Ide-cel or Standard Regimens in Relapsed and Refractory Multiple Myeloma.

BACKGROUND: Survival is poor among patients with triple-class-exposed relapsed and refractory multiple myeloma. Idecabtagene vicleucel (ide-cel), a B-cell maturation antigen-directed chimeric antigen receptor (CAR) T-cell therapy, previously led to deep, durable responses in patients with heavily pretreated relapsed and refractory multiple myeloma. METHODS: In this international, open-label, phase 3 trial involving adults with relapsed and refractory multiple myeloma who had received two to four regimens previously (including immunomodulatory agents, proteasome inhibitors, and daratumumab) and who had disease refractory to the last regimen, we randomly assigned patients in a 2:1 ratio to receive either ide-cel (dose range, 150×106 to 450×106 CAR-positive T cells) or one of five standard regimens. The primary end point was progression-free survival. Key secondary end points were overall response (partial response or better) and overall survival. Safety was assessed. RESULTS: A total of 386 patients underwent randomization: 254 to ide-cel and 132 to a standard regimen. A total of 66% of the patients had triple-class-refractory disease, and 95% had daratumumab-refractory disease. At a median follow-up of 18.6 months, the median progression-free survival was 13.3 months in the ide-cel group, as compared with 4.4 months in the standard-regimen group (hazard ratio for disease progression or death, 0.49; 95% confidence interval, 0.38 to 0.65; P<0.001). A response occurred in 71% of the patients in the ide-cel group and in 42% of those in the standard-regimen group (P<0.001); a complete response occurred in 39% and 5%, respectively. Data on overall survival were immature. Adverse events of grade 3 or 4 occurred in 93% of the patients in the ide-cel group and in 75% of those in the standard-regimen group. Among the 225 patients who received ide-cel, cytokine release syndrome occurred in 88%, with 5% having an event of grade 3 or higher, and investigator-identified neurotoxic effects occurred in 15%, with 3% having an event of grade 3 or higher. CONCLUSIONS: Ide-cel therapy significantly prolonged progression-free survival and improved response as compared with standard regimens in patients with triple-class-exposed relapsed and refractory multiple myeloma who had received two to four regimens previously. The toxicity of ide-cel was consistent with previous reports. (Funded by 2seventy bio and Celgene, a Bristol-Myers Squibb company; KarMMa-3 ClinicalTrials.gov number, NCT03651128.).

Over- and underreporting of seizures: How big is the problem?

OBJECTIVE: Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient-reported seizures and electroencephalographic (EEG) findings during video-EEG monitoring periods, chiefly highlighting underreporting of seizures, a well-recognized phenomenon. However, seizure overreporting is a significant problem discussed within the literature, although not in such a large cohort. Our aim is to quantify the over- and underreporting of seizures in a large cohort of ambulatory EEG patients. METHODS: We performed a retrospective data analysis on 3407 patients referred to a diagnostic service for ambulatory video-EEG between 2020 and 2022. Both patient-reported events and events discovered on review of the video-EEG were analyzed and classified as epileptic, psychogenic (typically clinical motor events, without accompanying EEG change), or noncorrelated events (NCEs; without perceivable clinical or EEG change). Events were analyzed by state of arousal and indication for referral. Subgroup analysis was performed in patients with focal and generalized epilepsies. RESULTS: A total of 21 024 events were recorded by 3407 patients. Fifty-eight percent of reported events were NCEs, whereas 27% of all events were epileptic. Sixty-four percent of epileptic seizures were not reported by the patient but discovered by the clinical service on review of the recording. NCEs were in the highest proportion in the awake and drowsy arousal states and were the most common event type for the majority of referral indications. Subgroup analysis found a significantly higher proportion of NCEs in the patients with focal epilepsy (23%) compared to generalized epilepsy (10%; p < .001, chi-squared proportion test). SIGNIFICANCE: Our results reaffirm the phenomenon of underreporting and highlight the prevalence of overreporting. Overreporting likely represents irrelevant symptoms or electrographic discharges not represented on scalp electrodes, identification of which has important clinical relevance. Future studies should analyze events by risk factors to elucidate relationships clinicians can use and investigate the etiology of NCEs.

Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability.

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy-so-called interictal epileptiform activity-with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy.

Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration.

Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

Diagnostic utility of prolonged ambulatory video-electroencephalography monitoring.

OBJECTIVES: Ambulatory video-electroencephalography (video-EEG) represents a low-cost, convenient and accessible alternative to inpatient video-EEG monitoring, however few studies have examined their diagnostic yield. In this large-scale retrospective study conducted in Australia, we evaluated the efficacy of prolonged ambulatory video-EEG recordings in capturing diagnostic events and resolving the referring question. METHODS: Sequential adult and paediatric ambulatory video-EEG reports from April 2020 to June 2021 were reviewed retrospectively. Data collection included patient demographics, clinical information, and details of events and EEG abnormalities. Clinical utility was assessed by examining i) time to first diagnostic event, and ii) ability to resolve the referring questions - seizure localisation, quantification, classification, and differentiation (differentiating seizures from non-epileptic events). RESULTS: Of the 600 reports analysed, 49 % captured at least one event, and 45 % captured interictal abnormalities (epileptiform or non-epileptiform). Seizures, probable psychogenic events (mostly non-convulsive), and other non-epileptic events occurred in 13 %, 23 % and 21 % of recordings respectively, with overlap. Unreported events were captured in 53 (9 %) recordings, and unreported seizures represented more than half of all seizures captured (51 %, 392/773). Nine percent of events were missing clinical, video or electrographic data. A diagnostic event occurred in 244 (41 %) recordings, of which 14 % were captured between the fifth and eighth day of recording. Reported event frequency ≥ 1/week was the only significant predictor of diagnostic event capture. In recordings with both seizures and psychogenic events, unrecognized seizures were frequent, and seizures may be missed if recording is terminated early. The referring question was resolved in 85 % of reports with at least one event, and 53 % of all reports. Specifically, this represented 46 % of reports (235/512) for differentiation of events, and 75 % of reports (27/36) for classification of seizures. CONCLUSION: Ambulatory video-EEG recordings are of high diagnostic value in capturing clinically relevant events and resolving the referring clinical questions.

User experience of a seizure risk forecasting app: A mixed methods investigation.

OBJECTIVE: Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy. METHODS: Active app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively. RESULTS: Surveys were completed by 111 users. Responders consisted of "app users" (n = 58), and "app and forecast users" (n = 53). Of the "app and forecast users", 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which 'frustrations with lack of direction' (regarding their current epilepsy management approach), 'benefits of increased self-knowledge' and 'current and anticipated usefulness of forecasting' were the most common. SIGNIFICANCE: Preliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.

Potential smoke-free dividend across local areas in England: a cross-sectional analysis.

BACKGROUND: The value that might be added to local economies each year through the money that people who smoke tobacco would save if everyone quit smoking is called the 'smoke-free dividend'. This study aimed to estimate the value of the smoke-free dividend across local areas in England, and how it relates to the average income in those areas. METHODS: The study was a cross-sectional descriptive analysis of tobacco expenditure from the Smoking Toolkit Study (STS) matched to income and smoking prevalence data for English local authorities. The STS sample was from 2014 to 2020 and comprised 18 721 adults who smoke cigarettes. Self-reported expenditure estimates from the STS were adjusted for under-reporting. This adjustment aimed to align the total expenditure estimate with figures derived from government tax receipts and national estimates of illicit tobacco use. The smoke-free dividend is calculated as 93% of spending on legal tobacco, which is the percentage estimated to leave the local economy, plus 100% of spending on illicit tobacco. RESULTS: The total dividend in England is estimated to be £10.9 billion each year, which equates to £1776 per person who smokes or £246 per adult regardless of smoking status. The estimated dividend is greater in areas with lower average income, with a correlation coefficient of -0.521 (95% CI -0.629, -0.392) between the average income of local areas and the dividend per adult. CONCLUSIONS: This study has estimated that local economies could gain a substantial dividend if everybody stopped smoking, which is larger in lower income areas, meaning that geographical economic inequalities could be reduced.

