Prognosis and management of acute symptomatic seizures: a prospective, multicenter, observational study.

BACKGROUND: Acute symptomatic epileptic seizures are frequently seen in neurocritical care. To prevent subsequent unprovoked seizures, long-term treatments with antiseizure medications are often initiated although supporting evidence is lacking. This study aimed at prospectively assessing the risk of unprovoked seizure relapse with respect to the use of antiseizure medications. It was hypothesized that after a first acute symptomatic seizure of structural etiology, the cumulative 12-month risk of unprovoked seizure relapse is ≤ 25%. METHODS: Inclusion criteria were age ≥ 18 and acute symptomatic first-ever epileptic seizure; patients with status epilepticus were excluded. Using telephone and mail interviews, participants were followed for 12 months after the acute symptomatic first seizure. Primary endpoint was the occurrence and timing of a first unprovoked seizure relapse. In addition, neuro-intensivists in Germany were interviewed about their antiseizure treatment strategies through an anonymous online survey. RESULTS: Eleven of 122 participants with structural etiology had an unprovoked seizure relapse, resulting in a cumulative 12-month risk of 10.7% (95%CI, 4.7%-16.7%). None of 19 participants with a non-structural etiology had a subsequent unprovoked seizure. Compared to structural etiology alone, combined infectious and structural etiology was independently associated with unprovoked seizure relapse (OR 11.1; 95%CI, 1.8-69.7). Median duration of antiseizure treatment was 3.4 months (IQR 0-9.3). Seven out of 11 participants had their unprovoked seizure relapse while taking antiseizure medication; longer treatment durations were not associated with decreased risk of unprovoked seizure relapse. Following the non-representative online survey, most neuro-intensivists consider 3 months or less of antiseizure medication to be adequate. CONCLUSIONS: Even in case of structural etiology, acute symptomatic seizures bear a low risk of subsequent unprovoked seizures. There is still no evidence favoring long-term treatments with antiseizure medications. Hence, individual constellations with an increased risk of unprovoked seizure relapse should be identified, such as central nervous system infections causing structural brain damage. However, in the absence of high-risk features, antiseizure medications should be discontinued early to avoid overtreatment.

High-Throughput Screening Assay for Detecting Drug-Induced Changes in Synchronized Neuronal Oscillations and Potential Seizure Risk Based on Ca2+ Fluorescence Measurements in Human Induced Pluripotent Stem Cell (hiPSC)-Derived Neuronal 2D and 3D Cultures.

Drug-induced seizure liability is a significant safety issue and the basis for attrition in drug development. Occurrence in late development results in increased costs, human risk, and delayed market availability of novel therapeutics. Therefore, there is an urgent need for biologically relevant, in vitro high-throughput screening assays (HTS) to predict potential risks for drug-induced seizure early in drug discovery. We investigated drug-induced changes in neural Ca2+ oscillations, using fluorescent dyes as a potential indicator of seizure risk, in hiPSC-derived neurons co-cultured with human primary astrocytes in both 2D and 3D forms. The dynamics of synchronized neuronal calcium oscillations were measured with an FDSS kinetics reader. Drug responses in synchronized Ca2+ oscillations were recorded in both 2D and 3D hiPSC-derived neuron/primary astrocyte co-cultures using positive controls (4-aminopyridine and kainic acid) and negative control (acetaminophen). Subsequently, blinded tests were carried out for 25 drugs with known clinical seizure incidence. Positive predictive value (accuracy) based on significant changes in the peak number of Ca2+ oscillations among 25 reference drugs was 91% in 2D vs. 45% in 3D hiPSC-neuron/primary astrocyte co-cultures. These data suggest that drugs that alter neuronal activity and may have potential risk for seizures can be identified with high accuracy using an HTS approach using the measurements of Ca2+ oscillations in hiPSC-derived neurons co-cultured with primary astrocytes in 2D.

Seizure forecasting: Where do we stand?

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.

An evidence-based approach to provide essential and desirable components to develop surveys on Sudden Unexpected Death in Epilepsy (SUDEP) for doctors: A focused review.

