Epilepsia最新文献

Objective: Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is difficult to predict. We explored whether spectral entropy, a fast computable electroencephalographic (EEG) feature, could estimate epileptic activity in intraoperative electrocorticography (ioECoG) and forecast postsurgical outcomes.

Methods: We included people with FCD pathology who underwent ioECoG-assisted resective surgery. We analyzed ioECoG recordings acquired before and after resection. We computed spectral entropy across eight frequency bands (1-500, 1-4, 4-8, 8-12, 12-20, 20-80, 80-250, 250-500 Hz) in 2-s epochs during 1 min, and the mean and SD per electrode. The preresection features were the input for a random forest machine learning model to classify channels covering resected from nonresected tissue. Explainable artificial intelligence was used to select features that positively influenced the model predicting resected tissue. We then related these markers measured after the resection to postsurgical outcome using trend analysis (Jonckheere-Terpstra) across outcome groups Engel IA without medication, Engel IA-D, Engel II, Engel III, and Engel IV.

Results: We analyzed ioECoG data from 37 patients (age range = 0-61 years, 21 patients were ≤16 years), including 2270 preresection and 1278 postresection channels. The random forest model discriminated between resected and nonresected cortex (validation fold area under the curve = .84, 95% confidence interval =.74-.95). Features with the strongest positive Shapley Additive Explanations values included high spectral entropy variability (SEV) in the 80-500-Hz bands. In postresection recordings, higher mean SEV and greater spatial variability of SEV were associated with poorer Engel outcome across several frequency bands. After multiple comparison correction, the positive relationship of high mean SEV (p = .004) and high spatial variability (p = .001) at 250-500 Hz with poor seizure outcome remained statistically significant.

Significance: SEV is a real-time computable invasive EEG marker that represents persisting epileptic activity after resection. SEV may be used to evaluate resection adequacy directly or may reflect residual epileptic activity. This would enable epilepsy surgery guidance and postsurgical counseling and decision-making.

Interictal epileptiform discharges (IEDs) are not just biomarkers but active contributors to cognitive and behavioral dysfunction. Antiseizure medications (ASMs) not only treat seizures but can also modulate IEDs. However, their broader neurocognitive impact remains underexplored. The goal of this review is to synthesize current evidence on ASM effects on IEDs and to examine their therapeutic implications for cognitive and behavioral improvement. A comprehensive literature search was conducted, focusing on studies that reported ASM-related IED modulation and associated cognitive or behavioral measures. ASMs demonstrate variable efficacy in reducing IEDs, with broad-spectrum agents like valproate and lamotrigine showing consistent suppression of IEDs resulting in cognitive benefit, particularly in children. The use of sodium channel blockers, such as lamotrigine and oxcarbazepine, produces cognitive improvements. Additionally, γ-aminobutyric acidergic agents, including clobazam and diazepam, are effective in treating developmental epileptic encephalopathies. Emerging therapies, including cannabidiol and perampanel, show promising IED and behavioral outcomes. Animal studies confirm that ASMs can suppress IEDs, leading to enhanced memory, attention, and social behaviors. Targeted reduction of IEDs may lead to improved cognitive and behavioral outcomes. This can be achieved by testing and recognizing ASMs in carefully designed prospective trials in animals and humans.

Objective: This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to predict individual seizure risk over time, comparing its performance to traditional clinical predictors such as interictal epileptiform discharges (IEDs).

Methods: We conducted a retrospective cohort study including 1014 consecutive routine EEGs from 994 patients recorded at a tertiary epilepsy center. We developed EEGSurvNet, a deep learning model that predicts time to next seizure over a 2-year horizon from a single EEG. Model performance was evaluated on a temporally shifted testing set of 135 EEGs from 115 patients using time-dependent area under the receiver operating characteristic curve (AUROC), AUROC integrated over 2 years (iAUROC), and C-index. We compared the deep survival model to a clinical Cox model incorporating standard risk factors as well as a random model based on baseline seizure risk.

