Risk Factors and Outcomes of Late Posttraumatic Seizures in Combat-Related Traumatic Brain Injury.

INTRODUCTION: Post-traumatic seizures (PTSs) contribute to morbidity after traumatic brain injury (TBI). Early PTS are rare in combat casualties sustaining TBI, but the prevalence of late PTS is poorly described. We sought to define the prevalence and risk factors of late PTS in combat casualties with computed tomography evidence of TBI. METHODS: From 2010 to 2015, 687 combat casualties were transferred to a military treatment facility and included in the Department of Defense Trauma Registry. 71 patients with radiographic evidence of TBI were analyzed. Data collection included demographics, injury characteristics, interventions, medications, and outcomes. RESULTS: Of the 71 patients with evidence of TBI, 66 patients survived hospitalization and were followed. No patients had early PTS, and most received antiepileptic drugs (AEDs) for prophylaxis. At a median follow-up of 7.4 y, late PTS occurred in 25.8% of patients. Patients with late PTS were more severely injured (median Injury severity score 30 versus 24, P = 0.005) and required more blood products (18 units versus 2, P = 0.045). Patients with late PTS were more likely to have had a penetrating TBI (76.5% versus 38.8%, P = 0.01), multiple types of intracranial hemorrhage (94.1% versus 63.3%, P = 0.02), and cranial decompression (76.5% versus 28.6%, P = 0.001). Six-month Glasgow outcome scores were worse (3.5 versus 4.1 P = 0.001) in the late PTS population. No significant relationship was observed between administration of AEDs for early PTS prophylaxis and late PTS. CONCLUSIONS: Combat casualties with TBI suffering late PTS are more severely injured and require more blood products. Penetrating TBI, intracranial hemorrhage, and need for cranial decompression are correlated with late PTS, and associated with worse Glasgow Outcome Score. The administration of prophylactic AEDs for early PTS was not associated with a difference in rates of late PTS.

EEG biomarkers for the prediction of post-traumatic epilepsy - a systematic review of an emerging field.

Traumatic brain injury (TBI) is often followed by post-traumatic epilepsy (PTE), a condition often difficult to treat and leading to a substantial decline in quality of life as well as increased long-term mortality. The latent period between TBI and the emergence of spontaneous recurrent seizures provides an opportunity for pharmacological intervention to prevent epileptogenesis. Biomarkers capable of predicting PTE development are urgently needed to facilitate clinical trials of putative anti-epileptogenic drugs. EEG is a widely available and flexible diagnostic modality that plays a fundamental role in epileptology. We systematically review the advances in the field of the discovery of EEG biomarkers for the prediction of PTE in humans. Despite recent progress, the field faces several challenges including short observation periods, a focus on early post-injury monitoring, difficulties in translating findings from animal models to scalp EEG, and emerging evidence indicating the importance of assessing altered background scalp EEG activity alongside epileptiform activity using quantitative EEG methods while also considering sleep abnormalities in future studies.

Prognostic Implications of Early Prediction in Posttraumatic Epilepsy.

Posttraumatic epilepsy (PTE) is a complication of traumatic brain injury that can increase morbidity, but predicting which patients may develop PTE remains a challenge. Much work has been done to identify a variety of risk factors and biomarkers, or a combination thereof, for patients at highest risk of PTE. However, several issues have hampered progress toward fully adapted PTE models. Such issues include the need for models that are well-validated, cost-effective, and account for competing outcomes like death. Additionally, while an accurate PTE prediction model can provide quantitative prognostic information, how such information is communicated to inform shared decision-making and treatment strategies requires consideration of an individual patient's clinical trajectory and unique values, especially given the current absence of direct anti-epileptogenic treatments. Future work exploring approaches integrating individualized communication of prediction model results are needed.

Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury.

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.

Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy.

BACKGROUND: Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1). METHODS: We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. RESULTS: In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). CONCLUSIONS: Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.

Chemical Kindling as an Experimental Model to Assess the Conventional Drugs in the Treatment of Post-traumatic Epilepsy.

