User experience of a seizure risk forecasting app: A mixed methods investigation.

OBJECTIVE: Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy. METHODS: Active app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively. RESULTS: Surveys were completed by 111 users. Responders consisted of "app users" (n = 58), and "app and forecast users" (n = 53). Of the "app and forecast users", 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which 'frustrations with lack of direction' (regarding their current epilepsy management approach), 'benefits of increased self-knowledge' and 'current and anticipated usefulness of forecasting' were the most common. SIGNIFICANCE: Preliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.

Over- and underreporting of seizures: How big is the problem?

OBJECTIVE: Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient-reported seizures and electroencephalographic (EEG) findings during video-EEG monitoring periods, chiefly highlighting underreporting of seizures, a well-recognized phenomenon. However, seizure overreporting is a significant problem discussed within the literature, although not in such a large cohort. Our aim is to quantify the over- and underreporting of seizures in a large cohort of ambulatory EEG patients. METHODS: We performed a retrospective data analysis on 3407 patients referred to a diagnostic service for ambulatory video-EEG between 2020 and 2022. Both patient-reported events and events discovered on review of the video-EEG were analyzed and classified as epileptic, psychogenic (typically clinical motor events, without accompanying EEG change), or noncorrelated events (NCEs; without perceivable clinical or EEG change). Events were analyzed by state of arousal and indication for referral. Subgroup analysis was performed in patients with focal and generalized epilepsies. RESULTS: A total of 21 024 events were recorded by 3407 patients. Fifty-eight percent of reported events were NCEs, whereas 27% of all events were epileptic. Sixty-four percent of epileptic seizures were not reported by the patient but discovered by the clinical service on review of the recording. NCEs were in the highest proportion in the awake and drowsy arousal states and were the most common event type for the majority of referral indications. Subgroup analysis found a significantly higher proportion of NCEs in the patients with focal epilepsy (23%) compared to generalized epilepsy (10%; p < .001, chi-squared proportion test). SIGNIFICANCE: Our results reaffirm the phenomenon of underreporting and highlight the prevalence of overreporting. Overreporting likely represents irrelevant symptoms or electrographic discharges not represented on scalp electrodes, identification of which has important clinical relevance. Future studies should analyze events by risk factors to elucidate relationships clinicians can use and investigate the etiology of NCEs.

Early discontinuation of ambulatory vEEG among individuals with intellectual disabilities: A retrospective chart review.

OBJECTIVE: This retrospective chart review aims to quantify the rate of patients with intellectual disability (ID) accessing an Australian ambulatory EEG service, and understand the clinical implications of discontinuing studies prematurely. METHODS: Electronic records of referrals, patient monitoring notes, and EEG reports were accessed retrospectively. Each referral was assessed to determine whether the patient had an ID. For each study where patients were discharged prematurely, the outcomes of their EEG report were assessed and compared between the ID and non-ID groups. Exploratory analysis was performed assessing the effects of age, the percentage of the requested monitoring undertaken, and outcome rates as a function of monitoring duration. RESULTS: There were significantly more patients in the ID group with early disconnection than the non-ID group (Chi squared test, p = 0.000). There was no significant difference in the rates of clinical outcomes between the ID and non-ID groups amongst patients who disconnected early. CONCLUSIONS: Although rates of early disconnection are higher in those with ID, study outcomes are largely similar between patients with and without ID in this retrospective analysis of an ambulatory EEG service. SIGNIFICANCE: Ambulatory EEG is a viable modality of EEG monitoring for patients with ID.

Diagnostic utility of prolonged ambulatory video-electroencephalography monitoring.

OBJECTIVES: Ambulatory video-electroencephalography (video-EEG) represents a low-cost, convenient and accessible alternative to inpatient video-EEG monitoring, however few studies have examined their diagnostic yield. In this large-scale retrospective study conducted in Australia, we evaluated the efficacy of prolonged ambulatory video-EEG recordings in capturing diagnostic events and resolving the referring question. METHODS: Sequential adult and paediatric ambulatory video-EEG reports from April 2020 to June 2021 were reviewed retrospectively. Data collection included patient demographics, clinical information, and details of events and EEG abnormalities. Clinical utility was assessed by examining i) time to first diagnostic event, and ii) ability to resolve the referring questions - seizure localisation, quantification, classification, and differentiation (differentiating seizures from non-epileptic events). RESULTS: Of the 600 reports analysed, 49 % captured at least one event, and 45 % captured interictal abnormalities (epileptiform or non-epileptiform). Seizures, probable psychogenic events (mostly non-convulsive), and other non-epileptic events occurred in 13 %, 23 % and 21 % of recordings respectively, with overlap. Unreported events were captured in 53 (9 %) recordings, and unreported seizures represented more than half of all seizures captured (51 %, 392/773). Nine percent of events were missing clinical, video or electrographic data. A diagnostic event occurred in 244 (41 %) recordings, of which 14 % were captured between the fifth and eighth day of recording. Reported event frequency ≥ 1/week was the only significant predictor of diagnostic event capture. In recordings with both seizures and psychogenic events, unrecognized seizures were frequent, and seizures may be missed if recording is terminated early. The referring question was resolved in 85 % of reports with at least one event, and 53 % of all reports. Specifically, this represented 46 % of reports (235/512) for differentiation of events, and 75 % of reports (27/36) for classification of seizures. CONCLUSION: Ambulatory video-EEG recordings are of high diagnostic value in capturing clinically relevant events and resolving the referring clinical questions.

