Are the days of counting seizures numbered?

Unseen yet overcounted: The paradox of seizure frequency reporting

OBJECTIVE

Seizure control is often assessed using patient-reported seizure frequencies. Despite its subjectivity, self-reporting remains essential for guiding anti-seizure medication (ASM) decisions and ongoing patient investigations. This study aims to compare patient-reported seizure frequencies with electrographic frequencies captured via ambulatory video EEG (avEEG).

METHODS

Data from intake forms and seizure diaries were collected from patients undergoing home-based avEEG in Australia (April 2020-April 2022). Intake forms included monthly seizure frequency estimates. Only avEEG-confirmed epilepsy cases were analyzed. Univariate and multivariate analyses compared seizure frequencies reported via EEG, diaries, and surveys.

RESULTS

Of 3,407 reports, 853 identified epilepsy cases, with 234 studies analyzed after excluding outliers. Diary-reported frequencies correlated with EEG frequency (p < 0.00001), but survey-reported frequencies did not (p > 0.05). Surveys significantly overestimated true seizure frequency (median = 3.98 seizures/month, p < 0.0001), while diaries showed substantially smaller differences (median = 0.01 seizures/month, p < 0.0001). Carer presence was associated with higher diary-reported frequencies (p = 0.047). Age negatively correlated with survey frequency estimation error (p = 0.016). Multivariate analysis identified age and carer status as significant predictors of residuals.

CONCLUSIONS

Most patients overestimate their true seizure frequency, potentially influencing therapeutic decisions and raising concerns about the reliability of some participants and carers to self-report seizures in clinical trials.

SIGNIFICANCE

An "over-reporting, over-prescribing" cascade may affect epilepsy treatment and highlights the potential issue of clinical drug trials relying on self-reported seizure rates for primary endpoints.

Real-world epilepsy monitoring with ultra long-term subcutaneous EEG: a 15-month prospective study

Objective

Novel subcutaneous electroencephalography (sqEEG) systems enable prolonged, near-continuous cerebral monitoring in real-world conditions. Nevertheless, the feasibility, acceptability and overall clinical utility of these systems remains unclear. We report on the longest observational study using ultra long-term sqEEG to date.

Methods

We conducted a 15-month prospective, observational study including ten adult people with treatment-resistant epilepsy. After device implantation, patients were asked to record sqEEG, to use an electronic seizure diary and to complete acceptability and usability questionnaires. sqEEG seizures were annotated visually, aided by automated detection. Seizure clustering was assessed via Fano Factor analysis and seizure periodicity at multiple timescales was investigated through circular statistics.

Results

Over a median duration of 438 days, ten patients recorded a median 18.8 hours/day, totalling 71,984 hours of real-world sqEEG data. Adherence and acceptability remained high throughout the study. While 754 sqEEG seizures were recorded across patients, over half (52%) of these were not reported in the patient diary. Of the 140 (27%) diary reports not associated with an identifiable sqEEG seizure, the majority (68%) were reported as seizures with preserved awareness. The sqEEG to diary F1 agreement score was highly variable, ranging from 0.06 to 0.97. Patient-specific patterns of seizure clustering and seizure periodicity were observed at multiple (circadian and multidien) timescales.

Interpretation

We demonstrate feasibility and high acceptability of ultra long-term (months-years) sqEEG monitoring. These systems help provide real-world, more objective seizure counting compared to patient diaries. It is possible to monitor individual temporal fluctuations of seizure occurrence, including seizure cycles.

Psychometric evaluation of clinician- and caregiver-reported clinical severity assessments for individuals with CDKL5 deficiency disorder

OBJECTIVE

The CDKL5 Clinical Severity Assessment (CCSA) is a comprehensive, content-validated measurement tool capturing the diverse challenges of cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD), a genetically caused developmental epileptic encephalopathy (DEE). The CCSA is divided into clinician-reported (CCSA-Clinician) and caregiver-reported (CCSA-Caregiver) assessments. The aim of this study was to evaluate the factor structure of these measures through confirmatory factor analysis (CFA) and evaluate their validity and reliability.

METHODS

Participants were recruited from the International CDKL5 Clinical Research Network to take part in an in-clinic CCSA-Clinician evaluation (n = 148) and/or complete the CCSA-Caregiver questionnaire (n = 198). CFA was used to determine domains, and factor loadings and validity were assessed. For the CCSA-Clinician, inter-rater reliability was assessed by nine CDD experienced clinicians via 14 pre-recorded evaluations. Eight clinicians re-viewed and re-scored the videos after 4 weeks to evaluate intra-rater reliability. The CCSA-Caregiver was completed on a second occasion by 34 caregivers after 2-4 weeks to assess test-retest reliability.

