The potential for prenatal exposure to antiseizure medications to increase the risk of congenital malformations has been a concern for decades. This study fills an information gap regarding congenital malformations and related factors in the offspring of mothers with epilepsy in Poland. The study uses prospectively collected data from an institutional database of a tertiary epilepsy centre between 2000 and 2024, including data from 1,467 pregnancies (mean maternal age = 29.02). These pregnancies resulted in live births (83.9%), miscarriages (15.4%), and perinatal deaths (0.7%). Major congenital malformations (MCM), identified at birth, were observed in 6.4% of children, most commonly affecting the cardiovascular system (33.0%), urogenital system (21.3%), and involving multiple defects (13.8%). Mothers of children with MCM were older than those of children without MCM, p = 0.026. No statistically significant associations were found between the occurrence of MCMs and the type of epilepsy, folic acid intake prior pregnancy, seizures in the first trimester, type of treatment (mono-vs polytherapy), or treatment versus no treatment. Among all medications, only valproate use was associated with a higher risk of MCM – 30.9% versus 20.5%, χ 2 = 5.60, p = 0.026, ϕ = 0.06. The use of valproate by women with epilepsy during pregnancy increases the risk of birth defects in the child. These findings support efforts to reduce VPA use during pregnancy and to encourage pregnancy planning. Mothers of children with major congenital malformations were statistically older – a weak but potentially clinically significant risk factor. Aside from maternal age, no other analysed factors were linked to a higher risk of malformations.
Summary Background Epileptic seizure is a transient period of signs and symptoms resulting from the abnormal excessive and synchronous neuronal activity in the brain, resulting in brain damage. To prevent complications, the antiseizure medication is needed. Several agents have a potency of anti-seizure effect, including ibuprofen. Unfortunately, there are few studies regarding the anti-seizure effect of ibuprofen. Aim To determine the anti-seizure effect of ibuprofen to reduce the symptoms of epileptic seizures. Materials and Methods A systematic review using the databases ScienceDirect, SpringerLink, Nature, and Pubmed. The article was obtained using the keywords “ibuprofen” and (“epilepsy” or “anticonvulsant” or “antiseizure” or “epileptic” or “neuroinflammation”) in the period 2012 to 2022 and screened with inclusion and exclusion criteria. Out of the 1376 articles screened, there are three studies included in this review. The SYRCLE Risk of Bias Tool was used to assess the risk of bias, and the data was synthesised using SWiM guidelines. Results Ibuprofen affects seizures by reducing the number, grade, and duration of seizure intensity in the PTZ-induced rats. Despite the risk of bias assessment revealing some potential bias in the studies, our study shows that ibuprofen has potency as an additional agent for epileptic seizures among febrile convulsive patients. Conclusion In animal models of seizures, ibuprofen has an antiseizure effect by reducing the number, grade, and duration of seizures.
Summary Background Seizure resolution is often difficult to attain promptly in status epilepticus (SE). Clobazam (CLB) is structurally different from typical benzodiazepines with an affinity to the α 2 subunit of GABA-A receptors, rendering unique efficacy in seizure termination. Aim To assess the response of clobazam in refractory status epilepticus (RSE) and to correlate this response with EEG patterns. Materials and Methods This was a single-center retrospective study conducted at a neurocritical care unit between January 2018 and June 2023. The study included fifty consecutive patients with RSE, out of which sixteen adult patients had continuous EEG monitoring both before and during treatment with clobazam until the resolution of RSE. Patients for whom clobazam was the last anti-seizure medication (ASM) used were included in the study. Thirty-four patients were excluded for various reasons, like lack of continuous EEG monitoring (cEEG), status post cardiac arrest, exclusion of clobazam due to medical issues or adverse events, or admission to the hospice floor. Results Out of the sixteen patients included in the study, twelve (75%) had focal nonconvulsive RSE on cEEG and four had focal convulsive RSE when clobazam was administered. Thirteen out of sixteen patients (81.25%) responded successfully to clobazam, with an average time of 23.5 hours since starting the medication. On average, three other ASMs were tried before clobazam, initiated at a mean time of 27 hours since SE onset. The study identified EEG patterns correlated with the use of clobazam :1) lateralised periodic discharges that evolved from 1–1.5 Hz to 2–3 Hz, then evolved to rhythmic theta/delta activity with embedded LPDs; 2) repetitive spikes that evolved to theta and delta frequencies (good responders to CLB), and iii) low voltage faster frequencies and evolved to theta delta frequencies (less responders to CLB). Conclusion Rapid pharmacoresistance to first-line benzodiazepines and ASM occurs as seizures persist during SE. Our study provides EEG correlates to the successful use of CLB in RSE based on its unique targeting of the GABA-A subtype receptors involved in postsynaptic phasic inhibition.
Summary Introduction In patients suspected of epilepsy, electroencephalography (EEG) is an essential tool in the diagnostic workup. Currently, visual analysis of interictal epileptiform discharges by experts is the gold standard. Neurophysiologists perform an analysis of EEG through visual inspection. This is very time-consuming and inefficient. There is an increasing need to develop reliable and validated automated EEG analysis methods. Methods based on artificial intelligence can potentially help achieve this task. Aim The present review paper aims to present the current state of knowledge regarding the ability of artificial intelligence to distinguish abnormal from normal EEG in patients suspected of epilepsy. Material and methods This review covers the most relevant and recent papers identified using the PubMed database. Results and discussion Artificial intelligence (AI) has the potential to improve the management of epilepsy. It was shown that AI could distinguish normal from abnormal recordings, detect seizures, or detect interictal epileptiform discharges. The AI model (SCORE-AI) was developed and validated to assess routine clinical EEGs comprehensively. The sensitivity of SCORE-AI (86.7%) was lower than the sensitivity of the human experts (93.3%) and two models, Encevis (96.7%) and Persyst (100%) but higher than the sensitivity of SpikeNet (66.7%). The accuracy of SCORE-AI was similar to that of human experts and higher than that of the three previously published AI models. SCORE-AI achieves high specificity similar to the human raters and significantly higher accuracy than the three previously published AI models. Conclusion Methods based on artificial intelligence can potentially be helpful in EEG analysis. SCORE-AI may reduce excessive workloads for experts who interpret high volumes of EEG recordings. The SCORE-AI is the first model capable of completing a fully automated and comprehensive clinically relevant assessment of routine EEGs.
SUMMARY Introduction Cenobamate (CNB) is a newly approved antiseizure medication in Europe. It is used as an add-on treatment for focal-onset seizures in adult patients with epilepsy that is not responding to other medications. Aim This report discusses the practical aspects of using cenobamate to treat adult patients with epilepsy based on current experiences. Discussion and conclusions Studies have shown that cenobamate is effective in reducing seizure frequency in adult patients with drug-resistant focal onset epilepsy when used as add-on therapy. It also has a high seizure freedom rate, a good treatment retention rate, and a favorable safety profile. The aspects discussed include using cenobamate in special populations and potential interactions with other drugs, management strategies to mitigate the risk of adverse reactions illustrated by a specific clinical case. Further studies involving larger patient groups are necessary to assess the drug’s efficacy and safety profile, particularly in special populations and patients with other types of epileptic seizures.
