Severe Syndrome of Inappropriate Antidiuretic Hormone Secretion (SIADH) Following the Initiation of Valbenazine for Tardive Dyskinesia: A Case Report.

Hyponatremia, a common electrolyte disorder, usually has a benign clinical course. However, patients with the syndrome of inappropriate antidiuretic hormone secretion (SIADH) can suffer unfavorable outcomes, including mortality. Atypical antipsychotics, which are among the drugs associated with SIADH, also cause tardive dyskinesia, a condition that physicians can now effectively manage with the recently approved agent - valbenazine. We herein report a case of severe hyponatremia due to SIADH in a 58-year-old man who developed hyponatremia-induced generalized seizures six weeks after valbenazine was added to his regimen to mitigate olanzapine-associated tardive dyskinesia. His electrolyte derangement and clinical course improved following prompt recognition and treatment of SIADH. The temporal association between the commencement of valbenazine and the onset of SIADH suggests a possible but previously unreported link between valbenazine and the development of SIADH. Awareness of this uncommon association is relevant to patient safety.

Status seizures as a secondary effect from the "squash drinking syndrome" in an infant.

Abstract: The "hypotonic drink syndrome" is characterized by loss of appetite, normal activity levels and, in some cases, intestinal disturbances in children with an intake of more than 30% of the recommended daily calories in the form of non-dairy drinks. Diarrhea and growth retardation are possible complications due to the amount of nonnutritive calorie intake ("empty calories") contained in this type of hypotonic beverages.We present the case of an 11-month-old boy who suffered a "Squash drinking syndrome" requiring admission to the pediatric intensive care unit because of a status seizure secondary to a severe hyponatremia (118 mmol/L) due to massive ingestion of hypotonic drinks, such as squash. The seizure did not subside until sodium levels reached 123 mmol/L with hypertonic saline (3%). Neurological, renal, digestive, endocrine and metabolic problems were all ruled out and normal sodium levels were maintained with dietary recommendations and a restriction of hypotonic fluid intake. Conclusions: To prevent these situations it is important to be aware of this entity and to know how to identify the possible complications that may appear after excessive ingestion of hypotonic drinks, as in the case of our patient, ranging from lack of appetite, growth failure and diarrhea, to a status seizure.

Focal Seizures in a Patient With Chronic Basal Ganglia Calcifications Secondary to Idiopathic Primary Hypoparathyroidism.

Patients with hypoparathyroidism can present with concurrent basal ganglia calcifications (BGCs). The exact pathogenesis is unknown, although it is thought to relate to calcium-phosphate deposition from chronic hypocalcemia and hyperphosphatemia. We present the case of a 65-year-old man with known idiopathic primary hypoparathyroidism and concurrent extensive BGC. Thirty years after diagnosis, he presented with focal seizures despite a decade of stable intracranial calcifications on imaging. Serum calcium, phosphate, 25-hydroxyvitamin D, and parathyroid hormone levels were well controlled during this period. He was commenced on lifelong levetiracetam with subsequent seizure remission. Given the scarcity of literature surrounding focal seizures and BGC, it is essential to raise awareness in this area.

Epileptic Seizure Prediction Using Spatiotemporal Feature Fusion on EEG.

Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure prediction has significant value for clinical treatment of epilepsy. Currently, prediction methods using Convolutional Neural Network (CNN) primarily focus on local features of EEG, making it challenging to simultaneously capture the spatial and temporal features from multi-channel EEGs to identify the preictal state effectively. In order to extract inherent spatial relationships among multi-channel EEGs while obtaining their temporal correlations, this study proposed an end-to-end model for the prediction of epileptic seizures by incorporating Graph Attention Network (GAT) and Temporal Convolutional Network (TCN). Low-pass filtered EEG signals were fed into the GAT module for EEG spatial feature extraction, and followed by TCN to capture temporal features, allowing the end-to-end model to acquire the spatiotemporal correlations of multi-channel EEGs. The system was evaluated on the publicly available CHB-MIT database, yielding segment-based accuracy of 98.71%, specificity of 98.35%, sensitivity of 99.07%, and F1-score of 98.71%, respectively. Event-based sensitivity of 97.03% and False Positive Rate (FPR) of 0.03/h was also achieved. Experimental results demonstrated this system can achieve superior performance for seizure prediction by leveraging the fusion of EEG spatiotemporal features without the need of feature engineering.