RESULTS

CFA resulted in three domains for the CCSA-Clinician (motor and movement, communication, vision) and four domains for the CCSA-Caregiver (seizures, behavior, alertness, feeding), with good item loadings across both measures. Structural statistics, internal consistency, discriminant validity, and reliability were satisfactory for both measures, and scores were consistent between known groups.

SIGNIFICANCE

This study provides strong evidence that the CCSA measures are suitable to assess the clinical severity of individuals with CDD, supporting their use in clinical trials. Further evaluation of responsiveness to change in a longitudinal assessment is planned. Use may also be appropriate in similar DEEs but would require validation in those populations.

High precision in epileptic seizure self-reporting with an app diary

People with epilepsy frequently under- or inaccurately report their seizures, which poses a challenge for evaluating their treatment. The introduction of epilepsy health apps provides a novel approach that could improve seizure documentation. This study assessed the documentation performance of an app-based seizure diary and a conventional paper seizure diary. At two tertiary epilepsy centers patients were asked to use one of two offered methods to report their seizures (paper or app diary) during their stay in the epilepsy monitoring unit. The performances of both methods were assessed based on the gold standard of video-EEG annotations. In total 89 adults (54 paper and 35 app users) with focal epilepsy were included in the analysis, of which 58 (33 paper and 25 app users) experienced at least one seizure and made at least one seizure diary entry. We observed a high precision of 85.7% for the app group, whereas the paper group's precision was lower due to overreporting (66.9%). Sensitivity was similar for both methods. Our findings imply that performance of seizure self-reporting is patient-dependent but is more precise for patients who are willing to use digital apps. This may be relevant for treatment decisions and future clinical trial design.

How accurate do self-reported seizures need to be for effective medication management in epilepsy?

Studies suggest that self-reported seizure diaries suffer from 50% under-reporting on average. It is unknown to what extent this impacts medication management. This study used simulation to predict the seizure outcomes of a large heterogeneous clinic population treated with a standardized algorithm based on self-reported seizures. Using CHOCOLATES, a state-of-the-art realistic seizure diary simulator, 100 000 patients were simulated over 10 years. A standard algorithm for medication management was employed at 3 month intervals for all patients. The impact on true seizure rates, expected seizure rates, and time-to-steady-dose were computed for self-reporting sensitivities 0%-100%. Time-to-steady-dose and medication use mostly did not depend on sensitivity. True seizure rate decreased minimally with increasing self-reporting in a non-linear fashion, with the largest decreases at low sensitivity rates (0%-10%). This study suggests that an extremely wide range of sensitivity will have similar seizure outcomes when patients are clinically treated using an algorithm similar to the one presented. Conversely, patients with sensitivity ≤10% would be expected to benefit (via lower seizure rates) from objective devices that provide even small improvements in seizure sensitivity.

Long-term seizure diary tracking habits in clinical studies: Evidence from the Human Epilepsy Project

OBJECTIVE

To characterize seizure tracking patterns of people with focal epilepsy using electronic seizure diary entries, and to assess for risk factors associated with poor tracking.

METHODS

We analyzed electronic seizure diary data from 410 participants with newly diagnosed focal epilepsy in the Human Epilepsy Project 1 (HEP1). Each participant was expected to record data each day during the study, regardless of seizure occurrence. The primary outcome of this post-hoc analysis was whether each participant properly tracked a seizure diary entry each day during their study participation. Using finite mixture modeling, we grouped patient tracking trajectories into data-driven clusters. Once defined, we used multinomial modeling to test for independent risk factors of tracking group membership.

RESULTS

Using over up to three years of daily seizure diary data per subject, we found four distinct seizure tracking groups: consistent, frequent at study onset, occasional, and rare. Participants in the consistent tracking group tracked a median of 92% (interquartile range, IQR: 82%, 99%) of expected days, compared to 47% (IQR:34%, 60%) in the frequent at study onset group, 37% (IQR: 26%, 49%) in the occasional group, and 9% (IQR: 3%, 15%) in the rare group. In multivariable analysis, consistent trackers had lower rates of seizure days per tracked year during their study participation, compared to other groups.

SIGNIFICANCE

Future efforts need to focus on improving seizure diary tracking adherence to improve quality of outcome data, particularly in those with higher seizure burden. In addition, accounting for missing data when using seizure diary data as a primary outcome is important in research trials. If not properly accounted for, total seizure burden may be underestimated and biased, skewing results of clinical trials.

Seizure Cycles under Pharmacotherapy

OBJECTIVE

This study was undertaken to determine the effects of antiseizure medications (ASMs) on multidien (multiday) cycles of interictal epileptiform activity (IEA) and seizures and evaluate their potential clinical significance.