Clinical outcomes of deep brain stimulation for obsessive-compulsive disorder: Insight as a predictor of symptom changes.

AIM: Deep brain stimulation (DBS) is a safe and effective treatment option for people with refractory obsessive-compulsive disorder (OCD). Yet our understanding of predictors of response and prognostic factors remains rudimentary, and long-term comprehensive follow-ups are lacking. We aim to investigate the efficacy of DBS therapy for OCD patients, and predictors of clinical response. METHODS: Eight OCD participants underwent DBS stimulation of the nucleus accumbens (NAc) in an open-label longitudinal trial, duration of follow-up varied between 9 months and 7 years. Post-operative care involved comprehensive fine tuning of stimulation parameters and adjunct multidisciplinary therapy. RESULTS: Six participants achieved clinical response (35% improvement in obsessions and compulsions on the Yale Brown Obsessive Compulsive Scale (YBOCS)) within 6-9 weeks, response was maintained at last follow up. On average, the YBOCS improved by 45% at last follow up. Mixed linear modeling elucidated directionality of symptom changes: insight into symptoms strongly predicted (P = 0.008) changes in symptom severity during DBS therapy, likely driven by initial changes in depression and anxiety. Precise localization of DBS leads demonstrated that responders most often had their leads (and active contacts) placed dorsal compared to non-responders, relative to the Nac. CONCLUSION: The clinical efficacy of DBS for OCD is demonstrated, and mediators of changes in symptoms are proposed. The symptom improvements within this cohort should be seen within the context of the adjunct psychological and biopsychosocial care that implemented a shared decision-making approach, with flexible iterative DBS programming. Further research should explore the utility of insight as a clinical correlate of response. The trial was prospectively registered with the ANZCTR (ACTRN12612001142820).

Influence of comorbidity on mortality in patients with epilepsy and psychogenic nonepileptic seizures.

OBJECTIVE: This study aims to determine the contribution of comorbidities to excess psychogenic nonepileptic seizures (PNES) mortality. METHODS: A retrospective cohort study was conducted of tertiary epilepsy outpatients from St. Vincent's Hospital Melbourne, Australia with an 8:1 comparison cohort, matched by age, sex, and socioeconomic status (SES) to national administrative databases between 2007 and 2017. Privacy-preserving data linkage was undertaken with the national prescription, National Death Index, and National Coronial Information System. Forty-five comorbid disease classes were derived by applying the Australian validated RxRisk-V to all dispensed prescriptions. We fitted Cox proportional hazard models controlling for age, sex, SES, comorbidity, disease duration, and number of concomitant antiseizure medications, as a marker of disease severity. We also performed a parallel forward-selection change in estimate strategy to explore which specific comorbidities contributed to the largest changes in the hazard ratio. RESULTS: A total of 13 488 participants were followed for a median 3.2 years (interquartile range = 2.4-4.0 years), including 1628 tertiary epilepsy outpatients, 1384 patients with epilepsy, 176 with PNES, and 59 with both. Eighty-two percent of epileptic seizures and 92% of typical PNES events were captured in an epilepsy monitoring unit. The age-/sex-/SES-adjusted hazard ratio was elevated for epilepsy (4.74, 95% confidence interval [CI] = 3.36-6.68) and PNES (3.46, 95% CI = 1.38-8.68) and remained elevated for epilepsy (3.21, 95% CI = 2.22-4.63) but not PNES (2.15, 95% CI = .77-6.04) after comorbidity adjustment. PNES had more pre-existing comorbidities (p = .0007), with a three times greater median weighted Rx-RiskV score. Psychotic illness, opioid analgesia, malignancies, and nonopioid analgesia had the greatest influence on PNES comorbid risk. SIGNIFICANCE: Higher comorbidity appears to explain the excess PNES mortality and may represent either a wider underrecognized somatoform disorder or a psychological response to physical illness. Better understanding and management of the bidirectional relationship of these wider somatic treatments in PNES could potentially reduce the risk of death.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review.

