Quantifying the burden of generalized tonic-clonic seizures in patients with drug-resistant epilepsy

Outcome of Absence Epilepsy With Onset at 8-11 Years of Age: Watershed Ages When Syndromes Overlap

Introduction: Absence seizures occur in various epilepsy syndromes, including childhood and juvenile absence epilepsy and juvenile myoclonic epilepsy. When children present with absence seizures at ages when syndromes overlap, initial syndrome designation is not always possible, making early prognostication challenging. For these children, the study objective is to determine clinical and initial electroencephalograph (EEG) findings to predict the development of generalized tonic-clonic seizures, which is a factor that affects outcome. Methods: Children with new-onset absence seizures between 8 and 11 years of age with at least 5 years of follow-up data were studied through the review of medical records and initial EEG tracings. Results: Ninety-eight patients were included in the study. The median age of absence seizure onset was 9 years (interquartile range [IQR] = 8.00, 10.00) and follow-up was 15 years (IQR = 13.00, 18.00). Forty-six percent developed generalized tonic-clonic seizures and 20% developed myoclonic seizures. On multiple regression analysis, a history of myoclonic seizures, anxiety, as well as bifrontal slowing and mild background slowing on initial EEG (P < .05) were associated with generalized tonic-clonic seizures. Although not statistically significant, a shorter duration of shortest EEG burst on baseline EEG was also associated with generalized tonic-clonic seizures. Conclusion: On initial EEG, bifrontal and background slowing and myoclonic seizures and anxiety are associated with developing generalized tonic-clonic seizures, which is of prognostic significance when early syndrome designation is difficult.

Explainable AI for wearable seizure logging: Impact of data quality, patient age, and antiseizure medication on performance

OBJECTIVE

Objective seizure count estimates are crucial for ambulatory epilepsy management. Wearables have shown promise for the detection of tonic-clonic seizures but may suffer from false alarms and undetected seizures. Seizure signatures recorded by wearables often occur over prolonged periods, including increased levels of electrodermal activity and heart rate long after seizure EEG onset, however, previous detection methods only partially exploited these signatures. Understanding the utility of these prolonged signatures for seizure count estimation and what factors generally determine seizure logging performance, including the role of data quality vs. algorithm performance, is thus crucial for improving wearables-based epilepsy monitoring and determining which patients benefit most from this technology.

METHODS

In this retrospective study we examined 76 pediatric epilepsy patients during multiday video-EEG monitoring equipped with a wearable (Empatica E4; records of electrodermal activity, EDA, accelerometry, ACC, heart rate, HR; 1983 h total recording time; 45 tonic-clonic seizures). To log seizures on prolonged data trends, we applied deep learning on continuous overlapping 1-hour segments of multimodal data in a leave-one-subject-out approach. We systematically examined factors influencing logging performance, including patient age, antiseizure medication (ASM) load, seizure type and duration, and data artifacts. To gain insights into algorithm function and feature importance we applied Uniform Manifold Approximation and Projection (UMAP, to represent the separability of learned features) and SHapley Additive exPlanations (SHAP, to represent the most informative data signatures).

RESULTS

Performance for tonic-clonic seizure logging increased systematically with patient age (AUC 0.61 for patients 〈 11 years, AUC 0.77 for patients between 11-15 years, AUC 0.85 for patients 〉 15 years). Across all ages, AUC was 0.75 corresponding to a sensitivity of 0.52 and a false alarm rate of 0.28/24 h. Seizures under high ASM load or with shorter duration were detected worse (P=.025, P=.033, respectively). UMAP visualized discriminatory power at the individual patient level, SHAP analyses identified clonic motor activity and peri/postictal increases in HR and EDA as most informative. In contrast, in missed seizures, these features were absent indicating that recording quality but not the algorithm caused the low sensitivity in these patients.

SIGNIFICANCE

Our results demonstrate the utility of prolonged, postictal data segments for seizure logging, contribute to algorithm explainability and point to influencing factors, including high ASM dose and short seizure duration. Collectively, these results may help to identify patients who particularly benefit from such technology.