METHODS

We retrospectively analyzed up to 10 years of data from 88 of the 256 total adults with pharmacoresistant focal epilepsy who participated in the clinical trials of the RNS System, an intracranial device that keeps records of IEA counts. Following adjunctive ASM trials, we evaluated changes over months in (1) rates of self-reported disabling seizures and (2) multidien IEA cycle strength (spectral power for periodicity between 4 and 40 days). We used a survival analysis and the receiver operating characteristics to assess changes in IEA as a predictor of seizure control.

RESULTS

Among 56 (33.3%) of the 168 adjunctive ASM trials suitable for analysis, ASM introduction was followed by an average 50 to 70% decrease in multidien IEA cycle strength and a concomitant 50 to 70% decrease in relative seizure rate for up to 12 months. Individuals with a ≥50% decrease in IEA cycle strength in the first 3 months of an ASM trial had a higher probability of remaining seizure responders (≥50% seizure rate reduction, p < 10-7) or super-responders (≥90%, p < 10-8) over the next 12 months.

INTERPRETATION

In this large cohort, a decrease in multidien IEA cycle strength following initiation of an adjunctive ASM correlated with seizure control for up to 12 months, suggesting that fluctuations in IEA mirror "disease activity" in pharmacoresistant focal epilepsy and may have clinical utility as a biomarker to predict treatment response. ANN NEUROL 2024;95:743-753.

Impact of antiseizure medication with a very long half-life on long term video-EEG monitoring in focal epilepsy

PURPOSE

To assess the impact of antiseizure medications (ASMs) with a very long half-life on long term video-EEG monitoring (LTM) in people with focal epilepsy (FE).

METHODS

In this retrospective cohort study, we searched our local database for people with FE who underwent ASM reduction during LTM at the University Hospital of 'La Fe', Valencia, from January 2013 to December 2019. Taking into account the half-life of the ASM, people with FE were divided into two groups: Group A contained individuals who were taking at least one ASM with a very long half-life at admission, and Group B consisted of those not taking very long half-life ASMs. Using multivariable analysis to control for important confounders, we compared the following outcomes between both groups: seizure rates per day, time to first seizure, and LTM duration.

RESULTS

Three hundred seventy individuals were included in the study (154 in Group A and 216 in Group B). The median recorded seizure rates (1.3 seizures/day, range 0-15.3 vs.1.3 seizures/day, range 0-9.3, p-value=0.68), median time to the first seizure (24 h, range 2-119 vs. 24 h, range 2-100, p-value=0.92), and median LTM duration (4 days, range 2-5 vs. 4 days, range 2-5, p-value=0.94) were similar in both groups. Multivariable analysis did not reveal any significant differences in the three outcomes between the two groups (all p-values>0.05).

CONCLUSION

ASMs with a very long half-life taken as co-medication do not significantly affect important LTM outcomes, including recorded seizure rates, time to the first seizure, or LTM duration. Therefore, in general, there is no need to discontinue ASMs with a very long half-life prior to LTM.

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.

Practical considerations in epilepsy neurostimulation

Neuromodulation is a key therapeutic tool for clinicians managing patients with drug-resistant epilepsy. Multiple devices are available with long-term follow-up and real-world experience. The aim of this review is to give a practical summary of available neuromodulation techniques to guide the selection of modalities, focusing on patient selection for devices, common approaches and techniques for initiation of programming, and outpatient management issues. Vagus nerve stimulation (VNS), deep brain stimulation of the anterior nucleus of the thalamus (DBS-ANT), and responsive neurostimulation (RNS) are all supported by randomized controlled trials that show safety and a significant impact on seizure reduction, as well as a suggestion of reduction in the risk of sudden unexplained death in epilepsy (SUDEP). Significant seizure reductions are observed after 3 months for DBS, RNS, and VNS in randomized controlled trials, and efficacy appears to improve with time out to 7 to 10 years of follow-up for all modalities, albeit in uncontrolled follow-up or retrospective studies. A significant number of patients experience seizure-free intervals of 6 months or more with all three modalities. Number and location of epileptogenic foci are important factors affecting efficacy, and together with comorbidities such as severe mood or sleep disorders, may influence the choice of modality. Programming has evolved-DBS is typically initiated at lower current/voltage than used in the pivotal trial, whereas target charge density is lower with RNS, however generalizable optimal parameters are yet to be defined. Noninvasive brain stimulation is an emerging stimulation modality, although it is currently not used widely. In summary, clinical practice has evolved from those established in pivotal trials. Guidance is now available for clinicians who wish to expand their approach, and choice of neuromodulation technique may be tailored to individual patients based on their epilepsy characteristics, risk tolerance, and preferences.