Sudden Unexpected Death in Epilepsy (SUDEP) is a major concern for people with epilepsy, their families, their care givers, and medical professionals. There is inconsistency in the SUDEP counselling doctors provide, compared to what is recommended in clinical guidelines. Numerous national and international surveys have highlighted how epilepsy professionals, usually doctors, deliver SUDEP risk counselling, particularly, when they deliver it and to whom. These surveys help understand the unmet need, develop suitable strategies, and raise awareness among clinicians with the eventual goal to reduce SUDEPs. However, there is no standardised survey or essential set of questions identified that can be used to evaluate SUDEP counselling practice globally. This focused review analyses the content of all published SUDEP counselling surveys for medical professionals (n=16) to date covering over 4000 doctors across over 30 countries and five continents. It identifies 36 question themes across three topics. The questions are then reviewed by an expert focus group of SUDEP communication experts including three doctors, an expert statistician and SUDEP Action, an UK based charity specialising in epilepsy deaths with a pre-set criterion. The review and focus group provide ten essential questions that should be included in all future surveys inquiring on SUDEP counselling. They could be used to evaluate current practice and compare findings over time, between services, across countries and between professional groups. They are provided as a template to download and use. The review also explores if there is a continued need in future for similar surveys to justify this activity.

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.

BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING.

A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure Tracker™ system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome, which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.

Exercise, medication adherence, and the menstrual cycle: How much do these change seizure risk?

People with epilepsy can experience tremendous stress from the uncertainty of when a seizure will occur. Three factors deemed important because of their potential influence on seizure risk are exercise, medication adherence, and the menstrual cycle. A narrative review was conducted through PubMed searching for relevant articles on how seizure risk is modified by 1) exercise, 2) medication adherence, and 3) the menstrual cycle. There was no consensus about the impact of exercise on seizure risk. Studies about medication nonadherence suggested an increase in seizure risk, but there was not a sufficient amount of data for a definitive conclusion. Most studies about the menstrual cycle reported an increase in seizures connected to a specific aspect of the menstrual cycle. No definitive studies were available to quantify this impact precisely. All three triggers reviewed had gaps in the research available, making it not yet possible to definitively quantify a relationship to seizure risk. More quantitative prospective studies are needed to ascertain the extent to which these triggers modify seizure risk.

Changes of interictal epileptiform discharges during medication withdrawal and seizures: A scalp EEG marker of epileptogenicity.

Objective: To determine the influence of antiseizure medication (ASM) withdrawal on interictal epileptogenic discharges (IEDs) in scalp-EEG and seizure propensity. Methods: We included 35 adult unifocal epilepsy patients admitted for presurgical evaluation in the EEG and Epilepsy Unit of Geneva between 2016 and 2020, monitored for at least 5 days. ASM was individually tapered down, and automated IED detection was performed using Epilog PreOp (Epilog NV, Belgium, Ghent). We compared spike rate per hour (SR) at day 1 when patients were on full medication (baseline) with SR at the day with the lowest dose of medication. To determine possible peri-ictal changes of SR, we compared SR 8 h before and after a seizure with the SR at the same time of the baseline day. Results: Our results showed a significant increase in spiking activity in the day of lowest drug load if compared to spike rate at day on full medication (p < 0.001). The total amount of spikes during 24 h correlated significantly with seizure occurrence (p < 0.0001). We also revealed significant increase in peri-ictal SR, in particular 2-4 h preceding a seizure (p = 0.05) extending up to 3 h after the seizure (p = 0.03) with a short decrease just before seizure occurrence. Conclusions: Our results suggest that SR increases with medication withdrawal and particularly before and after seizures. There is a complex pattern of increase and decrease around seizure onset which explains divergent results in previous studies. Significance: Precise spike counting at similar circadian periods for a patient could help to determine the risk of seizure occurrence in a personalized fashion.

Three Seizures Provoked by E-cigarette Use in a Five-Year Period: A Case Report.