BACKGROUND: Traumatic brain injury (TBI) is one of the leading causes of morbidity and mortality today, which will surpass many infectious diseases in the coming years/decades. Posttraumatic epilepsy (PTE) is one of the most common debilitating consequences of TBI. PTE is a secondary, acquired epilepsy that causes recurrent, spontaneous seizures more than a week after TBI. The extent of head injury in individuals who develop PTE is unknown; however, trauma is thought to account for 20% of symptomatic epilepsy worldwide. Understanding the mechanisms of epilepsy following TBI is crucial for the discovery of new anticonvulsant drugs for the treatment of PTE, as well as for improving the quality of life of patients with PTE. OBJECTIVE: This review article explains the rationale for the usage of a chemical model to access new treatments for post-traumatic epilepsy. RESULTS: There are multiple methods to control and manage PTE. The essential and available remedy for the management of epilepsy is the use of antiepileptic drugs. Antiepileptic drugs (AEDs) decrease the frequency of seizures without affecting the disease's causality. Antiepileptic drugs are administrated for the prevention and treatment of PTE; however, 30% of epilepsy patients are drug-resistant, and AED side effects are significant in PTE patients. There are different types of animal models, such as the liquid percussion model, intracortical ferric chloride injection, and cortical subincision model, to study PTE and neurophysiological mechanisms underlying the development of epilepsy after head injury. However, these animal models do not easily mimic the pathological events occurring in epilepsy. Therefore, animal models of PTE are an inappropriate tool for screening new and putatively effective AEDs. Chemical kindling is the most common animal model used to study epilepsy. There is a strong similarity between the kindling model and different types of human epilepsy. CONCLUSION: Today, researchers use experimental animal models to evaluate new anticonvulsant drugs. The chemical kindling models, such as pentylenetetrazol, bicuculline, and picrotoxin-induced seizures, are important experimental models to analyze the impact of putative antiepileptic drugs.

Thyroid function in the subacute phase of traumatic brain injury: a potential predictor of post-traumatic neurological and functional outcomes.

PURPOSE: That thyroid hormones exert pleiotropic effects and have a contributory role in triggering seizures in patients with traumatic brain injury (TBI) can be hypothesized. We aimed at investigating thyroid function tests as prognostic factors of the development of seizures and of functional outcome in TBI. METHODS: This retrospective study enrolled 243 adult patients with a diagnosis of mild-to-severe TBI, consecutively admitted to our rehabilitation unit for a 6-month neurorehabilitation program. Data on occurrence of seizures, brain imaging, injury characteristics, associated neurosurgical procedures, neurologic and functional assessments, and death during hospitalization were collected at baseline, during the workup and on discharge. Thyroid function tests (serum TSH, fT4, and fT3 levels) were performed upon admission to neurorehabilitation. RESULTS: Serum fT3 levels were positively associated with an increased risk of late post-traumatic seizures (LPTS) in post-TBI patients independent of age, sex and TBI severity (OR = 1.85, CI 95% 1.22-2.61, p < 0.01). Measured at admission, fT3 values higher than 2.76 pg/mL discriminated patients with late post-traumatic seizures from those without, with a sensitivity of 74.2% and a specificity of 60.9%. Independently from the presence of post-traumatic epilepsy and TBI severity, increasing TSH levels and decreasing fT3 levels were associated with worse neurological and functional outcome, as well as with higher risk of mortality within 6 months from the TBI event. CONCLUSIONS: Serum fT3 levels assessed in the subacute phase post-TBI are associated with neurological and functional outcome as well as with the risk of seizure occurrence. Further studies are needed to investigate the mechanisms underlying these associations.

Automatic Detection of EEG Epileptiform Abnormalities in Traumatic Brain Injury using Deep Learning.