Results: EEGSurvNet achieved a 2-year iAUROC of .69 (95% confidence interval [CI] = .64-.73) and C-index of .66 (95% CI = .60-.73), outperforming both clinical and random models. Performance was highest in the first months following EEG, peaking at 2 months (AUROC = .80). Combining EEGSurvNet with clinical predictors further improved performances (iAUROC = .70, C = .69). Notably, the model showed superior discrimination on EEGs without IEDs (iAUROC = .78 vs. .53). Model interpretation revealed that the temporal-occipital regions and 6-15-Hz frequencies contributed most to risk prediction.

Significance: EEGSurvNet demonstrates that deep learning can extract prognostic information from routine EEG beyond visible epileptiform abnormalities, potentially improving patient counseling and treatment decisions. Future prospective studies are needed to validate these findings and assess their clinical impact.

Objective: This study was undertaken to assess whether periconceptional folic acid (FA) supplementation affects seizures, maternal, and fetal outcomes in Chinese women with epilepsy.

Methods: We included pregnant women with epilepsy enrolled in the West China Pregnancy Registry of Epilepsy between 2012 and 2021. Detailed data on maternal health, FA intake, antiseizure medications (ASMs), pregnancy, and perinatal outcomes were obtained during regular visits to neurology clinics. Primary outcomes were seizure control status and adverse maternal and fetal outcomes. To adjust for potential confounders, subgroup and sensitivity analyses were performed.

Results: We included 1638 pregnancies of 1405 women with epilepsy. A total of 1299 (79.3%) pregnancies in 1173 women used FA supplements during periconception, and 1351 (82.5%) pregnancies were exposed to ASMs. The recurrence rate of convulsive seizures was significantly higher among non-FA users compared with FA users throughout pregnancy. Low FA dose, delayed initiation, and short use duration were associated with an increased risk of seizures during pregnancy. Most pregnancies (78.8%) in the non-FA group were lost compared to 13% in the FA group (p < .001). Non-FA users had 3.5-fold and 7.5-fold increased risks of spontaneous and elective abortions compared with FA users. The protective effect was more prevalent among those using ASMs. Pregnancies exposed to low-dose FA had higher rates of adverse maternal-fetal outcomes than those receiving medium- to high-dose FA supplements.

Significance: Periconceptional FA intake by women with epilepsy was associated with an approximately 66% decreased risk of abortion and improved seizure control during pregnancy. Low-dose FA may be insufficient to prevent adverse maternal-fetal outcomes in this population. Further studies are warranted to confirm these findings.

Objective: The diagnosis of functional/dissociative seizures (FDS) without ictal video-electroencephalography is challenging. The Functional/Dissociative Seizures Likelihood Score (FSLS) is a machine learning-based diagnostic score that aims to help clinicians identify FDS. We evaluated whether a human-in-the-loop implementation of the FSLS improved the performance of clinicians identifying FDS as compared to epileptic seizures (ES).

Methods: We constructed 117 anonymized cases about patients with ictal video-electroencephalography-documented FDS, epilepsy, co-occurring ES and FDS, or physiological seizurelike events. Text-based clinical history was presented followed by the FSLS. Readers were asked the most likely diagnosis after each piece of information. We used mixture modeling combined with mixed effects logistic regression to perform data-driven grouping of participants based on observed patterns of diagnostic performance.

Results: Overall, 163 readers saw 1142 cases (median = 4 cases/reader), and 146 (90%) had a performance higher than chance. More formal training in seizures was associated with better performance (epileptologist accuracy = 67%, mental health clinician accuracy = 52%). Data-driven groups including 66% of readers benefitted from the FSLS (accuracy improvement = 12%-15%, p < .05), including those in the reference and near highest baseline performance group. Other groups had no net change in performance (p > .75).

