Risk Of Post-traumatic Epilepsy Research Articles

Gut-microbiota-brain Axis and post-traumatic epilepsy.

There has been growing evidence that perturbations in gut-microbiota-brain axis (GMBA) are involved in mechanisms of chronic sequelae of traumatic brain injury (TBI). This review discusses the connection between GMBA and post-traumatic epilepsy (PTE), the latter being a common outcome of TBI. The focus is on two aspects of post-TBI GMBA dysfunction that are relevant to epilepsy. First are impairments in intestinal permeability with subsequent translocation of gut bacteria into the bloodstream. Specifically, endotoxemia following TBI may have a serendipitous protective effect against PTE through lipopolysaccharide conditioning, which may be leveraged for the development of therapeutic interventions. Second are changes in microbial composition (i.e., dysbiosis). Here, the GMBA-PTE connection is explored from predictive biomarker perspective, whereby the risk of PTE can be stratified based on specific microbial profiles. Finally, microbiota transplantation is discussed both as a tool to examine the role of gut microbiota in PTE and as a prelude to novel approaches for PTE therapy and prevention.

Development of individualized risk assessment models for predicting post-traumatic epilepsy 1 and 2 years after moderate-to-severe traumatic brain injury: A traumatic brain injury model system study.

Although traumatic brain injury (TBI) and posttraumatic epilepsy (PTE) are common, there are no prospective models quantifying individual epilepsy risk after moderate-to-severe TBI (msTBI). We generated parsimonious prediction models to quantify individual epilepsy risk between acute inpatient rehabilitation for individuals 2 years after msTBI. We used data from 6089 prospectively enrolled participants (≥16 years) in the TBI Model Systems National Database. Of these, 4126 individuals had complete seizure data collected over a 2-year period post-injury. We performed a case-complete analysis to generate multiple prediction models using least absolute shrinkage and selection operator logistic regression. Baseline predictors were used to assess 2-year seizure risk (Model 1). Then a 2-year seizure risk was assessed excluding the acute care variables (Model 2). In addition, we generated prognostic models predicting new/recurrent seizures during Year 2 post-msTBI (Model 3) and predicting new seizures only during Year 2 (Model 4). We assessed model sensitivity when keeping specificity ≥.60, area under the receiver-operating characteristic curve (AUROC), and AUROC model performance through 5-fold cross-validation (CV). Model 1 (73.8% men, 44.1 ± 19.7 years, 76.1% moderate TBI) had a model sensitivity = 76.00% and average AUROC = .73 ± .02 in 5-fold CV. Model 2 had a model sensitivity = 72.16% and average AUROC = .70 ± .02 in 5-fold CV. Model 3 had a sensitivity = 86.63% and average AUROC = .84 ± .03 in 5-fold CV. Model 4 had a sensitivity = 73.68% and average AUROC = .67 ± .03 in 5-fold CV. Cranial surgeries, acute care seizures, intracranial fragments, and traumatic hemorrhages were consistent predictors across all models. Demographic and mental health variables contributed to some models. Simulated, clinical examples model individual PTE predictions. Using information available, acute-care, and year-1 post-injury data, parsimonious quantitative epilepsy prediction models following msTBI may facilitate timely evidence-based PTE prognostication within a 2-year period. We developed interactive web-based tools for testing prediction model external validity among independent cohorts. Individualized PTE risk may inform clinical trial development/design and clinical decision support tools for this population.

Risk of epilepsy after traumatic brain injury: a nationwide Norwegian matched cohort study.

