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Objectives: Epilepsy and migraine are common chronic neurological disease. Epidemiologic studies and shared pathophysiology and treatment suggest that these two diseases overlap. However, migraine is often underestimated among patients with epilepsy. This study aimed to evaluate the prevalence of migraine and identify the related influencing factors among adult patients with epilepsy., Methods: Adult patients with epilepsy were recruited at the outpatient epilepsy clinic of 13 tertiary hospitals in China from February to September 2022. ID Migraine questionnaire was applied to evaluate for migraine. Both univariable and multivariable logistic regression models were used to explore the influencing factors of migraine., Results: A total of 1326 patients with epilepsy were enrolled in this study. The prevalence of migraine among patients with epilepsy was 19.2% (254/1326). In the multivariable analysis, being female (OR = 1.451, 95% CI: 1.068-1.975; p = 0.018), focal and focal to bilateral tonic-clonic seizures (OR = 1.583, 95% CI: 1.090-2.281; p = 0.015), and current seizure attack in the last 3 months (OR = 1.967, 95% CI: 1.282-3.063; p = 0.002) were the influencing factors for migraine. However, <10% of patients with epilepsy received analgesics for migraine., Significance: Approximately 20% of patients with epilepsy screened positive for migraine. Being female, focal and focal to bilateral tonic-clonic seizures, and current seizure attack in the last 3 months were the influencing factors for migraine. Neurologists should pay more attention to the screening and management of the migraine among patients with epilepsy in China., Plain Language Summary: Epilepsy and migraine are common chronic neurological disease with shared pathophysiological mechanisms and therapeutic options. However, migraine is often underestimated among patients with epilepsy. This multicenter study aimed to evaluate the prevalence of migraine and current status of treatment. In this study, approximately 20% of patients with epilepsy screened positive for migraine. Female, focal and focal to bilateral tonic-clonic seizures, and current seizure attack in the last 3 months were identified as independent influencing factors for migraine. Despite the high prevalence, the treatment for migraine was not optimistic, neurologists should pay more attention to the screening and management of migraine., (© 2024 The Author(s). Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal or spatial dimension, making it difficult to take both relationships into account. Therefore, the effective properties of electroencephalograms (EEGs) may not be fully evaluated. To solve this problem, we propose a spatiotemporal graph attention network (STGAT) based on synchronization. The spatial and functional connectivity information between EEG channels was extracted by using the phase locking values (PLVs) first, which allowed multichannel EEG signals to be modeled as graph signals. Afterward, the STGAT model was used to dynamically learn the temporal correlation properties of EEG sequences and explore the spatial topological structure information of multiple channels. Experimental results demonstrated that the STGAT model was able to obtain spatiotemporal correlations and achieve good results on two benchmark datasets. The accuracy, specificity and sensitivity were 98.74%, 99.21% and 98.87%, respectively, on the CHB-MIT dataset. Moreover, all evaluation indices of the private dataset had reached more than 98.8%, with the area under the curve (AUC) reaching 99.96%. The proposed method is superior or comparable to the state-of-the-art models. Extensive experiments demonstrate that our end-to-end automatic seizure prediction model can be extended to design clinical assistant decision systems.

Importance: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed., Objective: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients., Design, Setting, and Participants: This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables., Exposures: One of 7 antiseizure medications., Main Outcomes and Measures: With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models., Results: The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC., Conclusions and Relevance: In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.

Background: To minimize drug-related toxicity and monitor dosing regimens, an ultra-sensitive, simple and high-throughput analytical method for therapeutic drug monitoring is required. A novel LC-MS/MS bioassay of levetiracetam, lamotrigine and 10-hydroxycarbazepine in human plasma was established. The analytes were separated on a Hypersil GOLD™ C 18 column under a 2.5 min isocratic elution after one-step protein precipitation. MS detection was performed under electrospray ionization positive-mode fitted with selected reaction monitoring. The validated ranges were 0.1-20 μg/ml for LTG, 0.3-60 μg/ml for 10-hydroxycarbazepine and levetiracetam. The intra- and inter-batches of precision and accuracy was within ±15%. The novel method met all other criteria., Conclusion: This method can be used to monitor drug concentrations and decision-making in epileptic patients., (© 2022 John Wiley & Sons Ltd.)