Consequences of Epilepsy Through the Ages: When Is the Die Cast?

A Real-World Safety Profile in Neurological, Skin, and Sexual Disorders of Anti-Seizure Medications Using the Pharmacovigilance Database of the Korea Adverse Event Reporting System (KAERS)

(1) Background: The utilization of high-quality evidence regarding the safety of anti-seizure medications (ASMs) is constrained by the absence of standardized reporting. This study aims to examine the safety profile of ASMs using real-world data. (2) Methods: The data were collected from the Korea Adverse Event Reporting System Database (KAERS-DB) between 2012 and 2021. In total, 46,963 adverse drug reaction (ADR)-drug pairs were analyzed. (3) Results: At the system organ class level, the most frequently reported classes for sodium channel blockers (SCBs) were skin (37.9%), neurological (16.7%), and psychiatric disorders (9.7%). For non-SCBs, these were neurological (31.2%), gastrointestinal (22.0%), and psychiatric disorders (18.2%). The most common ADRs induced by SCBs were rash (17.8%), pruritus (8.2%), and dizziness (6.7%). Non-SCBs were associated with dizziness (23.7%), somnolence (13.0%), and nausea (6.3%). Rash, pruritus, and urticaria occurred, on average, two days later with SCBs compared to non-SCBs. Sexual/reproductive disorders were reported at a frequency of 0.23%. SCBs were reported as the cause more frequently than non-SCBs (59.8% vs. 40.2%, Fisher's exact test, p < 0.0001). (4) Conclusions: Based on real-world data, the safety profiles of ASMs were identified. The ADRs induced by SCBs exhibited different patterns when compared to those induced by non-SCBs.

Therapy outcomes associated with prescription cannabidiol use at 12 months post-initiation

OBJECTIVE

This study evaluated prescription cannabidiol (CBD) outcomes during the first 12 months of therapy.

METHODS

A single-center, prospective cohort study was performed including patients prescribed CBD from January 2019 - April 2020, excluding clinical trial patients and those using external specialty pharmacy services. The primary outcome wasepilepsy-related emergency healthcare service (EHS) use within 12 months of initation. Secondary outcomes included prescription CBD discontinuation rate and reason and concomitant anti-seizure medication (ASM) use. A multiple logistic regression model evaluated the odds of EHS use, adjusting for initial concomitant ASM count, age, and insurance type.

RESULTS

The 136 patients included were 85% white, 50% female, and 68% pediatric. EHS utilization occurred in 37% (n = 50) of patients; 29 patients (21%, n = 20 pediatric, n = 9 adult) had at least one emergency department (ED) visit, 9 patients (7%) had two or more; 30 patients (22%, n = 22 pediatric, n = 8 adult) had at least one hospitalizaion. Median time to first ED and hospitalization was 69 (IQR 31-196) and 104 (IQR 38-179) days, respectively. Prescription CBD was discontinued in 31 patients (23%, n = 18 pediatric, n = 13 adult), due to major side effects (n = 12, 39%), common side effects (n = 11, 36%), and unsatisfactory response (n = 11, 36%). There was no significant change in concomitant ASM use.

CONCLUSION

Despite potential benefits of prescription CBD, many patients utilize EHSs in the first 12 months of treatment with minimal changes in concomitant ASM use.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

Epileptic Disorder Detection of Seizures Using EEG Signals

Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.

Serious psychological distress among adults with active epilepsy in all racial/ethnic groups and among adults with inactive epilepsy in non-Hispanic whites is significantly higher than among adults without epilepsy-U.S. National Health Interview Survey, 2010, 2013, 2015, and 2017

Serious psychological distress (SPD) includes mental health problems severe enough to cause moderate-to-serious impairment in daily activities and to require treatment. Serious psychological distress is based on answers to six survey questions from the Kessler-6 scale used internationally in public health surveillance systems to assess recent feelings of sadness, restlessness, hopelessness, nervousness, worthlessness, and the sense that everything is an effort. We combined nationally representative samples in the National Health Interview Survey (NHIS) from 2010 (N = 27,157), 2013 (N = 34,557), 2015 (N = 33,672), and 2017 (N = 26,742). We used a validated surveillance case definition to classify adults as having epilepsy if they reported a history of doctor-diagnosed epilepsy or seizure disorder (n = 2251). We further classified those with epilepsy as having active epilepsy (n = 1380) if they reported either taking epilepsy medications or having at least one seizure in the past 12 months or as having inactive epilepsy (n = 871) if they did not take epilepsy medication and had not had any seizures in the past 12 months. We used an NHIS recoded variable that classifies adults by Hispanic origin and race. Following age adjustment, among adults with active epilepsy, SPD prevalence was 13.7% among non-Hispanic white adults, 11.2% among non-Hispanic black adults, 20.7% among Hispanic adults, and 17.5% among non-Hispanic other adults. Compared with adults without epilepsy, adults with active epilepsy were 4.8 times more likely, and adults with inactive epilepsy 2.6 times more likely, to report SPD. In each racial/ethnic group, SPD among adults with active epilepsy is significantly higher than in adults without epilepsy. Among adults with active epilepsy, SPD prevalence did not differ by racial/ethnic groups. However, only among non-Hispanic white adults with inactive epilepsy did SPD prevalence significantly exceed that among non-Hispanic white adults without epilepsy. Epilepsy stakeholders can use these estimates to target culturally appropriate community-based and clinic-based interventions to reduce the high burden of psychological distress among adults with active epilepsy and inactive epilepsy.