Post-traumatic epilepsy (PTE) is a well-known complication of traumatic brain injury (TBI). Although several risk factors have been identified, prediction of PTE is difficult. Changing demographics and advances in TBI treatment may affect the risk of PTE. Our aim was to provide an up-to-date estimate of the incidence of PTE by linking multiple nationwide registers. Patients with TBI admitted to hospital 2015-2018 were identified in the Norwegian Trauma Registry and matched to trauma-free controls on sex and birth year according to a matched cohort design. They were followed up for epilepsy in nationwide registers 2015-2020. Cumulative incidence of epilepsy in TBI patients and controls was estimated taking competing risks into account. Analyses stratified by the Abbreviated Injury Scale (AIS) severity score, Glasgow Coma Scale score and age were conducted for the TBI group. Occurrence of PTE in different injury types was visualized using UpSet plots. In total, 8,660 patients and 84,024 controls were included in the study. Of the patients, 3,029 (35%) had moderate to severe TBI. The cumulative incidence of epilepsy in the TBI group was 3.1% (95% Confidence Interval [CI] 2.8-3.5%) after 2 years and 4.0% (3.6-4.5%) after 5 years. Corresponding cumulative incidences in the control group were 0.2% (95% CI 0.2-0.3%) and 0.5% (0.5-0.6%). The highest incidence was observed in patients with severe TBI according to AIS (11.8% [95% CI 9.7-14.4%] after 2 years and 13.2% [10.8-16.0%] after 5 years) and in patients >40 years of age. Patients with TBI have significantly higher risk of developing epilepsy compared to population controls. However, PTE incidence following moderate-severe TBI was notably lower than what has been reported in several previously published 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.

Remote Network Effects of Post-traumatic Lesions and Risk of Post-traumatic Epilepsy (P5-1.010)

Remote Network Effects of Post-traumatic Lesions and Risk of Post-traumatic Epilepsy (P5-1.010)

Insights into epileptogenesis from post-traumatic epilepsy.

Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies. The incidence of PTE after traumatic brain injury (TBI) depends on the severity of injury, approaching one in three in groups with the most severe injuries. The repeated seizures that characterize PTE impair neurological recovery and increase the risk of poor outcomes after TBI. Given this high risk of recurrent seizures and the relatively short latency period for their development after injury, PTE serves as a model disease to understand human epileptogenesis and trial novel anti-epileptogenic therapies. Epileptogenesis is the process whereby previously normal brain tissue becomes prone to recurrent abnormal electrical activity, ultimately resulting in seizures. In this Review, we describe the clinical course of PTE and highlight promising research into epileptogenesis and treatment using animal models of PTE. Clinical, imaging, EEG and fluid biomarkers are being developed to aid the identification of patients at high risk of PTE who might benefit from anti-epileptogenic therapies. Studies in preclinical models of PTE have identified tractable pathways and novel therapeutic strategies that can potentially prevent epilepsy, which remain to be validated in humans. In addition to improving outcomes after TBI, advances in PTE research are likely to provide therapeutic insights that are relevant to all epilepsies.

Epidemiology, Risk Factors, and Biomarkers of Post-Traumatic Epilepsy: A Comprehensive Overview.

This paper presents an in-depth exploration of Post-Traumatic Epilepsy (PTE), a complex neurological disorder following traumatic brain injury (TBI), characterized by recurrent, unprovoked seizures. With TBI being a global health concern, understanding PTE is crucial for effective diagnosis, management, and prognosis. This study aims to provide a comprehensive overview of the epidemiology, risk factors, and emerging biomarkers of PTE, thereby informing clinical practice and guiding future research. The epidemiological aspect of the study reveals PTE as a significant contributor to acquired epilepsies, with varying incidence influenced by injury severity, age, and intracranial pathologies. The paper delves into the multifactorial nature of PTE risk factors, encompassing clinical, demographic, and genetic elements. Key insights include the association of injury severity, intracranial hemorrhages, and early seizures with increased PTE risk, and the roles of age, gender, and genetic predispositions. Advancements in neuroimaging, electroencephalography, and molecular biology are presented, highlighting their roles in identifying potential PTE biomarkers. These biomarkers, ranging from radiological signs to electroencephalography EEG patterns and molecular indicators, hold promise for enhancing PTE pathogenesis understanding, early diagnosis, and therapeutic guidance. The paper also discusses the critical roles of astrocytes and microglia in PTE, emphasizing the significance of neuroinflammation in PTE development. The insights from this review suggest potential therapeutic targets in neuroinflammation pathways. In conclusion, this paper synthesizes current knowledge in the field, emphasizing the need for continued research and a multidisciplinary approach to effectively manage PTE. Future research directions include longitudinal studies for a better understanding of TBI and PTE outcomes, and the development of targeted interventions based on individualized risk profiles. This research contributes significantly to the broader understanding of epilepsy and TBI.

