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OBJECTIVE: Lennox-Gastaut syndrome (LGS) is an archetypal developmental and epileptic encephalopathy, for which novel treatments are emerging. Diagnostic criteria for LGS have recently been defined by the International League Against Epilepsy (ILAE). We aimed to apply these criteria in a real-world setting. METHODS: We applied ILAE diagnostic criteria to a cohort of patients diagnosed with LGS by epileptologists following inpatient video-EEG monitoring (VEM) at tertiary comprehensive epilepsy centers between 1995 and 2015. We also assessed mortality in this cohort. RESULTS: Sixty patients diagnosed with LGS and had complete records available for review were identified. Among them, 29 (48%) patients met ILAE diagnostic criteria for LGS (ILAE-DC group). Thirty-one did not meet criteria (non-ILAE-DC) due to the absence of documented tonic seizures (n = 7), EEG features (n = 12), or both tonic seizures and EEG features (n = 10), intellectual disability (n = 1), or drug resistance (n = 1). The ILAE-DC group had a shorter duration of epilepsy at VEM than the non-ILAE-DC group (median = 12.0 years vs. 23.7 years, respectively; p = 0.015). The proportions of patients with multiple seizure types (100% vs. 96.7%), ≤2.5 Hz slow spike-and-wave EEG activity (100% vs. 90%), seizure-related injuries (27.6% vs. 25.8%), and mortality (standardized mortality ratio 4.60 vs. 5.12) were similar between the groups. SIGNIFICANCE: Up to 52% of patients diagnosed with LGS following VEM may not meet recently accepted ILAE criteria for LGS diagnosis. This may reflect both the limitations of retrospective medical record review and a historical tendency of applying the LGS diagnosis to a broad spectrum of severe, early-onset drug-resistant epilepsies with drop attacks. The ILAE criteria allow the delineation of LGS based on distinct electroclinical features, potentiating accurate diagnosis, prognostication, and management formulation. Nonetheless, mortality outcomes between those who did and di

Genetic factors contribute to the aetiology of epilepsy in >50% of cases, and information on the use of antiseizure medications in people with specific aetiologies will help guide treatment decisions. The PERMIT Extension study pooled data from two real-world studies (PERMIT and PROVE) to investigate the effectiveness and safety/tolerability of perampanel (PER) when used to treat people with focal and generalised epilepsy in everyday clinical practice. This post-hoc analysis of PERMIT Extension explored the use of PER when used to treat individuals presumed to have epilepsy with a genetic aetiology. Assessments included retention rate (evaluated at 3, 6 and 12 months), effectiveness (responder and seizure freedom rates; evaluated at 3, 6, 12 months and the last visit [last observation carried forward) and tolerability (adverse events [AEs]). Of the 6822 people with epilepsy included in PERMIT Extension, 1012 were presumed to have a genetic aetiology. The most common genetic aetiologies were idiopathic generalised epilepsy (IGE; 58.2%), tuberous sclerosis (1.1%), Dravet syndrome (0.8%) and genetic epilepsy with febrile seizures plus (GEFS+; 0.5%). Retention rates at 3, 6 and 12 months in the total genetic aetiology population were 89.3%, 79.7% and 65.9%, respectively. In the total genetic aetiology population, responder rates at 12 months and the last visit were 74.8% and 68.3%, respectively, and corresponding seizure freedom rates were 48.9% and 46.5%, respectively. For the specific aetiology subgroups, responder rates at 12 months and the last visit were, respectively: 90.4% and 84.4% (IGE), 100% and 57.1% (tuberous sclerosis), 100% and 71.4% (Dravet syndrome), and 33.3% and 20.0% (GEFS+). Corresponding seizure freedom rates were, respectively: 73.1% and 64.6% (IGE), 33.3% and 22.2% (tuberous sclerosis), 20.0% and 28.6% (Dravet syndrome), and 0% and 0% (GEFS+). The incidence of AEs was 46.5% for the total genetic aetiology population, 48.8% for IGE, 27.3% for tuber

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.

OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. METHODS: This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. RESULTS: The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. CONCLUSIONS: Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP

OBJECTIVE: New-onset seizures affect up to 10% of people over their lifetime, however, their health economic impact has not been well-studied. This prospective multicenter study will collect patient-reported outcome measures (PROMs) from adults with new-onset seizures seen in six Seizure Clinics across Melbourne, Australia and The University of Colorado, USA. METHODS: Approximately 450 eligible patients will be enrolled in the study at or following their initial attendance to Seizure Clinics at the study hospitals. Inclusion criteria for the study group are those with new-onset acute symptomatic seizures, new-onset unprovoked seizures, and new-onset epilepsy. Inclusion criteria for the three comparator groups are those with noncardiac syncope, those with psychogenic nonepileptic seizures, as well as published PROMs data from the Australian general population. Exclusion criteria are those aged less than 18 years, those with a preexisting epilepsy diagnosis, and those with intellectual disabilities or other impairments which would preclude them from comprehending and completing the questionnaires. Patients will complete eight online questionnaires regarding the effect that their seizures (or seizure mimics) have had on various aspects of their life. These questionnaires will be readministered at 6 and 12 months. Patients with new-diagnosis epilepsy will also be asked to share the reasons why they have accepted or declined antiseizure medications. ANALYSIS: Primary outcome measures will be quality of life, work productivity, informal care needs, and mood, at baseline compared to 6 and 12 months later for those with new-onset seizures and comparing these outcomes to those in the three comparator groups. Secondary outcomes include mapping of QoLIE-31 to the EQ-5D-5L in epilepsy, modelling indirect costs of new-onset seizures, and exploring why patients may or may not wish to take antiseizure medications. SIGNIFICANCE: These data will form an evidence-base for future stud

The PERaMpanel pooled analysIs of effecTiveness and tolerability (PERMIT) study was a pooled analysis of data from 44 real-world studies from 17 countries, in which people with epilepsy (PWE; focal and generalized) were treated with perampanel (PER). Retention and effectiveness were assessed after 3, 6, and 12 months, and at the last visit (last observation carried forward). Effectiveness assessments included 50% responder rate (≥ 50% reduction in seizure frequency from baseline) and seizure freedom rate (no seizures since at least the prior visit); in PWE with status epilepticus, response was defined as seizures under control. Safety and tolerability were assessed by evaluating adverse events (AEs) and discontinuation due to AEs. The Full Analysis Set included 5193 PWE. Retention, effectiveness and safety/tolerability were assessed in 4721, 4392 and 4617, respectively. Retention on PER treatment at 3, 6, and 12 months was 90.5%, 79.8%, and 64.2%, respectively. Mean retention time on PER treatment was 10.8 months. The 50% responder rate was 58.3% at 12 months and 50.0% at the last visit, and the corresponding seizure freedom rates were 23.2% and 20.5%, respectively; 52.7% of PWE with status epilepticus responded to PER treatment. Overall, 49.9% of PWE reported AEs and the most frequently reported AEs (≥ 5% of PWE) were dizziness/vertigo (15.2%), somnolence (10.6%), irritability (8.4%), and behavioral disorders (5.4%). At 12 months, 17.6% of PWEs had discontinued due to AEs. PERMIT demonstrated that PER is effective and generally well tolerated when used to treat people with focal and/or generalized epilepsy in everyday clinical practice.

Electroencephalography (EEG) has long been used as a versatile and noninvasive diagnostic tool in epilepsy. With the advent of digital EEG, more advanced applications of EEG have emerged. Compared with technologically advanced practice in focal epilepsies, the utilization of EEG in idiopathic generalized epilepsy (IGE) has been lagging, often restricted to a simple diagnostic tool. In this narrative review, we provide an overview of broader applications of EEG beyond this narrow scope, discussing how the current clinical and research applications of EEG may potentially be extended to IGE. The current literature, although limited, suggests that EEG can be used in syndromic classification, guiding antiseizure medication therapy, predicting prognosis, unraveling biorhythms, and investigating functional brain connectivity of IGE. We emphasize the need for longer recordings, particularly 24-h ambulatory EEG, to capture discharges reflecting circadian and sleep-wake cycle-associated variations for wider EEG applications in IGE. Finally, we highlight the challenges and limitations of the current body of literature and suggest future directions to encourage and enhance more extensive applications of this potent tool.

