Catastrophic Epilepsies of Childhood.

The tragedy of epilepsy emerges from the combination of its high prevalence, impact upon sufferers and their families, and unpredictability. Childhood epilepsies are frequently severe, presenting in infancy with pharmaco-resistant seizures; are often accompanied by debilitating neuropsychiatric and systemic comorbidities; and carry a grave risk of mortality. Here, we review the most current basic science and translational research findings on several of the most catastrophic forms of pediatric epilepsy. We focus largely on genetic epilepsies and the research that is discovering the mechanisms linking disease genes to epilepsy syndromes. We also describe the strides made toward developing novel pharmacological and interventional treatment strategies to treat these disorders. The research reviewed provides hope for a complete understanding of, and eventual cure for, these childhood epilepsy syndromes.

Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity.

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.

Early onset epileptic and developmental encephalopathy and MOGS variants: a new diagnosis in the whole exome sequencing (WES) ERA : Report of a new patient and review of the literature.

Mannosyl-oligosaccharide glucosidase - congenital disorder of glycosylation (MOGS-CDG) is determined by biallelic mutations in the mannosyl-oligosaccharide glucosidase (glucosidase I) gene. MOGS-CDG is a rare disorder affecting the processing of N-Glycans (CDG type II) and is characterized by prominent neurological involvement including hypotonia, developmental delay, seizures and movement disorders. To the best of our knowledge, 30 patients with MOGS-CDG have been published so far. We described a child who is compound heterozygous for two novel variants in the MOGS gene. He presented Early Infantile Developmental and Epileptic Encephalopathy (EI-DEE) in the absence of other specific systemic involvement and unrevealing first-line biochemical findings. In addition to the previously described features, the patient presented a Hirschprung disease, never reported before in individuals with MOGS-CDG.

Nonlinear analysis and recognition of epileptic EEG signals in different stages.

It is a hot problem in epilepsy research to detect and predict seizures by EEG signals. Clinically, it is generally observed that there are only sudden abnormal signals during the ictal stage, but there is no significant difference in the EEG signal between the interictal and preictal stages. To solve the problem that preictal signals are difficult to recognize clinically, and then effectively improve the recognition efficiency of epileptic seizures, so, in this paper, some nonlinear methods are comprehensively used to extract the hidden information in the EEG signals in different stages, namely, phase space reconstruction (PSR), Poincaré section (PS), synchroextracting transform (SET), and machine learning for EEG signal analysis. First, PSR based on C-C method is used, and the results show that there are different diffuse attractor trajectories of the signals in different stages. Second, the confidence ellipse (CE) is constructed by using the scatter diagram of the corresponding trajectory on PS, and the aspect ratio and area of the ellipse are calculated. The results show that there is an interesting transitional phenomenon in preictal stage. To recognize ictal and preictal signals, time-frequency (TF) spectrums, which are processed by SET, are fed into the convolutional neural network (CNN) classifier. The accuracy of recognizing ictal and preictal signals reaches 99.7% and 93.7%, respectively. To summarize, our results based on nonlinear method provide new research ideas for seizure detection and prediction.NEW & NOTEWORTHY Our results based on nonlinear method have better practical significance and clinical application value and improved the prediction efficiency of epileptic EEG signals effectively. This work provides direct insight into the application of these biomarkers for seizure detection and prediction.

Predictive models of epilepsy outcomes.

PURPOSE OF REVIEW: Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review. RECENT FINDINGS: Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65-73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72-81%). Improving their predictive performance is limited by the nomograms' inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications. SUMMARY: Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.

An update on psychogenic nonepileptic seizures.

PURPOSE OF REVIEW: The understanding of psychogenic nonepileptic seizures (PNES) has advanced steadily over recent decades. This update summarizes new insights from the last three years. RECENT FINDINGS: The process of diagnosing PNES has shifted from the exclusion of epilepsy to one based on the recognition of typical clinical features. While the diagnosis cannot rely on any single feature in isolation, a range of semiological features characterising PNES are now recognised and a number of studies hint at the potential for machine learning and AI to improve the diagnostic process. Advances in data processing and analysis may also help to make sense of the heterogeneity of PNES populations demonstrated by recent studies focussing on aetiology and patient subgroups. It is now clear that PNES are associated with high rates of mental and physical comorbidities and premature death, highlighting that they are only one manifestation of a complex disorder extending beyond the nervous system and the seizures themselves. SUMMARY: PNES are now understood as a manifestation of dysfunction in interacting brain networks. This understanding provides an explanation for the psychopathological and semiological heterogeneity of PNES patient populations. New insights into medical comorbidities and increased rates of premature death call for more research into associated pathological processes outside the nervous system.