We report a case of three seizures provoked by e-cigarette use (vaping) within the time span of five years from youth to young adult. At presentation, the neurological exam was unremarkable. Computerized tomography (CT) of the head, magnetic resonance image (MRI) of the brain, electroencephalograms (EEG), electrocardiogram (EKG), and transthoracic echocardiogram (TTE) were normal. Multiple toxicology screens were normal as well. Each seizure occurred within minutes of vaping, thereby suggesting a temporal association and a possible causal relationship between e-cigarettes and seizures.

A Case of Late-Onset Local Anesthetic Toxicity Observed as Seizure Activity.

Most of the local anesthetic toxicity cases develop within the first five minutes of peripheral block administration. Late local anesthetic toxicity has been rarely reported in the literature. However, it is an important life-threatening problem that can lead to seizures, hemodynamic collapse, and cardiac arrest if it is ignored and not considered. Here we present the case of an 18-year-old male patient who had ultrasonography-guided infraclavicular brachial plexus block administration with a 30 mL local anesthetic. The patient had convulsions 210 minutes after the block administration and was treated with intravenous diazepam. Intraoperative and postoperative courses were uneventful. He had no neurologic signs or symptoms afterward. All laboratory tests and radiologic investigation tests were normal. This report demonstrates that late local anesthetic toxicity is still possible after several hours of the uneventful peripheral neural blockade, although it is rarely reported.

Decrease in wearable-based nocturnal sleep efficiency precedes epileptic seizures.

Introduction: While it is known that poor sleep is a seizure precipitant, this association remains poorly quantified. This study investigated whether seizures are preceded by significant changes in sleep efficiency as measured by a wearable equipped with an electrocardiogram, respiratory bands, and an accelerometer. Methods: Nocturnal recordings from 47 people with epilepsy hospitalized at our epilepsy monitoring unit were analyzed (304 nights). Sleep metrics during nights followed by epileptic seizures (24 h post-awakening) were compared to those of nights which were not. Results: Lower sleep efficiency (percentage of sleep during the night) was found in the nights preceding seizure days (p < 0.05). Each standard deviation decrease in sleep efficiency and increase in wake after sleep onset was respectively associated with a 1.25-fold (95 % CI: 1.05 to 1.42, p < 0.05) and 1.49-fold (95 % CI: 1.17 to 1.92, p < 0.01) increased odds of seizure occurrence the following day. Furthermore, nocturnal seizures were associated with significantly lower sleep efficiency and higher wake after sleep onset (p < 0.05), as well as increased odds of seizure occurrence following wake (OR: 5.86, 95 % CI: 2.99 to 11.77, p < 0.001). Discussion: Findings indicate lower sleep efficiency during nights preceding seizures, suggesting that wearable sensors could be promising tools for sleep-based seizure-day forecasting in people with epilepsy.

A Comparison of Seizure Prophylaxis: Phenytoin Versus Levetiracetam.

Phenytoin and levetiracetam are both antiepileptic drugs (AEDs) used for seizure prophylaxis. However, to date, there is a paucity of literature comparing their relative efficacies. In this narrative review, we seek to determine if there is greater advantage between the two AEDs, levetiracetam and phenytoin. Phenytoin is the more traditional AED of the two as it has been medically used for a much longer time than levetiracetam. However, levetiracetam, the newer AED of the two, has fewer side effects than phenytoin and fewer drug-drug interactions. Although past studies have aimed to compare the efficacy of phenytoin versus levetiracetam, there is no clear consensus as to if there is a clinical advantage to one over the other. Here, we have analyzed several studies published between 2013 and 2020 in the hopes of having a better understanding of which AED is more efficient in preventing seizures. Many factors can contribute to determining which AED is the better fit for patients, including pricing, risk for adverse drug effects, and level of patient monitoring. After analysis of past research, the more advantageous AED still remains unclear. Future research must be conducted that involve large patient populations, stratifying age populations, and studies analyzing cost-effectiveness to clearly determine if there is indeed a more advantageous AED between levetiracetam and phenytoin.