Significance: Clinicians with more formal seizure training identified possible FDS more accurately than others, but formal training did not guarantee high diagnostic performance. Two performance-based groups, which included 66% readers, benefitted from the FSLS because they identified when to change their mind on the basis of the FSLS's suggestion. The implementation of machine learning in the diagnosis of FDS should focus on identifying clinical settings where it can effectively enhance clinicians' decision-making.

Objective: CDKL5 deficiency disorder (CDD) is a rare X-linked developmental and epileptic encephalopathy caused by loss-of-function variants in the CDKL5 gene. Preclinical experiments using enzyme replacement or gene therapies show promise and could be transformative therapies. This precompetitive consortium sought to harmonize nonseizure clinical endpoint selection for efficacy trials. Clinical Assessment of Neurodevelopmental Measures in CDD (CANDID) is an ongoing study evaluating the feasibility and suitability of neurocognitive tests and functioning scales in CDD patients.

Methods: CANDID is a 3-year, longitudinal, noninterventional global study involving children and adults with CDD. On-site and remote visits include clinical, behavioral, developmental, and quality of life assessments.

Results: We enrolled 112 patients (111 included in analyses); mean age = 8.3 years (range <1-28); 93% female; 10 participants were ≥18 years old. In the first 28 days, 82% had >16 seizures; six were seizure-free. Median seizure onset was at 1.5 months (range = 0-66). Patients used an average of 2.6 antiseizure medications at baseline. The most frequent comorbidities included gastrointestinal hypomotility, muscle tone abnormalities, and sleep disorders. Gross Motor Function Measure-88 (GMFM-88) scores indicated a floor effect in crawling, standing, and walking across all ages. Vineland-3 and Bayley-4 scores could be derived in most, with receptive language, interpersonal relationships, and fine and gross motor scores increasing with age. Bruni sleep questionnaire identified sleep initiation, sleep-awake transition, and excessive somnolence as the most disrupted components across all age groups. The mean Quality of Life Inventory-Disability total scores ranged from 53% to 64%, the independence domain being the most impacted.

Significance: The scales in the CANDID study capture disease-related deficits and phenotype variability in CDD. Floor effects in subdomains aligned with disease severity. The GMFM-88 lacks granularity, and its operational limitations make it unsuitable for CDD trials. Baseline analyses demonstrate the feasibility and potential value of most selected scales, supporting their use in optimizing trial design and endpoint selection for future CDD clinical trials.

Objective: Juvenile absence epilepsy (JAE) is an idiopathic generalized epilepsy characterized by absences, generalized tonic-clonic seizures, and cognitive difficulties. In contrast to juvenile myoclonic epilepsy (JME), where distinct functional and structural brain alterations are well established, it remains unclear whether comparable changes are identifiable in absence-predominant syndromes. We aimed to delineate functional and structural correlates of the cognitive profile in people with JAE and to explore potential familial imaging traits.

Methods: We acquired working memory functional magnetic resonance imaging (MRI) and high-resolution T1-weighted MRI in 23 individuals with JAE, 18 unaffected siblings, and 28 controls.

Results: Compared with both siblings and controls, patients showed increased motor cortex activation during the attention-only condition, but relative suppression of motor activity and inadequate default mode network deactivation with increasing working memory demand. Gray matter volume was reduced in sensorimotor regions and in the left inferior and middle frontal gyri in patients. Larger volumes in these frontal regions correlated with better language function. In contrast, increased gray matter volume in the dorsal midcingulate cortex was present in both patients and their siblings relative to controls.

Significance: Our findings in JAE differ from the patterns of functional reorganization reported in JME, indicating that each syndrome involves distinct motor-cognitive pathophysiological mechanisms aligned with its seizure profile. Inferior frontal structural abnormalities likely contribute to the well-recognized language difficulties in JAE, whereas increased midcingulate gray matter volume may serve as a familial marker linked to attentional vulnerability.

