Epilepsy Research最新文献

{"title":"Localization of epileptogenic zone based on time-varying effective networks","authors":"Ning Yin (尹宁) ,&nbsp;Yamei Han (韩雅美) ,&nbsp;Le Wang (王乐) ,&nbsp;Fan Yang (杨帆) ,&nbsp;Jicheng Li (李济丞) ,&nbsp;Guizhi Xu (徐桂芝)","doi":"10.1016/j.eplepsyres.2024.107409","DOIUrl":"10.1016/j.eplepsyres.2024.107409","url":null,"abstract":"<div><p>Surgical resection of the epileptogenic zone (EZ) is an effective method for treating drug-resistant epilepsy. At present, the accuracy of EZ localization needs to be further improved. The characteristics of graph theory based on partial directed coherence networks have been applied to the localization of EZ, but the application of network control theory to effective networks to locate EZ is rarely reported. In this study, the method of partial directed coherence analysis was utilized to construct the time-varying effective brain networks of stereo-electroencephalography (SEEG) signals from 20 seizures in 12 patients. Combined with graph theory and network control theory, the differences in network characteristics between epileptogenic and non-epileptogenic zones during seizures were analyzed. We also used dung beetle optimized support vector machine classification model to evaluate the localization effect of EZ based on brain network characteristics of graph theory and controllability. The results showed that the classification of the average controllability feature was the best, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.9505, which is 1.32 % and 1.97 % higher than the traditional methods. The AUC value increased to 0.9607 after integrating the average controllability with other features. This study proved the effectiveness of controllability characteristic in identifying the EZ and provided a theoretical basis for the clinical application of network controllability in the EZ.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"205 ","pages":"Article 107409"},"PeriodicalIF":2.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141603448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A medico-legal perspective on postictal violence: A case study and systematic review of postictal delirium","authors":"Mark Quigg ,&nbsp;Richard Bonnie ,&nbsp;Derek Bauer ,&nbsp;Howard P. Goodkin ,&nbsp;Jaideep Kapur","doi":"10.1016/j.eplepsyres.2024.107398","DOIUrl":"10.1016/j.eplepsyres.2024.107398","url":null,"abstract":"<div><p>Detailed descriptions of violent postictal episodes are rare. We provide evidence from an index case and from a systematic review of violent postictal episodes that demonstrates the encephalopathic features of some violent postictal behaviors. We discuss how these cases may fit in the legal framework of culpability. The data support the view that some episodes of violent postictal behavior are more accurately classified as a neurological delirium or encephalopathy rather than as a postictal psychosis. Current medical terminology may present unwarranted (and presumably unintended) barriers to exculpation for patients who exhibit post-ictal violence during an episode of delirium during which the patient was unaware of his or her violent conduct.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"205 ","pages":"Article 107398"},"PeriodicalIF":2.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population","authors":"Mahima Kaushik ,&nbsp;Siddhartha Mahajan ,&nbsp;Nitin Machahary ,&nbsp;Sarita Thakran ,&nbsp;Saransh Chopra ,&nbsp;Raj Vardhan Tomar ,&nbsp;Suman S. Kushwaha ,&nbsp;Rachna Agarwal ,&nbsp;Sangeeta Sharma ,&nbsp;Ritushree Kukreti ,&nbsp;Bibhu Biswal","doi":"10.1016/j.eplepsyres.2024.107404","DOIUrl":"https://doi.org/10.1016/j.eplepsyres.2024.107404","url":null,"abstract":"<div><h3>Purpose</h3><p>This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE).</p></div><div><h3>Methods</h3><p>Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into ‘good responder (GR)’ and ‘poor responder (PR)’ based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models.</p></div><div><h3>Results</h3><p>Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework.</p></div><div><h3>Significance</h3><p>Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient's response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier’s predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"205 ","pages":"Article 107404"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141595512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An rs-fMRI based neuroimaging marker for adult absence epilepsy","authors":"Ruoshi Liu ,&nbsp;Guozhong Zhu ,&nbsp;Yujun Gao ,&nbsp;Dongbin Li","doi":"10.1016/j.eplepsyres.2024.107400","DOIUrl":"https://doi.org/10.1016/j.eplepsyres.2024.107400","url":null,"abstract":"<div><h3>Objective</h3><p>Approximately 20–30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE.</p></div><div><h3>Methods</h3><p>This research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing.</p></div><div><h3>Results</h3><p>Compared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %.</p></div><div><h3>Significance</h3><p>According to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"204 ","pages":"Article 107400"},"PeriodicalIF":2.