Continuous EEG monitoring in the follow-up of convulsive status epilepticus patients: A proposal and preliminary validation of an EEG-based seizure build-up score (EaSiBUSSEs).

Continuous electroencephalography (EEG) is a major tool for monitoring patients admitted to the intensive care unit after refractory convulsive status epilepticus, following control of convulsive movements. We review the values of different EEG patterns observed in critically ill patients for prognosis and seizure risk, together with proposed criteria for non-convulsive status epilepticus diagnosis (Salzburg Criteria), the EEG scores for prognosis (Epidemiology-based Mortality score in Status Epilepticus, EMSE) and for seizure risk (2HELPS2B). These criteria and scores, based partially on continuous EEG, are not tailored to repetitively monitor the progressive build-up leading to seizure or status epilepticus recurrence. Therefore, we propose a new EEG-based seizure build-up score in status epilepticus (EaSiBUSSEs), based on the morphology and the prevalence of the EEG patterns observed in the follow-up of convulsive status epilepticus patients. It displays subscores from the least (no interictal activity) to the most associated with seizures (focal or generalized status epilepticus). We then evaluated the performance of the EaSiBUSSEs in a cohort of eleven patients who were admitted to intensive care unit for convulsive status epilepticus and who underwent continuous EEG recording. The receiver operating curve revealed good accuracy in identifying patients who would have seizures in the next 24 h, with excellent intra- and inter-rater reliability. We believe that this score is simple to perform, and suitable for repeated monitoring of EEG following refractory convulsive status epilepticus, with quantitative description of major EEG changes leading to seizures.

Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation.

BACKGROUND: An implanted device for brain-responsive neurostimulation (RNS® System) is approved as an effective treatment to reduce seizures in adults with medically-refractory focal epilepsy. Clinical trials of the RNS System demonstrate population-level reduction in average seizure frequency, but therapeutic response is highly variable. HYPOTHESIS: Recent evidence links seizures to cyclical fluctuations in underlying risk. We tested the hypothesis that effectiveness of responsive neurostimulation varies based on current state within cyclical risk fluctuations. METHODS: We analyzed retrospective data from 25 adults with medically-refractory focal epilepsy implanted with the RNS System. Chronic electrocorticography was used to record electrographic seizures, and hidden Markov models decoded seizures into fluctuations in underlying risk. State-dependent associations of RNS System stimulation parameters with changes in risk were estimated. RESULTS: Higher charge density was associated with improved outcomes, both for remaining in a low seizure risk state and for transitioning from a high to a low seizure risk state. The effect of stimulation frequency depended on initial seizure risk state: when starting in a low risk state, higher stimulation frequencies were associated with remaining in a low risk state, but when starting in a high risk state, lower stimulation frequencies were associated with transition to a low risk state. Findings were consistent across bipolar and monopolar stimulation configurations. CONCLUSION: The impact of RNS on seizure frequency exhibits state-dependence, such that stimulation parameters which are effective in one seizure risk state may not be effective in another. These findings represent conceptual advances in understanding the therapeutic mechanism of RNS, and directly inform current practices of RNS tuning and the development of next-generation neurostimulation systems.

Clinical and electrographic features of persistent seizures and status epilepticus associated with anti-NMDA receptor encephalitis (anti-NMDARE).