PURPOSE: To investigate the effectiveness, safety and tolerability of perampanel (PER) in treating myoclonic seizures in clinical practice, using data from the PERaMpanel pooled analysIs of effecTiveness and tolerability (PERMIT) study. METHODS: PERMIT was a pooled analysis of 44 real-world studies from 17 countries, in which patients with focal and generalised epilepsy were treated with PER. This post-hoc analysis included patients with myoclonic seizures at baseline. Retention and effectiveness were assessed after 3, 6, and 12 months; effectiveness was additionally assessed at the last visit (last observation carried forward). Effectiveness assessments included responder rate (≥50% seizure frequency reduction from baseline) and seizure freedom rate (no seizures since at least the prior visit). Safety and tolerability were assessed by evaluating adverse events (AEs) and discontinuation due to AEs. RESULTS: 156 patients had myoclonic seizures (59.0% female; mean age, 32.1 years; idiopathic generalised epilepsy, 89.1%; Juvenile Myoclonic Epilepsy, 63.1%; monthly median myoclonic seizure frequency [interquartile range], 1.7 [1.0-10.0]; mean [standard deviation] prior antiseizure medications, 2.9 [2.6]). Retention was assessed for 133 patients (mean time, 12.1 months), effectiveness for 142, and safety/tolerability for 156. Responder and seizure freedom rates were, respectively, 89.5% and 68.8% at 12 months, and 85.9% and 63.4% at the last visit. Incidence of AEs was 46.8%, the most frequent being dizziness/vertigo (19.2%), irritability (18.6%) and somnolence (9.6%). AEs led to discontinuation of 14.0% of patients over 12 months. CONCLUSION: PER was associated with reduction in myoclonic seizure frequency in patients with myoclonic seizures treated in everyday clinical practice.

Autoimmune encephalitis (AE) is a neurological disorder caused by autoimmune attack on cerebral proteins. Experts currently recommend staged immunotherapeutic management, with first-line immunotherapy followed by second-line immunotherapy if response to first-line therapy is inadequate. Meta-analysis of the evidence base may provide higher quality evidence to support this recommendation. We undertook a systematic review of observational cohort studies reporting AE patients treated with either second-line immunotherapy or first-line immunotherapy alone, and outcomes reported using the modified Rankin Scale (mRS; search date: April 22, 2020). We performed several one-stage multilevel individual patient data (IPD) meta-analyses to examine the association between second-line immunotherapy and final mRS scores (PROSPERO ID CRD42020181805). IPD were obtained for 356 patients from 25 studies. Most studies were rated as moderate to high risk of bias. Seventy-one patients (71/356, 19%) were treated with second-line immunotherapy. We did not find a statistically significant association between treatment with second-line immunotherapy and final mRS score for the cohort overall (odds ratio [OR] = 1.74, 95% confidence interval [CI] = .98-3.08, p = .057), or subgroups with anti-N-methyl-D-aspartate receptor encephalitis (OR = 1.03, 95% CI = .45-2.38, p = .944) or severe AE (maximum mRS score > 2; OR = 1.673, 95% CI = .93-3.00, p = .085). Treatment with second-line immunotherapy was associated with higher final mRS scores in subgroups with anti-leucine-rich glioma-inactivated 1 AE (OR = 6.70, 95% CI = 1.28-35.1, p = .024) and long-term (at least 12 months) follow-up (OR = 3.94, 95% CI = 1.67-9.27, p = .002). We did not observe an association between treatment with second-line immunotherapy and lower final mRS scores in patients with AE. This result should be interpreted with caution, given the risk of bias, limited adjustment for disease severity, and insensitivity of the mRS in e

BACKGROUND AND PURPOSE: Despite the rapid increase in research examining outcomes in autoimmune encephalitis (AE) patients, there are few cohort studies examining cognitive outcomes in this population. The current study aimed to characterise psychometric outcomes in this population, and explore variables that may predict psychometric outcomes. METHODS: This retrospective observational study collected psychometric data from 59 patients across six secondary and tertiary referral centres in metropolitan hospitals in Victoria, Australia between January 2008 and July 2019. Frequency and pattern analysis were employed to define and characterize psychometric outcomes. Univariable logistic regression was performed to examine predictors of intact and pathological psychometric outcomes. RESULTS: Deficits in psychometric markers of executive dysfunction were the most common finding in this cohort, followed by deficits on tasks sensitive to memory. A total of 54.2% of patients were classified as having psychometric impairments across at least two cognitive domains. Twenty-nine patterns were observed, suggesting outcomes in AE are complex. None of the demographic data, clinical features or auxiliary examination variables were predictors of psychometric outcome. CONCLUSIONS: Cognitive outcomes in AE are complex. Further detailed and standardized cognitive testing, in combination with magnetic resonance imaging volumetrics and serum/cerebrospinal fluid biomarkers, is required to provide rigorous assessments of disease outcomes.

