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Pease M, Elmer J, Shahabadi AZ, Mallela AN, Ruiz-Rodriguez JF, Sexton D, Barot N, Gonzalez-Martinez JA, Shutter L, Okonkwo DO, Castellano JF. Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury. Epilepsia 2023; 64:1842-1852. [PMID: 37073101 DOI: 10.1111/epi.17622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 04/20/2023]
Abstract
OBJECTIVE Posttraumatic epilepsy (PTE) develops in as many as one third of severe traumatic brain injury (TBI) patients, often years after injury. Analysis of early electroencephalographic (EEG) features, by both standardized visual interpretation (viEEG) and quantitative EEG (qEEG) analysis, may aid early identification of patients at high risk for PTE. METHODS We performed a case-control study using a prospective database of severe TBI patients treated at a single center from 2011 to 2018. We identified patients who survived 2 years postinjury and matched patients with PTE to those without using age and admission Glasgow Coma Scale score. A neuropsychologist recorded outcomes at 1 year using the Expanded Glasgow Outcomes Scale (GOSE). All patients underwent continuous EEG for 3-5 days. A board-certified epileptologist, blinded to outcomes, described viEEG features using standardized descriptions. We extracted 14 qEEG features from an early 5-min epoch, described them using qualitative statistics, then developed two multivariable models to predict long-term risk of PTE (random forest and logistic regression). RESULTS We identified 27 patients with and 35 without PTE. GOSE scores were similar at 1 year (p = .93). The median time to onset of PTE was 7.2 months posttrauma (interquartile range = 2.2-22.2 months). None of the viEEG features was different between the groups. On qEEG, the PTE cohort had higher spectral power in the delta frequencies, more power variance in the delta and theta frequencies, and higher peak envelope (all p < .01). Using random forest, combining qEEG and clinical features produced an area under the curve of .76. Using logistic regression, increases in the delta:theta power ratio (odds ratio [OR] = 1.3, p < .01) and peak envelope (OR = 1.1, p < .01) predicted risk for PTE. SIGNIFICANCE In a cohort of severe TBI patients, acute phase EEG features may predict PTE. Predictive models, as applied to this study, may help identify patients at high risk for PTE, assist early clinical management, and guide patient selection for clinical trials.
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Affiliation(s)
- Matthew Pease
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | - Jonathan Elmer
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Critical Care, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ameneh Zare Shahabadi
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Arka N Mallela
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | - Juan F Ruiz-Rodriguez
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Daniel Sexton
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jorge A Gonzalez-Martinez
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | - Lori Shutter
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Critical Care, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - David O Okonkwo
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | - James F Castellano
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
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