Confusing placebo effect with natural history in epilepsy: A big data approach

Association of reductions in rescue medication requirements with vagus nerve stimulation: Results of long-term community collected data from a seizure diary app

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.

Re-evaluating the placebo response in recent canine dietary epilepsy trials

The placebo response is a common phenomenon. Limited evidence is available about its magnitude in canine epilepsy trials, even though it can significantly influence the efficacy evaluation of new treatments. It was hypothesised that the placebo response is diminished when epilepsy trials are conducted in a prospective crossover design. Seizure data spanning six months from three previous multicenter epilepsy studies were analysed. The monthly seizure frequency of 60 dogs diagnosed with idiopathic epilepsy was calculated, comparing baseline data with placebo treatment. Furthermore, differentiation was made between dogs randomised to the placebo group early (Phase 1: first 3 months) or later during the study (Phase 2: second 3 months).The analysis did not reveal any placebo response in terms of monthly seizure frequency. Instead, an increase was noted during the placebo treatment period, with a mean of 2.95 seizures per month compared to 2.30 seizures per month before study entry (p = 0.0378). Additionally, a notable phase effect was observed. Dogs receiving the placebo in the second study phase exhibited a significant increase in monthly seizure frequency compared to baseline (p = 0.0036). Conversely, no significant difference from baseline was observed for dogs receiving the placebo in the first study phase. These findings underscore the considerable variability in placebo responses observed in trials for canine epilepsy, contrasting with previous limited data. The identified phase effect should be carefully considered in the design and evaluation of canine epilepsy trials to ensure a more accurate assessment of efficacy for new treatments.

Inductive reasoning with large language models: a simulated randomized controlled trial for epilepsy

Importance

The analysis of electronic medical records at scale to learn from clinical experience is currently very challenging. The integration of artificial intelligence (AI), specifically foundational large language models (LLMs), into an analysis pipeline may overcome some of the current limitations of modest input sizes, inaccuracies, biases, and incomplete knowledge bases.

Objective

To explore the effectiveness of using an LLM for generating realistic clinical data and other LLMs for summarizing and synthesizing information in a model system, simulating a randomized clinical trial (RCT) in epilepsy to demonstrate the potential of inductive reasoning via medical chart review.

Design

An LLM-generated simulated RCT based on a RCT for treatment with an antiseizure medication, cenobamate, including a placebo arm and a full-strength drug arm, evaluated by an LLM-based pipeline versus a human reader.

Setting

Simulation based on realistic seizure diaries, treatment effects, reported symptoms and clinical notes generated by LLMs with multiple different neurologist writing styles.

Participants

Simulated cohort of 240 patients, divided 1:1 into placebo and drug arms.

Intervention

Utilization of LLMs for the generation of clinical notes and for the synthesis of data from these notes, aiming to evaluate the efficacy and safety of cenobamate in seizure control either with a human evaluator or AI-pipeline.

Measures

The AI and human analysis focused on identifying the number of seizures, symptom reports, and treatment efficacy, with statistical analysis comparing the 50%-responder rate and median percentage change between the placebo and drug arms, as well as side effect rates in each arm.

Results

AI closely mirrored human analysis, demonstrating the drug's efficacy with marginal differences (<3%) in identifying both drug efficacy and reported symptoms.

Conclusions and Relevance

This study showcases the potential of LLMs accurately simulate and analyze clinical trials. Significantly, it highlights the ability of LLMs to reconstruct essential trial elements, identify treatment effects, and recognize reported symptoms, within a realistic clinical framework. The findings underscore the relevance of LLMs in future clinical research, offering a scalable, efficient alternative to traditional data mining methods without the need for specialized medical language training.

Delineating clinical and developmental outcomes in STXBP1-related disorders

STXBP1-related disorders are among the most common genetic epilepsies and neurodevelopmental disorders. However, the longitudinal epilepsy course and developmental end points, have not yet been described in detail, which is a critical prerequisite for clinical trial readiness. Here, we assessed 1281 cumulative patient-years of seizure and developmental histories in 162 individuals with STXBP1-related disorders and established a natural history framework. STXBP1-related disorders are characterized by a dynamic pattern of seizures in the first year of life and high variability in neurodevelopmental trajectories in early childhood. Epilepsy onset differed across seizure types, with 90% cumulative onset for infantile spasms by 6 months and focal-onset seizures by 27 months of life. Epilepsy histories diverged between variant subgroups in the first 2 years of life, when individuals with protein-truncating variants and deletions in STXBP1 (n = 39) were more likely to have infantile spasms between 5 and 6 months followed by seizure remission, while individuals with missense variants (n = 30) had an increased risk for focal seizures and ongoing seizures after the first year. Developmental outcomes were mapped using milestone acquisition data in addition to standardized assessments including the Gross Motor Function Measure-66 Item Set and the Grasping and Visual-Motor Integration subsets of the Peabody Developmental Motor Scales. Quantification of end points revealed high variability during the first 5 years of life, with emerging stratification between clinical subgroups. An earlier epilepsy onset was associated with lower developmental abilities, most prominently when assessing gross motor development and expressive communication. We found that individuals with neonatal seizures or early infantile seizures followed by seizure offset by 12 months of life had more predictable seizure trajectories in early to late childhood compared to individuals with more severe seizure presentations, including individuals with refractory epilepsy throughout the first year. Characterization of anti-seizure medication response revealed age-dependent response over time, with phenobarbital, levetiracetam, topiramate and adrenocorticotropic hormone effective in reducing seizures in the first year of life, while clobazam and the ketogenic diet were effective in long-term seizure management. Virtual clinical trials using seizure frequency as the primary outcome resulted in wide range of trial success probabilities across the age span, with the highest probability in early childhood between 1 year and 3.5 years. In summary, we delineated epilepsy and developmental trajectories in STXBP1-related disorders using standardized measures, providing a foundation to interpret future therapeutic strategies and inform rational trial design.

Quantifying and controlling the impact of regression to the mean on randomized controlled trials in epilepsy

OBJECTIVE

Randomized controlled trials (RCTs) in epilepsy for drug treatments are plagued by high costs. One potential remedy is to reduce placebo response via better control over regression to the mean (RTM). Here, RTM represents an initial observed seizure rate higher than the long-term average, which gradually settles closer to the average, resulting in apparent response to treatment. This study used simulation to clarify the relationship between eligibility criteria and RTM.

METHODS

Using a statistically realistic seizure diary simulator, the impact of RTM on placebo response and trial efficacy was explored by varying eligibility criteria for a traditional treatment phase II/III RCT for drug-resistant epilepsy.

RESULTS

When the baseline period was included in the eligibility criteria, increasingly larger fractions of RTM were observed (25%-47% vs. 23%-25%). Higher fractions of RTM corresponded with higher expected placebo responses (50% responder rate [RR50]: 2%-9% vs. 0%-8%) and lower statistical efficacy (RR50: 47%-67% vs. 47%-81%). The exclusion of baseline from eligibility criteria was shown to decrease the number of patients needed by roughly 30%.

SIGNIFICANCE

The manipulation of eligibility criteria for RCTs has a predictable and important impact on RTM, and therefore on placebo response; the difference between drug and placebo was more easily detected. This in turn impacts trial efficacy and therefore cost. This study found dramatic improvements in efficacy and cost when baseline was not included in eligibility.