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foramen ovale electrode investigation in the era of SEEG: Results and a reappraisal","authors":"Rohan Jha ,&nbsp;Melissa MJ Chua ,&nbsp;Noah Nawabi ,&nbsp;Sydney S. Cash ,&nbsp;John D. Rolston ,&nbsp;Andrew J. Cole","doi":"10.1016/j.eplepsyres.2024.107401","DOIUrl":"10.1016/j.eplepsyres.2024.107401","url":null,"abstract":"<div><h3>Introduction</h3><p>Patients with medication-resistant disabling epilepsy should be considered for potential epilepsy surgery. If noninvasive techniques are unable to identify the location of the seizure onset zone (SOZ), it becomes necessary to consider intracranial investigations. Stereo-electroencephalography (SEEG) is currently the preferred method for such monitoring, however foramen ovale (FO) electrodes offer a less invasive alternative that may be suitable in certain situations. Previous studies have demonstrated the effectiveness of FO electrodes in suspected mesial temporal epilepsy, nevertheless, increased experience with FO electrode use could further enhance their safety and efficacy. Therefore, we conducted an analysis of recent FO electrode investigations to assess their utility in surgical decision making, post resection outcomes, and complication rates.</p></div><div><h3>Methods</h3><p>We conducted a retrospective analysis of 61 patients who underwent FO placement at Mass General Brigham between 2009 and 2020. Patient and seizure characteristics, preoperative investigation data, and seizures outcomes were collected. In addition, identified predictors of FO utility using logistic regression.</p></div><div><h3>Results</h3><p>A total of 61 patients were identified. FO evaluation localized the SOZ in 56 % of patients. Complications were encountered in 1.6 % of patients. Subsequent surgical resection was pursued by 49 % of patients, with 56 % becoming seizure free, and 67 % having favorable seizure outcomes at last follow-up. Multivariate analysis identified younger patients with a higher number of preoperative ASMs as more likely to undergo subsequent treatment, however, these features were not predictive features of SOZ localization, seizure freedom, or favorable seizure outcomes. In patients with bitemporal or cross-over onsets on scalp EEG, FO was able to identify the SOZ in 79 %, whereas in patients with discordant or unclear onset, the rates were 71 % and 45 %, respectively.</p></div><div><h3>Conclusion</h3><p>In a contemporary cohort, FO electrode placement had a low complication rate and a high utility primarily in cases of unclear laterality of mesial temporal onsets or discordance between scalp EEG and other pre-FO investigation data in cases of suspected mesial temporal onsets.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"205 ","pages":"Article 107401"},"PeriodicalIF":2.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141563047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in ECG: Validating an ambulatory semiology labeller and predictor","authors":"Pooja Muralidharan ,&nbsp;Ravi Sankaran ,&nbsp;Perraju Bendapudi ,&nbsp;C. Santhosh Kumar ,&nbsp;A. Anand Kumar","doi":"10.1016/j.eplepsyres.2024.107403","DOIUrl":"10.1016/j.eplepsyres.2024.107403","url":null,"abstract":"<div><h3>Objectives</h3><p>Early prediction of epileptic seizures can help reduce morbidity and mortality. In this work, we explore using electrocardiographic (ECG) signal as input to a seizure prediction system and note that the performance can be improved by using selected signal processing techniques.</p></div><div><h3>Methods</h3><p>We used frequency domain analysis with a deep neural network backend for all our experiments in this work. We further analysed the effect of the proposed system for different seizure semiologies and prediction horizons. We explored refining the signal using signal processing to enhance the system's performance.</p></div><div><h3>Results</h3><p>Our final system using the Temple University Hospital’s Seizure (TUHSZ) corpus gave an overall prediction accuracy of 84.02 %, sensitivity of 87.59 %, specificity of 81.9 %, and an area under the receiver operating characteristic curve (AUROC) of 0.9112. Notably, these results surpassed the state-of-the-art outcomes reported using the TUHSZ database; all findings are statistically significant. We also validated our study using the Siena scalp EEG database. Using the frequency domain data, our baseline system gave a performance of 75.17 %, 79.17 %, 70.04 % and 0.82 for prediction accuracy, sensitivity, specificity and AUROC, respectively. After selecting the optimal frequency band of 0.8–15 Hz, we obtained a performance of 80.49 %, 89.51 %, 75.23 % and 0.89 for prediction accuracy, sensitivity, specificity and AUROC, respectively which is an improvement of 5.32 %, 10.34 %, 5.19 % and 0.08 for prediction accuracy, sensitivity, specificity and AUROC, respectively.</p></div><div><h3>Conclusions</h3><p>The seizure information in ECG is concentrated in a narrow frequency band. Identifying and selecting that band can help improve the performance of seizure detection and prediction.