Familial aggregation of seizure outcomes in four familial epilepsy cohorts.

OBJECTIVE: To assess the possible effects of genetics on seizure outcome by estimating the familial aggregation of three outcome measures: seizure remission, history of ≥4 tonic-clonic seizures, and seizure control for individuals taking antiseizure medication. METHODS: We analyzed families containing multiple persons with epilepsy in four previously collected retrospective cohorts. Seizure remission was defined as being 5 and 10 years seizure-free at last observation. Total number of tonic-clonic seizures was dichotomized at <4 and ≥4 seizures. Seizure control in patients taking antiseizure medication was defined as no seizures for 1, 2, and 3 years. We used Bayesian generalized linear mixed-effects model (GLMM) to estimate the intraclass correlation coefficient (ICC) of the family-specific random effect, controlling for epilepsy type, age at epilepsy onset, and age at last data collection as fixed effects. We analyzed each cohort separately and performed meta-analysis using GLMMs. RESULTS: The combined cohorts included 3644 individuals with epilepsy from 1463 families. A history of ≥4 tonic-clonic seizures showed strong familial aggregation in three separate cohorts and meta-analysis (ICC .28, 95% confidence interval [CI] .21-.35, Bayes factor 8 × 1016). Meta-analyses did not reveal significant familial aggregation of seizure remission (ICC .08, 95% CI .01-.17, Bayes factor 1.46) or seizure control for individuals taking antiseizure medication (ICC .13, 95% CI 0-.35, Bayes factor 0.94), with heterogeneity among cohorts. SIGNIFICANCE: A history of ≥4 tonic-clonic seizures aggregated strongly in families, suggesting a genetic influence, whereas seizure remission and seizure control for individuals taking antiseizure medications did not aggregate consistently in families. Different seizure outcomes may have different underlying biology and risk factors. These findings should inform the future molecular genetic studies of seizure outcomes.

Improving Seizure Frequency Documentation and Classification.

Background and Objectives: Accurate and reliable seizure data are essential for evaluating treatment strategies and tracking the quality of care in epilepsy clinics. This quality improvement project aimed to increase seizure documentation (i.e., documentation of seizure frequency from 80% to 100%, date of last seizure from 35% to 50%, and International League Against Epilepsy (ILAE) seizure classification from 35% to at least 50%) over 6 months. Methods: We surveyed 7 epileptologists to determine their perceived seizure frequency, ILAE classification, and date of last seizure documentation habits. Baseline data were collected weekly from September to December 2021. Subsequently, we implemented a newly created flowsheet in our Electronic Health Record (EHR) based on the Epilepsy Learning Healthcare System (ELHS) Case Report Forms to increase seizure documentation in a standardized way. Two epileptologists tested this flowsheet tool in their epilepsy clinics between February 2022 and July 2022. Data were collected weekly and compared with documentation from other epileptologists within the same group. Results: Epileptologists at our center believed they documented seizure frequency for 84%-87% of clinic visits, which aligned with baseline data collection, showing they recorded seizure frequency for 83% of clinic visits. Epileptologists believed they documented ILAE classification for 47%-52% of clinic visits, and baseline data showed this was documented in 33% of clinic visits. They also reported documenting the date of the last seizure for 52%-63% of clinic visits, but this occurred in only 35% of clinic visits. After implementing the new flowsheet, documentation increased to nearly 100% for all fields being completed by the providers who tested the flowsheet. Discussion: We demonstrated that by implementing an easy-to-use standardized EHR documentation tool, our documentation of critical metrics, as defined by the ELHS, improved dramatically. This shows that simple and practical interventions can substantially improve clinically meaningful documentation.

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.

EEG parameters as endpoints in epilepsy clinical trials - An expert panel opinion paper.

