The prevalence of drug-resistant-epilepsy and its associated factors in patients with epilepsy

Drug-resistant epilepsy in Morocco: description, prevalence and predictive factors in Casablanca-Settat region

Drug-resistant epilepsy (DRE) affects about one-third of people with epilepsy (PWE). Our study aims to estimate the DRE prevalence and its predictive factors in Morocco. A cross-sectional study was conducted over 18 months. PWE with clinical diagnosis of epilepsy, and with an antiseizure treatment duration >12 months were examined in the neurology, neurosurgery, psychiatry, and pediatrics departments, of different sampled clinical sectors for the Casablanca-Settat region. Sociodemographic and clinical data were collected using a questionnaire during consultations. Antiseizure multi-therapy, a seizure freedom duration <12 months, compliance, and adequate posology were the determining factors for classifying DRE. Data were analyzed using Statistical Package for Social Sciences (SPSS) software, version 21.0. Statistical significance was set at p < 0.05 and logistic regression was performed to determine the predictive factors. In our sample of 446 PWE, the median age is 25 years (IQR: 11.75-44.00). The DRE estimated prevalence was 29.4 %. Pseudo-resistant epilepsy (PRE) was 18.0 %. Multivariate logistic regression analysis reports that single marital status (ORa = 1.94; CI95%: 1.02-3.71), comorbidities and concomitant affections (ORa = 2.14; CI95%: 1.27-3.59), structural etiology (ORa = 1.96; CI95%: 1.16-3.30), pre-ictal aura (ORa = 1.90; CI95%: 1.09-3.29), inter-ictal EEG abnormalities (ORa = 2.45; CI95%: 1.24-4.84) and allopathic treatment use (ORa = 2.10; CI95%: 1.30-3.39) are the predictive factors for DRE. We report an alarming DRE prevalence. Associated factors found may contribute to the prognosis and early management. PWE awareness, facilitating healthcare access and the development of epilepsy surgery are the key points to limit DRE in Morocco and prevent its various complications, especially for the pediatric population.

Real-world anti-seizure treatment and adverse events among individuals living with drug-resistant focal epilepsy in the United States

OBJECTIVE

This study aimed to understand how people living with drug-resistant focal epilepsy (DRE) navigate through lines of antiseizure medications (ASM) and experience adverse events (AEs) in the real-world setting in the United States.

METHODS

A retrospective study was conducted with medical chart data from clinical practices in the United States. Eligible adults had a confirmed diagnosis of DRE and initiated a third-line ASM therapy between January 2013 and January 2020 (i.e., the index date). Subjects must have medical history data available for ≥1 year prior to (the baseline) and ≥2 years after the index date (the follow-up). Treatment patterns were captured from first to fourth lines. After the emergence of drug resistance, time to ASM discontinuation, reasons for discontinuation, AE experience and AE management were reported separately during third and fourth lines of treatment and beyond.

RESULTS

The study included a total of 345 individuals, with an average (standard deviation) age of 23.9 (11.9) years at first diagnosis. All individuals had at least three lines of ASMs with first and second lines during baseline, and third line during follow-up. The first line for most individuals was monotherapy. As individuals progressed through additional lines of ASM therapy, they were more likely to receive polytherapy. The regimens were more individualized after meeting drug resistance criteria. The top reasons for discontinuing were uncontrolled seizure and/or intolerance/AEs for both third and subsequent lines. More than a third of individuals experienced at least one AE. Among those with at least one AE, many individuals had to manage these AEs with dose adjustment (39.4%), discontinuation of offending ASM (37.9%), de novo pharmacotherapy (25.8%), emergency room visit (13.6%), and hospitalization (12.1%).

SIGNIFICANCE

This study demonstrated that individuals living with DRE experience significant AEs, and many of these AEs lead to treatment disruption and significant healthcare resource utilization.