Neuropharmacological insight into preventive intervention in posttraumatic epilepsy based on regulating glutamate homeostasis.

Posttraumatic epilepsy (PTE) is one of the most critical complications of traumatic brain injury (TBI), significantly increasing TBI patients' neuropsychiatric symptoms and mortality. The abnormal accumulation of glutamate caused by TBI and its secondary excitotoxicity are essential reasons for neural network reorganization and functional neural plasticity changes, contributing to the occurrence and development of PTE. Restoring glutamate balance in the early stage of TBI is expected to play a neuroprotective role and reduce the risk of PTE. To provide a neuropharmacological insight for drug development to prevent PTE based on regulating glutamate homeostasis. We discussed how TBI affects glutamate homeostasis and its relationship with PTE. Furthermore, we also summarized the research progress of molecular pathways for regulating glutamate homeostasis after TBI and pharmacological studies aim to prevent PTE by restoring glutamate balance. TBI can lead to the accumulation of glutamate in the brain, which increases the risk of PTE. Targeting the molecular pathways affecting glutamate homeostasis helps restore normal glutamate levels and is neuroprotective. Taking glutamate homeostasis regulation as a means for new drug development can avoid the side effects caused by direct inhibition of glutamate receptors, expecting to alleviate the diseases related to abnormal glutamate levels in the brain, such as PTE, Parkinson's disease, depression, and cognitive impairment. It is a promising strategy to regulate glutamate homeostasis through pharmacological methods after TBI, thereby decreasing nerve injury and preventing PTE.

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

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. 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). 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. 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.

337 Quantitative Analysis of Admission Electroencephalography Predicts Post-Traumatic Epilepsy After Severe Traumatic Brain Injury

INTRODUCTION: Up to one-third of severe traumatic brain injury (TBI) patients develop post-traumatic epilepsy (PTE), often years after their injury. Currently, there is no validated predictive model to identify patients at high risk for PTE. METHODS: We retrospectively analyzed a prospective database of severe TBI patients treated at a single level one trauma center from 2012 through 2018. Outcomes were recorded with the Expanded Glasgow Outcomes Scale (GOSE) at 6- and 12-months post-injury. We identified a cohort of patients who survived to two years and did not have an early post-traumatic seizure, defined as a seizure within 7 days of initial injury. From this cohort, we matched patients with PTE to those without using age and Glasgow coma scale score at admission. All patients underwent continuous EEG for 3-5 days upon admission, and a board-certified epileptologist identified 5-minute artifact-free segment for analysis. We extracted qEEG features, described them with qualitative statistics, and developed a logistic regression model using backwards selection. RESULTS: We identified 27 patients with PTE and 23 without who survived two years post-injury. The median time to onset of PTE was 7.2 months post-trauma and GOSE was similar when stratified by PTE at 6- and 12-months (p &gt; 0.73). On qEEG analysis, patients with PTE had higher spectral power in the delta frequencies (p = 0.003), more variance in the delta and theta frequencies (p &lt; 0.04), a higher mean amplitude (p = 0.01), and peak envelope (p = 0.01). On multivariate modeling, delta power (Odds Ratio [OR] 1.8), theta power (OR 0.2), and rhythmic spectrum (1.7) all increased risk of PTE and had an AUC of 0.85 for predicting PTE. CONCLUSIONS: In a cohort of severe TBI patients, quantitative qEEG features in the acute phase after injury identified future risk of PTE.

Abstract TP229: Impact Of Genetic Polymorphisms On The Risk Of Epilepsy Amongst Patients With Acute Brain Injury: A Systematic Review