County-level industrial composition of the labor force and drug overdose mortality rates in the United States in 2018-2021.

BACKGROUND: Drug mortality risks vary among industries, creating distinctive geographic patterns across US counties. However, less is known about how local labor market structure relates to drug overdose mortality amid the synthetic opioid era in the United States. This study investigates the relationship between industry-specific job composition and drug overdose mortality at the county level while exploring how fentanyl's presence in illicit drug supplies may moderate the relationship. METHODS: Data were derived from the National Center for Health Statistics' Multiple Cause of Death files for the rates of drug overdose mortality of any intent, linked with four other sources on industry-specific job shares, drug supply, and county-level sociodemographic characteristics and opioid prescribing rates from the US Census Bureau, the CDC, and the Drug Enforcement Administration. Negative binomial regression models were employed to examine associations between county industry-specific job composition and drug overdose mortality, with tests for moderating effects of state-level fentanyl seizure rates. RESULTS: Our models indicate negative associations between job shares of manufacturing, retail trade, and educational services industries and drug overdose mortality. Positive associations were found for arts/entertainment/recreation and public administration. State-level fentanyl seizure rates had moderating effects on administrative/support/waste management/remediation (A/S/WM/R) and educational services. CONCLUSION: Counties with a higher concentration of arts/entertainment/recreation and public administration jobs need targeted efforts to mitigate drug-related overdose risks. Additionally, areas with higher concentrations of A/S/WM/R service jobs, particularly where fentanyl seizure rates are higher, may require proactive harm reduction strategies for reducing overdose risks.

Additional Results from Two Randomized, Placebo-Controlled Trials of Stiripentol in Dravet Syndrome Highlight a Rapid Antiseizure Efficacy with Longer Seizure-Free Periods.

INTRODUCTION: The efficacy of stiripentol in Dravet syndrome children was evidenced in two randomized, double-blind, placebo-controlled, phase 3 studies, namely STICLO France (October 1996-August 1998) and STICLO Italy (April 1999-October 2000), but data were not fully exploited at the time. METHODS: This post-hoc analysis used additional information, notably collected during the open-label extension (OLE) month, or reported by caregivers in individual diaries, to evaluate new outcomes. RESULTS: Overall, 64 patients were included (31 in the placebo group, 33 in the stiripentol group) of whom 34 (53.1%) were female. Patients' mean and median (25%; 75%) age were 9.2 years (range 3.0-20.7 years) and 8.7 years (6.0; 12.1) respectively. At the end of the double-blind treatment period, 72% of the patients in the stiripentol group had a ≥ 50% decrease in generalized tonic-clonic seizure (GTCS) frequency, versus 7% in the placebo group (P < 0.001), 56% had a profound (≥ 75%) decrease versus 3% in the placebo group (P < 0.001), and 38% were free of GTCS, but none in the placebo group (P < 0.001). The onset of stiripentol efficacy was rapid, significant from the fourth day of treatment onwards. The median longest period of consecutive days with no GTCS was 32 days in the stiripentol group compared to 8.5 days in the placebo group (P < 0.001). Further to the switch to the third month OLE, an 80.2% decrease in seizure frequency from baseline was observed in patients previously receiving placebo, while no change in efficacy was observed in those already on stiripentol. Adverse events were more frequent in the stiripentol group, with significantly more episodes of somnolence, anorexia, and weight decrease than in the placebo group. CONCLUSION: Altogether these new analyses of the STICLO data reinforce the evidence for a remarkable efficacy of stiripentol in Dravet syndrome, with a demonstrated rapid onset of action and sustained response, as also evidenced in further post-randomized trials.