PLAIN LANGUAGE SUMMARY

This study examined how individuals with focal epilepsy are treated across various clinics in United States and reported the adverse events these individuals experienced during treatment, along with the consequence associated with these adverse events. We found that as individuals progressed through additional treatments, they were more and more likely to receive more than one antiseizure medication, and a significant portion of individuals experienced at least one adverse event, often manifested as headache, somnolence, dizziness, and fatigue.

Ficus platyphylla alleviates seizure severity and neurobehavioral comorbidities in pentylenetetrazole-kindled rats via modulation of oxidative stress

PTZ kindling induces oxidative stress, neuronal cell degeneration, and neurobehavioral alterations in rodents that mimic neuropsychiatric comorbidities of epilepsy, which could be initiated or aggravated by some antiepileptic drugs. Here, we investigated the effects of the methanol extract of Ficus platyphylla (FP) on severity scores for seizures, neuronal cell degeneration, and neurobehavioral alterations in rats kindled with pentylenetetrazole (PTZ) and probed the involvement of oxidative stress in these ameliorative effects of FP. FP (50 and 100 mg/kg, p.o.) ameliorated seizure severity, neuronal cell degeneration, depressive behaviors, cognitive dysfunctions, and oxidative stress in rats kindled with PTZ (42.5 mg/kg, i.p.). The findings from this study give additional insights into the potential values of FP in the treatment of persistent epilepsy and major neuropsychiatric comorbidities via modulation of oxidative stress.

Association of epilepsy with neuroimaging patterns in children with cerebral palsy

OBJECTIVES

In this study, we examined whether epilepsy and drug-resistant epilepsy are associated with neuroimaging findings in children with cerebral palsy (CP).

METHODS

Magnetic resonance imaging classification system (MRICS) proposed by Surveillance of Cerebral Palsy in Europe (SCPE) was used for classification of different MRI patterns in patients with cerebral palsy. We reviewed the brain MRI scans and medical records of children with CP who were followed-up in our clinic between 2019 and 2023. Patients were divided into three categories: CP without epilepsy, CP with controlled epilepsy and CP with DRE. MRI patterns were grouped as maldevelopments, predominant white matter injury, predominant gray matter injury, miscellaneous (delayed myelination, cerebral atrophy, cerebellar atrophy, brainstem lesions and calcifications, lesions that were not classified under any other group) and normal according to MRICS of the SCPE.

RESULTS

There were 325 CP patients. The most common MRI patterns were predominant white matter injury (47.6%) and gray matter injury (23.8%). There was a 1.5-fold reduction in the risk of epilepsy in patients with predominant white matter injury (OR = 1.54, 95% CI 1.23-1.94). In contrast, children in the miscellaneous group had significantly higher risks of epilepsy (p < 0.001), and we were able to determine that miscellaneous findings increased the risk by 1.8 times (OR = 1.77, 95% CI 1.47-2.12).

CONCLUSION

In conclusion, more than half of the children with CP had epilepsy, 40.7% of whom had DRE. On MRI, miscellaneous findings may indicate a poor prognosis for epilepsy, while predominant white matter injury may indicate a good outcome. Children with CP, especially those with miscellaneous findings on MRI, should be closely monitored for epilepsy development.

Early prediction of drug-resistant epilepsy using clinical and EEG features based on convolutional neural network

OBJECTIVE

Machine learning utilization in electroencephalogram (EEG) analysis and epilepsy care is fast evolving. Thus, we aim to develop and validate two one-dimensional convolutional neural network (CNN) algorithms for predicting drug-resistant epilepsy (DRE) in patients with newly-diagnosed epilepsy based on EEG and clinical features.

METHODS

We included a total of 1010 EEG signal epochs and 15 clinical features from 101 patients with epilepsy. Each patient had 10 epochs of EEG signal data, with each signal recorded for 90 s. The ratio of development set and validation set was 80:20, and ten-fold cross validation was performed. First, a CNN algorithm was used to extract EEG features automatically. Then, Two one-dimensional CNNs were crafted.. Accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, mean square error (MSE) and area under the curve (AUC) were calculated to evaluate the classifiers performance.