Current norms and practices in using a seizure diary for managing epilepsy: A scoping review

BACKGROUND

Epilepsy is a chronic and debilitating condition affecting people of all ages in many nations. Healthcare practitioners look for effective ways to track patients' seizures, and a seizure diary is one of the methods used. This scoping review sought to identify current norms and practices for using seizure diaries to manage epilepsy.

METHOD

A scoping review was performed by screening relevant studies and identifying themes, categories and subcategories.

RESULTS

A total of 1125 articles were identified from the database; 46 full-text articles were assessed for eligibility, of which 23 articles were selected. The majority (48%) of the studies were prospective studies. The majority (65%) of the articles were studies conducted in the United States. The themes identified were types of seizure diaries used in clinical practice, contents and structure of a standardised seizure diary, the use and efficacy of seizure diaries in medicine and challenges relating to using a seizure diary for patient management.

CONCLUSION

The study revealed that a seizure diary remains a relevant tool in managing epilepsy. The two forms of diaries in use are electronic and paper-based diaries. The high cost of data and the expensive devices required to access electronic diaries make it unsuitable in a resource-limited setting. Despite its disadvantages, imperfections and inadequacies, the paper-based diary is still relevant for managing patients with epilepsy in resource-limited settings.Contribution: This study reviewed the literature to find the current norms and practices in using seizure diaries. The benefits of the different formats were emphasised.

The Prospects of Non-EEG Seizure Detection Devices in Dogs

The unpredictable nature of seizures is challenging for caregivers of epileptic dogs, which calls the need for other management strategies such as seizure detection devices. Seizure detection devices are systems that rely on non-electroencephalographic (non-EEG) ictal changes, designed to detect seizures. The aim for its use in dogs would be to provide owners with a more complete history of their dog's seizures and to help install prompt (and potentially life-saving) intervention. Although seizure detection via wearable intracranial EEG recordings is associated with a higher sensitivity in humans, there is robust evidence for reliable detection of generalized tonic-clonic seizures (GTCS) using non-EEG devices. Promising non-EEG changes described in epileptic humans, include heart rate variability (HRV), accelerometry (ACM), electrodermal activity (EDA), and electromyography (EMG). Their sensitivity and false detection rate to detect seizures vary, however direct comparison of studies is nearly impossible, as there are many differences in study design and standards for testing. A way to improve sensitivity and decrease false-positive alarms is to combine the different parameters thereby profiting from the strengths of each one. Given the challenges of using EEG in veterinary clinical practice, non-EEG ictal changes could be a promising alternative to monitor seizures more objectively. This review summarizes various seizure detection devices described in the human literature, discusses their potential use and limitations in veterinary medicine and describes what is currently known in the veterinary literature.

A randomized, double-blind trial of triheptanoin for drug-resistant epilepsy in glucose transporter I deficiency syndrome (Glut1DS)

OBJECTIVE

Evaluate efficacy and long-term safety of triheptanoin in patients >1 year old, not on a ketogenic diet, with drug-resistant seizures associated with Glucose Transporter Type 1 Deficiency Syndrome (Glut1DS).

METHODS

UX007G-CL201 was a randomized, double-blind, placebo-controlled trial. Following a 6-week baseline period, eligible patients were randomized 3:1 to triheptanoin or placebo. Dosing was titrated to 35% total daily calories over 2 weeks. After an 8-week placebo-controlled period, all patients received open-label triheptanoin through Week 52.

RESULTS

The study included 36 patients (15 children; 13 adolescents; 8 adults). A median 12.6% reduction in overall seizure frequency was observed in the triheptanoin arm relative to baseline and a 13.5% difference was observed relative to placebo (p = .58). In patients with absence seizures only (n = 9), a median 62.2% reduction in seizure frequency was observed in the triheptanoin arm relative to baseline. Only one patient with absence seizures only was present in the control group, preventing comparison. No statistically significant differences in seizure frequency were observed. Common treatment-emergent adverse events (TEAEs) included diarrhea, vomiting, abdominal pain, and nausea, most mild or moderate in severity. No serious AEs were considered treatment related. One patient discontinued due to status epilepticus.

SIGNIFICANCE

Triheptanoin did not significantly reduce seizure frequency in patients with Glut1DS not on the ketogenic diet. Treatment was associated with mild to moderate GI treatment-related events; most resolved following dose reduction or interruption and/or medication for treatment. Triheptanoin was not associated with any long-term safety concerns when administered at dose levels up to 35% total daily caloric intake for up to one year.

Wearables in real life: A qualitative study of experiences of people with epilepsy who use home seizure monitoring devices

OBJECTIVE

To explore the experiences of people with epilepsy using wearables for home seizure monitoring.

METHODS

Nine people with epilepsy participated in eighteen semistructured individual interviews before and after home monitoring with wearable seizure monitoring equipment. An open-ended interview guide was used to encourage the participants to elaborate on their thoughts and experiences. Interviews were analyzed using a three-level process inspired by the philosopher Max van Manen.