Delineating clinical and developmental outcomes in STXBP1-related disorders

STXBP1-related disorders are among the most common genetic epilepsies and neurodevelopmental disorders. However, the longitudinal epilepsy course and developmental endpoints have not yet been described in detail, which is a critical prerequisite for clinical trial readiness. Here, we assessed 1,281 cumulative patient-years of seizure and developmental histories in 162 individuals with STXBP1-related disorders and established a natural history framework. STXBP1-related disorders are characterized by a dynamic pattern of seizures in the first year of life and high variability in neurodevelopmental trajectories in early childhood. Epilepsy onset differed across seizure types, with 90% cumulative onset for infantile spasms by 6 months and focal-onset seizures by 27 months of life. Epilepsy histories diverged between variant subgroups in the first 2 years of life, when individuals with protein-truncating variants and deletions in STXBP1 (n=39) were more likely to have infantile spasms between 5 and 6 months followed by seizure remission, while individuals with missense variants (n=30) had an increased risk for focal seizures and ongoing seizures after the first year. Developmental outcomes were mapped using milestone acquisition data in addition to standardized assessments including the Gross Motor Function Measure-66 Item Set and the Grasping and Visual-Motor Integration subsets of the Peabody Developmental Motor Scales. Quantification of endpoints revealed high variability during the first five years of life, with emerging stratification between clinical subgroups, most prominently between individuals with and without infantile spasms. We found that individuals with neonatal seizures or early infantile seizures followed by seizure offset by 12 months of life had more predictable seizure trajectories in early to late childhood than compared to individuals with more severe seizure presentations, including individuals with refractory epilepsy throughout the first year. Characterization of anti-seizure medication response revealed age-dependent response over time, with phenobarbital, levetiracetam, topiramate, and adrenocorticotropic hormone effective in reducing seizures in the first year of life, while clobazam and the ketogenic diet were effective in long-term seizure management. Virtual clinical trials using seizure frequency as the primary outcome resulted in wide range of trial success probabilities across the age span, with the highest probability in early childhood between 1 year and 3.5 years. In summary, we delineated epilepsy and developmental trajectories in STXBP1-related disorders using standardized measures, providing a foundation to interpret future therapeutic strategies and inform rational trial design.

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

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

Flexible realistic simulation of seizure occurrence recapitulating statistical properties of seizure diaries

OBJECTIVE

A realistic seizure diary simulator is currently unavailable for many research needs, including clinical trial analysis and evaluation of seizure detection and seizure-forecasting tools. In recent years, important statistical features of seizure diaries have been characterized. These include (1) heterogeneity of individual seizure frequencies, (2) the relation between average seizure rate and standard deviation, (3) multiple risk cycles, (4) seizure clusters, and (5) limitations on inter-seizure intervals. The present study unifies these features into a single model.

METHODS

Our approach, Cyclic Heterogeneous Overdispersed Clustered Open-source L-relationship Adjustable Temporally limited E-diary Simulator (CHOCOLATES) is based on a hierarchical model centered on a gamma Poisson generator with several modifiers. This model accounts for the aforementioned statistical properties. The model was validated by simulating 10 000 randomized clinical trials (RCTs) of medication to compare with 23 historical RCTs. Metrics of 50% responder rate (RR50) and median percent change (MPC) were evaluated. We also used CHOCOLATES as input to a seizure-forecasting tool to test the flexibility of the model. We examined the area under the receiver-operating characteristic (ROC) curve (AUC) for test data with and without cycles and clusters.

RESULTS

The model recapitulated typical findings in 23 historical RCTs without the necessity of introducing an additional "placebo effect." The model produced the following RR50 values: placebo: 17 ± 4%; drug 38 ± 5%; and the following MPC values: placebo: 13 ± 6%; drug 40 ± 4%. These values are similar to historical data: for RR50: placebo, 21 ± 10%, drug: 43 ± 13%; and for MPC: placebo: 17 ± 10%, drug: 41 ± 11%. The seizure forecasts achieved an AUC of 0.68 with cycles and clusters, whereas without them the AUC was 0.51.

SIGNIFICANCE

CHOCOLATES represents the most realistic seizure occurrence simulator to date, based on observations from thousands of patients in different contexts. This tool is open source and flexible, and can be used for many applications, including clinical trial simulation and testing of seizure-forecasting tools.

Brivaracetam as Early Add-On Treatment in Patients with Focal Seizures: A Retrospective, Multicenter, Real-World Study

INTRODUCTION

In randomized controlled trials, add-on brivaracetam (BRV) reduced seizure frequency in patients with drug-resistant focal epilepsy. Most real-world research on BRV has focused on refractory epilepsy. The aim of this analysis was to assess the 12-month effectiveness and tolerability of adjunctive BRV when used as early or late adjunctive treatment in patients included in the BRIVAracetam add-on First Italian netwoRk Study (BRIVAFIRST).

METHODS

BRIVAFIRST was a 12-month retrospective, multicenter study including adult patients prescribed adjunctive BRV. Effectiveness outcomes included the rates of sustained seizure response, sustained seizure freedom, and treatment discontinuation. Safety and tolerability outcomes included the rate of treatment discontinuation due to adverse events (AEs) and the incidence of AEs. Data were compared for patients treated with add-on BRV after 1-2 (early add-on) and ≥ 3 (late add-on) prior antiseizure medications.

RESULTS

A total of 1029 patients with focal epilepsy were included in the study, of whom 176 (17.1%) received BRV as early add-on treatment. The median daily dose of BRV at 12 months was 125 (100-200) mg in the early add-on group and 200 (100-200) in the late add-on group (p < 0.001). Sustained seizure response was reached by 97/161 (60.3%) of patients in the early add-on group and 286/833 (34.3%) of patients in the late add-on group (p < 0.001). Sustained seizure freedom was achieved by 51/161 (31.7%) of patients in the early add-on group and 91/833 (10.9%) of patients in the late add-on group (p < 0.001). During the 1-year study period, 29 (16.5%) patients in the early add-on group and 241 (28.3%) in the late add-on group discontinued BRV (p = 0.001). Adverse events were reported by 38.7% and 28.5% (p = 0.017) of patients who received BRV as early and late add-on treatment, respectively.

CONCLUSION

Brivaracetam was effective and well tolerated both as first add-on and late adjunctive treatment in patients with focal epilepsy.

Mobile Applications for Epilepsy: Where Are We? Where Should We Go? A Systematic Review

The development of mobile health for epilepsy has grown in the last years, bringing new applications (apps) to the market and improving already existing ones. In this systematic review, we analyse the scope of mobile apps for seizure detection and epilepsy self-management, with two research questions in mind: what are the characteristics of current solutions and do they meet users’ requirements? What should be considered when designing mobile health for epilepsy? We used PRISMA methodology to search within App Store and Google Play Store from February to April of 2021, reaching 55 potential apps. A more thorough analysis regarding particular features was performed on 26 of those apps. The content of these apps was evaluated in five categories, regarding if there was personalisable content; features related to medication management; what aspects of seizure log were present; what type of communication prevailed; and if there was any content related to seizure alarm or seizure action plans. Moreover, the 26 apps were evaluated through using MARS by six raters, including two neurologists. The analysis of MARS categories was performed for the top and bottom apps, to understand the core differences. Overall, the lowest MARS scores were related to engagement and information, which play a big part in long-term use, and previous studies raised the concern of assuring continuous use, especially in younger audiences. With that in mind, we identified conceptual improvement points, which were divided in three main topics: customisation, simplicity and healthcare connection. Moreover, we summarised some ideas to improve m-health apps catered around long-term adherence. We hope this work contributes to a better understanding of the current scope in mobile epilepsy management, endorsing healthcare professionals and developers to provide off-the-shelf solutions that engage patients and allows them to better manage their condition.

Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

Natural History Studies and Clinical Trial Readiness for Genetic Developmental and Epileptic Encephalopathies

The developmental and epileptic encephalopathies (DEEs) are the most severe group of epilepsies. They usually begin in infancy or childhood with drug-resistant seizures, epileptiform EEG patterns, developmental slowing or regression, and cognitive impairment. DEEs have a high mortality and profound morbidity; comorbidities are common including autism spectrum disorders. With advances in genetic sequencing, over 400 genes have been implicated in DEEs, with a genetic cause now identified in over 50% patients. Each genetic DEE typically has a broad genotypic-phenotypic spectrum, based on the underlying pathophysiology. There is a pressing need to improve health outcomes by developing novel targeted therapies for specific genetic DEE phenotypes that not only improve seizure control, but also developmental outcomes and comorbidities. Clinical trial readiness relies firstly on a deep understanding of phenotype-genotype correlation and evolution of a condition over time, in order to select appropriate patients for clinical trials. Understanding the natural history of the disorder informs assessment of treatment efficacy in terms of both clinical outcome and biomarker utility. Natural history studies (NHS) provide a high quality, integrated, comprehensive approach to understanding a complex disease and underpin clinical trial design for novel therapies. NHS are pre-planned observational studies designed to track the course of a disease and identify demographic, genetic, environmental, and other variables, including biomarkers, that correlate with the disease's evolution and outcomes. Due to the rarity of individual genetic DEEs, appropriately funded high-quality DEE NHS will be required, with sustainable frameworks and equitable access to affected individuals globally.

Cannabidiol for Treating Lennox-Gastaut Syndrome and Dravet Syndrome in Korea

BACKGROUND

For the first time in Korea, we aimed to study the efficacy and safety of cannabidiol (CBD), which is emerging as a new alternative in treating epileptic encephalopathies.

METHODS

This study was conducted retrospectively with patients between the ages of 2-18 years diagnosed with Lennox-Gastaut syndrome (LGS) or Dravet syndrome (DS) were enrolled from March to October 2019, who visited outpatient unit at 3 and 6 months to evaluate medication efficacy and safety based on caregiver reporting. Additional evaluations, such as electroencephalogram and blood tests, were conducted at each period also. CBD was administered orally at a starting dose of 5 mg/kg/day, and was maintained at 10 mg/kg/day.

RESULTS

We analyzed 34 patients in the LGS group and 10 patients in the DS group between the ages of 1.2-15.8 years. In the 3-month evaluation, the overall reduction of seizure frequency in the LGS group was 52.9% (>50% reduction in 32.3% of the cases), and 29.4% in the 6-month evaluation (more than 50% reduction in 20.6%). In DS group, the reduction of seizure frequency by more than 50% was 30% and 20% in the 3-month and 6-month evaluation, respectively. Good outcomes were defined as the reduction of seizure frequency by more than 50% and similar results were observed in both LGS and DS groups. Adverse events were reported in 36.3% of total patients of which most common adverse events were gastrointestinal problems. However, no life-threatening adverse event was reported in both LGS and DS during the observation period.

CONCLUSION

In this first Korean study, CBD was safe and tolerable for use and could be expected to potentially reduce the seizure frequency in pediatric patients with LGS or DS.

Placebo in epilepsy

Placebos impact epilepsy in a number of ways. Through randomized clinical trials, explicit clinical use, and also through implicit clinical use, placebos play a role in epilepsy. This chapter will discuss the reasons placebo is used, the determinants of placebo response in epilepsy, observations about placebo specific to epilepsy, and ways in which clinical trial design is impacted by placebo.

Predicting seizure freedom with AED treatment in newly diagnosed patients with MRI-negative epilepsy: A large cohort and multicenter study

OBJECTIVE

We developed and validated a prediction score for predicting the probability of 6-month and 12-month seizure freedom of antiepileptic drug (AED) treatment in newly diagnosed patients with magnetic resonance imaging (MRI)-negative epilepsy.

METHODS

The development cohort included 543 consecutive patients from the Epilepsy Center of Henan Provincial People's Hospital, while the validation cohorts included 493 consecutive patients in two independent cohorts. Univariate analysis and a forward and backward elimination of multivariate Cox regression analysis were used to select predictive factors. The performance of the score was evaluated with C-index, calibration plots, and decision curve analysis. The risk stratification was also performed.

RESULTS

The score included five routinely available predictors including Circadian rhythms, Electroencephalography before AED treatment, Neuropsychiatric disorders, Perinatal brain injury, and History of central nervous system infection (CENPH score). When applied to the external validation cohort, the score showed good discrimination with C-index (development group: 0.83; validation group: 0.78), and calibration plots indicated well calibration, as well as the decision curve analysis showed good predictive accuracy and clinical values in four cohorts. The points of the score were categorized to the following three probability levels for predicting seizure freedom: high probability (0-83.11 points), medium probability (83.11-122.71 points), and low probability (>122.71 points). And online calculator was established to make this score easily applicable in clinical practice.

CONCLUSIONS

We established a simple, practical, and evidence-based prediction score for predicting seizure freedom with AEDs to aid in the clinical consultation and treatment decision for the newly diagnosed patients with MRI-negative epilepsy.

Natural variability in seizure frequency: Implications for trials and placebo

BACKGROUND

Changes in patient-reported seizure frequencies are the gold standard used to test efficacy of new treatments in randomized controlled trials (RCTs). Recent analyses of patient seizure diary data suggest that the placebo response may be attributable to natural fluctuations in seizure frequency, though the evidence is incomplete. Here we develop a data-driven statistical model and assess the impact of the model on interpretation of placebo response.

METHODS

A synthetic seizure diary generator matching statistical properties seen across multiple epilepsy diary datasets was constructed. The model was used to simulate the placebo arm of 5000 RCTs. A meta-analysis of 23 historical RCTs was compared to the simulations.

RESULTS

The placebo 50 %-responder rate (RR50) was 27.3 ± 3.6 % (simulated) and 21.1 ± 10.0 % (historical). The placebo median percent change (MPC) was 22.0 ± 6.0 % (simulated) and 16.7 ± 10.3 % (historical).

CONCLUSIONS

A statistical model of daily seizure count generation which incorporates quantities related to the natural fluctuations of seizure count data produces a placebo response comparable to those seen in historical RCTs. This model may be useful in better understanding the seizure count fluctuations seen in patients in other clinical settings.

Drug Development for Rare Paediatric Epilepsies: Current State and Future Directions

Rare diseases provide a challenge in the evaluation of new therapies. However, orphan drug development is of increasing interest because of the legislation enabling facilitated support by regulatory agencies through scientific advice, and the protection of the molecules with orphan designation. In the landscape of the rare epilepsies, very few syndromes, namely Dravet syndrome, Lennox-Gastaut syndrome and West syndrome, have been subject to orphan drug development. Despite orphan designations for rare epilepsies having dramatically increased in the past 10 years, the number of approved drugs remains limited and restricted to a handful of epilepsy syndromes. In this paper, we describe the current state of orphan drug development for rare epilepsies. We identified a large number of compounds currently under investigation, but mostly in the same rare epilepsy syndromes as in the past. A rationale for further development in rare epilepsies could be based on the match between the drug mechanisms of action and the knowledge of the causative gene mutation or by evidence from animal models. In case of the absence of strong pathophysiological hypotheses, exploratory/basket clinical studies could be helpful to identify a subpopulation that may benefit from the new drug. We provide some suggestions for future improvements in orphan drug development such as promoting paediatric drug investigations, better evaluation of the incidence and the prevalence, together with the natural history data, and the development of new primary outcomes.

Individualizing the definition of seizure clusters based on temporal clustering analysis

OBJECTIVE

Seizure clusters are often encountered in people with poorly controlled epilepsy. Detection of seizure clusters is currently based on simple clinical rules, such as two seizures separated by four or fewer hours or multiple seizures in 24 h. Current definitions fail to distinguish between statistically significant clusters and those that may result from natural variation in the person's seizures. Ability to systematically define when a seizure cluster is significant for the individual carries major implications for treatment. However, there is no uniform consensus on how to define seizure clusters. This study proposes a principled statistical approach to defining seizure clusters that addresses these issues.