Seizure freedom without seizure medication following stereoelectroencephalography implantation: a case report of drug-resistant post-traumatic epilepsy.

Achieving seizure freedom following failure of several antiseizure medications (ASMs) is rare, with the likelihood of achieving further control decreasing with each successive ASM trial. When cases of drug-resistant epilepsy arise, a diagnostic procedure known as stereoelectroencephalography (sEEG) can be used to identify epileptogenic zones (EZ) within the brain. After localization of these zones, they can be targeted for future surgical intervention. Here, we describe a case of complete seizure freedom off medication after sEEG without resection or other therapeutic intervention. In 2017, a 36-year-old right-handed male presented with drug-resistant epilepsy stemming from prior traumatic brain injury. Due to ongoing seizures, in 2020 a robotic-assisted sEEG electrode placement procedure was employed to localize the seizure onset zone. During sEEG monitoring, a single event was captured where the patient had dysarthric speech, left arm dystonic flexion, and difficulty responding to questioning. Notably, this event had no sEEG correlate, suggesting seizure occurrence in a region not monitored by implanted electrodes, which prompted the placement of scalp electrodes following this event. However, no further clinical events consistent with seizure were provoked through the remainder of recording. Following the 13-day admission, the patient chose to self-discontinue all seizure medications and has remained seizure free as of October 2023, more than 3.5 years later. While sEEG is considered a relatively safe procedure for seizure localization in drug resistant epilepsy, the possibility of microlesions created by sEEG depth electrodes remains largely unexplored. Further evaluation should be performed into potential tissue injury produced by depth electrode insertion.

The effects of slow wave sleep characteristics on semantic, episodic, and procedural memory in people with epilepsy.

Slow wave sleep (SWS) is highly relevant for verbal and non-verbal/spatial memory in healthy individuals, but also in people with epilepsy. However, contradictory findings exist regarding the effect of seizures on overnight memory retention, particularly relating to procedural and non-verbal memory, and thorough examination of episodic memory retention with ecologically valid tests is missing. This research explores the interaction of SWS duration with epilepsy-relevant factors, as well as the relation of spectral characteristics of SWS on overnight retention of procedural, verbal, and episodic memory. In an epilepsy monitoring unit, epilepsy patients (N = 40) underwent learning, immediate and 12 h delayed testing of memory retention for a fingertapping task (procedural memory), a word-pair task (verbal memory), and an innovative virtual reality task (episodic memory). We used multiple linear regression to examine the impact of SWS duration, spectral characteristics of SWS, seizure occurrence, medication, depression, seizure type, gender, and epilepsy duration on overnight memory retention. Results indicated that none of the candidate variables significantly predicted overnight changes for procedural memory performance. For verbal memory, the occurrence of tonic-clonic seizures negatively impacted memory retention and higher psychoactive medication load showed a tendency for lower verbal memory retention. Episodic memory was significantly impacted by epilepsy duration, displaying a potential nonlinear impact with a longer duration than 10 years negatively affecting memory performance. Higher drug load of anti-seizure medication was by tendency related to better overnight retention of episodic memory. Contrary to expectations longer SWS duration showed a trend towards decreased episodic memory performance. Analyses on associations between memory types and EEG band power during SWS revealed lower alpha-band power in the frontal right region as significant predictor for better episodic memory retention. In conclusion, this research reveals that memory modalities are not equally affected by important epilepsy factors such as duration of epilepsy and medication, as well as SWS spectral characteristics.

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal.

Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.

Patient-reported outcomes in epilepsy: a case study exploring their usage and impact.