RESULTS

The overall findings illustrate that patients experienced being placed in the spotlight when wearing wearables. The meaning of being in this spotlight is reflected in three themes: Becoming vulnerable through exposure, Standing alone while being with others, and Having a renewed life situation. The analysis and interpretation showed that although the participants expressed readiness to use the wearables, they were less willing to do so after a few days of monitoring. The visibility of the devices influenced how they experienced themselves and were perceived by others.

CONCLUSION

For people with epilepsy, wearables are more than just technical tools; they have a significant existential impact on everyday life. Wearables spotlight the epilepsy condition, and this causes people with epilepsy to experience an existential disruption, as they experience being exposed and vulnerable. This results in a renewed way of perceiving oneself. Nevertheless, wearables also validate epilepsy symptoms, thereby reducing the uncertainty related to epilepsy.

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.

A journey into the unknown: An ethnographic examination of drug-resistant epilepsy treatment and management in the United States

Patients often recognize unmet needs that can improve patient-provider experiences in disease treatment management. These needs are rarely captured and may be hard to quantify in difficult-to-treat disease states such as drug-resistant epilepsy (DRE). To further understand challenges living with and managing DRE, a team of medical anthropologists conducted ethnographic field assessments with patients to qualitatively understand their experience with DRE across the United States. In addition, healthcare provider assessments were conducted in community clinics and Comprehensive Epilepsy Centers to further uncover patient-provider treatment gaps. We identified four distinct stages of the treatment and management journey defined by patients' perceived control over their epilepsy: Gripped in the Panic Zone, Diligently Tracking to Plan, Riding a Rollercoaster in the Dark, and Reframing Priorities to Redefine Treatment Success. We found that patients sought resources to streamline communication with their care team, enhanced education on treatment options beyond medications, and long-term resources to protect against a decline in control over managing their epilepsy once drug-resistant. Likewise, treatment management optimization strategies are provided to improve current DRE standard of care with respect to identified patient-provider gaps. These include the use of digital disease management tools, standardizing neuropsychiatrists into patients' initial care team, and introducing surgical and non-pharmacological treatment options upon epilepsy and DRE diagnoses, respectively. This ethnographic study uncovers numerous patient-provider gaps, thereby presenting a conceptual framework to advance DRE treatment. Further Incentivization from professional societies and healthcare systems to support standardization of the treatment optimization strategies provided herein into clinical practice is needed.

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).

Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).

Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).

Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

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.

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-electroencephalography (EEG) monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially heralding a more quantitative approach to therapeutic outcomes. There is substantial research into the automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic, and despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG. This study reports on a deep learning algorithm for computer-assisted EEG review. Deep convolutional neural networks were trained to detect epileptic discharges using a preexisting dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data were curated and confirmed independently by two epilepsy specialists (Seneviratne et al., 2016). The resulting automated detection algorithm was then used to review diagnostic scalp EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting. The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes. This article is part of the Special Issue "NEWroscience 2018".

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.

Transcutaneous vagus nerve stimulation in the treatment of drug-resistant epilepsy

Epilepsy is a common chronic neurological disease with a high burden of illness. Invasive vagus nerve stimulation (iVNS) is a well-established treatment option in patients with epilepsy (PWE). More recently, transcutaneous vagus nerve stimulation (tVNS) was introduced, an alternative option which is particularly interesting because it does not require surgery and is instantaneously removable. Here, we thoroughly reviewed clinical data on efficacy and safety of tVNS in epilepsies. Five prospective trials in 118 patients with drug-resistant epilepsies and 3 randomized controlled trials in 280 patients with drug-resistant epilepsies were carried out. Study protocols were heterogeneous in terms of patients' characteristics, used device, stimulation parameters, study duration and endpoints. Seizure reduction amounted up to 64%, with responder rates (seizure reduction ≥50%) up to 65%. Seizure freedom was reached in up to 24%, and even to 31% in a small pediatric study group. Seizure severity scores were provided in 4 studies, showing significant improvement in two of them. Adverse side effects were mostly headache, ear pain and skin alteration and rated as mild to moderate. Drowsiness might be depend on stimulation intensity. Quality of life scores reflecting burden of illness showed significant improvement in two studies. Efficacy and safety of tVNS in PWE has to be interpreted as promising. Multicenter randomized double-blind clinical trials with standardized stimulation protocols and long-term follow-up studies are necessary to finally assess tVNS treatment outcome in drug-resistant epilepsies.

Seizure Cycles in Focal Epilepsy

Importance

Focal epilepsy is characterized by the cyclical recurrence of seizures, but, to our knowledge, the prevalence and patterns of seizure cycles are unknown.