INTRODUCTION: The lack of ideal measurement of treatment efficacy is a well acknowledged problem in the epilepsy community, both in clinical care and clinical trials. Whilst still the current gold-standard, self-reported seizure frequency significantly underestimates the true number of seizures and does not account for any other at least equally important outcome parameters, such as neurodevelopment and cognition. With the rise of disease modifying treatments, the need for more reliable endpoints in practice and clinical trials becomes more pressing. In this paper we assembled an expert panel to discuss the nature of these needs, current limitations, and obstacles based on a survey amongst these experts who were queried about the most important issues regarding the use of electroencephalography (EEG) parameters as endpoints in clinical drug and device development. METHODS: A structured survey was sent to a group of experts in the design and conduct of epilepsy trials in adults and children. This was followed by a virtual in-person meeting discussing the results of the trial and identifying a list of most important issues. RESULTS: Six clinical trialists and 5 individuals from pharmaceutical companies returned the survey containing 14 questions, and 8 clinical trialists and 10 pharma-representatives attended the meeting. Three main issues were identified (1) lack of accuracy of seizure diaries due to nocturnal seizures, subtle motor seizures, impairment of consciousness and lack of awareness of the seizure by the patient (2) inter-rater variability of EEG assessment (3) lack of standardization regarding definition(s) of seizures (clinical and electrographic), EEG recording methods and EEG data management. Recommended solutions included (1) validation of EEG parameters as biomarkers and use of wearables (2) development of a manual that describes EEG rating criteria, protocol for validation by > 1 central reader and use of a resolution of disagreements reporting template (3) standardization of EEG recording, data management and reporting. DISCUSSION & CONCLUSION: Current developments in research and technology seem promising to advance the use of EEG parameters as potential endpoints and offer partial solutions to the current needs. However, continuous, focused and collaborative efforts of all stakeholders (academia, industry and regulatory agencies) are needed to formulate guidelines, validate emerging technologies and approve them for use in trials. It is the intent of this opinion "position paper" to stimulate those efforts.

A replicable, open-source, data integration method to support national practice-based research & quality improvement systems.

OBJECTIVES: The Epilepsy Learning Healthcare System (ELHS) was created in 2018 to address measurable improvements in outcomes for people with epilepsy. However, fragmentation of data systems has been a major barrier for reporting and participation. In this study, we aimed to test the feasibility of an open-source Data Integration (DI) method that connects real-life clinical data to national research and quality improvement (QI) systems. METHODS: The ELHS case report forms were programmed as EPIC SmartPhrases at Mass General Brigham (MGB) in December 2018 and subsequently as EPIC SmartForms in June 2021 to collect actionable, standardized, structured epilepsy data in the electronic health record (EHR) for subsequent pull into the external national registry of the ELHS. Following the QI methodology in the Chronic Care Model, 39 providers, epileptologists and neurologists, incorporated the ELHS SmartPhrase into their clinical workflow, focusing on collecting diagnosis of epilepsy, seizure type according to the International League Against Epilepsy, seizure frequency, date of last seizure, medication adherence and side effects. The collected data was stored in the Enterprise Data Warehouse (EDW) without integration with external systems. We developed and validated a DI method that extracted the data from EDW using structured query language and later preprocessed using text mining. We used the ELHS data dictionary to match fields in the preprocessed notes to obtain the final structured dataset with seizure control information. For illustration, we described the data curated from the care period of 12/2018-12/2021. RESULTS: The cohort comprised a total of 1806 patients with a mean age of 43 years old (SD: 17.0), where 57% were female, 80% were white, and 84% were non-Hispanic/Latino. Using our DI method, we automated the data mining, preprocessing, and exporting of the structured dataset into a local database, to be weekly accessible to clinicians and quality improvers. During the period of SmartPhrase implementation, there were 5168 clinic visits logged by providers documenting each patient's seizure type and frequency. During this period, providers documented 59% patients having focal seizures, 35% having generalized seizures and 6% patients having another type. Of the cohort, 45% patients had private insurance. The resulting structured dataset was bulk uploaded via web interface into the external national registry of the ELHS. CONCLUSIONS: Structured data can be feasibly extracted from text notes of epilepsy patients for weekly reporting to a national learning healthcare system.

