OBJECTIVE:Benzodiazepine rescue medications are established as therapy for acute termination of seizure clusters. A post-hoc analysis of a clinical trial of seizure cluster treatment with diazepam nasal spray found a potential longer-term impact over a year of treatment. In this retrospective analysis, we tested the hypothesis that benzodiazepine-treated seizure clusters are associated with prolonged time to the next seizure cluster compared with untreated seizure clusters in a patient-reported real-world database. METHODS:We analyzed data on self-reported seizures and benzodiazepine rescue medication administration in the Seizure Tracker™ database between 2007 and 2022. Kaplan-Meier analysis was used to compare treated vs untreated seizure clusters with respect to time to start of the next seizure cluster or immediate-use medication administration. Mixed-effects analysis was used to compare the number of seizures per cluster for treated and untreated seizure clusters. Robustness of findings was evaluated across three operational seizure-cluster definitions: ≥2 seizures in 4 hours as primary analysis and in 6 and 24 hours as sensitivity analyses. RESULTS:A total of 10 889 benzodiazepine immediate-use medication administrations (n = 220 patients) met inclusion criteria. Benzodiazepine rescue administrations were followed by longer time to the next seizure cluster or rescue administration, compared with untreated seizure clusters, corresponding to a median of 4.9 days following treated seizure clusters and a median of 0.8 days following untreated seizure clusters. This prolongation was driven by a minority of patients (accounting for 45.9% of seizure clusters in the sample) and patients were more likely to be women. The number of seizures per cluster was lower when treatment was administered earlier in the seizure cluster. SIGNIFICANCE:These retrospective real-world data suggest that the effect of benzodiazepines on termination of seizure clusters may be more pronounced when administration occurs earlier after onset, and support a hypothesis of a possible longer-term effect of benzodiazepines beyond immediate-use acute seizure termination.

Work is ongoing to advance seizure forecasting, but the performance metrics used to evaluate model effectiveness can sometimes lead to misleading outcomes. For example, some metrics improve when tested on patients with a particular range of seizure frequencies (SF). This study illustrates the connection between SF and metrics. Additionally, we compared benchmarks for testing performance: a moving average (MA) or the commonly used permutation benchmark. Three data sets were used for the evaluations: (1) Self-reported seizure diaries of 3,994 Seizure Tracker patients; (2) Automatically detected (and sometimes manually reported or edited) generalized tonic-clonic seizures from 2,350 Empatica Embrace 2 and Mate App seizure diary users, and (3) Simulated datasets with varying SFs. Metrics of calibration and discrimination were computed for each dataset, comparing MA and permutation performance across SF values. Most metrics were found to depend on SF. The MA model outperformed or matched the permutation model in all cases. The findings highlight SF's role in seizure forecasting accuracy and the MA model's suitability as a benchmark. This underscores the need for considering patient SF in forecasting studies and suggests the MA model may provide a better standard for evaluating future seizure forecasting models.

Objective To assess the impact of vagus nerve stimulation (VNS) on quality of life contributors such as rescue medications. Methods Using the seizure diary application SeizureTracker™ database, we examined trends in rescue administration frequency before and after the first recorded VNS magnet swipe in patients with drug-resistant epilepsy who had 1) At least one VNS magnet swipe recorded in the diary, and 2) Recorded usage of a benzodiazepine rescue medication (RM) within 90 days prior to the first swipe. A paired Wilcoxon rank-sum test was used to assess changes in RM usage frequency between 30-, 60-, 90-, 180- and 360-day intervals beginning 30 days after first magnet swipe. Longitudinal changes in RM usage frequency were assessed with a generalized estimating equation model. Results We analyzed data of 95 patients who met the inclusion criteria. Median baseline seizure frequency was 8.3 seizures per month, with median baseline rescue medication usage frequency of 2.1 administrations per month (SD 3.3). Significant reductions in rescue medication usage were observed in the 91 to 180 day interval after first VNS magnet swipe, and at 181 to 360 days and at 361 to 720 days, with the magnitude of reduction increasing over time. Decreases in rescue medication usage were sustained when controlling for patients who did not record rescue medication use after the first VNS magnet swipe (N=91). Significant predictors of reductions in rescue medication included baseline frequency of rescue medication usage and time after first VNS magnet swipe. Significance This retrospective analysis suggests that usage of rescue medications is reduced following the start of VNS treatment in patients with epilepsy, and that the magnitude of reduction may progressively increase over time.