Objective

To establish the prevalence, strength, and temporal patterns of seizure cycles over timescales of hours to years.

Design, Setting, and Participants

This retrospective cohort study analyzed data from continuous intracranial electroencephalography (cEEG) and seizure diaries collected between January 19, 2004, and May 18, 2018, with durations up to 10 years. A total of 222 adults with medically refractory focal epilepsy were selected from 256 total participants in a clinical trial of an implanted responsive neurostimulation device. Selection was based on availability of cEEG and/or self-reports of disabling seizures.

Exposures

Antiseizure medications and responsive neurostimulation, based on clinical indications.

Main Outcomes and Measures

Measures involved (1) self-reported daily seizure counts, (2) cEEG-based hourly counts of electrographic seizures, and (3) detections of interictal epileptiform activity (IEA), which fluctuates in daily (circadian) and multiday (multidien) cycles. Outcomes involved descriptive characteristics of cycles of IEA and seizures: (1) prevalence, defined as the percentage of patients with a given type of seizure cycle; (2) strength, defined as the degree of consistency with which seizures occur at certain phases of an underlying cycle, measured as the phase-locking value (PLV); and (3) seizure chronotypes, defined as patterns in seizure timing evident at the group level.

Results

Of the 222 participants, 112 (50%) were male, and the median age was 35 years (range, 18-66 years). The prevalence of circannual (approximately 1 year) seizure cycles was 12% (24 of 194), the prevalence of multidien (approximately weekly to approximately monthly) seizure cycles was 60% (112 of 186), and the prevalence of circadian (approximately 24 hours) seizure cycles was 89% (76 of 85). Strengths of circadian (mean [SD] PLV, 0.34 [0.18]) and multidien (mean [SD] PLV, 0.34 [0.17]) seizure cycles were comparable, whereas circannual seizure cycles were weaker (mean [SD] PLV, 0.17 [0.10]). Across individuals, circadian seizure cycles showed 5 peaks: morning, mid-afternoon, evening, early night, and late night. Multidien cycles of IEA showed peak periodicities centered around 7, 15, 20, and 30 days. Independent of multidien period length, self-reported and electrographic seizures consistently occurred during the days-long rising phase of multidien cycles of IEA.

Conclusions and Relevance

Findings in this large cohort establish the high prevalence of plural seizure cycles and help explain the natural variability in seizure timing. The results have the potential to inform the scheduling of diagnostic studies, the delivery of time-varying therapies, and the design of clinical trials in epilepsy.

Mood and quality of life in patients treated with brain-responsive neurostimulation: The value of earlier intervention

OBJECTIVE

To establish whether earlier treatment using direct brain-responsive neurostimulation for medically intractable focal-onset seizures is associated with better mood and Quality of Life (QoL) compared to later treatment intervention.

METHODS

Data were retrospectively analyzed from prospective clinical trials of a direct brain-responsive neurostimulator (RNS® System) for treatment of adults with medically intractable focal-onset epilepsy. Participants completed the Quality of Life in Epilepsy Inventory (QOLIE-31) yearly through 9 years of follow-up and the Beck Depression Inventory-II (BDI-II) through 2 years of follow-up. Changes in each assessment after treatment with responsive neurostimulation were calculated for patients who began treatment within 10 years of seizure onset (early) and those who began treatment 20 years or more after seizure onset (late).

RESULTS

The median duration of epilepsy was 18.3 years at enrollment. At 9 years, both the early (N = 51) and late (N = 109) treatment groups experienced similar and significant reductions in the frequency of disabling seizures (73.4% and 77.8%, respectively). The early treatment patients had significant improvements in QoL and mood. However, the late treatment patients not only failed to show these improvements but also declined in the emotional QoL subscale.

CONCLUSIONS

Patients treated with brain-responsive neurostimulation earlier in the course of their epilepsy show significant improvements in multiple domains of QoL and mood that are not observed in patients treated later in the course of their epilepsy despite similar efficacy in seizure reduction. Even with similar and substantial reductions in seizure frequency, the comorbidities of uncontrolled epilepsy may be less responsive to treatment when too many years have passed. The results of this study suggest that, as with resective and ablative surgery, treatment with brain-responsive neurostimulation should be delivered as early as possible in the course of medically resistant epilepsy to maximize the opportunity for improvements in mood and QoL.

Under-sampling in epilepsy: Limitations of conventional EEG

The cyclical structure of epilepsy was recently (re)-discovered through years-long intracranial electroencephalography (EEG) obtained with implanted devices. In this review, we discuss how new revelations from chronic EEG relate to the practice and interpretation of conventional EEG. We argue for an electrographic definition of seizures and highlight the caveats of counting epileptiform discharges in EEG recordings of short duration. Limitations of conventional EEG have practical implications with regard to titrating anti-seizure medications and allowing patients to drive, and we propose that chronic monitoring of brain activity could greatly improve epilepsy care. An impending paradigm shift in epilepsy will involve using next-generation devices for chronic EEG to leverage known biomarkers of disease state.