Introduction: Stroke and traumatic brain injury (TBI) accounts for 70% of secondary epilepsy in the adult population. The genetic architecture of epilepsy secondary to TBI or stroke is poorly understood. Objective: We undertook a systematic review to test the association of single nucleotide polymorphisms (SNPs) with the risk of posttraumatic epilepsy (PTE) and post-stroke epilepsy (PSE). Methods: We conducted a comprehensive literature search until 5 July 2022 in PubMed, Embase, PsycINFO, Web of Science, and Google Scholar. We preregistered the protocol of this systematic review on PROSPERO (CRD42022325617). We collated the association statistics from the articles to assess the association of SNPs with the risk of epilepsy amongst TBI or stroke patients. We assessed the study quality using the Quality of Genetic Association (Q-Genie) tool. We report Odds Ratio (OR) and Hazard Ratio (HR) with a 95% confidence interval (CI), including combined OR where a meta-analysis was possible. Results: The literature search yielded 420 articles, of which 16 were included in our systematic review. Q-Genie-based assessment of the literature found that 58% of the included studies were of poor quality. We examined published data on 127 SNPs from 32 genes identified in PTE and PSE patients. Twelve studies reported that 718 TBI patients (21%) suffered from PTE. Four studies reported PSE in 1192 stroke patients (50%). Eleven SNPs were associated with an increased risk of PTE. Three SNPs, TRMP6 rs2274924, ALDH2 rs671, CD40 -1C/T, were significantly associated with an increased risk of PSE, while two SNPs, AT1R rs12721273 and rs55707609, were significantly associated with reduced risk. Only two studies tested the association of APOE E4 with PTE; no other studies validated previously reported genetic association data. Hence, the limited data precluded meta-analysis of all but one SNP, i.e., the APOE E4 allele. The meta-analysis for the association of the APOE E4 allele with PTE was non-significant (OR 1.8, CI 0.6-5.6). Conclusions: The current evidence on the association of genetic polymorphisms in epilepsy secondary to TBI or stroke is of low quality and lacks validation. A collaborative effort to pool genetic data linked to epileptogenesis in stroke and TBI patients is warranted.

Establishment and validation of PTE prediction model in patients with cerebral contusion

Post-traumatic epilepsy (PTE) is an important cause of poor prognosis in patients with cerebral contusions. The primary purpose of this study is to evaluate the high-risk factors of PTE by summarizing and analyzing the baseline data, laboratory examination, and imaging features of patients with a cerebral contusion, and then developing a Nomogram prediction model and validating it. This study included 457 patients diagnosed with cerebral contusion who met the inclusion criteria from November 2016 to November 2019 at the Qinghai Provincial People's Hospital. All patients were assessed for seizure activity seven days after injury. Univariate analysis was used to determine the risk factors for PTE. Significant risk factors in univariate analysis were selected for binary logistic regression analysis. P < 0.05 was statistically significant. Based on the binary logistic regression analysis results, the prediction scoring system of PTE is established by Nomogram, and the line chart model is drawn. Finally, external validation was performed on 457 participants to assess its performance. Univariate and binary logistic regression analyses were performed using SPSS software, and the independent predictors significantly associated with PTE were screened as Contusion site, Chronic alcohol use, Contusion volume, Skull fracture, Subdural hematoma (SDH), Glasgow coma scale (GCS) score, and Non late post-traumatic seizure (Non-LPTS). Based on this, a Nomogram model was developed. The prediction accuracy of our scoring system was C-index = 98.29%. The confidence interval of the C-index was 97.28% ~ 99.30%. Internal validation showed that the calibration plot of this model was close to the ideal line. This study developed and verified a highly accurate Nomogram model, which can be used to individualize PTE prediction in patients with a cerebral contusion. It can identify individuals at high risk of PTE and help us pay attention to prevention in advance. The model has a low cost and is easy to be popularized in the clinic. This model still has some limitations and deficiencies, which need to be verified and improved by future large-sample and multicenter prospective studies.

Incidence and risk factors of posttraumatic epilepsy following pediatric traumatic brain injury: A systematic review and meta-analysis.