Based on a multicenter cohort of people with anti-NMDA receptor encephalitis (anti-NMDARE), we describe seizure phenotypes, electroencephalographic (EEG) findings, and anti-seizure treatment strategies. We also investigated whether specific electrographic features are associated with persistent seizures or status epilepticus after acute presentation. In this retrospective cohort study, we reviewed records of children and adults with anti-NMDARE between 2010 and 2014 who were included in the Rare Epilepsy of New York City database, which included the text of physician notes from five academic medical centers. Clinical history (e.g., seizure semiology) and EEG features (e.g., background organization, slowing, epileptiform activity, seizures, sleep architecture, extreme delta brush) were abstracted. We compared clinical features associated with persistent seizures (ongoing seizures after one month from presentation) and status epilepticus, using bivariate and multivariable analyses. Among the 38 individuals with definite anti-NMDARE, 32 (84%) had seizures and 29 (76%) had seizures captured on EEG. Electrographic-only seizures were identified in five (13%) individuals. Seizures started at a median of four days after initial symptoms (IQR: 3-6 days). Frontal lobe-onset focal seizures were most common (n=12; 32%). Most individuals (31/38; 82%) were refractory to anti-seizure medications. Status epilepticus was associated with younger age (15 years [9-20] vs. 23 years [18-27]; p=0.04) and Hispanic ethnicity (30 [80%] vs. 8 [36%]; p=0.04). Persistent seizures (ongoing seizures after one month from presentation) were associated with younger age (nine years [3-14] vs. 22 years [15-28]; p<0.01). Measured electrographic features were not associated with persistent seizures. Seizures associated with anti-NMDARE are primarily focal seizures originating in the frontal lobes. Younger patients may be at increased risk of epileptogenesis and status epilepticus. Continuous EEG monitoring helps identify subclinical seizures, but specific EEG findings may not predict the severity or persistence of seizures during hospitalization.

Improving epilepsy management with EpSMon: A Templar to highlight the multifaceted challenges of incorporating digital technologies into routine clinical practice.

The digital epilepsy self-monitor (EpSMon) app was developed to address the challenge of improving risk education and management in the UK. The tool, which has emerged out of quality improvement methodology, demonstrates efficacy and has been met with peer-reviewed support and international awards. The focus of this paper is about the development and integration into care of a digital self-assessment epilepsy risk empowerment tool into the UK health system. This paper provides detail into the specific challenges of incorporating a digital epilepsy intervention into routine clinical practice. Despite a strong narrative and evidence, the engagement of commissioners, clinicians, and people with epilepsy is slow. A breakdown of the strategies used, the current governance landscape, and emerging opportunities to develop an informed implementation strategy is provided to support others who seek to create impact with digital solutions for people with epilepsy. This paper is for the Special Issue: Prevent 21: SUDEP Summit - Time to Listen".

Prospective validation study of an epilepsy seizure risk system for outpatient evaluation.

OBJECTIVE: We conducted clinical testing of an automated Bayesian machine learning algorithm (Epilepsy Seizure Assessment Tool [EpiSAT]) for outpatient seizure risk assessment using seizure counting data, and validated performance against specialized epilepsy clinician experts. METHODS: We conducted a prospective longitudinal study of EpiSAT performance against 24 specialized clinician experts at three tertiary referral epilepsy centers in the United States. Accuracy, interrater reliability, and intra-rater reliability of EpiSAT for correctly identifying changes in seizure risk (improvements, worsening, or no change) were evaluated using 120 seizures from four synthetic seizure diaries (seizure risk known) and 120 seizures from four real seizure diaries (seizure risk unknown). The proportion of observed agreement between EpiSAT and clinicians was evaluated to assess compatibility of EpiSAT with clinical decision patterns by epilepsy experts. RESULTS: EpiSAT exhibited substantial observed agreement (75.4%) with clinicians for assessing seizure risk. The mean accuracy of epilepsy providers for correctly assessing seizure risk was 74.7%. EpiSAT accurately identified seizure risk in 87.5% of seizure diary entries, corresponding to a significant improvement of 17.4% (P = .002). Clinicians exhibited low-to-moderate interrater reliability for seizure risk assessment (Krippendorff's α = 0.46) with good intrarater reliability across a 4- to 12-week evaluation period (Scott's π = 0.89). SIGNIFICANCE: These results validate the ability of EpiSAT to yield objective clinical recommendations on seizure risk which follow decision patterns similar to those from specialized epilepsy providers, but with improved accuracy and reproducibility. This algorithm may serve as a useful clinical decision support system for quantitative analysis of clinical seizure frequency in clinical epilepsy practice.

Bridging the gap of risk communication and management using the SUDEP and Seizure Safety Checklist.