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Seizure occurrence is linked to multiday cycles in diverse physiological signals.

OBJECTIVE: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. METHODS: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. RESULTS: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SIGNIFICANCE: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.

Optimizing the technique for eliciting antidromic sural and superficial fibular sensory nerve action potentials.

INTRODUCTION/AIMS: Lower limb sensory nerve action potentials are an important component of nerve conduction studies. Most testing of the sural and superficial fibular nerves involves antidromic techniques above the ankle, which result in a falsely unobtainable response in 2%-6% of healthy people. Cadaver, surgical, and more recent ultrasound series suggest this may relate to the site of fascia penetration of the nerve, and it is hypothesized that a modified technique may be more likely to produce reliable responses and reduce false-negative errors. METHODS: This article evaluates a variety of recording distances for both nerves in 100 healthy controls, including varying recording electrode positions and techniques, to provide the optimal electrodiagnostic information in healthy control subjects. RESULTS: Shorter stimulation distances produce higher-amplitude responses but become confounded by increasing stimulation artifact at very short distances, with the best balance found at around 10 cm. In both sural and superficial fibular nerves, amplitude increases by approximately 10%/cm compared with the standard 14 cm distance. The Daube superficial fibular technique produced a higher amplitude than the Izzo Intermediate technique (by 22.46%, p < .001). The calculated upper limit of normal for side-to-side variation in amplitude was around 50% in the sural nerve but over 70% in the superficial fibular nerve. DISCUSSION: It is proposed that the 10 cm recording distance for both nerves is optimal, with minimal false-negatives and a higher amplitude elicited than with existing techniques.

Long latency reflexes of the median nerves in healthy adults.

INTRODUCTION/AIMS: Long latency reflexes (LLRs) are late responses in nerve conduction studies seen after peripheral nerve stimulation during submaximal muscle contraction. They follow a short latency reflex, also known as the H reflex, and are thought to involve transcortical pathways, providing a measure of proximal nerve and central conduction. For this reason, they have been evaluated in several central nervous system diseases, but reference values are not widely published and are mostly based on old studies with very small numbers of participants. Therefore, in this work we aim to provide comprehensive reference values for LLR testing. METHODS: LLRs were tested in a cohort of 100 healthy participants, testing the median nerve bilaterally. RESULTS: Mean latencies for short latency reflex (SLR), LLR1, LLR2, and LLR3 were 27.00, 38.50, 47.60, and 67.34 milliseconds, respectively. The allowable side-to-side difference was approximately 3 to 4 milliseconds. No significant sex-related differences were seen. Height correlated moderately with the SLR latency, but only weakly with LLR1, LLR2, and LLR3. DISCUSSION: This work provides normal LLR values for comparison with future studies in disease. The technique used may allow for improved evaluation of central nervous system or proximal peripheral nerve disorders.

The prevalence and clustering of alcohol consumption, gambling, smoking, and excess weight in an English adult population.

BACKGROUND: The aim of this study was to examine the prevalence and clustering of four health risks (increasing-/higher-risk drinking, current smoking, overweight/obesity, and at-risk gambling), and to examine variation across sociodemographic groups in the English adult population. METHODS: We analysed data from the 2012, 2015, 2016, and 2018 Health Survey for England (n = 20,698). Prevalence odds ratios (POR) were calculated to examine the clustering of risks. We undertook a multinomial multilevel regression model to examine sociodemographic variation in the clustering of health risks. RESULTS: Overall, 23.8% of the adult English population had two or more co-occurring health risks. The most prevalent was increasing-/higher-risk drinking and overweight/obesity (17.2%). Alcohol consumption and smoking were strongly clustered, particularly higher-risk drinking and smoking (POR = 2.68; 95% CI = 2.31, 3.11; prevalence = 1.7%). Higher-risk drinking and at-risk gambling were also clustered (POR = 2.66; 95% CI = 1.76, 4.01), albeit with a very low prevalence (0.2%). Prevalence of multiple risks was higher among men for all risk combinations except smoking and obesity. The odds of multiple risks were highest for men and women aged 35-64 years. Unemployed men and women with lower educational qualifications had a higher odds of multiple risks. The relationship between deprivation and multiple risks depended on the definition of multiple risks, with the clearest socioeconomic gradients seen for the highest risk health behaviours. CONCLUSION: An understanding of the prevalence, clustering, and risk factors for multiple health risks can help inform effective prevention and treatment approaches and may support the design and use of multiple behaviour change interventions.