RESULTS

The clinical-EEG model showed good performance and clinical practical value, with the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.72, 0.82, 0.96, 0.89, 0.83, 32.00, 0.81, respectively, and the accuracy in validation set was 0.84. In the EEG model, the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.59, 0.82, 0.90, 0.86, 0.72, 181.76, 0.76, respectively, and the accuracy in validation set was 0.81.

CONCLUSION

We constructed a clinical-EEG model showed good potential for predicting DRE in patients with newly-diagnosed epilepsy, which could help identify patients at high risk of developing DRE at earlier stages.

The efficacy and safety of first-line anti-seizure medications as substitution therapy for children with drug-resistant epilepsy: a randomized controlled trial protocol

Although many anti-seizure medications (ASMs) are available, treatment failure, known as drug-resistant epilepsy (DRE), still occurs in around 30% of children with epilepsy. Second-line ASMs are usually used as substitution therapy in DRE to control seizures, although international consensus is not available yet. Previous studies focus on comparing the ASMs, whether as add-on or substitution therapy, mainly conducted in newly diagnosed epilepsy. However, the study that investigated first-line ASMs as substitution therapy compared to second-line ones, particularly among DRE children, is still lacking. A randomized controlled trial (RCT) enrolling 102 participants, aged 1-18, at three referral hospitals in Indonesia will be conducted, dividing them into intervention and control groups. The intervention group will be treated with first-line ASMs as the substitution therapy, while the other in the control group will get second-line ASMs. The primary outcome measure is the proportion difference of responders between groups who get first-line and second-line ASMs in 14 weeks of intervention. Clinical trial registration: ClinicalTrials.gov, identifier NCT05697614.

Non-invasive mapping of epileptogenic networks predicts surgical outcome

Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.

Neurological update: structural and functional imaging in epilepsy surgery

Structural and functional imaging prior to surgery in drug-resistant focal epilepsy, has an important role to play alongside electroencephalography (EEG) techniques, in planning the surgical approach and predicting post-operative outcome. This paper reviews the role of structural and functional imaging of the brain, namely computed tomography (CT), magnetic resonance imaging (MRI), functional MRI (fMRI), single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging in the preoperative work-up of people with medically refractory epilepsy. In MRI-negative patients, the precise localisation of the epileptogenic zone may be established by demonstrating hypometabolism on PET imaging or hyperperfusion on SPECT imaging in the area surrounding the seizure focus. These imaging modalities are far less invasive than intracranial EEG, which is the gold standard but requires surgical placement of electrodes or recording grids. Even when intracranial EEG is needed, PET or SPECT imaging can assist in the planning of EEG electrode placement, due to its' limited spatial sampling. Multimodal imaging techniques now allow the multidisciplinary epilepsy surgery team to identify and better characterise focal pathology, determine its' relationship to eloquent areas of the brain and the degree of interconnectedness within both physiological and pathological networks, as well as improve planning and surgical outcomes for patients. This paper will update the reader on this whole field and provide them with a practical guide, to aid them in the selection of appropriate investigations, interpretation of the findings and facilitating patient discussions in individuals with drug-resistant focal epilepsy.

Potential clinical and biochemical markers for the prediction of drug-resistant epilepsy: A literature review

Drug resistance is a major challenge in the treatment of epilepsy. Drug-resistant epilepsy (DRE) accounts for 30% of all cases of epilepsy and is a matter of great concern because of its uncontrollability and the high burden, mortality rate, and degree of damage. At present, considerable research has focused on the development of predictors to aid in the early identification of DRE in an effort to promote prompt initiation of individualized treatment. While multiple predictors and risk factors have been identified, there are currently no standard predictors that can be used to guide the clinical management of DRE. In this review, we discuss several potential predictors of DRE and related factors that may become predictors in the future and perform evidence rating analysis to identify reliable potential predictors.