Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury

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.

Early hippocampal high-amplitude rhythmic spikes predict post-traumatic epilepsy in mice

“…Both acute and subacute EEG have been increasingly collected as a routine procedure after TBI to facilitate diagnosis and indicate the prognosis of TBI 16 . Our findings align with a recent case-control study showing quantitative EEG features, including higher spectral power in the delta wave during early admission (within 3-5 days), can discriminate the risk of PTE after severe TBI 5 . Secondly, EEG provides a noninvasive real-time monitoring of the brain state and offers non-linear measurement of the dynamic changes of oscillations, reflecting the biological functions of the brain.…”

“…Many descriptive and quantitative EEG alterations have been documented following TBI, including early post-traumatic seizures, high amplitude sharp waves, high-frequency oscillation (HFO), repetitive HFOs and spikes, sleep spindle duration, and EEG power change in both directions 2,3 . Retrospective case-controlled studies suggest some of these quantitative EEG features hold promise as predictive indicators for PTE [4][5][6][7] . However, these patients studies are face limitions such as poorly controlled environments, bias toward severe injury in participant selection, and insufficient consideration of patients' genetics, demographic information, comorbid conditions, or medication history 8 .…”