Background This study aims to illustrate the connection between seizure frequency (SF) and performance metrics in seizure forecasting, and to compare the effectiveness of a moving average (MA) model versus the commonly used permutation benchmark. Methods Metrics of calibration and discrimination were computed for each dataset, comparing MA and permutation performance across SF values. Three datasets were used: (1) self-reported seizure diaries from 3994 Seizure Tracker patients, (2) automatically detected and sometimes manually reported or edited generalized tonic-clonic seizures from 2350 Empatica Embrace 2 and Mate App users, and (3) simulated datasets with varying SFs. Results Most metrics were found to depend on SF. The MA model outperformed or matched the permutation model in all cases. These more advanced metrics show that comparison to permutation will falsely elevate poor forecasting models. Conclusions The findings highlight SF's role in seizure forecasting accuracy and the MA model's suitability as a benchmark. This study underscores the need for considering patient SF in forecasting studies and suggests the MA model may provide a better standard for evaluating future seizure forecasting models.

OBJECTIVE:Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm.METHODS:We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI.RESULTS:The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts.SIGNIFICANCE:The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.

The authors regret that we did not include a complete list of funding sources. The authors are deeply grateful to all who supported this work. Dr. Goldenholz's contributions were supported by NIH NINDS 5KL2TR002542. The authors would like to apologise for any inconvenience caused. Patterns of Recording Epileptic Spasms in an Electronic Seizure Diary Compared With Video-EEG and Historical CohortsPediatric NeurologyVol. 122PreviewUse of electronic seizure diaries (e-diaries) by caregivers of children with epileptic spasms is not well understood. We describe the demographic and seizure-related information of children with epileptic spasms captured in a widely used e-diary and explore the potential biases in how caregivers report these data. Full-Text PDF

Objective: In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy. Methods: A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria. Results: A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n = 95, 87.2%; caregivers, n = 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n = 43, 78.2%; caregivers, n = 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity. Significance: Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

BACKGROUND:Use of electronic seizure diaries (e-diaries) by caregivers of children with epileptic spasms is not well understood. We describe the demographic and seizure-related information of children with epileptic spasms captured in a widely used e-diary and explore the potential biases in how caregivers report these data.METHODS:We analyzed children with epileptic spasms in an e-diary, Seizure Tracker, from 2007 to 2018. We described variables including sex, time of seizure, percentage of spasms occurring as individual spasms (versus in clusters), cluster duration, and number of spasms per cluster. We compared seizure characteristics in the e-diary cohort with published cohorts to identify biases in caregiver-reported epileptic spasms. We also reviewed seizure patterns in a small cohort of children with epileptic spasms monitored on overnight video-electroencephalography (vEEG).RESULTS:There were 314 children in the e-diary cohort and nine children in the vEEG cohort. The e-diary cohort was more likely than expected to report counts divisible by five. The e-diary cohort had a lower proportion of nighttime spasms than expected based on data from published cohorts. The e-diary cohort had a significantly lower percentage of spasms as individual spasms, a greater number of spasms per cluster, and a greater cluster duration relative to the vEEG cohort.CONCLUSIONS:Caregivers using e-diaries for epileptic spasms may miss individual spams, be more likely to report long clusters, round counts to the nearest five, and underreport nighttime spasms. Clinicians should be aware of these reporting biases when using e-diary data to guide care for children with epileptic spasms.

A major issue in the clinical management of epilepsy is the unpredictabil-ity 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 nonhomo-geneous 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 signifi-cant 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 tech-niques. 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 TrackerTM 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 charac-terizing 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.

OBJECTIVE:Our aim was to describe the risk factors known to be related to sudden unexpected death in epilepsy (SUDEP) that can be extracted from patients that utilizes an online seizure diary tool (SeizureTracker™).METHOD:We conducted a descriptive analysis of SeizureTracker™ users across factors relevant to SUDEP risk. We also compared our app-using cohort to published SUDEP case-control studies.RESULTS:We report across seven risk factors from 30,813 users of SeizureTracker™ who had a median length of time using the app of 5.69 years (range from 1 month to 15 years). We found that they are at greater risk for SUDEP than groups from published studies (p < .00001) based on the risk factor of generalized tonic-clonic seizures.SIGNIFICANCE:We demonstrated that the population using the SeizureTracker™ tool can be a valuable population for expanding investigation of SUDEP risk factors and is a first step towards establishing a large sample with a method to ascertain data prospectively that might be critical to developing a SUDEP risk algorithm.