Objective: To examine the prognostic value of CSF abnormalities in seropositive autoimmune encephalitis (AE). Method(s): We retrospectively studied 57 cases of seropositive AE. Primary outcomes were mortality and modified Rankin Scale, while secondary outcomes were first line treatment failure, ICU admission and relapse. Regression analysis was performed. Result(s): CSF white cell count (WCC) was higher in the NMDAR group, while elevated protein was more common amongst other subtypes. We found an association between WCC >5 cells/mm3 and treatment failure (OR 16.0, p = 0.006)), and between WCC >20 cells/mm3 and ICU admission (OR 19.3, p = 0.026). Conclusion(s): Different subsets of AE have characteristic CSF abnormalities, which may aid recognition during early evaluation. CSF WCC had prognostic significance in our study.Copyright © 2021

Electroencephalography (EEG) has long been used as a versatile and noninvasive diagnostic tool in epilepsy. With the advent of digital EEG, more advanced applications of EEG have emerged. Compared with technologically advanced practice in focal epilepsies, the utilization of EEG in idiopathic generalized epilepsy (IGE) has been lagging, often restricted to a simple diagnostic tool. In this narrative review, we provide an overview of broader applications of EEG beyond this narrow scope, discussing how the current clinical and research applications of EEG may potentially be extended to IGE. The current literature, although limited, suggests that EEG can be used in syndromic classification, guiding antiseizure medication therapy, predicting prognosis, unraveling biorhythms, and investigating functional brain connectivity of IGE. We emphasize the need for longer recordings, particularly 24-h ambulatory EEG, to capture discharges reflecting circadian and sleep-wake cycle-associated variations for wider EEG applications in IGE. Finally, we highlight the challenges and limitations of the current body of literature and suggest future directions to encourage and enhance more extensive applications of this potent tool.Copyright © 2021 International League Against Epilepsy

Objective: Bilateral tonic-clonic seizures are encountered in both focal and generalized epilepsies. We reviewed the literature regarding the presence of focal signs in generalized-onset tonic-clonic seizures (GOTCS) and the utility of semiology in differentiating those from focal to bilateral tonic-clonic seizures (FBTCS). Method(s): We conducted a comprehensive literature search using four electronic databases (Medline, Embase, Web of Science, and Psychinfo) and constructed a systematic review in keeping with the Preferred Items for Systematic Reviews and Meta-analyses guidelines. Result(s): We included 13 studies on focal semiological features of GOTCS. These studies included a total of 952 participants. The key focal signs described in GOTCS included: early head version, figure of four sign, asymmetric seizure termination, and a multitude of auras as well as automatisms. Additionally, we reviewed five studies that investigated the use of semiology to differentiate GOTCS from FBTCS; these studies had a total of 289 participants. Asymmetry in clonic phase, side-to-side axial movements, asymmetrical seizure termination, figure of four sign, index finger pointing, and fanning posture of the hand were found to be significantly more frequent in FBTCS compared with GOTCS. Furthermore, combinations of focal semiological features occurring in a single seizure were found to be suggestive of FBTCS rather than GOTCS. Conclusion(s): Focal signs are often evident in GOTCS. Though the observation of multiple focal signs within a given seizure may be in favor of an FBTCS, our findings caution against differentiating between the two seizure types based on semiology alone due to considerable overlap in focal features.Copyright © 2021 Elsevier Inc.