Posttraumatic epilepsy (PTE) is a well-known chronic complication following traumatic brain injury (TBI). Despite some evidence that age at the time of injury may influence the likelihood of PTE, the incidence of PTE in pediatric populations remains unclear. We therefore conducted a systematic review to determine the overall reported incidence of PTE, and explore potential risk factors associated with PTE after pediatric TBI. A comprehensive literature search of the PubMed, Embase, and Web of Science databases was conducted, including randomized controlled trials and cohort studies assessing the incidence of PTE in TBI pediatric patients. We excluded studies with a sample size of <10 patients and those in which a pediatric cohort was not clearly discernable. The review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We found that the overall incidence of PTE following pediatric TBI was 10% (95% confidence interval [CI]=5.9%-15%). Subgroup analysis of a small number of studies demonstrated that the occurrence of early seizures (cumulative incidence ratio [CIR]=7.28, 95% CI=1.09-48.4, p= .040), severe TBI (CIR=1.81, 95% CI=1.23-2.67, p< .001), and intracranial hemorrhage (CIR=1.60, 95% CI=1.06-2.40, p= .024) increased the risk of PTE in this population. Other factors, including male sex and neurosurgical intervention, were nonsignificantly associated with a higher incidence of PTE. In conclusion, PTE is a significant chronic complication following childhood TBI, similar to in the adult population. Further standardized investigation into clinical risk factors and management guidelines is warranted. PROSPERO ID# CRD42021245802.

Modification of post-traumatic epilepsy by fecal microbiota transfer

It has been well established that traumatic brain injury (TBI) modifies the composition of gut microbiome. Epilepsy, which represents one of the common sequelae of TBI, has been associated with dysbiosis. Earlier study showed that the risk of post-traumatic epilepsy (PTE) after lateral fluid percussion injury (LFPI) in rats can be stratified based on pre-existing (i.e., pre-TBI) gut microbiome profile. In the present study, we examined whether fecal microbiota transfer (FMT) from naïve rats with different prospective histories of PTE would affect the trajectory of PTE in recipients. Fecal samples were collected from naïve adult male Sprague–Dawley rats, followed by LFPI. Seven months later, upon four weeks of vide-EEG monitoring (vEEG), the rats were categorized as those with and without PTE. Recipients were subjected to LFPI, followed by FMT from donors with and without impending PTE. Control groups included auto-FMT and no-FMT subjects. Seven month after LFPI, recipients underwent four-week vEEG to detect spontaneous seizures. After completing vEEG, rats of all groups underwent kindling of basolateral amygdala. Fecal microbiota transfer from donors with impending PTE exerted mild-to-moderate pro-epileptic effects in recipients, evident as marginal increase in multiple spontaneous seizure incidence, and facilitation of kindling. Analysis of fecal samples in selected recipients and their respective donors confirmed that FMT modified microbiota in recipients along the donors’ lines, albeit without full microbiome conversion. The findings provide further evidence that gut microbiome may actively modulate the susceptibility to epilepsy.

Hippocampal position and orientation as prognostic biomarkers for posttraumatic epileptogenesis: An experimental study in a rat lateral fluid percussion model.

ObjectiveThis study was undertaken to identify prognostic biomarkers for posttraumatic epileptogenesis derived from parameters related to the hippocampal position and orientation.MethodsData were derived from two preclinical magnetic resonance imaging (MRI) follow‐up studies: EPITARGET (156 rats) and Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx; University of Eastern Finland cohort, 43 rats). Epileptogenesis was induced with lateral fluid percussion‐induced traumatic brain injury (TBI) in adult male Sprague Dawley rats. In the EPITARGET cohort, T2∗‐weighted MRI was performed at 2, 7, and 21 days and in the EpiBioS4Rx cohort at 2, 9, and 30 days and 5 months post‐TBI. Both hippocampi were segmented using convolutional neural networks. The extracted segmentation mask was used for a geometric construction, extracting 39 parameters that described the position and orientation of the left and right hippocampus. In each cohort, we assessed the parameters as prognostic biomarkers for posttraumatic epilepsy (PTE) both individually, using repeated measures analysis of variance, and in combination, using random forest classifiers.ResultsThe extracted parameters were highly effective in discriminating between sham‐operated and TBI rats in both the EPITARGET and EpiBioS4Rx cohorts at all timepoints (t; balanced accuracy > .9). The most discriminating parameter was the inclination of the hippocampus ipsilateral to the lesion at t = 2 days and the volumes at t ≥ 7 days after TBI. Furthermore, in the EpiBioS4Rx cohort, we could effectively discriminate epileptogenic from nonepileptogenic animals with a longer MRI follow‐up, at t = 150 days (area under the curve = .78, balanced accuracy = .80, p = .0050), based on the orientation of both hippocampi. We found that the ipsilateral hippocampus rotated outward on the horizontal plane, whereas the contralateral hippocampus rotated away from the vertical direction.SignificanceWe demonstrate that assessment of TBI‐induced hippocampal deformation by clinically translatable MRI methodologies detects subjects with prior TBI as well as those at high risk of PTE, paving the way toward subject stratification for antiepileptogenesis studies.