Background: This study aimed to obtain insights from epilepsy specialists on the use of Patient-Reported Outcome (PRO) measures and how they can affect the management of people with epilepsy and healthcare resource utilization. Methods: The heads of two referral units for people with epilepsy at one tertiary care hospital were invited to respond to a structured survey. Results: Paper-based questionnaires and face-to-face interviews were the main modalities used to measure the quality of life of people with epilepsy. The Quality of Life in Epilepsy Inventory-31 (QOLIE-31), the Adverse Event Profile (adult centre), the Generalized Anxiety Disorder-7, Short-Form Health Survey 36, PSY-Flex, SAFA and Child Behavior Checklist (paediatric centre) were the most used scales. There was consensus about the favourable impact of PRO upon patient management, disease management and measurement of the success of a treatment. Both respondents considered the PRO as important as other main indicators like efficacy and tolerability of the treatment. Lack of time, personnel and economic resources was identified as a barrier on the use of PRO. The PRO could reduce the number of visits, exams and treatments, and increase the time spent on each patient and the number of neuropsychological, psychological and rehabilitation services. The standardized use of PRO was considered useful and the increase in human resources was considered a priority to achieve this goal. Conclusions: Despite the heterogeneity in the actual collection of PRO, there was a uniform perception about their role to optimize the care of people with epilepsy.

Efficacy and tolerability of perampanel in pediatric patients with Dravet syndrome.

Background: In 1978, Charlotte Dravet first described a form of epilepsy termed Dravet syndrome (DS). It is a form of genetic epilepsy with early-onset, intractable epilepsy episodes, and neurodevelopmental delay. In children, DS can lead to refractory seizures that are resistant to standard therapy. Recently, perampanel (PER) was approved as an antiepileptic drug for patients as young as 4 years old. Methods: The medical records were retrospectively reviewed and patients with DS who used PER were included in this study. The diagnosis was established using whole-exome sequencing, and the collected data included the patients' demographic characteristics, seizure pattern, PER dosage, laboratory and imaging findings. Results: This study included 18 pediatric patients with a clinical diagnosis of DS. The mean age of PER initiation was 7.67±3.865. Most patients had two types of seizures (61.1%) followed by three types (22.2%), with generalized tonic-clonic being the most frequently reported type of seizure. The mean efficacy of PER was 29.17%±29.368%, and only one patient had an efficacy of 100%. Moreover, patients aged 8 years and younger presented with higher efficacy than those who were older (49.17%±34.120% vs. 19.17%±21.829%, P=0.03). Conclusions: This study presented supporting evidence of the promising therapeutic effect of PER among patients with DS. PER can be considered one of the treatment options for this group of patients. However, several patients presented with unfavorable side effects that led to medication cessation. Future multicenter studies are required to explore further treatment options for patients with DS.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data.

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

Shining a Light on Trace Elements: A Systematic Review and Meta-analysis of Serum Concentrations in Febrile seizure.

Febrile seizures (FS) are a common occurrence in pediatric patients and are typically triggered by high fevers above 100.4°F (38°C), often associated with viral or bacterial infections such as respiratory or gastrointestinal infections. Recent research suggests that the serum concentration of trace elements may play a role in the occurrence of FS. This study aimed to assess the association between serum levels of trace elements and FS in pediatric patients. A comprehensive search of four databases, including Scopus, Web of Science, PubMed, and Google Scholar, was conducted up to February 2024. The study followed the PICO structure, focusing on the Population (pediatric patients with FS), Intervention (serum concentrations of selenium, zinc, magnesium, and copper), Comparison (with or without controls), and Outcome (occurrence of FS). The methodological quality of the included observational studies was assessed using the Newcastle-Ottawa Scale (NOS) tool. Out of a total of 168 papers, 37 met the inclusion criteria for this meta-analysis, covering studies published between 2018 and 2023. Lower serum zinc levels were observed in pediatric patients with FS compared to control groups (SMD: -1.25, 95% CI: -1.47, -1.03). Conversely, higher serum copper levels were found in control groups compared to those with FS (SMD: 0.43, 95% CI: 0.04, 0.82). Additionally, lower serum magnesium levels were detected in the FS group compared to controls (SMD: -0.76, 95% CI: -1.57, 0.05), while serum selenium levels were approximately two times lower in the FS group than in controls (SMD: -2.23, 95% CI: -2.76, -1.70). Our meta-analysis suggests that pediatric patients with FS have lower serum concentrations of trace elements compared to controls. Further research is warranted to elucidate the potential role of trace elements in the pathogenesis of FS. This meta-analysis and systematic review was registered in the International prospective register of systematic reviews (PROSPERO ID: CRD42024519163). Registry URL: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024519163 registry number: CRD42024519163.