</p></div><div><h3>Significance</h3><p>EEG is susceptible to artefacts and is not preferred in a low-cost ambulatory device. ECG can be used in wearable devices (like chest bands) and is feasible for developing a low-cost ambulatory device for seizure prediction. Early seizure prediction can provide patients and clinicians with the required alert to take necessary precautions and prevent a fatality, significantly improving the patient’s quality of life.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"204 ","pages":"Article 107403"},"PeriodicalIF":2.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141467045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy","authors":"Yue Yu ,&nbsp;Zhibin Chen ,&nbsp;Yong Yang ,&nbsp;Jiajun Zhang ,&nbsp;Yan Wang","doi":"10.1016/j.eplepsyres.2024.107397","DOIUrl":"10.1016/j.eplepsyres.2024.107397","url":null,"abstract":"<div><h3>Background</h3><p>Epilepsy is a serious complication after an ischemic stroke. Although two studies have developed prediction model for post-stroke epilepsy (PSE), their accuracy remains insufficient, and their applicability to different populations is uncertain. With the rapid advancement of computer technology, machine learning (ML) offers new opportunities for creating more accurate prediction models. However, the potential of ML in predicting PSE is still not well understood. The purpose of this study was to develop prediction models for PSE among ischemic stroke patients.</p></div><div><h3>Methods</h3><p>Patients with ischemic stroke from two stroke centers were included in this retrospective cohort study. At the baseline level, 33 input variables were considered candidate features. The 2-year PSE prediction models in the derivation cohort were built using six ML algorithms. The predictive performance of these machine learning models required further appraisal and comparison with the reference model using the conventional triage classification information. The Shapley additive explanation (SHAP), based on fair profit allocation among many stakeholders according to their contributions, is used to interpret the predicted outcomes of the naive Bayes (NB) model.</p></div><div><h3>Results</h3><p>A total of 1977 patients were included to build the predictive model for PSE. The Boruta method identified NIHSS score, hospital length of stay, D-dimer level, and cortical involvement as the optimal features, with the receiver operating characteristic curves ranging from 0.709 to 0.849. An additional 870 patients were used to validate the ML and reference models. The NB model achieved the best performance among the PSE prediction models with an area under the receiver operating curve of 0.757. At the 20 % absolute risk threshold, the NB model also provided a sensitivity of 0.739 and a specificity of 0.720. The reference model had poor sensitivities of only 0.15 despite achieving a helpful AUC of 0.732. Furthermore, the SHAP method analysis demonstrated that a higher NIHSS score, longer hospital length of stay, higher D-dimer level, and cortical involvement were positive predictors of epilepsy after ischemic stroke.</p></div><div><h3>Conclusions</h3><p>Our study confirmed the feasibility of applying the ML method to use easy-to-obtain variables for accurate prediction of PSE and provided improved strategies and effective resource allocation for high-risk patients. In addition, the SHAP method could improve model transparency and make it easier for clinicians to grasp the prediction model's reliability.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"205 ","pages":"Article 107397"},"PeriodicalIF":2.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morbidity Associated with Deviation from Pediatric Status Epilepticus Guidelines","authors":"Jillian Gregory ,&nbsp;Andrew Cohen ,&nbsp;Anya Cutler ,&nbsp;Alexa Craig","doi":"10.1016/j.eplepsyres.2024.107394","DOIUrl":"10.1016/j.eplepsyres.2024.107394","url":null,"abstract":"<div><p>Treatment guidelines for the management of pediatric status epilepticus (PSE) are often institution-specific. We aim to characterize deviation from our hospital-based PSE treatment guidelines, the total dosage of benzodiazepines administered, and the need for intubation. The study population included all patients with an ICD −10 code for PSE who required admission to the Pediatric Intensive Care Unit (PICU) from April 2019 to April 2022. There were 66 PICU admissions. All patients with concern for PSE and altered mental status are admitted to the PICU. The cohort was divided between those treated according to the PSE protocol (benzodiazepine dose (0.05 mg/kg- 0.2 mg/kg) versus those who had low dose (≤0.05 mg/kg) and high-dose benzodiazepine (&gt; 0.2 mg/kg) totals. The dosage was calculated as the total dose of benzodiazepines received pre-hospital and in the ED before intubation or transport. Forty-one (62 %) of patients received high-dose benzodiazepines (median 0.34 mg/kg [IQR 0.29–0.56], 19 (29 %) received recommended-dose benzodiazepines (median 0.13 mg/kg [IQR 0.09,0.15] and 6 (9 %) received low-dose (median 0.05 mg/kg [IQR 0.03,0.05]. The high-dose group was 15.9 (95 % CI = 3.7, 99.9) times more likely to be intubated controlling for the location of care (tertiary versus community hospital), and the age of the patient. The recommended-dose and low-dose groups required intubation with much less frequency.