METHODS

A total of 533,968 clinical seizures from 1,748 people with epilepsy in the Seizure Tracker™ seizure diary database were used for algorithm development. We propose an algorithm for automated individualized seizure cluster identification combining cumulative sum change-point analysis with bootstrapping and aberration detection, which provides a new approach to personalized seizure cluster identification at user-specified levels of clinical significance. We develop a standalone user interface to make the proposed algorithm accessible for real-time seizure cluster identification (ClusterCalc™). Clinical impact of systematizing cluster identification is demonstrated by comparing empirically-defined clusters to those identified by routine seizure cluster definitions. We also demonstrate use of the Hurst exponent as a standardized measure of seizure clustering for comparison of seizure clustering burden within or across patients.

RESULTS

Seizure clustering was present in 26.7 % (95 % CI, 24.5-28.7 %) of people with epilepsy. Empirical tables were provided for standardizing inter- and intra-patient comparisons of seizure cluster tendency. Using the proposed algorithm, we found that 37.7-59.4 % of seizures identified as clusters based on routine definitions had high probability of occurring by chance. Several clusters identified by the algorithm were missed by conventional definitions. The utility of the ClusterCalc algorithm for individualized seizure cluster detection is demonstrated.

SIGNIFICANCE

This study proposes a principled statistical approach to individualized seizure cluster identification and demonstrates potential for real-time clinical usage through ClusterCalc. Using this approach accounts for individual variations in baseline seizure frequency and evaluates statistical significance. This new definition has the potential to improve individualized epilepsy treatment by systematizing identification of unrecognized seizure clusters and preventing unnecessary intervention for random events previously considered clusters.

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.

A perspective on cannabinoids for treating epilepsy: Do they really change the landscape?

With the licensing of cannabidiol for drug resistant seizures in Dravet and Lennox Gastaut syndromes in the United states in 2018, interest in the potential for cannabis-based-medicinal products to meet currently unmet needs for people with epilepsy continues to grow. This review summarizes current knowledge and discusses the implications for future research and practice. Both cannabidiol and tetrahydrocannabinol, the main components, have been extensively studied in animal models, with multimodal mechanisms of action proposed. Only pure cannabidiol formulations have been rigorously evaluated in controlled trials thus far, with modest but significant improvements in motor seizures. Adverse effects include diarrhoea, somnolence and reduced appetite, with mostly acceptable tolerability, but a not insignificant (up to 1 in 23) risk of serious adverse events. Recognized drug interactions include with valproate (increased risk of hepatotoxicity) and clobazam (contributing to somnolence, increased secretions, probably chest infections, and potentially efficacy). Whilst there is public (and producer) interest in products also containing tetrahydrocannabinol, clinicians have justifiable concerns about exposing a group already vulnerable to mental health and neurobehavioural comorbidities to the associated additional risks in these domains. Artisanal preparations, with often inconsistent/unknown constituents are frequently used but not recommended. A gulf exists between the actual evidence, including a lack of comparative studies and public beliefs, fuelled by media and anecdote. Continued education of the public, policymakers, researchers and healthcare providers about what is and isn't yet known, together with on-going good quality research is essential to mitigate against future potential risks, particularly in relation to vulnerable populations. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

A double-blinded randomised dietary supplement crossover trial design to investigate the short-term influence of medium chain fatty acid (MCT) supplement on canine idiopathic epilepsy: study protocol

Background

Epilepsy is the most common brain disease in dogs. Recently, diets have been reported to have a positive impact on seizure activity and behaviour in various species including dogs with idiopathic epilepsy (IE). Historically, classic high fat ketogenic diets (KD) and medium chain triglycerides (MCT) KD have been successfully used to manage drug-resistant epilepsy. Similarly, an MCT enriched diet has been shown to improve seizure control and behavioural comorbidities in some dogs with IE. However, it is unknown whether an MCT dietary supplement (DS) may provide similar positive effects.

Methods

A 6-month prospective, randomised, double-blinded, placebo-controlled, crossover, multicentre dietary trial is designed comparing a 9% metabolic energy based calculated medium-chain triglyceride (MCT) oil supplement to a conventional ‘control’ DS. Only dogs which will have an International Veterinary Epilepsy Task Force Tier II level like diagnosis of IE which satisfied the following inclusion criteria are included: age between 6 months and ≤ 12 years; weighing between 4 and ≤ 65 kg; unremarkable interictal neurological examinations; no clinically significant findings on routine laboratory diagnostics; unremarkable brain MRI scan; have had at least 3 seizures in the previous 3 months prior to enrolment; treated with at least one ASD and being classified as resistant. All dogs are fed initially for 90 ± 2 days with either the control oil or the MCT oil alongside their normal diet, followed by 97 ± 2 days with the other supplement including a 7-day washout period. Overall, the aim is to recruit thirty-six patients at five different centres and to investigate the effect of MCTs as DS on seizure activity, tolerability, behavioural comorbidities and quality of life (QoL).

Discussion

Dietary interventions are rarely studied in a standardised form in veterinary medicine. The background diet, the cohort of animals and ASD received is standardised in this prospective diet trial to ensure representative data about the potential effect of MCT DS. If the study data confirms former findings, this would provide further evidence for the efficacy of MCTs as a management option for canine epilepsy. This publication should offer a repository of trial conditions and variable description with forecasted statistical analysis.

Electronic supplementary material

The online version of this article (10.1186/s12917-019-1915-8) contains supplementary material, which is available to authorized users.

The placebo response in myasthenia gravis assessed by quantitative myasthenia gravis score: A meta-analysis

INTRODUCTION

This meta-analysis investigates the placebo response in generalized myasthenia gravis (MG) trials by means of Quantitative Myasthenia Gravis (QMG) scores.

METHODS

PubMed, Scopus, Web of Science, Cochrane Controlled Trial Register, and EMBASE were searched. QMG score, dropouts rate, adverse events (AEs), and AEs responsible for dropouts were examined, together with treatment moderators.

RESULTS

The magnitude of placebo response showed an effect size of 0.24, which was significantly lower than 0.67 of the drug response (P = 0.019). Furthermore, the forest plot revealed that, overall, active treatments showed a significantly higher impact on QMG scores than placebos.

CONCLUSIONS

Placebo and drug responses in MG trials are small and moderate, respectively. The lack of MG trials with a pure placebo arm or a no-treatment control arm made it impossible to disentangle improvements due to the placebo psychological effect from other effects such as natural history and/or regression to the mean. Muscle Nerve 59:671-678, 2019.

Are the days of counting seizures numbered?

PURPOSE OF REVIEW

The estimation of seizure frequency is a cornerstone of clinical management of epilepsy and the evaluation of new therapies. Current estimation approaches are significantly limited by several factors. Comparing patient diaries and objective estimates (through both inpatient video-EEG monitoring of and long-term ambulatory EEG studies) reveal that patients document seizures inaccurately. So far, few practical alternative methods of estimation have been available.

RECENT FINDINGS

We review the systems of counting currently utilized and their limitations, as well as the limitations imposed by problems defining clinical events. Alternative methodologies that permit the volatility of seizure rates to be accommodated, and possible alternative measures of brain excitability will be outlined. Recent developments in technologies around data capture, such as wearable and implantable devices, as well as significant advances in the ability to analyse the large data-sets supplied by these systems have provided a wealth of information.

SUMMARY

There are now unprecedented opportunities to utilize and apply these insights in routine clinical management and assessment of therapies. The rapid adoption of long-term, wearable monitoring systems will permit major advances in our understanding of the natural history of epilepsy, and lead to more effective therapies and improved patient safety.

Long-Term Safety, Tolerability, and Efficacy of Cannabidiol in Children with Refractory Epilepsy: Results from an Expanded Access Program in the US

BACKGROUND

Purified cannabidiol is a new antiepileptic drug that has recently been approved for use in patients with Lennox-Gastaut and Dravet syndromes, but most published studies have not extended beyond 12-16 weeks.