Traumatic brain injury (TBI) is a sudden injury that causes damage to the brain. TBI can have wide-ranging physical, psychological, and cognitive effects. TBI outcomes include acute injuries, such as contusion or hematoma, as well as chronic sequelae that emerge days to years later, including cognitive decline and seizures. Some TBI patients develop posttraumatic epilepsy (PTE), or recurrent and unprovoked seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis, the process by which a normal brain becomes capable of generating seizures. These biomarkers would allow for a higher standard of care by identifying patients at risk of developing PTE as candidates for antiepileptogenic interventions. In this paper, we use deep neural network architectures to automatically detect potential biomarkers of PTE from electroencephalogram (EEG) data collected between post-injury day 1-7 from patients with moderate-to-severe TBI. Continuous EEG is often part of multimodal monitoring for TBI patients in intensive care units. Clinicians review EEG to identify the presence of epileptiform abnormalities (EAs), such as seizures, periodic discharges, and abnormal rhythmic delta activity, which are potential biomarkers of epileptogenesis. We show that a recurrent neural network trained with continuous EEG data can be used to identify EAs with the highest accuracy of 80.78%, paving the way for robust, automated detection of epileptiform activity in TBI patients.

[Early predictive biomarkers of posttraumatic epilepsy]. / Rannie prediktivnye biomarkery posttravmaticheskoi epilepsii.

Traumatic brain injury (TBI) affects about 50 million people in the world every year. Posttraumatic epilepsy (PTE) is a significant complication of TBI of any severity. PTE occurs in 20% of patients with TBI. Treatment of patients with PTE is particularly difficult due to obvious tendency towards drug resistance. Currently, there are no validated predictive biomarkers for PTE. Development of a system of validated predictive markers would improve PTE prediction quality and therapeutic approach for these patients. This review is devoted to the current data on the most perspective predictive biomarkers of PTE for clinical practice.

Biomarkers for posttraumatic epilepsy.

A biomarker is a characteristic that can be objectively measured as an indicator of normal biologic processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarker modalities include molecular, histologic, radiographic, or physiologic characteristics. To improve the understanding and use of biomarker terminology in biomedical research, clinical practice, and medical product development, the Food and Drug Administration (FDA)-National Institutes of Health (NIH) Joint Leadership Council developed the BEST Resource (Biomarkers, EndpointS, and other Tools). The seven BEST biomarker categories include the following: (a) susceptibility/risk biomarkers, (b) diagnostic biomarkers, (c) monitoring biomarkers, (d) prognostic biomarkers, (e) predictive biomarkers, (f) pharmacodynamic/response biomarkers, and (g) safety biomarkers. We hypothesize some potential overlap between the reported biomarkers of traumatic brain injury (TBI), epilepsy, and posttraumatic epilepsy (PTE). Here, we tested this hypothesis by reviewing studies focusing on biomarker discovery for posttraumatic epileptogenesis and epilepsy. The biomarker modalities reviewed here include plasma/serum and cerebrospinal fluid molecular biomarkers, imaging biomarkers, and electrophysiologic biomarkers. Most of the reported biomarkers have an area under the receiver operating characteristic curve greater than 0.800, suggesting both high sensitivity and high specificity. Our results revealed little overlap in the biomarker candidates between TBI, epilepsy, and PTE. In addition to using single parameters as biomarkers, machine learning approaches have highlighted the potential for utilizing patterns of markers as biomarkers. Although published data suggest the possibility of identifying biomarkers for PTE, we are still in the early phase of the development curve. Many of the seven biomarker categories lack PTE-related biomarkers. Thus, further exploration using proper, statistically powered, and standardized study designs with validation cohorts, and by developing and applying novel analytical methods, is needed for PTE biomarker discovery.

Progress in Research on Biomarkers of Post-Traumatic Epilepsy.

ABSTRACT: Post traumatic epilepsy （PTE） is a serious complication of traumatic brain injury and a difficult problem in forensic justice practice. In recent years, many biomarkers have been applied to the diagnosis, treatment and prognosis of injuries and diseases. There have been many studies on the biomarkers of PTE in the field of epilepsy. This paper reviews the progress in research on biomarkers of PTE in recent years in order to provide reference for the forensic identification of PTE.

A review of seizures and epilepsy following traumatic brain injury.