Objective: Topiramate has been linked to increased glaucoma risk, potentially through mechanisms involving ocular fluid shifts. However, comparative risks vs other antiseizure medications (ASMs) and variation by sex or indication remain uncertain. This study evaluates glaucoma incidence in topiramate initiators compared to valproate or lamotrigine users among patients with epilepsy or migraine.

Methods: We conducted a retrospective active-comparator, new-user cohort study using electronic health records from the IQVIA Medical Research Data among patients with epilepsy or migraine initiating topiramate, valproate, or lamotrigine. Patients with prior ASM use, limited washout period and follow-up, or pre-existing glaucoma were excluded. The outcome was incident glaucoma within 1 year, censored at glaucoma occurrence, death, discontinuation, switch, or September 30, 2023. Covariates included age, sex, race, lifestyle factors, comorbidities, and medication history. Propensity score-based inverse probability weighting balanced characteristics, and crude and weighted Cox models estimated hazard ratios (HRs) with 95% confidence intervals (CIs). Subgroup analyses were conducted by sex, age, and indication.

Results: The cohort included 688 topiramate, 4490 valproate, and 4179 lamotrigine initiators. After weighting, the 1-year absolute risk increase was approximately 2.4% when comparing topiramate to valproate, and about 2.0% compared to lamotrigine. Topiramate was associated with higher glaucoma risk vs valproate (adjusted HR 2.66, 95% CI 1.12-6.32) and lamotrigine (adjusted HR 3.57, 95% CI 1.76-7.26). Risks were elevated in female (vs valproate: HR 5.31, 95% CI 1.48-19.08; vs lamotrigine: HR 5.73, 95% CI 2.38-13.79) or epilepsy patients (vs valproate: HR 2.23, 95% CI 1.04-4.76; vs lamotrigine: HR 5.08, 95% CI 2.32-11.14), but not in male or migraine patients.

Significance: Topiramate use substantially increases glaucoma risk compared with valproate and lamotrigine, particularly among female or epilepsy patients. No significant association in male or migraine patients was observed. These findings may inform targeted ophthalmologic monitoring in high-risk groups and use of alternative ASMs.

Clear documentation and transfer of information between health care providers is key to ensuring the delivery of high-quality patient care. Our aim was to determine how to optimize and standardize physician documentation in outpatient epilepsy clinics as well as to highlight challenges and barriers to their implementation. We conducted a scoping review of studies implementing standardization and optimization of physician documentation within outpatient epilepsy clinics. The study is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. The following databases were searched from inception to March 2025: MEDLINE, Embase, APA PsycInfo, CINAHL, and Cochrane Library. All abstracts, full texts, and data charting were completed in duplicate. The search yielded 10 268 studies, of which 16 met eligibility criteria. Studies were primarily from the United States (n = 12, 75.0%) and focused on adult practices (n = 9, 56.25%). Studies were quality improvement framework/consensus guideline investigations (n = 9, 56.3%), observational audits/cross-sectional designs (n = 2, 12.5%), and retrospective cohort/chart reviews (n = 2, 12.5%), followed by several mixed-methods development, observational field study, and observational toolkit implementation designs (n = 1, 6.25% each). Most clinical notes were in electronic medical records (n = 14, 87.5%) using free-text fields (n = 4, 25.0%), structured fields (n = 2, 12.5%), or hybrid approaches (n = 7, 43.8%). Common Data Elements included seizure information and treatment counseling information. Outcomes associated with standardized note implementation included reduced epilepsy-related adverse events, better seizure control, and more consistent documentation of pertinent patient information. Highlighted challenges to implementation included workflow disruptions, hesitation to initial uptake, and cost-related barriers such as information technology support. Implementation of standardized documentation was associated with fewer adverse events and better seizure control. Future efforts should prioritize inclusive design, have expanded quality indicators, be easy to use at point of care, and have robust evaluation metrics to optimize their utility for epilepsy care.