OBJECTIVE

The objective of this study was to evaluate the long-term safety, tolerability, and efficacy of cannabidiol in children with epilepsy.

METHODS

Patients aged 1-17 years with refractory epilepsy were enrolled in an open-label prospective study through individual patient and expanded access programs between April 2013 and December 2014. Seizure types were video-electroencephalogram confirmed prior to enrollment. After a 28-day evaluation period, during which baseline seizure frequency was assessed, cannabidiol was given as add-on therapy at 5 mg/kg/day and titrated weekly by 5-mg/kg increments to a dose of 25 mg/kg/day. Blood tests were performed at baseline, after 1, 2, and 3 months, and every 3 months thereafter. Trough concentrations of concomitant antiepileptic drugs were measured at baseline, after 1, 2, and 3 months of therapy, and as clinically indicated afterwards. Concomitant antiepileptic drugs, ketogenic diet ratio, and vagal nerve stimulator settings remained unchanged during the baseline period and the first 3 months of treatment, unless there was a significant increase in plasma concentrations. Seizure frequency was reported daily in seizure diaries by parents or caregivers. Clinical assessments occurred after 15 days of treatment, at 1 month, at 3 months, and every 3 months thereafter. Diaries of seizure frequency and adverse events were reviewed at each visit. The primary efficacy outcome was a reduction in seizure frequency and responders were defined as those patients achieving a > 50% reduction in motor seizures.

RESULTS

Twenty-six children were enrolled. Most had genetic epilepsies with daily or weekly seizures and multiple seizure types. All were refractory to prior antiepileptic drugs (range 4-11, mean 7), and were taking two antiepileptic drugs on average. Duration of therapy ranged from 4 to 53 months (mean 21 months). Adverse events were reported in 21 patients (80.8%), including reduced appetite in ten (38.4%), diarrhea in nine (34.6%), and weight loss in eight (30.7%). Four (15.4%) had changes in antiepileptic drug concentrations and three had elevated aspartate aminotransferase and alanine aminotransferase levels when cannabidiol was administered together with valproate. Serious adverse events, reported in six patients (23.1%), included status epilepticus in three, catatonia in two, and hypoalbuminemia in one. Fifteen patients (57.7%) discontinued cannabidiol for lack of efficacy, one because of status epilepticus, and one for severe weight loss. The retention rate declined rapidly in the first 6 months and more gradually thereafter. At 24 months, the number of patients continuing cannabidiol as adjunctive therapy was nine of the original 26 (34.6%). Of these patients, seven (26.9%) had a sustained > 50% reduction in motor seizures, including three (11.5%) who remain seizure free.

CONCLUSION

Over a 4-year period, cannabidiol was effective in 26.9% of children with otherwise refractory epilepsy. It was well tolerated in about 20% of patients, but 80.8% had adverse events, including 23.1% with serious adverse events. Decreased appetite and diarrhea were frequent along with weight loss that became evident only later in the treatment.

Cannabis and epilepsy

Click here to listen to the Podcast The one-third of people who do not gain seizure control through current treatment options need a revolution in epilepsy therapeutics. The general population appears to be showing a fundamental and rapid shift in its opinion regarding cannabis and cannabis-related drugs. It is quite possible that cannabidiol, licensed in the USA for treating rare genetic epilepsies, may open the door for the widespread legalisation of recreational cannabis. It is important that neurologists understand the difference between artisanal cannabidiol products available legally on the high street and the cannabidiol medications that have strong trial evidence. In the UK in 2018 there are multiple high-profile reports of the response of children taking cannabis-derived medication, meaning that neurologists are commonly asked questions about these treatments in clinic. We address what an adult neurologist needs to know now, ahead of the likely licensing of Epidiolex in the UK in 2019.

Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study

BACKGROUND

Epilepsy has long been suspected to be governed by cyclic rhythms, with seizure rates rising and falling periodically over weeks, months, or even years. The very long scales of seizure patterns seem to defy natural explanation and have sometimes been attributed to hormonal cycles or environmental factors. This study aimed to quantify the strength and prevalence of seizure cycles at multiple temporal scales across a large cohort of people with epilepsy.

METHODS

This retrospective cohort study used the two most comprehensive databases of human seizures (SeizureTracker [USA] and NeuroVista [Melbourne, VIC, Australia]) and analytic techniques from circular statistics to analyse patients with epilepsy for the presence and frequency of multitemporal cycles of seizure activity. NeuroVista patients were selected on the basis of having intractable focal epilepsy; data from patients with at least 30 clinical seizures were used. SeizureTracker participants are self selected and data do not adhere to any specific criteria; we used patients with a minimum of 100 seizures. The presence of seizure cycles over multiple time scales was measured using the mean resultant length (R value). The Rayleigh test and Hodges-Ajne test were used to test for circular uniformity. Monte-Carlo simulations were used to confirm the results of the Rayleigh test for seizure phase.

FINDINGS

We used data from 12 people from the NeuroVista study (data recorded from June 10, 2010, to Aug 22, 2012) and 1118 patients from the SeizureTracker database (data recorded from Jan 1, 2007, to Oct 19, 2015). At least 891 (80%) of 1118 patients in the SeizureTracker cohort and 11 (92%) of 12 patients in the NeuroVista cohort showed circadian (24 h) modulation of their seizure rates. In the NeuroVista cohort, patient 8 had a significant cycle at precisely 1 week. Two others (patients 1 and 7) also had approximately 1-week cycles. Patients 1 and 4 had 2-week cycles. In the SeizureTracker cohort, between 77 (7%) and 233 (21%) of the 1118 patients showed strong circaseptan (weekly) rhythms, with a clear 7-day period. Between 151 (14%) and 247 (22%) patients had significant seizure cycles that were longer than 3 weeks. Seizure cycles were equally prevalent in men and women, and peak seizure rates were evenly distributed across all days of the week.

INTERPRETATION

Our results suggest that seizure cycles are robust, patient specific, and more widespread than previously understood. They align with the accepted consensus that most epilepsies have some diurnal influence. Variations in seizure rate have important clinical implications. Detection and tracking of seizure cycles on a patient-specific basis should be standard in epilepsy management practices.

FUNDING

Australian National Health and Medical Research Council.

Composition and Use of Cannabis Extracts for Childhood Epilepsy in the Australian Community

Recent surveys suggest that many parents are using illicit cannabis extracts in the hope of managing seizures in their children with epilepsy. In the current Australian study we conducted semi-structured interviews with families of children with diverse forms of epilepsy to explore their attitudes towards and experiences with using cannabis extracts. This included current or previous users of cannabis extracts to treat their child's seizures (n = 41 families), and families who had never used (n = 24 families). For those using cannabis, extracts were analysed for cannabinoid content, with specific comparison of samples rated by families as "effective" versus those rated "ineffective". Results showed that children given cannabis extracts tended to have more severe epilepsy historically and had trialled more anticonvulsants than those who had never received cannabis extracts. There was high variability in the cannabinoid content and profile of cannabis extracts rated as "effective", with no clear differences between extracts perceived as "effective" and "ineffective". Contrary to family's expectations, most samples contained low concentrations of cannabidiol, while Δ9-tetrahydrocannabinol was present in nearly every sample. These findings highlight profound variation in the illicit cannabis extracts being currently used in Australia and warrant further investigations into the therapeutic value of cannabinoids in epilepsy.

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

Objective

To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.

Methods

Zero‐inflated negative binomial mixed‐effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed‐effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed‐effects models, respectively.

Results

A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: “Overtired or irregular sleep,” “Bright or flashing lights,” and “Emotional stress” (p < 0.004).

Significance

This study explored a large cohort of patients with self‐reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient‐recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.

