara Inati; William H Theodore Journal: Epilepsy Res Date: 2016-09-22 Impact factor: 3.045
Authors: Joseph J Tharayil; Sharon Chiang; Robert Moss; John M Stern; William H Theodore; Daniel M Goldenholz Journal: Epilepsia Date: 2017-03-30 Impact factor: 5.864
Authors: Stephen V Gliske; Zachary T Irwin; Cynthia Chestek; Garnett L Hegeman; Benjamin Brinkmann; Oren Sagher; Hugh J L Garton; Greg A Worrell; William C Stacey Journal: Nat Commun Date: 2018-06-01 Impact factor: 14.919