Traumatic brain injury (TBI) is one of the commonest presentations to emergency departments and is associated with seizures carrying different significance at different stages following injury. We describe the epidemiology of early and late seizures following TBI, the significance of intracranial haemorrhage of different types in the risk of later epilepsy and the gaps in current understanding of risk factors contributing to the risk of post-traumatic epilepsy (PTE). The delay from injury to epilepsy presents an opportunity to understand the mechanisms underlying changes in the brain and how they may reveal potential targets for anti-epileptogenic therapy. We review existing treatments, both medical and surgical and conclude that current research is not tailored to differentiate between PTE and other forms of focal epilepsy. Finally, we review the increasing understanding of the frequency and significance of dissociative seizures following mild TBI.

Epileptogenesis, traumatic brain injury, and biomarkers.

Epilepsy is one of the most common brain disorders, causing serious disability and premature death worldwide. Approximately 1.2% of the U.S. population has active epilepsy, and 30 to 40% have seizures that do not respond to antiseizure drugs. There currently is no treatment available that prevents epilepsy following a potential epileptogenic insult, and the search for disease or syndrome modifying interventions for epilepsy is a high priority of neurobiological research. This requires better understanding of neuronal mechanisms underlying the development of epilepsy, and biomarkers of this process that would permit cost-effective drug discovery, and validation in clinical trials, for potential antiepileptogenic compounds. EpiBioS4Rx is an NIH-funded Center without Walls consisting of collaborative investigations in the United States, Europe, and Australia of traumatic brain injury in patients, and a standardized animal model, to identify biomarkers of epileptogenesis and to determine their ability to assess the effectiveness of potential antiepileptogenic agents. Successful completion of this project is expected to result in design of an economically feasible, full-scale clinical trial of at least one antiepileptogenic intervention.

Breakdown of blood brain barrier as a mechanism of post-traumatic epilepsy.

Traumatic brain injury (TBI) accounts for approximately 16% of acute symptomatic seizures which usually occur in the first week after trauma. Children are at higher risk for post-traumatic seizures than adults. Post-traumatic seizures are a risk factor for delayed development of epilepsy. Delayed, chronic post-traumatic epilepsy is preceded by a silent period during which therapeutic interventions may arrest, revert or prevent epileptogenesis. A number of recent review articles summarize the most important features of post-traumatic seizures and epilepsy; this review will instead focus on the link between cerebrovascular permeability, epileptogenesis and ictal events after TBI. The possibility of acting on the blood-brain barrier (BBB) and the neurovascular unit to prevent, disrupt or treat post-traumatic epilepsy is also discussed. Finally, we describe the latest quest for biomarkers of epileptogenesis which may allow for a more targeted intervention.

Modelling traumatic brain injury and posttraumatic epilepsy in rodents.

Posttraumatic epilepsy (PTE) is one of the most debilitating and understudied consequences of traumatic brain injury (TBI). It is challenging to study the effects, underlying pathophysiology, biomarkers, and treatment of TBI and PTE purely in human patients for a number of reasons. Rodent models can complement human PTE studies as they allow for the rigorous investigation into the causal relationship between TBI and PTE, the pathophysiological mechanisms of PTE, the validation and implementation of PTE biomarkers, and the assessment of PTE treatments, in a tightly controlled, time- and cost-efficient manner in experimental subjects known to be experiencing epileptogenic processes. This article will review several common rodent models of TBI and/or PTE, including their use in previous studies and discuss their relative strengths, limitations, and avenues for future research to advance our understanding and treatment of PTE.

The epilepsy bioinformatics study for anti-epileptogenic therapy (EpiBioS4Rx) clinical biomarker: Study design and protocol.

The Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy (EpiBioS4Rx) is a longitudinal prospective observational study funded by the National Institute of Health (NIH) to discover and validate observational biomarkers of epileptogenesis after traumatic brain injury (TBI). A multidisciplinary approach has been incorporated to investigate acute electrical, neuroanatomical, and blood biomarkers after TBI that may predict the development of post-traumatic epilepsy (PTE). We plan to enroll 300 moderate-severe TBI patients with a frontal and/or temporal lobe hemorrhagic contusion. Acute evaluation with blood, imaging and electroencephalographic monitoring will be performed and then patients will be tracked for 2 years to determine the incidence of PTE. Validation of selected biomarkers that are discovered in planned animal models will be a principal feature of this work. Specific hypotheses regarding the discovery of biomarkers have been set forth in this study. An international cohort of 13 centers spanning 2 continents will be developed to facilitate this study, and for future interventional studies.

Algorithm for automatic detection of spontaneous seizures in rats with post-traumatic epilepsy.

BACKGROUND: Labor intensive electroencephalogram (EEG) analysis is a major bottleneck to identifying anti-epileptogenic treatments in experimental models of post-traumatic epilepsy. We aimed to develop an algorithm for automated seizure detection in experimental post-traumatic epilepsy. NEW METHOD: Continuous (24/7) 1-month-long video-EEG monitoring with three epidural screw electrodes was started 154 d after lateral fluid-percussion induced traumatic brain injury (TBI; n = 97) or sham-injury (n = 29) in adult male Sprague-Dawley rats. First, an experienced researcher screened a total of 90,720 h of digitized recordings on a computer screen to annotate the occurrence of spontaneous seizures. The same files were then analyzed using an algorithm in Spike2 (ver.9), which searching for temporally linked power peaks (14-42 Hz) in all three EEG channels, and then positive events were marked as a probable seizures. Finally, an experienced researcher confirmed all seizure candidates visually on the computer screen. RESULTS: Visual analysis identified 197 seizures in 29 rats. Automatic detection identified 4346 seizure candidates in 109 rats, of which 202 in the same 29 rats were true positives, resulting in a false positive rate of 0.046/h or 1.10/d. The algorithm demonstrated 5% specificity and 100% sensitivity. The algorithm analyzed 1-month 3-channel EEG in 7 cohorts in 2 h, whereas analysis by an experienced technician took ∼500 h. COMPARISON WITH EXISTING METHODS: The algorithm had 100% sensitivity. It performed slightly better and was substantially faster than investigator-performed visual analysis. CONCLUSIONS: We present a novel seizure detection algorithm for automated detection of seizures in a rat model of post-traumatic epilepsy.

Epileptiform activity in traumatic brain injury predicts post-traumatic epilepsy.

We hypothesize that epileptiform abnormalities (EAs) in the electroencephalogram (EEG) during the acute period following traumatic brain injury (TBI) independently predict first-year post-traumatic epilepsy (PTE1 ). We analyze PTE1 risk factors in two cohorts matched for TBI severity and age (n = 50). EAs independently predict risk for PTE1 (odds ratio [OR], 3.16 [0.99, 11.68]); subdural hematoma is another independent risk factor (OR, 4.13 [1.18, 39.33]). Differences in EA rates are apparent within 5 days following TBI. Our results suggest that increased EA prevalence identifies patients at increased risk for PTE1 , and that EAs acutely post-TBI can identify patients most likely to benefit from antiepileptogenesis drug trials. Ann Neurol 2018;83:858-862.

[Posttraumatic seizures: a prospective cohort study]. / Posttravmaticheskie pristupy: prospektivnoe kogortnoe issledovanie.

AIM: To evaluate the incidence and risk factors for posttraumatic seizures (PTS). MATERIAL AND METHODS: The authors conducted a prospective study of 237 patients with TBI of varying severity. The patients were hospitalized and examined in Moscow neurosurgery departments. Then they participated in the follow-up observation for 2 years. PTS were classified as early (occurred from 1 to 7 days after TBI) and late (occurred later than 7 days). RESULTS AND CONCLUSION: Forty-three people (18.1%) experienced early seizures, 15 patients (6.3%) had late seizures. The early seizures were the significant predictor of the late seizures. In the group of patients with early seizures, the proportion of severe TBI was significantly higher. Subdural hematoma, depressed skull fracture, alcohol abuse were reliable predictors of early and late PTS. Thus, these factors increased the risk of posttraumatic epilepsy (PTE).