ArticleEpilepsia2023
Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury.
Article in Epilepsia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 17 citations in OpenAlex.
- High-frequency oscillations after acute hemorrhagic traumatic brain injury: insights into posttraumatic epilepsy development.Epilepsia · 2026Article
- Fast ripple-delta coupling as an early biomarker for post-traumatic epileptogenesis in repetitive brain injury.Epilepsia open · 2026Article
- Paroxysmal Cortical Slowing Predicts Posttraumatic Epilepsy After Severe Traumatic Brain Injury.Neurocritical care · 2026Article
- A Multifractal-Guided Machine Learning Framework for Late Post-Traumatic Seizure Prediction Following Hemorrhagic Traumatic Brain Injury.Research square · 2026Article
- Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.AIMS neuroscience · 2026Review
- Fast Ripple-Delta Coupling as Early Biomarker for Post-Traumatic Epileptogenesis in Repetitive Brain Injury.bioRxiv : the preprint server for biology · 2025Article
- Antiseizure Medication Prescription Patterns for Treatment of Post-Traumatic Epilepsy.Neurology. Clinical practice · 2025Article
- Prognostic models for seizures and epilepsy after stroke, tumors and traumatic brain injury.Clinical neurophysiology practice · 2025Review
- Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Utility of Electroencephalograms for Enhancing Clinical Care and Rehabilitation of Children with Acquired Brain Injury.International journal of environmental research and public health · 2024Review
- Insights into epileptogenesis from post-traumatic epilepsy.Nature reviews. Neurology · 2024Review
- Early hippocampal high-amplitude rhythmic spikes predict post-traumatic epilepsy in mice.bioRxiv : the preprint server for biology · 2024Article
- The predicative value of early quantitative electroencephalograph in epilepsy after severe traumatic brain injury in children.Frontiers in pediatrics · 2024Article
- Early hippocampal high-amplitude rhythmic spikes predict post-traumatic epilepsy in mice.Frontiers in neuroscience · 2024Article
- Computational Prognostic Modeling in Traumatic Brain Injury.Advances in experimental medicine and biology · 2024Review
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
objectivePosttraumatic 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.
methodsWe 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).
resultsWe 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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