Systematic review of seizure-onset patterns in stereo-electroencephalography: Current state and future directions.

OBJECTIVE: Increasing evidence suggests that the seizure-onset pattern (SOP) in stereo-electroencephalography (SEEG) is important for localizing the "true" seizure onset. Specifically, SOPs with low-voltage fast activity (LVFA) are associated with seizure-free outcome (Engel I). However, several classifications and various terms corresponding to the same pattern have been reported, challenging its use in clinical practice. METHOD: Following the Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guideline, we performed a systematic review of studies describing SOPs along with accompanying figures depicting the reported SOP in SEEG. RESULTS: Of 1799 studies, 22 met the selection criteria. Among the various SOPs, we observed that the terminology for low frequency periodic spikes exhibited the most variability, whereas LVFA is the most frequently used term of this pattern. Some SOP terms were inconsistent with standard EEG terminology. Finally, there was a significant but weak association between presence of LVFA and seizure-free outcome. CONCLUSION: Divergent terms were used to describe the same SOPs and some of these terms showed inconsistencies with the standard EEG terminology. Additionally, our results confirmed the link between patterns with LVFA and seizure-free outcomes. However, this association was not strong. SIGNIFICANCE: These results underline the need for standardization of SEEG terminology.

Ketogenic Diet in the Treatment of Epilepsy.

Epilepsy is one of the most disabling neurological diseases. Despite proper pharmacotherapy and the availability of 2nd and 3rd generation antiepileptic drugs, deep brain stimulation, and surgery, up to 30-40% of epilepsy patients remain drug-resistant. Consequences of this phenomenon include not only decreased a quality of life, and cognitive, behavioral, and personal disorders, but also an increased risk of death, i.e., in the mechanism of sudden unexpected death in epilepsy patients (SUDEP). The main goals of epilepsy treatment include three basic issues: achieving the best possible seizure control, avoiding the undesired effects of treatment, and maintaining/improving the quality of patients' lives. Therefore, numerous attempts are made to offer alternative treatments for drug-resistant seizures, an example of which is the ketogenic diet. It is a long-known but rarely used dietary therapy for intractable seizures. One of the reasons for this is the unpalatability of the classic ketogenic diet, which reduces patient compliance and adherence rates. However, its antiseizure effects are often considered to be worth the effort. Until recently, the diet was considered the last-resort treatment. Currently, it is believed that a ketogenic diet should be used much earlier in patients with well-defined indications. In correctly qualified patients, seizure activity may be reduced by over 90% or even abolished for long periods after the diet is stopped. A ketogenic diet can be used in all age groups, although most of the available literature addresses pediatric epilepsy. In this article, we focus on the mechanisms of action, effectiveness, and adverse effects of different variants of the ketogenic diet, including its classic version, a medium-chain triglyceride diet, a modified Atkins diet, and a low glycemic index treatment.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

PYRIDOXINE-dependent epilepsy (PDE): An observational study of neonatal cases on the role of pyridoxine in patients treated with standard anti-seizure medications.