Deep learning for automated detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome.

OBJECTIVES: Generalized paroxysmal fast activity (GPFA) is a key electroencephalographic (EEG) feature of Lennox-Gastaut Syndrome (LGS). Automated analysis of scalp EEG has been successful in detecting more typical abnormalities. Automatic detection of GPFA has been more challenging, due to its variability from patient to patient and similarity to normal brain rhythms. In this work, a deep learning model is investigated for detection of GPFA events and estimating their overall burden from scalp EEG. METHODS: Data from 10 patients recorded during four ambulatory EEG monitoring sessions are used to generate and validate the model. All patients had confirmed LGS and were recruited into a trial for thalamic deep-brain stimulation therapy (ESTEL Trial). RESULTS: The correlation coefficient between manual and model estimates of event counts was r2 = 0.87, and for total burden was r2 = 0.91. The average GPFA detection sensitivity was 0.876, with an average false-positive rate of 3.35 per minute. There was no significant difference found between patients with early or delayed deep brain stimulation (DBS) treatment, or those with active vagal nerve stimulation (VNS). CONCLUSIONS: Overall, the deep learning model was able to accurately detect GPFA and provide accurate estimates of the overall GPFA burden and electrographic event counts, albeit with a high false-positive rate. SIGNIFICANCE: Automated GPFA detection may enable automated calculation of EEG biomarkers of burden of disease in LGS.

Brainwaves beyond diagnosis: Wider applications of electroencephalography in idiopathic generalized epilepsy.

Electroencephalography (EEG) has long been used as a versatile and noninvasive diagnostic tool in epilepsy. With the advent of digital EEG, more advanced applications of EEG have emerged. Compared with technologically advanced practice in focal epilepsies, the utilization of EEG in idiopathic generalized epilepsy (IGE) has been lagging, often restricted to a simple diagnostic tool. In this narrative review, we provide an overview of broader applications of EEG beyond this narrow scope, discussing how the current clinical and research applications of EEG may potentially be extended to IGE. The current literature, although limited, suggests that EEG can be used in syndromic classification, guiding antiseizure medication therapy, predicting prognosis, unraveling biorhythms, and investigating functional brain connectivity of IGE. We emphasize the need for longer recordings, particularly 24-h ambulatory EEG, to capture discharges reflecting circadian and sleep-wake cycle-associated variations for wider EEG applications in IGE. Finally, we highlight the challenges and limitations of the current body of literature and suggest future directions to encourage and enhance more extensive applications of this potent tool.

Seizure forecasting: Bifurcations in the long and winding road.

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.

Ambient air pollution and epileptic seizures: A panel study in Australia.