Generic antiepileptic drugs-Safe or harmful in patients with epilepsy?

Generic antiepileptic drugs (AED) are significantly cheaper than brand name drugs, and may reduce overall health care expenditures. Regulatory bodies in Europe and North America require bioequivalence between generic and innovator drugs with regard to area under the plasma concentration-time curve (AUC) and peak plasma concentration (C_max ); strict cutoff values have been defined. The main issue is if bioequivalence ensures therapeutic equivalence. Are switches from brand to generic, or between generic AEDs entirely safe or potentially harmful in patients with epilepsy? We summarized and evaluated the available evidence from bioequivalence, health care utilization, and clinical studies on safety of generic AEDs. In most cases, variations in AUC and C_max were negligible when comparing innovator and generic AEDs. Due to interindividual pharmacokinetic and pharmacodynamic variability, measured differences between innovator and generic drugs may be the same as differences between different lots of the same brand. Studies from several countries based on insurance data have reported an increase in health care usage after switch from brand to generic AEDs; switchback rates are significantly higher for AEDs compared to other compounds. Patients may be confused, and nonadherence may increase, when AEDs are switched between manufacturers, perhaps due to changes in medication shape and color. But clinical studies do not report changes in seizure frequency and tolerability attributable to generics. Sufficient evidence indicates that most generics are bioequivalent to innovator AEDs; they do not pose a relevant risk for patients with epilepsy. However, some patients are reluctant towards variations in color and shape of their AEDs which may result in nonadherence. We recommend administering generics when a new AED is initiated. Switches from brand to generic AEDs for cost reduction and between generics, which is rarely required, generally seem to be safe, but should be accompanied by thorough counseling of patients on low risks.

Designing a trial for neonatal seizure treatment

Neonatal seizures are widely considered a neurological emergency with a need for prompt treatment, yet they are known to present a highly elusive target for bedside clinicians. Recent studies have suggested that the design of a neonatal seizure treatment trial will profoundly influence the sample size, which may readily increase to hundreds or even thousands as the achieved effect size diminishes to clinical irrelevance. The self-limiting and rapidly resolving nature of neonatal seizures diminishes the measurable treatment effect every hour after seizure onset and any effect may potentially be confused with spontaneous resolution, precluding the value of many observational studies. The large individual variability in seizure occurrence over time and between etiologies challenges group comparisons, while the absence of clinical signs mandates quantification of seizure occurrence with continuous multi-channel EEG monitoring. A biologically sound approach that views neonatal seizures as a functional cot-side biomarker rather than an object to treat can overcome these challenges.

Different as night and day: Patterns of isolated seizures, clusters, and status epilepticus

Using approximations based on presumed U.S. time zones, we characterized day and nighttime seizure patterns in a patient-reported database, Seizure Tracker. A total of 632 995 seizures (9698 patients) were classified into 4 categories: isolated seizure event (ISE), cluster without status epilepticus (CWOS), cluster including status epilepticus (CIS), and status epilepticus (SE). We used a multinomial mixed-effects logistic regression model to calculate odds ratios (ORs) to determine night/day ratios for the difference between seizure patterns: ISE versus SE, ISE versus CWOS, ISE versus CIS, and CWOS versus CIS. Ranges of OR values were reported across cluster definitions. In adults, ISE was more likely at night compared to CWOS (OR = 1.49, 95% adjusted confidence interval [CI] = 1.36-1.63) and to CIS (OR = 1.61, 95% adjusted CI = 1.34-1.88). The ORs for ISE versus SE and CWOS versus SE were not significantly different regardless of cluster definition. In children, ISE was less likely at night compared to SE (OR = 0.85, 95% adjusted CI = 0.79-0.91). ISE was more likely at night compared to CWOS (OR = 1.35, 95% adjusted CI = 1.26-1.44) and CIS (OR = 1.65, 95% adjusted CI = 1.44-1.86). CWOS was more likely during the night compared to CIS (OR = 1.22, 95% adjusted CI = 1.05-1.39). With the exception of SE in children, our data suggest that more severe patterns favor daytime. This suggests distinct day/night preferences for different seizure patterns in children and adults.

Common data elements for epilepsy mobile health systems

OBJECTIVE

Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data.

METHODS

Using a group of stakeholders from industry, academia, and patient advocacy organizations, we offer a consensus statement for the elements that may facilitate communication among different systems.

RESULTS

A consensus statement is presented for epilepsy mHealth CDEs.

SIGNIFICANCE

Although it is not exclusive, we believe that the use of a minimal common information denominator, specifically these CDEs, will promote innovation, accelerate scientific discovery, and enhance clinical usage across applications and devices in the epilepsy mHealth space. As a consequence, people with epilepsy will have greater flexibility and ultimately more powerful tools to improve their lives.

Is seizure frequency variance a predictable quantity?

Background

There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting.

Methods

Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions.

Results

A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R² > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%.

Conclusion

Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.

A multi-dataset time-reversal approach to clinical trial placebo response and the relationship to natural variability in epilepsy

PURPOSE

Clinical epilepsy drug trials have been measuring increasingly high placebo response rates, up to 40%. This study was designed to examine the relationship between the natural variability in epilepsy, and the placebo response seen in trials. We tested the hypothesis that 'reversing' trial direction, with the baseline period as the treatment observation phase, would reveal effects of natural variability.

METHOD

Clinical trial simulations were run with time running forward and in reverse. Data sources were: SeizureTracker.com (patient reported diaries), a randomized sham-controlled TMS trial, and chronically implanted intracranial EEG electrodes. Outcomes were 50%-responder rates (RR50) and median percentage change (MPC).

RESULTS

The RR50 results showed evidence that temporal reversal does not prevent large responder rates across datasets. The MPC results negative in the TMS dataset, and positive in the other two.

CONCLUSIONS

Typical RR50s of clinical trials can be reproduced using the natural variability of epilepsy as a substrate across multiple datasets. Therefore, the placebo response in epilepsy clinical trials may be attributable almost entirely to this variability, rather than the "placebo effect".

Clinical Research Informatics: Supporting the Research Study Lifecycle

Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

Does accounting for seizure frequency variability increase clinical trial power?

OBJECTIVE

Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, Z_V.

METHODS

Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, Z_V. Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the Z_V method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05).

RESULTS

Prediction accuracy across datasets was, Z_V: 91-100%, RR50: 42-80%. Simulated RCT Z_V analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm.

SIGNIFICANCE

Z_V may increase the statistical power of an RCT relative to the traditional RR50.

Methodological issues associated with clinical trials in epilepsy

INTRODUCTION

despite methodological advances in epilepsy clinical trials, the proportion of patients reaching seizure-freedom has not substantially changed over the years. We review the main methodological limitations of current trials, the possible strategies to overcome these limits, and the issues that need to be addressed in next future. Area covered: references were identified by PubMed search until March 2017 and unpublished literature was searched on ClinicalTrials.gov. Add-on trials mainly involve refractory epilepsy subjects, reducing overall response to the investigational drug. The inclusion of subjects with earlier disease from less developed countries has partially allowed overcoming this limitation, but has introduced more random variability of results. Monotherapy trials rise methodological, economical, and ethical concerns with different regulatory requirements in European Union and in the United States of America. Newer trial designs, such as futility trials or 'time-to-event' design, have been implemented. Moreover, both add-on and monotherapy trials results might be affected by patient's ability to recognize and record seizures, and by randomness of seizures occurrence over time. Possible strategies to achieve more reliable outcomes are detailed. Expert commentary: clinical trial methodology needs to be optimized to better address regulatory agencies requirements and to encounter both patients' and clinicians' needs.