Improving Care for Families and Children with Neurodevelopmental Disorders and Co-occurring Chronic Health Conditions Using a Care Coordination Intervention.

OBJECTIVE: This clinical intervention study aimed to improve care integration and health service delivery for children with concurrent neurodevelopmental disorders and chronic health conditions. This population has significant unmet needs and disproportionate deficits in service delivery. A lack of coordination across child service sectors is a common barrier to successful treatment and support of children with neurodevelopmental disorders with complex medical needs. METHODS: This project implemented an innovative care coordination model, involving one-on-one supports from a trained care coordinator who liaised with the broader intersectoral care team to improve joint care planning, integration of services, and the experience of both families and care providers. To evaluate the impact of care coordination activities, a single-group interventional study was conducted using a repeated-measures framework (at 0, 6, and 12 months) using previously established outcome measures. RESULTS: Over 2 years, this project provided care coordination to 84 children and their families, with an age range from 2 to 17 years. The care coordination intervention demonstrated positive impacts for children, families, and care teams and contributed to clinical efficiencies. Children had fewer visits to the emergency department and less frequent acute care use. Improvement in access to services, joint care planning and communication across providers, and better linkage with school supports were demonstrated. Families reported that the program decreased their stress around coordinating care for their child. CONCLUSION: This work demonstrated that intersectoral care coordination is attainable through innovative and collaborative practice for children with complex neurodevelopmental and medical needs.

Standardization of seizure response times and data collection in an epilepsy monitoring unit.

OBJECTIVE: We sought to improve seizure response times in the epilepsy monitoring unit, improve the accuracy and reliability of seizure response time data collection, and develop a standardized and automated approach for seizure response data collection in the EMU. METHODS: We used Quality Improvement (QI) methodology to understand the EMU workflow involved in responding to seizures (a process map); to create a theory of change that stated the desired aim, potential drivers/barriers and interventions (i.e., key driver diagram) and perform iterative interventions to address some of the drivers plan-do-study-act (PDSA) cycles. We performed three PDSA cycles with a focus on improving the seizure alert system in our EMU. Adjustments were made to the methodology as it became clear that this was a systems issue, and our project would need to focus on improving the system rather than iteratively improving a functioning (stable) system. RESULTS: Over a 6-month period, 252 seizure response times were recorded and analyzed. We performed 3 interventions. The first was initiating twice monthly meetings with nursing and EEG techs to discuss the project and provide feedback on response times. The second was the implementation of a new Hill-Rom seizure alert system to reduce alert times and automate data tracking. The third was implementing a new alert deactivation system to reduce variability in the data. Following these 3 interventions, variation, and data collection methods were improved while also maintaining improvements in seizure response times. SIGNIFICANCE: We identified and implemented an alert system in our EMU which led to more efficient and accurate data collection while maintaining improved response times that resulted from the first intervention. This lays the groundwork for future QI initiatives and has created a framework for standardizing seizure response time recording and data collection that can be replicated at other centers with similar infrastructure, personnel and workflows.

The rescue therapy in epilepsy project Part 2: Insights from people with epilepsy and families on expert-derived preferred practices.

BACKGROUND: The number and type of therapies available to treat seizure clusters (SCs) or periods of increased seizure activity have risen in recent years. Gaps still exist on defining SCs, when to use them, and educating patients and families. The Epilepsy Foundation developed and published expert-derived consensus on preferred practices for rescue therapies (RTs), 79% of which were agreed upon by a larger group of healthcare professionals (HCPs). This paper describes insights from people with epilepsy (PWE) and families/caregivers (FCGs) on these practices to assess similarities and trends between PWE, FCGs, an expert panel, and HCPs. METHODS: Online survey including expert-derived preferred practices for RT was completed by a convenience sample of 176 PWE/FCGs. Respondents rated agreement with each preferred practice using a 0-8 point Likert scale. Results were examined by relationship to epilepsy, prior use of RTs, and comparison to the expert panel and larger group of HCPs. RESULTS: 41.5% of respondents were PWE and 54.6% were FCGs; 70% represented PWE age 18 and over or those who cared for adults with epilepsy. Levels of agreement were similar to those of HCPs - consensus was obtained on 79% of preferred practices. Differences were noted on which items achieved consensus and strength of consensus for some items. Differences between PWE and FCG, and between those who had and had not previously used a RT were found. A proposed definition of SCs did not reach consensus, but there was strong consensus for individualized seizure action plans and more RT education.