Epilepsy is a disorder characterized by paroxysmal transitions between multistable states. Dynamical systems have been useful for modeling the paroxysmal nature of seizures. At the same time, intracranial electroencephalography (EEG) recordings have recently discovered that an electrographic measure of epileptogenicity, interictal epileptiform activity, exhibits cycling patterns ranging from ultradian to multidien rhythmicity, with seizures phase-locked to specific phases of these latent cycles. However, many mechanistic questions about seizure cycles remain unanswered. Here, we provide a principled approach to recast the modeling of seizure chronotypes within a statistical dynamical systems framework by developing a Bayesian switching linear dynamical system (SLDS) with variable selection to estimate latent seizure cycles. We propose a Markov chain Monte Carlo algorithm that employs particle Gibbs with ancestral sampling to estimate latent cycles in epilepsy and apply unsupervised learning on spectral features of latent cycles to uncover clusters in cycling tendency. We analyze the largest database of patient-reported seizures in the world to comprehensively characterize multidien cycling patterns among 1,012 people with epilepsy, spanning from infancy to older adulthood. Our work advances knowledge of cycling in epilepsy by investigating how multidien seizure cycles vary in people with epilepsy, while demonstrating an application of an SLDS to frame seizure cycling within a nonlinear dynamical systems framework. It also lays the groundwork for future studies to pursue data-driven hypothesis generation regarding the mechanistic drivers of seizure cycles.

Northern Virginia Community College, Alexandria, VA, United States, W. T. Woodson High School, Fairfax, VA, United States, 3 Seizure Tracker, LLC, Springfield, VA, United States, 4 TSC Alliance, Silver Spring, MD, United States, Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States, Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States

Background: Use of electronic seizure diaries (e-diaries) by caregivers of children with epileptic spasms is not well understood. We describe the demographic and seizure-related information of children with epileptic spasms captured in a widely used e-diary and explore the potential biases in how caregivers report these data. Methods: We analyzed children with epileptic spasms in an e-diary, Seizure Tracker, from 2007 to 2018. We described variables including sex, time of seizure, percentage of spasms occurring as individual spasms (versus in clusters), cluster duration, and number of spasms per cluster. We compared seizure characteristics in the e-diary cohort with published cohorts to identify biases in caregiver-reported epileptic spasms. We also reviewed seizure patterns in a small cohort of children with epileptic spasms monitored on overnight video-electroencephalography (vEEG). Results: There were 314 children in the e-diary cohort and nine children in the vEEG cohort. The e-diary cohort was more likely than expected to report counts divisible by five. The e-diary cohort had a lower proportion of nighttime spasms than expected based on data from published cohorts. The e-diary cohort had a significantly lower percentage of spasms as individual spasms, a greater number of spasms per cluster, and a greater cluster duration relative to the vEEG cohort. Conclusions: Caregivers using e-diaries for epileptic spasms may miss individual spams, be more likely to report long clusters, round counts to the nearest five, and underreport nighttime spasms. Clinicians should be aware of these reporting biases when using e-diary data to guide care for children with epileptic spasms. (c) 2021 Elsevier Inc. All rights reserved.

Preemptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of preempting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm.

Abstract Objective. This study sought to understand issues facing people with epilepsy (PWE) during the lockdown period of the COVID‐19 pandemic in the United States. Methods. We conducted a cross‐sectional study using a 20‐question survey that used SeziureTracker.com, sent to eligible PWE and their caregivers on May 6th, 2020. Questions about demographics and medical history were used to calculate COVID mortality risk odds ratios (OR) compared to a low baseline risk group. Results. In total, 505 responses were collected. Of these, 71% reported no change in seizure rates and 25% reported an increase in seizures, which they attributed primarily to disrupted sleep (63%) and decreased exercise (42%). Mortality risks from COVID‐19 had median OR of 1.67, ranging 1.00‐906.98. Fear about hospitalization (53%) and concern for loved ones (52%) were prominent concerns. Of the respondents, 5% reported stopping or reducing anti‐seizure medications due to problems communicating with doctors, access or cost. Lower‐risk COVID patients reported more fear of hospitalization (55% versus 38%, p<0.001) and anxiety about medication access (43% versus 28%, p=0.03) compared with higher‐risk COVID patients. Increased anxiety was reported in 47%, and increased depression in 28%. Ten percent without generalized convulsions and 8% with did not know anything about epilepsy devices (VNS, RNS, DBS). Significance. The COVID‐19 pandemic presents unique challenges to PWE, including increased seizure rates, problems with access and cost of life‐saving medications. Those with lower COVID‐19 risk may have been marginalized more than those with higher risk. Efforts to protect PWE during major public health emergencies should take these findings into account.