Objectives: To determine the long-term outcomes in patients undergoing intracranial EEG (iEEG) evaluation for epilepsy surgery in terms of seizure freedom, mood, and quality of life at St. Vincent's Hospital, Melbourne. Method(s): Patients who underwent iEEG between 1999 and 2016 were identified. Patients were retrospectively assessed between 2014 and 2017 by specialist clinic record review and telephone survey with standardized validated questionnaires for: 1) seizure freedom using the Engel classification; 2) Mood using the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E); 3) Quality-of-life outcomes using the QOLIE-10 questionnaire. Summary statistics and univariate analysis were performed to investigate variables for significance. Result(s): Seventy one patients underwent iEEG surgery: 49 Subdural, 14 Depths, 8 Combination with 62/68 (91.9%) of those still alive, available at last follow-up by telephone survey or medical record review (median of 8.2 years). The estimated epileptogenic zone was 62% temporal and 38% extra-temporal. At last follow-up, 69.4% (43/62) were Engel Class I and 30.6% (19/62) were Engel Class II-IV. Further, a depressive episode (NDDI-E > 15) was observed in 34% (16/47), while a 'better quality of life' (QOLIE-10 score < 25) was noted in 74% (31/42). Quality of life (p < 0.001) but not mood (p = 0.24) was associated with seizure freedom. Significance: Long-term seizure freedom can be observed in patients undergoing complex epilepsy surgery with iEEG evaluation and is associated with good quality of life.Copyright © 2021 Elsevier Inc.

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 intracrania

Objective: To examine the utility of the peripheral blood neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) as biomarkers of prognosis in seropositive autoimmune encephalitis (AE). Method(s): In this multicenter study, we retrospectively analyzed 57 cases of seropositive AE with hospital admissions between January 2008 and June 2019. The initial full blood examination was used to determine each patients' NLR and MLR. The modified Rankin Scale (mRS) was utilized to assess the patients' follow-up disability at 12 months and then at final follow-up. Primary outcomes were mortality and mRS, while secondary outcomes were failure of first line treatment, ICU admission, and clinical relapse. Univariate and multivariable regression analysis was performed. Result(s): During initial hospital admission 44.7% of patients had unsuccessful first line treatment. After a median follow-up of 700 days, 82.7% had good functional outcome (mRS <=2) while five patients had died. On multivariable analysis, high NLR was associated with higher odds of first line treatment failure (OR 1.32, 95% CI 1.03-1.69, p = 0.029). Increased MLR was not associated with any short or long-term outcome. Conclusion(s): NLR on initial hospital admission blood tests may be provide important prognostic information for cases of seropositive AE. This study demonstrates the potential use of NLR as a prognostic marker in the clinical evaluation of patients with seropositive AE.© Copyright © 2021 Broadley, Wesselingh, Seneviratne, Kyndt, Beech, Buzzard, Nesbitt, D'Souza, Brodtmann, Kalincik, Butzkueven, O'Brien, Monif and Australian Autoimmune Encephalitis Consortium.

OBJECTIVE: To explore the impact of psychiatric comorbidities on all-cause mortality in adults with epilepsy from a cohort of patients admitted for video-EEG monitoring (VEM) over 2 decades. METHODS: A retrospective medical record audit was conducted on 2,709 adults admitted for VEM and diagnosed with epilepsy at 3 Victorian comprehensive epilepsy programs from 1995 to 2015. A total of 1,805 patients were identified in whom the record of a clinical evaluation by a neuropsychiatrist was available, excluding 27 patients who died of a malignant brain tumor known at the time of VEM admission. Epilepsy and lifetime psychiatric diagnoses were determined from consensus opinion of epileptologists and neuropsychiatrists involved in the care of each patient. Mortality and cause of death were determined by linkage to the Australian National Death Index and National Coronial Information System. RESULTS: Compared with the general population, mortality was higher in people with epilepsy (PWE) with a psychiatric illness (standardized mortality ratio [SMR] 3.6) and without a psychiatric illness (SMR 2.5). PWE with a psychiatric illness had greater mortality compared with PWE without (hazard ratio 1.41, 95% confidence interval 1.02-1.97) after adjusting for age and sex. No single psychiatric disorder by itself conferred increased mortality in PWE. The distribution of causes of death remained similar between PWE with psychiatric comorbidities and those without. CONCLUSION: The presence of comorbid psychiatric disorders in adults with epilepsy is associated with increased mortality, highlighting the importance of identifying and treating psychiatric comorbidities in these patients.

