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Zelig D, Goldberg I, Shor O, Ben Dor S, Yaniv-Rosenfeld A, Milikovsky DZ, Ofer J, Imtiaz H, Friedman A, Benninger F. Paroxysmal slow wave events predict epilepsy following a first seizure. Epilepsia 2021; 63:190-198. [PMID: 34750812 PMCID: PMC9298770 DOI: 10.1111/epi.17110] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/14/2021] [Accepted: 10/14/2021] [Indexed: 11/30/2022]
Abstract
Objective Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp electroencephalogram (EEG) to detect abnormal epileptiform activity. The decision to use antiseizure medication can be challenging when objective findings are missing. There is a need for new biomarkers to better diagnose epilepsy following a first seizure. Recently, an EEG‐based novel analytical method was reported to detect paroxysmal slowing in the cortical network of patients with epilepsy. The aim of our study is to test this method's sensitivity and specificity to predict epilepsy following a first seizure. Methods We analyzed interictal EEGs of 70 patients admitted to the emergency department of a tertiary referral center after a first seizure. Clinical data from a follow‐up period of at least 18 months were available. EEGs of 30 healthy controls were also analyzed and included. For each EEG, we applied an automated algorithm to detect paroxysmal slow wave events (PSWEs). Results Of patients presenting with a first seizure, 40% had at least one additional recurring seizure and were diagnosed with epilepsy. Sixty percent did not report additional seizures. A significantly higher occurrence of PSWEs was detected in the first interictal EEG test of those patients who were eventually diagnosed with epilepsy. Conducting the EEG test within 72 h after the first seizure significantly increased the likelihood of detecting PSWEs and the predictive value for epilepsy up to 82%. Significance The quantification of PSWEs by an automated algorithm can predict epilepsy and help the neurologist in evaluating a patient with a first seizure.
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Affiliation(s)
- Daniel Zelig
- Departments of Physiology and Cell Biology, Cognitive, and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ilan Goldberg
- Department of Neurology, Rabin Medical Center, Petach Tikva, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Oded Shor
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Felsenstein Medical Research Center, Beilinson Hospital, Petach Tikva, Israel
| | - Shira Ben Dor
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | | | - Dan Z Milikovsky
- Departments of Physiology and Cell Biology, Cognitive, and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jonathan Ofer
- Departments of Physiology and Cell Biology, Cognitive, and Brain Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Hamza Imtiaz
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alon Friedman
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Petach Tikva, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Felsenstein Medical Research Center, Beilinson Hospital, Petach Tikva, Israel
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