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"204 ","pages":"Article 107394"},"PeriodicalIF":2.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141467046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality of life during usual epilepsy care for anxiety or depression symptoms: Secondary patient-reported outcomes in a randomized trial of remote assessment methods","authors":"Heidi M. Munger Clary ,&nbsp;Beverly M. Snively ,&nbsp;Yaw Kumi-Ansu ,&nbsp;Halley B. Alexander ,&nbsp;James Kimball ,&nbsp;Pamela Duncan ,&nbsp;Kelly Conner ,&nbsp;Jerryl Christopher ,&nbsp;Paneeni Lohana ,&nbsp;Gretchen A. Brenes","doi":"10.1016/j.eplepsyres.2024.107396","DOIUrl":"10.1016/j.eplepsyres.2024.107396","url":null,"abstract":"<div><h3>Background and objectives</h3><p>Anxiety and depression are highly prevalent and impactful in epilepsy. American Academy of Neurology quality measures emphasize anxiety and depression screening and quality of life (QOL) measurement, yet usual epilepsy care QOL and anxiety/depression outcomes are poorly characterized. The main objective was to assess 6-month QOL, anxiety and depression during routine care among adults with epilepsy and baseline anxiety or depression symptoms; these were prespecified secondary outcomes within a pragmatic randomized trial of remote assessment methods.</p></div><div><h3>Methods</h3><p>Adults with anxiety or depression symptoms and no suicidal ideation were recruited from a tertiary epilepsy clinic via an electronic health record (EHR)-embedded process. Participants were randomized 1:1 to 6 month outcome collection via patient portal EHR questionnaires vs. telephone interview. This report focuses on an a priori secondary outcomes of the overall trial, focused on patient-reported health outcomes in the full sample. Quality of life, (primary health outcome), anxiety, and depression measures were collected at 3 and 6 months (Quality of Life in Epilepsy-10, QOLIE-10, Generalized Anxiety Disorder-7, Neurological Disorders Depression Inventory-Epilepsy). Change values and 95 % confidence intervals were calculated. In post-hoc exploratory analyses, patient-reported anxiety/depression management plans at baseline clinic visit and healthcare utilization were compared with EHR-documentation, and agreement was calculated using the kappa statistic.</p></div><div><h3>Results</h3><p>Overall, 30 participants (15 per group) were recruited and analyzed, of mean age 42.5 years, with 60 % women. Mean 6-month change in QOLIE-10 overall was 2.0(95 % CI −6.8, 10.9), and there were no significant differences in outcomes between the EHR and telephone groups. Mean anxiety and depression scores were stable across follow-up (all 95 % CI included zero). Outcomes were similar regardless of whether an anxiety or depression action plan was documented. During the baseline interview, most participants with clinic visit EHR documentation indicating action to address anxiety and/or depression reported not being offered a treatment(7 of 12 with action plan, 58 %), and there was poor agreement between patient report and EHR documentation (kappa=0.22). Healthcare utilization was high: 40 % had at least one hospitalization or emergency/urgent care visit reported and/or identified via EHR, but a third (4/12) failed to self-report an EHR-identified hospitalization/urgent visit.</p></div><div><h3>Discussion</h3><p>Over 6 months of usual care among adults with epilepsy and anxiety or depression symptoms, there was no significant average improvement in quality of life or anxiety/depression, suggesting a need for interventions to enhance routine neurology care and achieve quality of life improvement for this group.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"204 ","pages":"Article 107396"},"PeriodicalIF":2.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141400290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rat strain differences in seizure frequency and hilar neuron loss after systemic treatment with pilocarpine","authors":"Kristina Junghans ,&nbsp;Megan Wyeth ,&nbsp;Paul S. Buckmaster","doi":"10.1016/j.eplepsyres.2024.107384","DOIUrl":"10.1016/j.eplepsyres.2024.107384","url":null,"abstract":"<div><p>At least 3 months after systemic treatment with pilocarpine to induce status epilepticus, Long-Evans and Sprague-Dawley rats were video-EEG monitored for seizures continuously for 1 month. Rats were then perfused, hippocampi were processed for Nissl staining, and hilar neurons were quantified. Seizure frequency in Long-Evans rats was 1/10th of that in Sprague-Dawley rats, and more variable. Hilar neuron loss was also less severe in Long-Evans rats. However, there was no correlation between hilar neuron loss and seizure frequency in either strain. The low and variable seizure frequency suggests limited usefulness of pilocarpine-treated Long-Evans rats for some epilepsy experiments.</p></div>","PeriodicalId":11914,"journal":{"name":"Epilepsy Research","volume":"204 ","pages":"Article 107384"},"PeriodicalIF":2.2,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141330566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}