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 intracrani

BACKGROUND: Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy). METHODS: We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase. FINDINGS: Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles. INTERPRETATION: Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the lin

OBJECTIVE: To examine the utility of the peripheral blood neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) as biomarkers of prognosis in seropositive autoimmune encephalitis (AE). METHODS: In this multicenter study, we retrospectively analyzed 57 cases of seropositive AE with hospital admissions between January 2008 and June 2019. The initial full blood examination was used to determine each patients' NLR and MLR. The modified Rankin Scale (mRS) was utilized to assess the patients' follow-up disability at 12 months and then at final follow-up. Primary outcomes were mortality and mRS, while secondary outcomes were failure of first line treatment, ICU admission, and clinical relapse. Univariate and multivariable regression analysis was performed. RESULTS: During initial hospital admission 44.7% of patients had unsuccessful first line treatment. After a median follow-up of 700 days, 82.7% had good functional outcome (mRS ≤2) while five patients had died. On multivariable analysis, high NLR was associated with higher odds of first line treatment failure (OR 1.32, 95% CI 1.03-1.69, p = 0.029). Increased MLR was not associated with any short or long-term outcome. CONCLUSIONS: NLR on initial hospital admission blood tests may be provide important prognostic information for cases of seropositive AE. This study demonstrates the potential use of NLR as a prognostic marker in the clinical evaluation of patients with seropositive AE.

The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using smartphone seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 min in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure an

BACKGROUND: While the effects of prolonged sleep deprivation (≥24 h) on seizure occurrence has been thoroughly explored, little is known about the effects of day-to-day variations in the duration and quality of sleep on seizure probability. A better understanding of the interaction between sleep and seizures may help to improve seizure management. METHODS: To explore how sleep and epileptic seizures are associated, we analysed continuous intracranial electroencephalography (EEG) recordings collected from 10 patients with refractory focal epilepsy undergoing ordinary life activities between 2010 and 2012 from three clinical centres (Austin Health, The Royal Melbourne Hospital, and St Vincent's Hospital of the Melbourne University Epilepsy Group). A total of 4340 days of sleep-wake data were analysed (average 434 days per patient). EEG data were sleep scored using a semi-automated machine learning approach into wake, stages one, two, and three non-rapid eye movement sleep, and rapid eye movement sleep categories. FINDINGS: Seizure probability changes with day-to-day variations in sleep duration. Logistic regression models revealed that an increase in sleep duration, by 1·66 ± 0·52 h, lowered the odds of seizure by 27% in the following 48 h. Following a seizure, patients slept for longer durations and if a seizure occurred during sleep, then sleep quality was also reduced with increased time spent aroused from sleep and reduced rapid eye movement sleep. INTERPRETATION: Our results suggest that day-to-day deviations from regular sleep duration correlates with changes in seizure probability. Sleeping longer, by 1·66 ± 0·52 h, may offer protective effects for patients with refractory focal epilepsy, reducing seizure risk. Furthermore, the occurrence of a seizure may disrupt sleep patterns by elongating sleep and, if the seizure occurs during sleep, reducing its quality.

Rationale: Product switching (brand/manufacturer or dose/formulation of the same drug) is associated with non-adherence and breakthrough seizures in patients with epilepsy. Rates of product switching and discontinuation were characterized in patients on levetiracetam (LEV) brands experiencing shortages in Australia, and compared with non-shortage periods to understand how antiseizure medication (ASM) shortages affect product switching and discontinuation in patients with epilepsy. LEV was chosen as the focus due to multiple shortages in the Medicine Shortage Information Initiative (MSII), widespread usage in multiple seizure types and ages, and limited use outside epilepsy. Methods: Data were obtained from the Australian Therapeutic Goods Administration MSII on all LEV shortages between Jan 2019 and Nov 2020. Shortage dates were cross-referenced with IQVIA-NostraData Longitudinal Dispensation data, which collects pharmacy dispensing data from 25 million patients in Australia with 75% coverage of retail pharmacies nationwide. Patients dispensed a LEV product (brand-formulation-strength combination) appearing in MSII in the 90 days before a shortage were designated as “on therapy.” Patients on therapy and with two consecutive dispensations of a relevant product before start of a shortage were classified as “continuing” (only dispensation of index product throughout shortage), “switching” (dispensation of ≥ 1 different LEV ASM during the shortage), or “discontinuing” (no further LEV dispensations throughout shortage). Switching patterns were compared with the corresponding (non-shortage) period in the prior year to estimate how shortages were associated with rates of product switching and discontinuation. Results: 118 LEV product shortages were identified and 100% were generic brands. Overlapping shortages of same product were consolidated, leaving 23 distinct shortages (Table 1). Median shortage was 133 days (interquartile range 80, 229.5). Of 11 government funded gen