How to do things with words: Two seminars on the naming of functional (psychogenic, non-epileptic, dissociative, conversion, ) seizures.

Amongst the most important conditions in the differential diagnosis of epilepsy is the one that manifests as paroxysms of altered behaviour, awareness, sensation or sense of bodily control in ways that often resemble epileptic seizures, but without the abnormal excessive or synchronous electrical activity in the brain that defines these. Despite this importance, there remains little agreement - and frequent debate - on what to call this condition, known inter alia as psychogenic non-epileptic seizures (PNES), dissociative seizures (DS), functional seizures (FS), non-epileptic attack disorder (NEAD), pseudoseizures, conversion disorder with seizures, and by many other labels besides. This choice of terminology is not merely academic - it affects patients' response to and understanding of their diagnosis, and their ability to navigate health care systems.This paper summarises two recent discussions hosted by the American Epilepsy Society and Functional Neurological Disorders Society on the naming of this condition. These discussions are conceptualised as the initial step of an exploration of whether it might be possible to build consensus for a new diagnostic label.

Seizure documentation in people living with epilepsy.

Seizure documentation is an essential component of epilepsy management. Not all persons with epilepsy choose to document their seizures, but many view the practice as essential to managing their disease. While seizure documentation is a valuable aspect of patient care, clinicians and patients must remain aware that seizure underreport and overreport commonly occur due to lack of seizure awareness. Additionally, in rare cases, persons with epilepsy may intentionally conceal their seizures from clinicians. The continued development of electronic seizure diaries and epilepsy self-management software provides patients with new and expanding options for seizure documentation and disease management. In order for these tools to be utilized most effectively, patient input must be central to their development. Given the limitations of seizure documentation, the development of accurate, non-invasive seizure detection devices is crucial for accurate seizure monitoring.

Creation and implementation of an electronic health record note for quality improvement in pediatric epilepsy: Practical considerations and lessons learned.

OBJECTIVE: To describe the development of the Pediatric Epilepsy Outcome-Informatics Project (PEOIP) at Alberta Children's Hospital (ACH), which was created to provide standardized, point-of-care data entry; near-time data analysis; and availability of outcome dashboards as a baseline on which to pursue quality improvement. METHODS: Stakeholders involved in the PEOIP met weekly to determine the most important outcomes for patients diagnosed with epilepsy, create a standardized electronic note with defined fields (patient demographics, seizure and syndrome type and frequency and specific outcomes- seizure type and frequency, adverse effects, emergency department visits, hospitalization, and care pathways for clinical decision support. These were embedded in the electronic health record from which the fields were extracted into a data display platform that provided patient- and population-level dashboards updated every 36 hours. Provider satisfaction and family experience surveys were performed to assess the impact of the standardized electronic note. RESULTS: In the last 5 years, 3,245 unique patients involving 13, 831 encounters had prospective, longitudinal, standardized epilepsy data accrued via point-of-care data entry into an electronic note as part of routine clinical care. A provider satisfaction survey of the small number of users involved indicated that the vast majority believed that the note makes documentation more efficient. A family experience survey indicated that being provided with the note was considered "valuable" or "really valuable" by 86% of respondents and facilitated communication with family members, school, and advocacy organizations. SIGNIFICANCE: The PEOIP serves as a proof of principle that information obtained as part of routine clinical care can be collected in a prospective, standardized, efficient manner and be used to construct filterable process/outcome dashboards, updated in near time (36 hours). This information will provide the necessary baseline data on which multiple of QI projects to improve meaningful outcomes for children with epilepsy will be based.