BACKGROUND: The main objective of this study was to evaluate the neurological consequences of delayed pyridoxine administration in patients diagnosed with Pyridoxin Dependent Epilepsies (PDE). MATERIALS AND METHODS: We reviewed 29 articles, comprising 52 genetically diagnosed PDE cases, ensuring data homogeneity. Three additional cases were included from the General Pediatric Operative Unit of San Marco Hospital. Data collection considered factors like age at the first seizure's onset, EEG reports, genetic analyses, and more. Based on the response to first-line antiseizure medications, patients were categorized into four distinct groups. Follow-up evaluations employed various scales to ascertain neurological, cognitive, and psychomotor developments. RESULTS: Our study includes 55 patients (28 males and 27 females), among whom 15 were excluded for the lack of follow-up data. 21 patients were categorized as "Responder with Relapse", 11 as "Resistant", 6 as "Pyridoxine First Approach", and 2 as "Responders". The neurological outcome revealed 37,5 % with no neurological effects, 37,5 % showed complications in two developmental areas, 15 % in one, and 10 % in all areas. The statistical analysis highlighted a positive correlation between the time elapsed from the administration of pyridoxine after the first seizure and worse neurological outcomes. On the other hand, a significant association was found between an extended latency period (that is, the time that elapsed between the onset of the first seizure and its recurrence) and worse neurological outcomes in patients who received an unfavorable score on the neurological evaluation noted in a subsequent follow-up. CONCLUSIONS: The study highlights the importance of early recognition and intervention in PDE. Existing medical protocols frequently overlook the timely diagnosis of PDE. Immediate administration of pyridoxine, guided by a swift diagnosis in the presence of typical symptoms, might improve long-term neurological outcomes, and further studies should evaluate the outcome of PDE neonates promptly treated with Pyridoxine.

Latarjet operation carries three times the risk of failure in seizure versus non-seizure recurrent anterior dislocation of the shoulder joint: outcome of a systematic review with meta-analysis.

Background: Recurrent anterior shoulder dislocation (RASD) in cases of seizure disorders (SDs) total 50%-80% of all SD-associated shoulder instabilities. Based on the extent of bone loss, treatment options include bony and soft-tissue reconstructions, arthroplasty, and arthrodesis. The primary objective of this paper was to review the treatment options for RASD in SDs. Methods: Several bibliographic databases were searched for RASD treatment options in SD patients. The demographic outcome measures, the failure rate (defined as the relative risk of recurrence of dislocation postoperation), and the postoperative seizure recurrence rate were recorded. Results: We pooled 171 cases (187 shoulders) from 11 studies. Of these, one, five, two, two, and one reports studied Bankart's operation with remplissage (27 cases/29 shoulders), the Latarjet procedure (106/118), bone block operation (21/23), arthroplasty (11/11), and arthrodesis (6/6), respectively, in treating SD-associated RASD. The relative risk of failure between SD and non-SD patients was 3.76 (1.36-10.38) after the Latarjet operation. The failure rates were 17% and 13% for Bankart's operation with remplissage and the Latarjet procedure in SD patients, respectively, but 0% each for bone block operation, arthroplasty, and arthrodesis. The total rate of seizure recurrence after operation was 33% of the pooled cases. Conclusions: SD recurrence in the postoperative period, the size of the bone block, and the muscular attachments to a small coracoid autograft are the determinants of failure among various reconstructive operations in SD-associated RASD.

Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Deep brain stimulation of the subthalamic nucleus for a patient with drug resistant juvenile myoclonic epilepsy: 1 year follow-up.

BACKGROUND: Drug-resistant juvenile myoclonic epilepsy (DR-JME) remains a significant challenge in neurology. Traditional management strategies often fail to achieve satisfactory control, necessitating innovative treatments. OBJECTIVE: This case report aims to evaluate the efficacy and safety of deep brain stimulation (DBS) targeting the subthalamic nucleus (STN-DBS) in a patient with DR-JME. METHODS: We describe the treatment of a patient with DR-JME using STN-DBS. The patient underwent implantation and received high-frequency stimulation (HFS) at the STN. RESULTS: One year post-implantation, the patient demonstrated a substantial reduction in motor seizure frequency by 87.5%, with improvements in quality of life and seizure severity by 52.0% and 46.7%, respectively. No adverse events were reported during the follow-up period. CONCLUSIONS: This case represents the first report of favorable outcomes with STN-DBS in a patient with DR-JME, suggesting that long-term HFS of the STN may be a promising treatment option for patients suffering from this condition.