Objective: To investigate the impact of sleep onset and offset on the rate of epileptiform discharges (ED) in idiopathic generalized epilepsies (IGE). Method(s): We studied the temporal distribution of EDs with mixed-effects Poisson regression modeling in a cohort of patients diagnosed with IGE who underwent 24-hour ambulatory electroencephalography (EEG) recordings. We defined the mean number discharges per hour per subject as the mean ED rate. The association between each hour and the mean ED rate was quantified with incidence rate ratio (IRR) as the metric. We calculated the IRR of each hourly block for the total cohort in relation to sleep onset and offset. Finally, we admitted secondary risk factors into our Poisson regression model and quantified changes in IRR in order to investigate the impact of those variables on the outcome. The secondary risk factors included: epilepsy syndrome, duration of seizure freedom, duration of epilepsy, number of antiepileptic drugs (AED), type of AED, and age. Result(s): A total of 39 patients with a mean age of 29.1 y (SD = 10.1) were studied. The distribution of ED rate demonstrated a highly significant abrupt increase in the first hour after sleep onset (IRR = 3.96; p < 0.001). On the contrary, the ED rate significantly dropped in the second hour after the sleep offset compared with the last hour block before sleep offset (IRR = 0.39; p < 0.001). None of the secondary risk factors demonstrated any significant impact on this pattern. Conclusion(s): Sleep onset is a very significant trigger for the generation of EDs in IGE. Significance: Our results support the hypothesis that there is a "critical zone of vigilance" in the sleep-wake boundary from which generalized EDs are more likely to emerge.Copyright © 2020 International Federation of Clinical Neurophysiology

The International League Against Epilepsy (ILAE) groups seizures into "focal", "generalized" and "unknown" based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.Copyright © 2020 Creaser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.

The International League Against Epilepsy (ILAE) groups seizures into "focal", "generalized" and "unknown" based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.

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 therapeu

ObjectiveTo identify clinical and EEG biomarkers of drug resistance in adults with idiopathic generalized epilepsy.MethodsWe conducted a case-control study consisting of a discovery cohort and a replication cohort independently assessed at 2 different centers. In each center, patients with the idiopathic generalized epilepsy phenotype and generalized spike-wave discharges on EEG were classified as drug-resistant or drug-responsive. EEG changes were classified into predefined patterns and compared between the 2 groups in the discovery cohort. Factors associated with drug resistance in multivariable analysis were tested in the replication cohort.ResultsThe discovery cohort included 85 patients (29% drug-resistant and 71% drug-responsive). Their median age at assessment was 32 years and 50.6% were female. Multivariable analysis showed that higher number of seizure types ever experienced (3 vs 1: odds ratio [OR] = 31.1, 95% confidence interval [CI]: 4.5-214, p < 0.001; 3 vs 2: OR = 14.6, 95% CI: 2.3-93.1, p = 0.004) and generalized polyspike train (burst of generalized rhythmic spikes lasting less than 1 second) during sleep were associated with drug resistance (OR = 10.8, 95% CI: 2.4-49.4, p = 0.002). When these factors were tested in the replication cohort of 80 patients (27.5% drug-resistant and 72.5% drug-responsive; 71.3% female; median age 27.5 years), the proportion of patients with generalized polyspike train during sleep was also higher in the drug-resistant group (OR = 4.0, 95% CI: 1.35-11.8, p = 0.012).ConclusionGeneralized polyspike train during sleep may be an EEG biomarker for drug resistance in adults with idiopathic generalized epilepsy.Copyright © 2018 American Academy of Neurology.