OBJECTIVE: Emerging evidence has shown that ambient air pollution affects brain health, but little is known about its effect on epileptic seizures. This work aimed to assess the association between daily exposure to ambient air pollution and the risk of epileptic seizures. METHODS: This study used epileptic seizure data from two independent data sources (NeuroVista and Seer App seizure diary). In the NeuroVista data set, 3273 seizures were recorded using intracranial electroencephalography (iEEG) from 15 participants with refractory focal epilepsy in Australia in 2010-2012. In the seizure diary data set, 3419 self-reported seizures were collected through a mobile application from 34 participants with epilepsy in Australia in 2018-2021. Daily average concentrations of carbon monoxide (CO), nitrogen dioxide (NO2 ), ozone (O3 ), particulate matter ≤10 µm in diameter (PM10 ), and sulfur dioxide (SO2 ) were retrieved from the Environment Protection Authority (EPA) based on participants' postcodes. A patient-time-stratified case-crossover design with the conditional Poisson regression model was used to determine the associations between air pollutants and epileptic seizures. RESULTS: A significant association between CO concentrations and epileptic seizure risks was observed, with an increased seizure risk of 4% (relative risk [RR]: 1.04, 95% confidence interval [CI]: 1.01-1.07) for an interquartile range (IQR) increase of CO concentrations (0.13 parts per million), whereas no significant associations were found for the other four air pollutants in the whole study population. Female participants had a significantly increased risk of seizures when exposed to elevated CO and NO2 , with RRs of 1.05 (95% CI: 1.01-1.08) and 1.09 (95% CI: 1.01-1.16), respectively. In addition, a significant association was observed between CO and the risk of subclinical seizures (RR: 1.20, 95% CI: 1.12-1.28). SIGNIFICANCE: Daily exposure to elevated CO concentrations may be associated with an increased risk of epileptic seizures, especially for subclinical seizures.

Epileptic seizure onset predicts its duration.

BACKGROUND: Epilepsy is characterized by recurrent seizures that have a variety of manifestations. The severity of, and risks for patients associated with, seizures are largely linked to the duration of seizures. Methods that determine seizure duration based on seizure onsets could be used to help mitigate the risks associated with what might be extended seizures by guiding timely interventions. METHODS: Using long-term intracranial electroencephalography (iEEG) recordings, this article presents a method for predicting whether a seizure is going to be long or short by analyzing the seizure onset. The definition of long and short depends on each patient's seizure distribution. By analyzing 2954 seizures from 10 patients, patient-specific classifiers were built to predict seizure duration given the first few seconds from the onset. RESULTS: The proposed methodology achieved an average area under the receiver operating characteristic curve (AUC) performance of 0.7 for the 5 of 10 patients with above chance prediction performance (p value from 0.04 to 10-9 ). CONCLUSIONS: Our results imply that the duration of seizures can be predicted from the onset in some patients. This could form the basis of methods for predicting status epilepticus or optimizing the amount of electrical stimulation delivered by seizure control devices.

Semiologic differences between bilateral tonic-clonic seizures of focal onset and generalized onset.

BACKGROUND: Focal semiologies have been described in idiopathic generalized epilepsies (IGE) and generalized-onset bilateral tonic-clonic seizures (GBTCS). These focal signs may lead to wrong diagnosis and inappropriate choice of antiseizure medications. We sought to investigate the differences in focal semiologic features between GBTCS and focal-onset bilateral tonic-clonic seizures (FBTCS). METHODS: We retrospectively reviewed video-EEG data of captured GBTCS and FBTCS over a period of five years. The presence or absence of 12 focal signs as well seizure duration and time to head version was tabulated for each seizure. We used the chi-square test for independence and Fisher's exact test to investigate the occurrence of each focal sign in FBTCS compared with GBTCS. Additionally, we used receiver operating characteristic (ROC) curves to explore if the seizure duration and time to head version from the ictal onset can reliably differentiate between FBTCS and GBTCS. Finally, we employed hierarchical cluster analysis to visualize how these focal signs appear in combination. RESULTS: Head version (p <.001), preceding automatisms (p <.001), eye version (p <.001), unilateral facial clonic activity (p <.001), and mouth deviation (p =.004) were found to be significantly more frequent in FBTCS. Longer seizures were highly in favor of FBTCS whereas shorter time to head version from the ictal onset indicated GBTCS in the ROC curve analysis. CONCLUSIONS: Though focal signs occur in GBTCS, careful evaluation of semiology can help the clinician distinguish FBTCS from GBTCS.