Susceptibility to epilepsy after traumatic brain injury is associated with preexistent gut microbiome profile.

We examined whether posttraumatic epilepsy (PTE) is associated with measurable perturbations in gut microbiome. Adult Sprague Dawley rats were subjected to lateral fluid percussion injury (LFPI). PTE was examined 7months after LFPI, during 4-week continuous video-electroencephalographic monitoring. 16S ribosomal RNA gene sequencing was performed in fecal samples collected before LFPI/sham-LFPI and 1week, 1 month, and 7months thereafter. Longitudinal analyses of alpha diversity, beta diversity, and differential microbial abundance were performed. Short-chain fatty acids (SCFAs) were measured in fecal samples collected before LFPI by liquid chromatography with tandem mass spectrometry. Alpha diversity changed over time in both LFPI and sham-LFPI subjects; no association was observed between alpha diversity and LFPI, the severity of post-LFPI neuromotor impairments, and PTE. LFPI produced significant changes in beta diversity and selective changes in microbial abundances associated with the severity of neuromotor impairments. No association between LFPI-dependent microbial perturbations and PTE was detected. PTE was associated with beta diversity irrespective of timepoint vis-à-vis LFPI, including at baseline. Preexistent fecal microbial abundances of four amplicon sequence variants belonging to the Lachnospiraceae family (three enriched and one depleted) predicted the risk of PTE, with area under the curve (AUC) of .73. Global SCFA content was associated with the increased risk of PTE, with AUC of .722, and with 2-methylbutyric (depleted), valeric (depleted), isobutyric (enriched), and isovaleric (enriched) acids being the most important factors (AUC = .717). When the analyses of baseline microbial and SCFA compositions were combined, AUC to predict PTE increased to .78. Whereas LFPI produces no perturbations in the gut microbiome that are associated with PTE, the risk of PTE can be stratified based on preexistent microbial abundances and SCFA content.

Post-Traumatic Epilepsy and Comorbidities: Advanced Models, Molecular Mechanisms, Biomarkers, and Novel Therapeutic Interventions

Post-traumatic epilepsy (PTE) is one of the most devastating long-term, network consequences of traumatic brain injury (TBI). There is currently no approved treatment that can prevent onset of spontaneous seizures associated with brain injury, and many cases of PTE are refractory to antiseizure medications. Post-traumatic epileptogenesis is an enduring process by which a normal brain exhibits hypersynchronous excitability after a head injury incident. Understanding the neural networks and molecular pathologies involved in epileptogenesis are key to preventing its development or modifying disease progression. In this article, we describe a critical appraisal of the current state of PTE research with an emphasis on experimental models, molecular mechanisms of post-traumatic epileptogenesis, potential biomarkers, and the burden of PTE-associated comorbidities. The goal of epilepsy research is to identify new therapeutic strategies that can prevent PTE development or interrupt the epileptogenic process and relieve associated neuropsychiatric comorbidities. Therefore, we also describe current preclinical and clinical data on the treatment of PTE sequelae. Differences in injury patterns, latency period, and biomarkers are outlined in the context of animal model validation, pathophysiology, seizure frequency, and behavior. Improving TBI recovery and preventing seizure onset are complex and challenging tasks; however, much progress has been made within this decade demonstrating disease modifying, anti-inflammatory, and neuroprotective strategies, suggesting this goal is pragmatic. Our understanding of PTE is continuously evolving, and improved preclinical models allow for accelerated testing of critically needed novel therapeutic interventions in military and civilian persons at high risk for PTE and its devastating comorbidities.Significance StatementPost-traumatic epilepsy is a chronic seizure condition after brain injury. With few models and limited understanding of the underlying progression of epileptogenesis, progress is extremely slow to find a preventative treatment for PTE. This study reviews the current state of modeling, pathology, biomarkers, and potential interventions for PTE and comorbidities. There’s new optimism in finding a drug therapy for preventing PTE in people at risk, such as after traumatic brain injury, concussion, and serious brain injuries, especially in military persons.