OBJECTIVE:Seizure forecasting algorithms have become increasingly accurate and may reduce the morbidity and mortality caused by seizure unpredictability. Translating these benefits into meaningful health outcomes for people with epilepsy requires effective data visualization of algorithm outputs. To date, no studies have investigated patient and physician perspectives on effective translation of algorithm outputs into data visualizations through health information technology. MATERIALS AND METHODS:We developed front-end data visualizations as part of a Seizure Forecast Visualization Toolkit. We surveyed 627 people living with epilepsy and caregivers, and 28 epilepsy healthcare providers. Respondents scored each visualization in terms of international standardized software quality criteria for functionality, appropriateness, and usability. RESULTS:People with epilepsy and caregivers ranked hourly radar charts highest for protecting against errors in interpreting forecasts, reducing anxiety from seizure unpredictability, and understanding seizure patterns. Accuracy in interpreting visuals, such as a risk gauge, was dependent on seizure frequency. Visuals showing hourly/daily forecasts were more useful for patients who experienced seizure cycling than those who did not. Hourly line graphs and monthly heat maps were rated highest among clinicians for ease of understanding, anticipated integration into clinical practice, and the likelihood of clinical usage. Epilepsy providers indicated that daily heat maps, daily line graphs, and hourly line graphs were most useful for interpreting seizure diary patterns, assessing therapy impact, and counseling on seizure safety. DISCUSSION:The choice of data visualization impacts the effective translation of seizure forecast algorithms into meaningful health outcomes. CONCLUSION:This effort underlines the importance of incorporating standardized, quantitative methods for assessing the effectiveness of data visualization to translate seizure forecast algorithms into clinical practice.

Preemptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of preempting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm.

OBJECTIVE:Adults living with intellectual and developmental disability (IDD) and epilepsy (IDD-E) face challenges in addition to those faced by the general population of adults with epilepsy, which may be associated with distinct priorities for improving health-related quality of life (HR-QOL). This study sought to (1) conduct a survey of HR-QOL priorities identified by adults with IDD-E and caregivers, and (2) perform an exploratory cross-sectional comparison to adults with epilepsy who do not have IDD.METHODS:This cross-sectional study recruited 65 adults with IDD-E and 134 adults with epilepsy without IDD and caregivers. Using a three-step development process, 256 items from existing quality-of-life scales recommended by the American Academy of Neurology (AAN) were rated by patients/caregivers for their importance as HR-QOL priorities. HR-QOL items identified as critical to the majority of the sample of adults with IDD-E were reported. Health-related quality of life priorities were compared between adults with IDD-E and adults with epilepsy without IDD.RESULTS:Health-related quality of life was significantly lower in adults with IDD-E. Health-related quality of life domains identified as critical priorities by adults with IDD-E included seizure burden, anti-seizure medication side effects, seizure unpredictability, and family impact. Priorities for improving HR-QOL differed between adults with and without IDD-E, with concerns about family impact, difficulty finding appropriate living conditions, inadequate assistance, and difficulty transitioning from pediatric-to-adult care valued significantly more among those with IDD-E.SIGNIFICANCE:Intellectual and developmental disability is an important determinant of HR-QOL among adults with epilepsy. We report HR-QOL priorities identified by adults with IDD-E and their caregivers. These results may help epilepsy clinicians and researchers develop tailored strategies to address priorities of the patient with IDD-E/caregiver community.

Introduction: The unpredictability of epilepsy has a severe impact on health-related quality of life (HR-QOL) for people with epilepsy. Seizure detection devices have the potential to improve HR-QOL by improving seizure safety, reducing caregiver hypervigilance, and reducing seizure anxiety. Emerging data have led to an improved understanding of characteristics that promote acceptability of detection devices for people with epilepsy and caregivers. However, whether usage of seizure detection devices is associated with clinically meaningful improvement in anxiety and HR-QOL remains poorly understood. Methods: We analyzed cross-sectional survey data collected first-hand from 371 people with epilepsy and care-givers on seizure detection device and HR-QOL using an enriched population of electronic seizure diary users. Metrics related to quality of life and anxiety reduction were compared between users and nonusers of seizure detection devices. Results: Compared with nonusers of seizure detection devices, device users were significantly more likely to have been impacted by epilepsy in multiple HR-QOL domains, including anxiety, mood, emotional regulation/aggression, speech/language, sleep quality, social life, activities of daily living, independence, and education/academic potential. The majority (80.2%) of people using seizure detection devices experienced moderate or greater anxiety reduction from seizure detection device usage, while 11.1% reported that detection devices did not help at all with anxiety. Despite potential benefit, seizure detection devices were used only by a minority (21.8%) of people with epilepsy surveyed, and usage tended to be skewed toward younger patient age, higher income, and caregivers. There was no significant difference in overall HR-QOL between users and nonusers. Conclusions: Seizure detection devices provide moderate or greater anxiety reduction among the majority of people with epilepsy and their caregivers, but current translatability into improvements in overall HR-QOL may be limited. Affordability and technological support are potential barriers to maximizing benefit equally among the epilepsy community. These considerations may be useful to help guide future device development and inform patient-dinician discussions on device usage and HR-QOL. (C) 2020 Elsevier Inc. All rights reserved.