Cannabis Pharmacogenomics: A Path to Personalized Medicine

Cannabis and related compounds have created significant research interest as a promising therapy in many disorders. However, the individual therapeutic effects of cannabinoids and the incidence of side effects are still difficult to determine. Pharmacogenomics may provide the answers to many questions and concerns regarding the cannabis/cannabinoid treatment and help us to understand the variability in individual responses and associated risks. Pharmacogenomics research has made meaningful progress in identifying genetic variations that play a critical role in interpatient variability in response to cannabis. This review classifies the current knowledge of pharmacogenomics associated with medical marijuana and related compounds and can assist in improving the outcomes of cannabinoid therapy and to minimize the adverse effects of cannabis use. Specific examples of pharmacogenomics informing pharmacotherapy as a path to personalized medicine are discussed.

Pharmacogenetic Predictors of Cannabidiol Response and Tolerability in Treatment-Resistant Epilepsy

In patients with treatment‐resistant epilepsy (TRE), cannabidiol (CBD) produces variable improvement in seizure control. Patients in the University of Alabama at Birmingham CBD Expanded Access Program (EAP) were enrolled in the genomic study and genotyped using the Affymetrix Drug Metabolizing Enzymes and Transporters plus array. Associations between variants and CBD response (≥50% seizure reduction) and tolerability (diarrhea, sedation, and abnormal liver function) was evaluated under dominant and recessive models. Expression quantitative trait loci (eQTL) influencing potential CBD targets was evaluated in the UK Brain Expression Consortium data set (Braineac), and genetic co‐expression examined. Of 169 EAP patients, 112 (54.5% pediatric and 50.0% female) were included in the genetic analyses. Patients with AOX1 rs6729738 CC (aldehyde oxidase; odds ratio (OR) 6.69, 95% confidence interval (CI) 2.19–20.41, P = 0.001) or ABP1 rs12539 (diamine oxidase; OR 3.96, 95% CI 1.62–9.73, P = 0.002) were more likely to respond. Conversely, patients with SLC15A1 rs1339067 TT had lower odds of response (OR 0.06, 95% CI 0.01–0.56, P = 0.001). ABCC5 rs3749442 was associated with lower likelihood of response and abnormal liver function tests, and higher likelihood of sedation. The eQTL revealed that rs1339067 decreased GPR18 expression (endocannabinoid receptor) in white matter (P = 5.6 × 10⁻³), and rs3749442 decreased hippocampal HTR3E expression (serotonin 5‐HT_3E; P = 8.5 × 10⁻⁵). Furthermore, 75% of genes associated with lower likelihood of response were co‐expressed. Pharmacogenetic variation is associated with CBD response and influences expression of CBD targets in TRE. Implicated pathways, including cholesterol metabolism and glutathione conjugation, demonstrate potential interactions between CBD and common medications (e.g., statins and acetaminophen) that may require closer monitoring. These results highlight the role of pharmacogenes in fundamental biologic processes and potential genetic underpinnings of treatment‐resistance.

Clinical Management of Drug Resistant Epilepsy: A Review on Current Strategies

Drug resistant epilepsy (DRE) is defined as the persistence of seizures despite at least two syndrome-adapted antiseizure drugs (ASD) used at efficacious daily dose. Despite the increasing number of available ASD, about a third of patients with epilepsy still suffer from drug resistance. Several factors are associated with the risk of evolution to DRE in patients with newly diagnosed epilepsy, including epilepsy onset in the infancy, intellectual disability, symptomatic epilepsy and abnormal neurological exam. Pharmacological management often consists in ASD polytherapy. However, because quality of life is driven by several factors in patients with DRE, including the tolerability of the treatment, ASD management should try to optimize efficacy while anticipating the risks of drug-related adverse events. All patients with DRE should be evaluated at least once in a tertiary epilepsy center, especially to discuss eligibility for non-pharmacological therapies. This is of paramount importance in patients with drug resistant focal epilepsy in whom epilepsy surgery can result in long-term seizure freedom. Vagus nerve stimulation, deep brain stimulation or cortical stimulation can also improve seizure control. Lastly, considering the effect of DRE on psychologic status and social integration, comprehensive care adaptations are always needed in order to improve patients' quality of life.