Preferred practices for rescue treatment of seizure clusters: A consensus-driven, multi-stakeholder approach.

BACKGROUND: Some of the most difficult issues in the care of people living with epilepsy (PWE) regard the definition, treatment, and communication of unexpected increase(s) in seizure frequency over a relatively short duration of time. In order to address this issue, the Epilepsy Foundation established the Rescue Therapy Project in Epilepsy to understand the gaps, needs, and barriers facing people with epilepsy who use or may benefit from rescue therapies (RTs) for "seizure clusters". The intent was to provide consensus-derived recommendations from a broad stakeholder group including PWE, their caregivers, epilepsy specialist physicians, nurses, pharmacists, and representatives of epilepsy & neurology advocacy and professional organizations. METHODS: During Phase 1, a group of epilepsy experts and stakeholders (N = 54) were divided into 3 workgroups that met by conference calls and in-person. Content of workgroups was developed into preferred practices related to RTs. In Phase 2, these recommendations were evaluated by a larger more diverse group of healthcare professionals, PWE, and caregivers. Agreement with recommended preferred practices at 80% or greater was set as the level to achieve consensus. RESULTS: The preferred practices were centered around four core themes identified by the experts and key stakeholders: the importance of a common language; when RTs should be prescribed; assessing the need for RTs; and education/communication about RTs. Consensus from experts and key stakeholders was reached for 27 recommended preferred practices using the Delphi method. "Rescue therapy" or "rescue medicine" was the preferred term to describe what to name a treatment intervention in this context, and seizure action plans was the preferred term to communicate how to respond to a seizure or SCs and the use of RTs. In Phase 2, 23 of the recommendations reached final consensus, including the need for a common language, and the need to consider RTs and seizure action plans in all PWE in an individualized manner, several circumstances in which RTs should be prescribed, and the importance of education regarding RTs and SAPs.

Improving Patient-Centered Communication about Sudden Unexpected Death in Epilepsy through Computerized Clinical Decision Support.

BACKGROUND: Sudden unexpected death in epilepsy (SUDEP) is a rare but fatal risk that patients, parents, and professional societies clearly recommend discussing with patients and families. However, this conversation does not routinely happen. OBJECTIVES: This pilot study aimed to demonstrate whether computerized decision support could increase patient communication about SUDEP. METHODS: A prospective before-and-after study of the effect of computerized decision support on delivery of SUDEP counseling. The intervention was a screening, alerting, education, and follow-up SUDEP module for an existing computerized decision support system (the Child Health Improvement through Computer Automation [CHICA]) in five urban pediatric primary care clinics. Families of children with epilepsy were contacted by telephone before and after implementation to assess if the clinician discussed SUDEP at their respective encounters. RESULTS: The CHICA-SUDEP module screened 7,154 children age 0 to 21 years for seizures over 7 months; 108 (1.5%) reported epilepsy. We interviewed 101 families after primary care encounters (75 before and 26 after implementation) over 9 months. After starting CHICA-SUDEP, the number of caregivers who reported discussing SUDEP with their child's clinician more than doubled from 21% (16/75) to 46% (12/26; p = 0.03), and when the parent recalled who brought up the topic, 80% of the time it was the clinician. The differences between timing and sampling methodologies of before and after intervention cohorts could have led to potential sampling and recall bias. CONCLUSION: Clinician-family discussions about SUDEP significantly increased in pediatric primary care clinics after introducing a systematic, computerized screening and decision support module. These tools demonstrate potential for increasing patient-centered education about SUDEP, as well as incorporating other guideline-recommended algorithms into primary and subspecialty cares. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov, NCT03502759.

Establishing a learning healthcare system to improve health outcomes for people with epilepsy.