A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

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.

Natural history of generalized motor seizures: A retrospective analysis

PURPOSE

This study aims to characterize the natural history of generalized motor seizures through longitudinal stratification of patient-reported clinical seizures into high, medium and low rates of generalized motor seizures (also known as generalized tonic-clonic seizures or GTCs).

METHODS

From 2007 to 2018, 1.4 million seizures were recorded by 12,402 SeizureTracker.com users that met inclusion/exclusion criteria. The number of GTCs per year since the first seizure diary entry was calculated for each user and categorized as: Low (0 GTCs/year), Medium (1-2 GTCs/year), or High (>3 GTCs/year) GTC rates.

RESULTS

Kaplan-Meier survival curves for the time until exiting the initial category were computed. There was a global difference between risk groups (p < 0.01). Further pairwise log rank tests revealed a difference between each pair of risk groups (p < 0.01). At 3 years, 40.8% of people initially presenting with high GTC rates remained in their initial category, while 77.3% of people initially presenting with low GTC rates remained in their initial category.

CONCLUSION

A patient with a low rate of GTCs is likely to remain at low risk for future GTCs, whereas higher GTC rate patients (at least one GTC/year) may leave their initial risk stratification. Thus, yearly re-assessment may be prudent when considering risk of further GTCs. Given the association between higher yearly rates of GTCs with increased SUDEP risk and morbidity in epilepsy, further validation of these findings is important for prognostication.

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.

Event marker compliance in actigraphy

Actigraphy is a versatile tool for evaluating sleep-wake cycles over time in the home-environment. Patients using the Phillips Actiwatch place an event marker when going to sleep and upon awakening. We investigate compliance in pressing the Actiwatch event marker button for patients referred for insomnia, hypersomnia and disorders of circadian rhythm. We retrospectively analysed event markers from 150 patients undergoing actigraphy for 2,117 nights combined. Compliance was evaluated from inspection of actigraphy records, and coded as full or partial. From patient records, a construct called the C-factor, designed to describe poor social resources and chronic unemployment, was used together with age and sex to predict compliance. We found a mean compliance between 54.0% and 76.3% for a median monitoring duration of 14 days. There was an overall insignificant effect of age (p = .081), but when analysed only for females there was a significant effect of 0.56% pr. year (p = .0038). Compliance was higher for women, Cohen's d = 0.65 (p = .01). The C-factor predicts 18.3% (confidence interval 9%-27.5%) lower compliance. Morning and evening compliance are correlated at r = .65. In conclusion, actigraphy event marker compliance is generally moderate or high, with older women exhibiting the highest compliance. C-factor predicts lower compliance, and this pattern may further translate to other circumstances. If compliance is important, clinicians may want to consider the effects of age, sex and C-factor.

Ultra-long-term subcutaneous home monitoring of epilepsy-490 days of EEG from nine patients

Objective

To explore the feasibility of home monitoring of epilepsy patients with a novel subcutaneous electroencephalography (EEG) device, including clinical implications, safety, and compliance via the first real‐life test.

Methods

We implanted a beta‐version of the 24/7 EEG SubQ (UNEEG Medical A/S, Denmark) subcutaneously in nine participants with temporal lobe epilepsy. Data on seizures, adverse events, compliance in using the device, and use of antiepileptic drugs (AEDs) were collected. EEG was recorded for up to 3 months, and all EEG data were reviewed visually to identify electrographic seizures. These were descriptively compared to seizure counts and AED changes reported in diaries from the same period.

Results

Four hundred ninety days of EEG and 338 electrographic seizures were collected. Eight participants completed at least 9 weeks of home monitoring, while one cancelled participation after 4 weeks due to postimplantation soreness. In total, 13 cases of device‐related adverse events were registered, none of them serious. Recordings obtained from the device covered 73% of the time, on average (range 45%‐91%). Descriptively, electrographic seizure counts were substantially different from diary seizure counts. We uncovered several cases of underreporting and revealed important information on AED response. Electrographic seizure counts revealed circadian distributions of seizures not visible from seizure diaries.

Significance

The study shows that home monitoring for up to 3 months with a subcutaneous EEG device is feasible and well tolerated. No serious adverse device‐related events were reported. An objective seizure count can be derived, which often differs substantially from self‐reported seizure counts. Larger clinical trials quantifying the benefits of objective seizure counting should be a priority for future research as well as development of algorithms for automated review of data.