Technological Vanguard: the outstanding performance of the LTY-CNN model for the early prediction of epileptic seizures.

BACKGROUND: Epilepsy is a common neurological disorder that affects approximately 60 million people worldwide. Characterized by unpredictable neural electrical activity abnormalities, it results in seizures with varying intensity levels. Electroencephalography (EEG), as a crucial technology for monitoring and predicting epileptic seizures, plays an essential role in improving the quality of life for people with epilepsy. METHOD: This study introduces an innovative deep learning model, a lightweight triscale yielding convolutional neural network" (LTY-CNN), that is specifically designed for EEG signal analysis. The model integrates a parallel convolutional structure with a multihead attention mechanism to capture complex EEG signal features across multiple scales and enhance the efficiency achieved when processing time series data. The lightweight design of the LTY-CNN enables it to maintain high performance in environments with limited computational resources while preserving the interpretability and maintainability of the model. RESULTS: In tests conducted on the SWEC-ETHZ and CHB-MIT datasets, the LTY-CNN demonstrated outstanding performance. On the SWEC-ETHZ dataset, the LTY-CNN achieved an accuracy of 99.9%, an area under the receiver operating characteristic curve (AUROC) of 0.99, a sensitivity of 99.9%, and a specificity of 98.8%. Furthermore, on the CHB-MIT dataset, it recorded an accuracy of 99%, an AUROC of 0.932, a sensitivity of 99.1%, and a specificity of 93.2%. These results signify the remarkable ability of the LTY-CNN to distinguish between epileptic seizures and nonseizure events. Compared to other existing epilepsy detection classifiers, the LTY-CNN attained higher accuracy and sensitivity. CONCLUSION: The high accuracy and sensitivity of the LTY-CNN model demonstrate its significant potential for epilepsy management, particularly in terms of predicting and mitigating epileptic seizures. Its value in personalized treatments and widespread clinical applications reflects the broad prospects of deep learning in the health care sector. This also highlights the crucial role of technological innovation in enhancing the quality of life experienced by patients.

Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion.

BACKGROUND: Epilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient's work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals. The study proposes an epileptic seizure model based on a multidimensional Transformer with recurrent neural network(LSTM-GRU) fusion for seizure classification of EEG signals. METHODOLOGY: Firstly, a short-time Fourier transform was employed in the extraction of time-frequency features from EEG signals. Second, the extracted time-frequency features are learned using the Multidimensional Transformer model. Then, LSTM and GRU are then used for further learning of the time and frequency characteristics of the EEG signals. Next, the output features of LSTM and GRU are spliced and categorized using the gating mechanism. Subsequently, seizure prediction is conducted. RESULTS: The model was tested on two datasets: the Bonn EEG dataset and the CHB-MIT dataset. On the CHB-MIT dataset, the average sensitivity and average specificity of the model were 98.24% and 97.27%, respectively. On the Bonn dataset, the model obtained about 99% and about 98% accuracy on the binary classification task and the tertiary upper classification task, respectively. CONCLUSION: The findings of the experimental investigation demonstrate that our model is capable of exploiting the temporal and frequency characteristics present within EEG signals.

Overnight Electroencephalogram to Forecast Epilepsy Development in Children with Autism Spectrum Disorders.