OBJECTIVE: To describe the organization of the Epilepsy Learning Healthcare System (ELHS), a network that aims to improve care outcomes for people with epilepsy (PWE). MATERIALS AND METHODS: Patients and family partners, providers, researchers, epidemiologists, and other leaders collaborated to recruit epilepsy centers and community services organizations into a novel learning network. A multidisciplinary Coordinating Committee developed ELHS governance and organizational structure, including four key planning Cores (Community, Clinical, Quality Improvement, and Data). Through Quality Improvement (QI) methodology grounded in the Institute for Healthcare Improvement (IHI) model, including iterative Plan-Do-Study-Act (PDSA) rapid learning cycles and other learning and sharing sessions, ELHS equipped epilepsy centers and community organizations with tools to standardize, measure, share, and improve key aspects of epilepsy care. The initial learning cycles addressed provider documentation of seizure frequency and type, and also screening for medication adherence barriers. Rapid learning cycles have been carried out on these initial measures in both clinical centers and community-based settings. Additional key measures have been defined for quality of life, screening, and treatment for mental health and behavioral comorbidities, transition from pediatric to adult care, counseling for women and girls living with epilepsy, referral for specialty care, and prevention and treatment of seizure clusters and status epilepticus. RESULTS: It is feasible to adopt a learning healthcare system framework in epilepsy centers and community services organizations. Through structured collaboration between epilepsy care providers, community support organizations, PWE, and their families/caregivers we have identified new opportunities to improve outcomes that are not available in traditional care models.

Contribution of rare genetic variants to drug response in absence epilepsy.

OBJECTIVE: We investigated the possible significance of rare genetic variants to response to valproic acid (VPA) and ethosuximide (ETX) in patients with absence epilepsy. Our primary hypothesis was that rare CACNA1H variants are more frequent in ETX-non-responsive patients compared to ETX-responsive. Our secondary hypothesis was that rare variants in GABA-receptor genes are more frequent in VPA-non-responsive patients compared to VPA-responsive. METHODS: We recruited patients with absence epilepsy treated with both VPA and ETX, and performed whole exome sequencing in order to investigate the potential role of rare variants in CACNA1H, other voltage-gated calcium channel (VGCC) genes, or GABA-receptor genes in predicting response to ETX or VPA. RESULTS: Sixty-two patients were included; 12 were ETX-responsive, 14 VPA-responsive, and 36 did not have a clear positive response to either medication. We did not find significant enrichment inCACNA1H rare variants in ETX-responsive patients (odds ratio 3.43; 0.43-27.65; p = 0.20), nor was there enrichment for other VGCC genes. No significant enrichment of GABA-receptor gene rare variants was seen for VPA-non-responsive patients versus VPA-responsive. We found enrichment of rare GABA-receptor variants in our absence cohort compared to controls (odds ratio 3.82; 1.68-8.69). There was no difference in frequency of CACNA1H rs61734410 and CACNA1I rs3747178 polymorphisms between ETX-responsive and ETX-non-responsive groups; these polymorphisms have previously been reported to predict lack of response to ETX in absence epilepsy. SIGNIFICANCE: We conclude that if CACNA1H rare variants predict lack of response to ETX, a larger sample is necessary to test this with sufficient power. Increased GABA-receptor gene rare variant frequency in absence epilepsy patients who fail initial anti-seizure therapy suggests subtle GABA receptor dysfunction may contribute to the underlying pathophysiology.

Design and implementation of electronic health record common data elements for pediatric epilepsy: Foundations for a learning health care system.