When can we trust responders? Serious concerns when using 50% response rate to assess clinical trials

Individual seizure rates are highly volatile, with large fluctuations from month-to-month. Nevertheless, changes in individual mean seizure rates are used to measure whether or not trial participants successfully respond to treatment. This study aims to quantify the challenges in identifying individual treatment responders in epilepsy. A power calculation was performed to determine the trial duration required to detect a significant 50% decrease in seizure rates (P < .05) for individuals. Seizure rate simulations were also performed to determine the number of people who would appear to be 50% responders by chance. Seizure rate statistics were derived from long-term seizure counts recorded during a previous clinical trial for an implantable seizure monitoring device. We showed that individual variance in monthly seizure rates can lead to an unacceptably high false-positive rate in the detection of individual treatment responders. This error rate cannot be reduced by increasing the trial population; however, it can be reduced by increasing the duration of clinical trials. This finding suggests that some drugs may be incorrectly evaluated as effective; or, conversely, that helpful drugs could be rejected based on 50% response rates. It is important to pursue more nuanced approaches to measuring individual treatment response, which consider the patient-specific distributions of seizure rates.

Gauging seizure risk

The current paradigm for treatment of epilepsy begins with trials of antiepileptic drugs, followed by evaluation for resective brain surgery in drug-resistant patients. If surgery is not possible or fails to control seizures, some patients benefit from implanted neurostimulation devices. In addition to their therapeutic benefit, some of these devices have diagnostic capability enabling recordings of brain activity with unprecedented chronicity. Two recent studies using different devices for chronic EEG (i.e., over months to years) yielded convergent findings of daily and multiday cycles of brain activity that help explain seizure timing. Knowledge of these patient-specific cycles can be leveraged to gauge and forecast seizure risk, empowering patients to adopt risk-stratified treatment strategies and behavioral modifications. We review evidence that epilepsy is a cyclical disorder, and we argue that implanted monitoring devices should be offered earlier in the treatment paradigm. Chronic EEG would allow pharmacologic treatments tailored to days of high seizure risk-here termed chronotherapy-and would help characterize long timescale seizure dynamics to improve subsequent surgical planning. Coupled with neuromodulation, the proposed approach could improve quality of life for patients and decrease the number ultimately requiring resective surgery. We outline challenges for chronic monitoring and seizure forecasting that demand close collaboration among engineers, neurosurgeons, and neurologists.

Caring electronically for young outpatients who have epilepsy

PURPOSE

The purpose of this study was to review electronic tools that might improve the delivery of epilepsy care, reduce medical care costs, and empower families to improve self-management capability.

METHOD

We reviewed the epilepsy-specific literature about self-management, electronic patient-reported or provider-reported outcomes, on-going remote surveillance, and alerting/warning systems.

CONCLUSIONS

The improved care delivery system that we envision includes self-management, electronic patient (or provider)-reported outcomes, on-going remote surveillance, and alerting/warning systems. This system and variants have the potential to reduce seizure burden through improved management, keep children out of the emergency department and hospital, and even reduce the number of outpatient visits.

Can a collaborative healthcare network improve the care of people with epilepsy?

New opportunities are now available to improve care in ways not possible previously. Information contained in electronic medical records can now be shared without identifying patients. With network collaboration, large numbers of medical records can be searched to identify patients most like the one whose complex medical situation challenges the physician. The clinical effectiveness of different treatment strategies can be assessed rapidly to help the clinician decide on the best treatment for this patient. Other capabilities from different components of the network can prompt the recognition of what is the best available option and encourage the sharing of information about programs and electronic tools. Difficulties related to privacy, harmonization, integration, and costs are expected, but these are currently being addressed successfully by groups of organizations led by those who recognize the benefits.

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

Objective

A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk.

Methods

Using data from SeizureTracker.com, a patient‐reported seizure diary tool containing over 1.2 million recorded seizures across 8 years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed‐effects hidden Markov model for zero‐inflated count data was developed to estimate changes in underlying seizure risk using patient‐reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (TSC) were assessed using data from SeizureTracker.com.

Results

EpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to TSC, four underlying seizure risk states were identified. The expected duration of each state was <12 months, providing a data‐driven estimate of the amount of time a person with TSC would be expected to remain at the same seizure risk level according to the natural course of epilepsy.

Significance

We propose a novel Bayesian statistical approach for evaluating seizure risk on an individual patient level using patient‐reported seizure diaries, which allows for the incorporation of external clinical variables to assess impact on seizure risk. This tool may improve the ability to distinguish true changes in seizure risk from natural variations in seizure frequency in clinical practice. Incorporation of systematic statistical approaches into antiepileptic drug (AED) management may help improve understanding of seizure unpredictability as well as timing of treatment interventions for people with epilepsy.