Model Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.

The durations of epileptic seizures are linked to severity and risk for patients. It is unclear if the spatiotemporal evolution of a seizure has any relationship with its duration. Understanding such mechanisms may help reveal treatments for reducing the duration of a seizure. Here, we present a novel method to predict whether a seizure is going to be short or long at its onset using features that can be interpreted in the parameter space of a brain model. The parameters of a Jansen-Rit neural mass model were tracked given intracranial electroencephalography (iEEG) signals, and were processed as time series features using MINIROCKET. By analysing 2954 seizures from 10 patients, patient-specific classifiers were built to predict if a seizure would be short or long given 7 s of iEEG at seizure onset. The method achieved an area under the receiver operating characteristic curve (AUC) greater than 0.6 for five of 10 patients. The behaviour in the parameter space has shown different mechanisms are associated with short/long seizures.Clinical relevance-This shows that it is possible to classify whether a seizure will be short or long based on its early characteristics. Timely interventions and treatments can be applied if the duration of the seizures can be predicted.

Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability.

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy-so-called interictal epileptiform activity-with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy.

Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states.

Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterized the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states. We then compared seizure network state occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. In most patients, the occurrence or duration of at least one seizure network state was associated with the time since implantation. Some patients had one or more seizure network states that were associated with phases of circadian and/or multidien spike rate cycles. A given seizure network state's occurrence and duration were usually not associated with the same timescale. Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a seizure network state's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures and similar principles likely apply to other neurological conditions.

Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column.

Strong inhibitory recurrent connections can reduce the tendency for a neural network to become unstable. This is known as inhibitory stabilization; networks that are unstable in the absence of strong inhibitory feedback because of their unstable excitatory recurrent connections are known as Inhibition Stabilized Networks (ISNs). One of the characteristics of ISNs is their "paradoxical response", where perturbing the inhibitory neurons with additional excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we develop a model of populations of neurons across different layers of cortex. Within each layer, there is one population of inhibitory neurons and one population of excitatory neurons. The connectivity weights across different populations in the model are derived from a synaptic physiology database provided by the Allen Institute. The model shows a gradient of excitation-inhibition balance across different layers in the cortex, where superficial layers are more inhibitory dominated compared to deeper layers. To investigate the presence of ISNs across different layers, we measured the membrane potentials of neural populations in the model after perturbing inhibitory populations. The results show that layer 2/3 in the model does not operate in the ISN regime but layers 4 and 5 do operate in the ISN regime. These results accord with neurophysiological findings that explored the presence of ISNs across different layers in the cortex. The results show that there may be a systematic macroscopic gradient of inhibitory stabilization across different layers in the cortex that depends on the level of excitation-inhibition balance, and that the strength of the paradoxical response increases as the model moves closer to bifurcation points.

Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.

Objective. Brain-computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.Approach. Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments.Main results. Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by naïve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech.Significance.As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.

Ambulatory video EEG extended to 10 days: A retrospective review of a large database of ictal events.

OBJECTIVE: This work aims to determine the ambulatory video electroencephalography monitoring (AVEM) duration and number of captured seizures required to resolve different clinical questions, using a retrospective review of ictal recordings. METHODS: Patients who underwent home-based AVEM had event data analyzed retrospectively. Studies were grouped by clinical indication: differential diagnosis, seizure type classification, or treatment assessment. The proportion of studies where the conclusion was changed after the first seizure was determined, as was the AVEM duration needed for at least 99% of studies to reach a diagnostic conclusion. RESULTS: The referring clinical question was not answered entirely by the first event in 29.6% (n = 227) of studies. Diagnostic and classification indications required a minimum of 7 days for at least 99% of studies to be answered, whilst treatment-assessment required at least 6 days. CONCLUSIONS: At least 7 days of monitoring, and potentially multiple events, are required to adequately answer these clinical questions in at least 99% of patients. The widely applied 72 h or single event recording cut-offs may be inadequate to adequately answer these three indications in a substantial proportion of patients. SIGNIFICANCE: Extended duration of monitoring and capturing multiple events should be considered when attempting to capture seizures on video-EEG.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study.