OBJECTIVE: Common data elements (CDEs) are standardized questions and answer choices that allow aggregation, analysis, and comparison of observations from multiple sources. Clinical CDEs are foundational for learning health care systems, a data-driven approach to health care focused on continuous improvement of outcomes. We aimed to create clinical CDEs for pediatric epilepsy. METHODS: A multiple stakeholder group (clinicians, researchers, parents, caregivers, advocates, and electronic health record [EHR] vendors) developed clinical CDEs for routine care of children with epilepsy. Initial drafts drew from clinical epilepsy note templates, CDEs created for clinical research, items in existing registries, consensus documents and guidelines, quality metrics, and outcomes needed for demonstration projects. The CDEs were refined through discussion and field testing. We describe the development process, rationale for CDE selection, findings from piloting, and the CDEs themselves. We also describe early implementation, including experience with EHR systems and compatibility with the International League Against Epilepsy classification of seizure types. RESULTS: Common data elements were drafted in August 2017 and finalized in January 2020. Prioritized outcomes included seizure control, seizure freedom, American Academy of Neurology quality measures, presence of common comorbidities, and quality of life. The CDEs were piloted at 224 visits at 10 centers. The final CDEs included 36 questions in nine sections (number of questions): diagnosis (1), seizure frequency (9), quality of life (2), epilepsy history (6), etiology (8), comorbidities (2), treatment (2), process measures (5), and longitudinal history notes (1). Seizures are categorized as generalized tonic-clonic (regardless of onset), motor, nonmotor, and epileptic spasms. Focality is collected as epilepsy type rather than seizure type. Seizure frequency is measured in nine levels (all used during piloting). The CDEs were implemented in three vendor systems. Early clinical adoption included 1294 encounters at one center. SIGNIFICANCE: We created, piloted, refined, finalized, and implemented a novel set of clinical CDEs for pediatric epilepsy.

Psychogenic non-epileptic seizures in children - psychophysiology & dissociative characteristics.

The purpose of this study was to determine psychophysiology and dissociative characteristics of psychogenic non-epileptic seizures (PNES) in a clinical pediatric setting. A retrospective chart review was conducted over a 5-year period that included children meeting criteria for probable, clinically established or documented PNES. Of these, 33 patients (81%) underwent psychophysiology assessment as part of standardized care and were selected for study inclusion. Ages ranged from 10 to 17 years inclusive (70% female). The majority of patients were found to have some form of autonomic decompensation at baseline (82%) and lack of autonomic recovery from a cognitive stressor (58%). Inhibition of electrodermal skin response to laboratory stressor was associated with significantly longer duration of PNES illness (t=2.65, p=.013), while elevated heart rate (above 90th percentile) was associated with significantly higher frequency of PNES events in the month preceding diagnosis (t=3.1, p=.004). High levels of dissociation and hyperventilation symptoms were self-reported by adolescent patients (n=19) with a moderate degree of positive association (r=0.35, p=.038). The majority of patients (n= 25, 89%) were taught to correct respiratory CO2 levels during a single biofeedback training session. Conclusions: Child PNES populations appear to be characterized by chronic autonomic hyperarousal reflecting severity of their symptoms, which can feasibly be targeted for behavioral treatment.

Driving a motor vehicle and psychogenic nonepileptic seizures: ILAE Report by the Task Force on Psychogenic Nonepileptic Seizures.

OBJECTIVES: This International League Against Epilepsy (ILAE) Report: (a) summarizes the literature about "driving and psychogenic nonepileptic seizures (PNES)"; (b) presents the views of international experts; and (c) proposes an approach to assessing the ability of persons with PNES (PwPNES) to drive. METHODS: Phase 1: Systematic literature review. Phase 2: Collection of international expert opinion using SurveyMonkey®. Experts included the members of the ILAE PNES Task Force and individuals with relevant publications since 2000. Phase 3: Joint analysis of the findings and refinement of conclusions by all participants using email. As an ILAE Report, the resulting text was reviewed by the Psychiatry Commission, the ILAE Task Force on Driving Guidelines, and Executive Committee. RESULTS: Eight studies identified by the systematic review process failed to provide a firm evidence base for PNES-related driving regulations, but suggest that most health professionals think restrictions are appropriate. Twenty-six experts responded to the survey. Most held the view that decisions about driving privileges should consider individual patient and PNES characteristics and take account of whether permits are sought for private or commercial driving. Most felt that those with active PNES should not be allowed to drive unless certain criteria were met and that PNES should be thought of as "active" if the last psychogenic seizure had occurred within 6 months. SIGNIFICANCE: Recommendations on whether PwPNES can drive should be made at the individual patient level. Until future research has determined the risk of accidents in PwPNES a proposed algorithm may guide decisions about driving advice.

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy.

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.