Comparison of brand versus generic antiepileptic drug adverse event reporting rates in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS)

OBJECTIVE

Despite the cost saving role of generic anti-epileptic drugs (AEDs), debate exists as to whether generic substitution of branded AEDs may lead to therapeutic failure and increased toxicity. This study compared adverse event (AE) reporting rates for brand vs. authorized generic (AG) vs. generic AEDs. Since AGs are pharmaceutically identical to brand but perceived as generics, the generic vs. AG comparison minimized potential bias against generics.

METHODS

Events reported to the U.S. Food and Drug Administration Adverse Event Reporting System between January 2004 to March 2015 with lamotrigine, carbamazepine, and oxcarbazepine listed as primary or secondary suspect were classified as brand, generic, or AG based on the manufacturer. Disproportionality analyses using the reporting odds ratio (ROR) assessed the relative rate of reporting of labeled AEs compared to reporting these events with all other drugs. The Breslow-Day statistic compared RORs across brand, AG, and other generics using a Bonferroni-corrected P<0.01.

RESULTS

A total of 27,150 events with lamotrigine, 13,950 events with carbamazepine, and 5077 events with oxcarbazepine were reported, with generics accounting for 27%, 41%, and 32% of reports, respectively. Although RORs for the majority of known AEs were different between brand and generics for all three drugs of interest (Breslow-Day P<0.001), RORs generally were similar for AG and generic comparisons. Generic lamotrigine and carbamazepine were more commonly involved in reports of suicide or suicidal ideation compared with the respective AGs based on a multiple comparison-adjusted P<0.01.

SIGNIFICANCE

Similar AED reporting rates were observed for the AG and generic comparisons for most outcomes and drugs, suggesting that brands and generics have similar reporting rates after accounting for generic perception biases. Disproportional suicide reporting was observed for generics compared with AGs and brand, although this finding needs further study.

Open-label versus double-blind placebo treatment in irritable bowel syndrome: study protocol for a randomized controlled trial

BACKGROUND

Placebo medications, by definition, are composed of inactive ingredients that have no physiological effect on symptoms. Nonetheless, administration of placebo in randomized controlled trials (RCTs) and in clinical settings has been demonstrated to have significant impact on many physical and psychological complaints. Until recently, conventional wisdom has suggested that patients must believe that placebo pills actually contain (or, at least, might possibly contain) active medication in order to elicit a response to placebo. However, several recent RCTs, including patients with irritable bowel syndrome (IBS), chronic low back pain, and episodic migraine, have demonstrated that individuals receiving open-label placebo (OLP) can still experience symptomatic improvement and benefit from honestly described placebo treatment.

METHODS AND DESIGN

This paper describes an innovative multidisciplinary trial design (n = 280) that attempts to replicate and expand upon an earlier IBS OLP study. The current study will compare OLP to double-blind placebo (DBP) administration which is made possible by including a nested, double-blind RCT comparing DBP and peppermint oil. The study also examines possible genetic and psychological predictors of OLP and seeks to better understand participants' experiences with OLP and DBP through a series of extensive interviews with a randomly selected subgroup.

DISCUSSION

OLP treatment is a novel strategy for ethically harnessing placebo effects. It has potential to re-frame theories of placebo and to influence how physicians can optimize watch-and-wait strategies for common, subjective symptoms. The current study aims to dramatically expand what we know about OLP by comparing, for the first time, OLP and DBP administration. Adopting a unique, multidisciplinary approach, the study also explores genetic, psychological and experiential dimensions of OLP. The paper ends with an extensive discussion of the "culture" of the trial as well as potential mechanisms of OLP and ethical implications.

TRIAL REGISTRATION

ClinicalTrials.gov, identifier: NCT02802241 . Registered on 14 June 2016.

Monte Carlo simulations of randomized clinical trials in epilepsy

Background

The placebo response in epilepsy randomized clinical trials (RCTs) has recently been shown to largely reflect underlying natural variability in seizure frequency. Based on this observation, we sought to explore the parameter space of RCT design to optimize trial efficiency and cost.

Methods

We used one of the world's largest patient reported seizure diary databases, SeizureTracker.com to derive virtual patients for simulated RCTs. We ran 1000 randomly generated simulated trials using bootstrapping (sampling with replacement) for each unique combination of trial parameters, sweeping a large set of parameters in durations of the baseline and test periods, number of patients, eligibility criteria, drug effect size, and patient dropout. We studied the resulting trial efficiency and cost.

Results

A total of 6,732,000 trials were simulated, drawing from 5097 patients in the database. We found that the strongest regression predictors of placebo response were durations of baseline and test periods. Drug effect size had a major impact on trial efficiency and cost. Dropout did not have a major impact on trial efficiency or cost. Eligibility requirements impacted trial efficiency to a limited extent. Cost was minimized while maintaining statistical integrity with very short RCT durations.

Discussion

This study suggests that RCT parameters can be improved over current practice to reduce costs while maintaining statistical power. In addition, use of a large‐scale population dataset in a massively parallel computing analysis allows exploration of the wider parameter space of RCT design prior to running a trial, which could help accelerate drug discovery and approval.

A big data approach to the development of mixed-effects models for seizure count data

OBJECTIVE

Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy.

METHODS

Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies.

RESULTS

For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day.

SIGNIFICANCE

The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

Simulating Clinical Trials With and Without Intracranial EEG Data

OBJECTIVE

It is currently unknown if knowledge of clinically silent (electrographic) seizures improves the statistical efficiency of clinical trials.

METHODS

Using data obtained from 10 patients with chronically implanted subdural electrodes over an average of 1 year, a Monte Carlo bootstrapping simulation study was performed to estimate the statistical power of running a clinical trial based on A) patient reported seizures with intracranial EEG (icEEG) confirmation, B) all patient reported events, or C) all icEEG confirmed seizures. A "drug" was modeled as having 10%, 20%, 30%, 40% and 50% efficacy in 1000 simulated trials each. Outcomes were represented as percentage of trials that achieved p<0.05 using Fisher Exact test for 50%-responder rates (RR50), and Wilcoxon Rank Sum test for median percentage change (MPC).

RESULTS

At each simulated drug strength, the MPC method showed higher power than RR50. As drug strength increased, statistical power increased. For all cases except RR50 with drug of 10% efficacy, using patient reported events (with or without icEEG confirmation) was not as statistically powerful as using all available intracranially confirmed seizures (p<0.001).

SIGNIFICANCE

This study demonstrated using simulation that additional accuracy in seizure detection using chronically implanted icEEG improves statistical power of clinical trials. Newer invasive and noninvasive seizure detection devices may have the potential to provide greater statistical efficiency, accelerate drug discovery and lower trial costs.

Can Matching-Adjusted Indirect Comparison Methods Mitigate Placebo Response Differences Among Patient Populations in Adjunctive Trials of Brivaracetam and Levetiracetam?

BACKGROUND AND OBJECTIVE

Patients with focal seizures recruited into adjunctive antiepileptic drug (AED) trials have become more refractory and severe over time; concurrently, placebo responses have increased. To attempt to account for heterogeneity among trials, propensity-score weighted patient-level data were used to indirectly compare placebo responses reported in brivaracetam and levetiracetam trials.

METHODS

Patient-level data from randomised, placebo-controlled brivaracetam (recruited 2007-2014) and levetiracetam (1993-1998) trials were pooled. Consistent inclusion/exclusion criteria were applied and outcomes were defined consistently. Potentially confounding baseline characteristics were adjusted for using propensity score weighting. Weighting success was assessed using placebo response.

RESULTS

In total, 707 and 473 active drug and 399 and 253 placebo patients comprised the brivaracetam and levetiracetam groups, respectively. Before weighting, several baseline variables were significantly different between groups; after weighting, prior vagal nerve stimulation, co-morbid depression and co-morbid anxiety remained different. Before weighting, median seizure frequency reduction was 21.7 and 3.9% in the brivaracetam and levetiracetam placebo arms, respectively; after weighting, median reduction was 15.0 and 6.0%. The comparison of non-randomised groups could be biased by unobserved confounding factors and region of residence. Lifetime AED history was unavailable in the brivaracetam trials and excluded from analysis.