BACKGROUND: Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. METHOD: Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. FINDINGS: The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. INTERPRETATION: The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. FUNDING: This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Classification of Epileptic and Psychogenic Non-Epileptic Seizures Using Electroencephalography and Electrocardiography.

Patients with psychogenic non-epileptic seizures (PNES) may exhibit similar clinical features to patients with epileptic seizures (ES). Misdiagnosis of PNES and ES can lead to inappropriate treatment and significant morbidity. This study investigates the use of machine learning techniques for classification of PNES and ES based on electroencephalography (EEG) and electrocardiography (ECG) data. Video-EEG-ECG of 150 ES events from 16 patients and 96 PNES from 10 patients were analysed. Four preictal periods (time before event onset) in EEG and ECG data were selected for each PNES and ES event (60-45 min, 45-30 min, 30-15 min, 15-0 min). Time-domain features were extracted from each preictal data segment in 17 EEG channels and 1 ECG channel. The classification performance using k-nearest neighbour, decision tree, random forest, naive Bayes, and support vector machine classifiers were evaluated. The results showed the highest classification accuracy was 87.83% using the random forest on 15-0 min preictal period of EEG and ECG data. The performance was significantly higher using 15-0 min preictal period data than 30-15 min, 45-30 min, and 60-45 min preictal periods ( [Formula: see text]). The classification accuracy was improved from 86.37% to 87.83% by combining ECG data with EEG data ( [Formula: see text]). The study provided an automated classification algorithm for PNES and ES events using machine learning techniques on preictal EEG and ECG data.

Brain model state space reconstruction using an LSTM neural network.

Objective. Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to electroencephalography (EEG). However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically a long short-term memory (LSTM) neural network.Approach. An LSTM filter was trained on simulated EEG data generated by a NMM using a wide range of parameters. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input.Main results. Test results using simulated data yielded correlations withRsquared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures.Significance. Tracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any NMM and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure.

Individualized Seizure Cluster Prediction Using Machine Learning and Chronic Ambulatory Intracranial EEG.

Epilepsy patients often experience acute repetitive seizures, known as seizure clusters, which can progress to prolonged seizures or status epilepticus if left untreated. Predicting the onset of seizure clusters is crucial to enable patients to receive preventative treatments. Additionally, studying the patterns of seizure clusters can help predict the seizure type (isolated or cluster) after observing a just occurred seizure. This paper presents machine learning models that use bivariate intracranial EEG (iEEG) features to predict seizure clustering. Specifically, we utilized relative entropy (REN) as a bivariate feature to capture potential differences in brain region interactions underlying isolated and cluster seizures. We analyzed a large ambulatory iEEG dataset collected from 15 patients and spanned up to 2 years of recordings for each patient, consisting of 3341 cluster seizures (from 427 clusters) and 369 isolated seizures. The dataset's substantial number of seizures per patient enabled individualized analyses and predictions. We observed that REN was significantly different between isolated and cluster seizures in majority of the patients. Machine learning models based on REN: 1) predicted whether a seizure will occur soon after a given seizure with up to 69.5% Area under the ROC Curve (AUC), 2) predicted if a seizure is the first one in a cluster with up to 55.3% AUC, outperforming baseline techniques. Overall, our findings could be beneficial in addressing the clinical burden associated with seizure clusters, enabling patients to receive timely treatments and improving their quality of life.

Seizure occurrence is linked to multiday cycles in diverse physiological signals.

OBJECTIVE: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. METHODS: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. RESULTS: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SIGNIFICANCE: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.

Rates of event capture of home video EEG.

OBJECTIVE: Recording electrographic and behavioral information during epileptic and other paroxysmal events is important during video electroencephalography (EEG) monitoring. This study was undertaken to measure the event capture rate of an home service operating across Australia using a shoulder-worn EEG device and telescopic pole-mounted camera. METHODS: Neurologist reports were accessed retrospectively. Studies with confirmed events were identified and assessed for event capture by recording modality, whether events were reported or discovered, and physiological state. RESULTS: 6,265 studies were identified, of which 2,788 (44.50%) had events. A total of 15,691 events were captured, of which 77.89% were reported. The EEG amplifier was active for 99.83% of events. The patient was in view of the camera for 94.90% of events. 84.89% of studies had all events on camera, and 2.65% had zero events on camera (mean = 93.66%, median = 100.00%). 84.42% of events from wakefulness were reported, compared to 54.27% from sleep. CONCLUSIONS: Event capture was similar to previously reported rates from home studies, with higher capture rates on video. Most patients have all events captured on camera. SIGNIFICANCE: Home monitoring is capable of high rates of event capture, and the use of wide-angle cameras allows for all events to be captured in the majority of studies.