Risk factors for post-traumatic epilepsy

PurposeThe aim of the current study was to investigate the risk factors for post-traumatic epilepsy (PTE) in a large cohort of patients after severe non-penetrating civilian traumatic brain injury (TBI). MethodsThis was a longitudinal study. All patients with a severe non-penetrating TBI, who were admitted at the neuro-intensive care unit of Shahid Rajaee Trauma Hospital, affiliated with Shiraz University of Medical Sciences, Shiraz, Iran, from 2015 until 2019, were studied. Severe TBI was defined as a Glasgow Coma Scale-Motor score below six. Post-traumatic epilepsy was defined as any seizures that occurred after being discharged from the hospital. ResultsIn total, 803 patients with severe non-penetrating TBI were studied; 82 patients (10.2%) reported any late post-traumatic seizures (PTSs). A higher Glasgow outcome scale (extended) at discharge was significantly inversely associated with PTE [Odds Ratio (OR)= 0.76, 95% Confidence Interval (CI): 0.65–0.87; p = 0.0001]. Depressed skull fracture (OR= 1.88, 95% CI: 0.92–3.80; p = 0.081), epi‑dural hematoma (OR= 1.67, 95% CI: 0.93–2.97; p = 0.083), and sub-dural hematoma (OR= 1.64, 95% CI: 0.96–2.78; p = 0.068) were associated with PTE as trends. ConclusionOur study adds to the literature on the risk of PTE after severe non-penetrating civilian TBI. Our large sample size and also the application of a logistic regression analysis model may suggest that other variables (e.g., depressed skull fracture and intracranial hematoma) are indeed associated with the Glasgow outcome scale (extended) at discharge and that is why they lost their significance in the model.

Post-traumatic epilepsy in adults: a nationwide register-based study

ObjectiveTraumatic brain injury (TBI) is a leading cause of epilepsy. Our aim was to characterise the risk of epilepsy in adults after hospitalisation for TBI.MethodsRegister-based cohort study. All individuals aged...

An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: A Retrospective Cohort Study

Background: Artificial neural network (ANN) is commonly used to develop risk prediction model and outperformed than conventional model. Here we aimed to train and validate ANN to predict risk of posttraumatic epilepsy (PTE). Methods: 1301 individuals diagnosed as traumatic brain injury (TBI) were enrolled from West China Hospital. We used a 5-fold cross-validation approach to train and test ANN models. Sensitivity, specificity, accuracy of ANN model was obtained and compared it with a nomogram prediction model built in our former work. The performance of this model was evaluated in patients registered at Chengdu Shang Jin Nan Fu Hospital (testing 1 cohort) and Sichuan Provincial People’s Hospital (testing 2 cohort). Findings: The ANN performed well in the training (AUC = 0.907, 95% CI: 0.889-0.924)) and testing cohorts (testing 1 cohort: AUC = 0.867, 95% CI: 0.842-0.893; testing 2 cohort: AUC = 0.859, 95% CI: 0.826-0.890). The average precision (AP) was 0.557 (95% CI: 0.510-0.620) in training cohort, 0.470 (95% CI: 0.414-0.526) in testing 1 cohort and 0.344 (95% CI: 0.287-0.401) in testing 2 cohort. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) in the training cohort (testing 1 and testing 2 cohort) were 0.80 (0.83 and 0.80), 0.86 (0.80 and 0.84), 91% (85% and 78%) and 86% (80% and 83%). ANN model had a higher accuracy than nomogram model (P = 0.04). Interpretation: This study suggests the ANN model could be used to predict the risk of PTE and outperformed than nomogram model and we need to calibrate this model on large-sample-size set. Funding Statement: This study was funded by 1·3·5 project for disciplines of excellence– Clinical Research Incubation Project, West China Hospital, Sichuan University (2019HXFH048). Declaration of Interests: The authors have declared that they have no competing interests. Ethics Approval Statement: The West China Hospital of Sichuan University Ethics Committee approved this study (approval no. 2019-936). Subjects or their proxies gave informed verbal consent to participate in this study.