Sudden unexpected death in epilepsy (SUDEP) is a tragic condition and, despite varied risk levels among the population with epilepsy, is the cause of significant premature mortality. In the last 20 years, though awareness of SUDEP has increased among epilepsy professionals, little has changed with regard to the death rates per se, in rates of informing people with epilepsy (PWE) of their person-centered SUDEP risks, or in the awareness levels of nonepilepsy clinicians, such as, primary care practitioners and hospital doctors. The challenges to make aware and inform PWE have been multifold, in particular, 'when', 'what', and 'how' to tell about SUDEP. Current guidance recognizes that to improve SUDEP rates, it is important to engage proactively with PWE. There is a need to bring shared responsibility between clinicians and PWE to help mitigate the risk of SUDEP. To enable this, a meaningful evidence-based person-centered conversation is essential. The SUDEP and Seizure Safety Checklist ("Checklist") was created to facilitate this. This paper showcases the background, concept, development, implementation, feasibility and validity studies undertaken, challenges, barriers, and limitations of the eight-year Checklist project, which has moved from a single clinic to an international presence. It outlines the need to further reform SUDEP risk communication recognizing the differences between a basic risk message at time of diagnosis as advocated by current good practice guidance and the need for a more person-centered discussion on a regular basis for which the Checklist can be a key catalyst. This article is part of the Special Issue "Prevent 21: SUDEP Summit - Time to Listen".

Timing matters: Impact of anticonvulsant drug treatment and spikes on seizure risk in benign epilepsy with centrotemporal spikes.

OBJECTIVE: Benign epilepsy with centrotemporal spikes (BECTS) is a common, self-limited epilepsy syndrome affecting school-age children. Classic interictal epileptiform discharges (IEDs) confirm diagnosis, and BECTS is presumed to be pharmacoresponsive. As seizure risk decreases in time with this disease, we hypothesize that the impact of IEDs and anticonvulsive drug (ACD) treatment on the risk of subsequent seizure will differ based on disease duration. METHODS: We calculate subsequent seizure risk following diagnosis in a large retrospective cohort of children with BECTS (n = 130), evaluating the impact of IEDs and ACD treatment in the first, second, third, and fourth years of disease. We use a Kaplan-Meier survival analysis and logistic regression models. Patients were censored if they were lost to follow-up or if they changed group status. RESULTS: Two-thirds of children had a subsequent seizure within 2 years of diagnosis. The majority of children had a subsequent seizure within 3 years despite treatment. The presence of IEDs on electroencephalography (EEG) did not impact subsequent seizure risk early in the disease. By the fourth year of disease, all children without IEDs remained seizure free, whereas one-third of children with IEDs at this stage had a subsequent seizure. Conversely, ACD treatment corresponded with lower risk of seizure early in the disease but did not impact seizure risk in later years. SIGNIFICANCE: In this cohort, the majority of children with BECTS had a subsequent seizure despite treatment. In addition, ACD treatment and IEDs predicted seizure risk at specific points of disease duration. Future prospective studies are needed to validate these exploratory findings.

Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability.

OBJECTIVE: A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk. METHODS: Using data from SeizureTracker.com, a patient-reported seizure diary tool containing over 1.2 million recorded seizures across 8 years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed-effects hidden Markov model for zero-inflated count data was developed to estimate changes in underlying seizure risk using patient-reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (TSC) were assessed using data from SeizureTracker.com. RESULTS: EpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to TSC, four underlying seizure risk states were identified. The expected duration of each state was <12 months, providing a data-driven estimate of the amount of time a person with TSC would be expected to remain at the same seizure risk level according to the natural course of epilepsy. SIGNIFICANCE: We propose a novel Bayesian statistical approach for evaluating seizure risk on an individual patient level using patient-reported seizure diaries, which allows for the incorporation of external clinical variables to assess impact on seizure risk. This tool may improve the ability to distinguish true changes in seizure risk from natural variations in seizure frequency in clinical practice. Incorporation of systematic statistical approaches into antiepileptic drug (AED) management may help improve understanding of seizure unpredictability as well as timing of treatment interventions for people with epilepsy.