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.

Influence of comorbidity on mortality in patients with epilepsy and psychogenic nonepileptic seizures.

OBJECTIVE: This study aims to determine the contribution of comorbidities to excess psychogenic nonepileptic seizures (PNES) mortality. METHODS: A retrospective cohort study was conducted of tertiary epilepsy outpatients from St. Vincent's Hospital Melbourne, Australia with an 8:1 comparison cohort, matched by age, sex, and socioeconomic status (SES) to national administrative databases between 2007 and 2017. Privacy-preserving data linkage was undertaken with the national prescription, National Death Index, and National Coronial Information System. Forty-five comorbid disease classes were derived by applying the Australian validated RxRisk-V to all dispensed prescriptions. We fitted Cox proportional hazard models controlling for age, sex, SES, comorbidity, disease duration, and number of concomitant antiseizure medications, as a marker of disease severity. We also performed a parallel forward-selection change in estimate strategy to explore which specific comorbidities contributed to the largest changes in the hazard ratio. RESULTS: A total of 13 488 participants were followed for a median 3.2 years (interquartile range = 2.4-4.0 years), including 1628 tertiary epilepsy outpatients, 1384 patients with epilepsy, 176 with PNES, and 59 with both. Eighty-two percent of epileptic seizures and 92% of typical PNES events were captured in an epilepsy monitoring unit. The age-/sex-/SES-adjusted hazard ratio was elevated for epilepsy (4.74, 95% confidence interval [CI] = 3.36-6.68) and PNES (3.46, 95% CI = 1.38-8.68) and remained elevated for epilepsy (3.21, 95% CI = 2.22-4.63) but not PNES (2.15, 95% CI = .77-6.04) after comorbidity adjustment. PNES had more pre-existing comorbidities (p = .0007), with a three times greater median weighted Rx-RiskV score. Psychotic illness, opioid analgesia, malignancies, and nonopioid analgesia had the greatest influence on PNES comorbid risk. SIGNIFICANCE: Higher comorbidity appears to explain the excess PNES mortality and may represent either a wider underrecognized somatoform disorder or a psychological response to physical illness. Better understanding and management of the bidirectional relationship of these wider somatic treatments in PNES could potentially reduce the risk of death.

Risk of sudden unexpected death in epilepsy (SUDEP) with lamotrigine and other sodium channel-modulating antiseizure medications.

OBJECTIVE: In vitro data prompted U.S Food and Drug Administration warnings that lamotrigine, a common sodium channel modulating anti-seizure medication (NaM-ASM), could increase the risk of sudden death in patients with structural or ischaemic cardiac disease, however, its implications for Sudden Unexpected Death in Epilepsy (SUDEP) are unclear. METHODS: This retrospective, nested case-control study identified 101 sudden unexpected death in epilepsy (SUDEP) cases and 199 living epilepsy controls from Epilepsy Monitoring Units (EMUs) in Australia and the USA. Differences in proportions of lamotrigine and NaM-ASM use were compared between cases and controls at the time of admission, and survival analyses from the time of admission up to 16 years were conducted. Multivariable logistic regression and survival analyses compared each ASM subgroup adjusting for SUDEP risk factors. RESULTS: Proportions of cases and controls prescribed lamotrigine (P = 0.166), one NaM-ASM (P = 0.80), or ≥2NaM-ASMs (P = 0.447) at EMU admission were not significantly different. Patients taking lamotrigine (adjusted hazard ratio [aHR] = 0.56; P = 0.054), one NaM-ASM (aHR = 0.8; P = 0.588) or ≥2 NaM-ASMs (aHR = 0.49; P = 0.139) at EMU admission were not at increased SUDEP risk up to 16 years following admission. Active tonic-clonic seizures at EMU admission associated with >2-fold SUDEP risk, irrespective of lamotrigine (aHR = 2.24; P = 0.031) or NaM-ASM use (aHR = 2.25; P = 0.029). Sensitivity analyses accounting for incomplete ASM data at follow-up suggest undetected changes to ASM use are unlikely to alter our results. SIGNIFICANCE: This study provides additional evidence that lamotrigine and other NaM-ASMs are unlikely to be associated with an increased long-term risk of SUDEP, up to 16 years post-EMU admission.