CONCLUSIONS

Placebo responses remained different between brivaracetam and levetiracetam trials after propensity score weighting, indicating the presence of residual confounding factors associated with placebo response in these trials. It therefore remains problematic to conduct reliable indirect comparisons of brivaracetam and levetiracetam given the current evidence base, which may apply to comparisons between other AED trials.

Treatment Trials for Neonatal Seizures: The Effect of Design on Sample Size

Neonatal seizures are common in the neonatal intensive care unit. Clinicians treat these seizures with several anti-epileptic drugs (AEDs) to reduce seizures in a neonate. Current AEDs exhibit sub-optimal efficacy and several randomized control trials (RCT) of novel AEDs are planned. The aim of this study was to measure the influence of trial design on the required sample size of a RCT. We used seizure time courses from 41 term neonates with hypoxic ischaemic encephalopathy to build seizure treatment trial simulations. We used five outcome measures, three AED protocols, eight treatment delays from seizure onset (T_d) and four levels of trial AED efficacy to simulate different RCTs. We performed power calculations for each RCT design and analysed the resultant sample size. We also assessed the rate of false positives, or placebo effect, in typical uncontrolled studies. We found that the false positive rate ranged from 5 to 85% of patients depending on RCT design. For controlled trials, the choice of outcome measure had the largest effect on sample size with median differences of 30.7 fold (IQR: 13.7–40.0) across a range of AED protocols, T_d and trial AED efficacy (p<0.001). RCTs that compared the trial AED with positive controls required sample sizes with a median fold increase of 3.2 (IQR: 1.9–11.9; p<0.001). Delays in AED administration from seizure onset also increased the required sample size 2.1 fold (IQR: 1.7–2.9; p<0.001). Subgroup analysis showed that RCTs in neonates treated with hypothermia required a median fold increase in sample size of 2.6 (IQR: 2.4–3.0) compared to trials in normothermic neonates (p<0.001). These results show that RCT design has a profound influence on the required sample size. Trials that use a control group, appropriate outcome measure, and control for differences in T_d between groups in analysis will be valid and minimise sample size.

Patient-centered design criteria for wearable seizure detection devices

INTRODUCTION

Epilepsy is a common neurological condition. Seizure diary reports and patient- or caregiver-reported seizure counts are often inaccurate and underestimated. Many caregivers express stress and anxiety about the patient with epilepsy having seizures when they are not present. Therefore, a need exists for the ability to recognize and/or detect a seizure in the home setting. However, few studies have inquired on detection device features that are important to patients and their caregivers.

METHODS

A survey instrument utilizing a population of patients and caregivers was created to obtain information on the design criteria most desired for patients with epilepsy in regard to wearable devices.

RESULTS

One thousand one hundred sixty-eight responses were collected. Respondents thought that sensors for muscle signal (61.4%) and heart rate (58.0%) would be most helpful followed by the O₂ sensor (41.4%). There was more interest in these three sensor types than for an accelerometer (25.5%). There was very little interest in a microphone (8.9%), galvanic skin response sensor (8.0%), or a barometer (4.9%). Based on a rating scale of 1-5 with 5 being the most important, respondents felt that "detecting all seizures" (4.73) is the most important device feature followed by "text/email alerts" (4.53), "comfort" (4.46), and "battery life" (4.43) as an equally important group of features. Respondents felt that "not knowing device is for seizures" (2.60) and "multiple uses" (2.57) were equally the least important device features. Average ratings differed significantly across age groups for the following features: button, multiuse, not knowing device is for seizures, alarm, style, and text ability. The p-values were all<0.002. Eighty-two point five percent of respondents [95% confidence interval: 80.0%, 84.7%] were willing to pay more than $100 for a wearable seizure detection device, and 42.8% of respondents [95% confidence interval: 39.8%, 45.9%] were willing to pay more than $200.

CONCLUSIONS

Our survey results demonstrated that patients and caregivers have design features that are important to them in regard to a wearable seizure detection device. Overall, the ability to detect all seizures rated highest among respondents which continues to be an unmet need in the community with epilepsy in regard to seizure detection. Additional uses for a wearable were not as important. Based on our results, it is important that an alert (via test and/or email) for events be a portion of the system. A reasonable price point appears to be around $200 to $300. An accelerometer was less important to those surveyed when compared with the use of heart rate, oxygen saturation, or muscle twitches/signals. As further products become developed for use in other health arenas, it will be important to consider patient and caregiver desires in order to meet the need and address the gap in devices that currently exist.

Response to placebo in clinical epilepsy trials--Old ideas and new insights

Randomized placebo-controlled trials are a mainstay of modern clinical epilepsy research; the success or failure of innovative therapies depends on proving superiority to a placebo. Consequently, understanding what drives response to placebo (including the "placebo effect") may facilitate evaluation of new therapies. In this review, part one will explore observations about placebos specific to epilepsy, including the relatively higher placebo response in children, apparent increase in placebo response over the past several decades, geographic variation in placebo effect, relationship to baseline epilepsy characteristics, influence of nocebo on clinical trials, the possible increase in (SUDEP) in placebo arms of trials, and patterns that placebo responses appear to follow in individual patients. Part two will discuss the principal causes of placebo responses, including regression to the mean, anticipation, classical conditioning, the Hawthorne effect, expectations from symbols, and the natural history of disease. Included in part two will be a brief overview of recent advances using simulations from large datasets that have afforded new insights into causes of epilepsy-related placebo responses. In part three, new developments in study design will be explored, including sequential parallel comparison, two-way enriched design, time to pre-randomization, delayed start, and cohort reduction techniques.

Incorporating Natural Products, Pharmaceutical Drugs, Self-Care and Digital/Mobile Health Technologies into Molecular-Behavioral Combination Therapies for Chronic Diseases

Merging pharmaceutical and digital (mobile health, mHealth) ingredients to create new therapies for chronic diseases offers unique opportunities for natural products such as omega-3 polyunsaturated fatty acids (n-3 PUFA), curcumin, resveratrol, theanine, or α-lipoic acid. These compounds, when combined with pharmaceutical drugs, show improved efficacy and safety in preclinical and clinical studies of epilepsy, neuropathic pain, osteoarthritis, depression, schizophrenia, diabetes and cancer. Their additional clinical benefits include reducing levels of TNFα and other inflammatory cytokines. We describe how pleiotropic natural products can be developed as bioactive incentives within the network pharmacology together with pharmaceutical drugs and self-care interventions. Since approximately 50% of chronically-ill patients do not take pharmaceutical drugs as prescribed, psychobehavioral incentives may appeal to patients at risk for medication non-adherence. For epilepsy, the incentive-based network therapy comprises anticonvulsant drugs, antiseizure natural products (n-3 PUFA, curcumin or/and resveratrol) coupled with disease-specific behavioral interventions delivered by mobile medical apps. The add-on combination of antiseizure natural products and mHealth supports patient empowerment and intrinsic motivation by having a choice in self-care behaviors. The incentivized therapies offer opportunities: (1) to improve clinical efficacy and safety of existing drugs, (2) to catalyze patient-centered, disease self-management and behavior-changing habits, also improving health-related quality-of-life after reaching remission, and (3) merging copyrighted mHealth software with natural products, thus establishing an intellectual property protection of medical treatments comprising the natural products existing in public domain and currently promoted as dietary supplements. Taken together, clinical research on synergies between existing drugs and pleiotropic natural products, and their integration with self-care, music and mHealth, expands precision/personalized medicine strategies for chronic diseases via pharmacological-behavioral combination therapies.