Objective: The objective of this study was to evaluate the current evidence regarding the effect of antiepileptic drugs (AEDs)on epileptiform discharge (ED)burden in genetic generalized epilepsy (GGE). Method(s): We conducted a comprehensive literature search of PubMed, Embase, PsycINFO, and the Web of Science Core Collection databases using the keywords 'genetic generalized epilepsy', 'antiepileptic drugs' and 'epileptiform discharge'. Primary human studies published in English that reported the effect of AEDs on EDs captured on electroencephalogram (EEG)recordings of at least 24 h in duration in patients with GGE were included. Result(s): Six studies published between 1984 and 2017, which reported the effect of AEDs on EDs, involving a total of 116 patients with GGE, were analyzed. Our systematic review found a tendency for AEDs to reduce ED density, frequency, cumulative duration, and burst duration in GGE. Furthermore, we found evidence that the AED-mediated reduction in ED burden was associated with improved seizure control and cognitive outcomes. Conclusion(s): Antiepileptic drugs tend to reduce ED burden in GGE, but the significance of this association remains uncertain.Copyright © 2019 Elsevier Inc.

Purpose: To identify clinical and EEG biomarkers of drug resistance in adults with idiopathic generalized epilepsy. Method(s): We conducted a case-control study consisting of a discovery cohort and a replication cohort independently assessed at two different centres. In each centre patients with idiopathic generalized epilepsy phenotype and generalized spike-wave discharges on EEG were classified as drug-resistant or drug-responsive. EEG changes were classified into pre-defined patterns and compared between the two groups in the discovery cohort. Factors associated with drug resistance in multivariable analysis were tested in the replication cohort. Result(s): The discovery cohort included 85 patients (29% drug-resistant and 71% drug-responsive). Their median age at assessment was 32 years and 50.6% were female. Multivariable analysis showed that having higher number of seizure types (3 vs. 1 odds ratio [OR]= 31.1, 95% confidence interval [CI] 4.5-214, p < 0.001; 3 vs. 2 OR=14.6, 95% CI 2.3-93.1, p = 0.004) and generalized polyspike train (burst of generalized rhythmic spikes lasting less than 1 second) during sleep were associated with drug resistance (OR=10.8, 95% CI 2.4-49.4, p = 0.002). When these factors were tested in the replication cohort of 80 patients (27.5% drug-resistant and 72.5% drug-responsive; 71.3% female; median age 27.5 years), the proportion of patients with generalized polyspike train during sleep was also higher in the drug-resistant group (OR=4.0, 95% CI 1.35- 11.8, p = 0.012). Conclusion(s): Generalized polyspike train during sleep may be an EEG biomarker for drug resistance in adults with idiopathic generalized epilepsy.

OBJECTIVE: We report on patient-specific durations of postictal periods in long-term intracranial electroencephalography (iEEG) recordings. The objective was to investigate the relationship between seizure duration and postictal suppression duration. METHODS: Long-term recording iEEG from 9 patients (>50 seizures recorded) were analyzed. In total, 2310 seizures were recorded during a total of 13.8 years of recording. Postictal suppression duration was calculated as the duration after seizure termination until total signal energy returned to background levels. The relationship between seizure duration and postictal suppression duration was quantified using the correlation coefficient (r). The effects of populations of seizures within patients, on correlations, were also considered. Populations of seizures within patients were distinguished by seizure duration thresholds and k-means clustering along the dimensions of seizure duration and postictal suppression duration. The effects of bursts of seizures were also considered by defining populations based on interseizure interval (ISI). RESULTS: Seizure duration accounted for 40% of postictal suppression duration variance, aggregated across all patients and seizures. Seizure duration accounted for more than 25% of the variance in postictal suppression duration in 2 patients and accounted for less than 25% in the remaining 7. In 3 patients, heat maps showed multiple distinct postictal patterns indicating multiple populations of seizures. When accounting for these populations, seizure duration accounted for less than 25% of the variance in postictal duration in all populations. Variance in postictal suppression duration accounted for less than 10% of ISI variance in all patients. SIGNIFICANCE: We have previously demonstrated that some patients have multiple seizure populations distinguishable by seizure duration. This article shows that different seizure populations have distinct and consistent postictal behaviors. The exi