OBJECTIVE: To establish the utility of long-term electroencephalogram (EEG) in forecasting epilepsy onset in children with autism spectrum disorder (ASD). STUDY DESIGN: A single-institution, retrospective analysis of children with ASD, examining long-term overnight EEG recordings collected over a period of 15 years, was conducted. Clinical EEG findings, patient demographics, medical histories, and additional Autism Diagnostic Observation Schedule data were examined. Predictors for the timing of epilepsy onset were evaluated using survival analysis and Cox regression. RESULTS: Among 151 patients, 17.2% (n = 26) developed unprovoked seizures (Sz group), while 82.8% (n = 125) did not (non-Sz group). The Sz group displayed a higher percentage of interictal epileptiform discharges (IEDs) in their initial EEGs compared with the non-Sz group (46.2% vs 20.0%, P = .01). The Sz group also exhibited a greater frequency of slowing (42.3% vs 13.6%, P < .01). The presence of IEDs or slowing predicted an earlier seizure onset, based on survival analysis. Multivariate Cox proportional hazards regression revealed that the presence of any IEDs (HR 3.83, 95% CI 1.38-10.65, P = .01) or any slowing (HR 2.78, 95% CI 1.02-7.58, P = .046 significantly increased the risk of developing unprovoked seizures. CONCLUSION: Long-term EEGs are valuable for predicting future epilepsy in children with ASD. These findings can guide clinicians in early education and potential interventions for epilepsy prevention.

Genetic and phenotypic landscape of pediatric-onset epilepsy in 142 Indian families: Counseling and therapeutic implications.

The application of genomic technologies has led to unraveling of the complex genetic landscape of disorders of epilepsy, gaining insights into their underlying disease mechanisms, aiding precision medicine, and providing informed genetic counseling. We herein present the phenotypic and genotypic insights from 142 Indian families with epilepsy with or without comorbidities. Based on the electroclinical findings, epilepsy syndrome diagnosis could be made in 44% (63/142) of the families adopting the latest proposal for the classification by the ILAE task force (2022). Of these, 95% (60/63) of the families exhibited syndromes with developmental epileptic encephalopathy or progressive neurological deterioration. A definitive molecular diagnosis was achieved in 74 of 142 (52%) families. Infantile-onset epilepsy was noted in 81% of these families (61/74). Fifty-five monogenic, four chromosomal, and one imprinting disorder were identified in 74 families. The genetic variants included 65 (96%) single-nucleotide variants/small insertion-deletions, 1 (2%) copy-number variant, and 1 (2%) triplet-repeat expansion in 53 epilepsy-associated genes causing monogenic disorders. Of these, 35 (52%) variants were novel. Therapeutic implications were noted in 51% of families (38/74) with definitive diagnosis. Forty-one out of 66 families with monogenic disorders exhibited autosomal recessive and inherited autosomal dominant disorders with high risk of recurrence.

Identification of genetic causes in children with unexplained epilepsy based on trio-whole exome sequencing.

Genotype and clinical phenotype analyses of 128 children were performed based on whole exome sequencing (WES), providing a reference for the provision of genetic counseling and the precise diagnosis and treatment of epilepsy. A total of 128 children with unexplained epilepsy were included in this study, and all their clinical data were analyzed. The children's treatments, epilepsy control, and neurodevelopmental levels were regularly followed up every 3 months. The genetic diagnostic yield of the 128 children with epilepsy is 50.8%, with an SNV diagnostic yield of 39.8% and a CNV diagnostic yield of 12.5%. Among the 128 children with epilepsy, 57.0% had onset of epilepsy in infancy, 25.8% have more than two clinical seizure forms, 62.5% require two or more anti-epileptic drug treatments, and 72.7% of the children have varying degrees of psychomotor development retardation. There are significant differences between ages of onset, neurodevelopmental levels and the presence of drug resistance in the genetic diagnostic yield (all p < 0.05). The 52 pathogenic/likely pathogenic SNVs involve 31 genes, with genes encoding ion channels having the largest number of mutations (30.8%). There were 16 cases of pathogenic/possibly pathogenic CNVs, among which the main proportions of CNVs were located in chromosome 15 and chromosome 16. Trio-WES is an essential tool for the genetic diagnosis of unexplained epilepsy, with a genetic diagnostic yield of up to 50.8%. Early genetic testing can provide an initiate appropriate therapies and accurate molecular diagnosis.

The clinical and genetic landscape of developmental and epileptic encephalopathies in Egyptian children.

Developmental and epileptic encephalopathies (DEEs) are a heterogeneous group of epilepsies characterized by early-onset, refractory seizures associated with developmental regression or impairment, with a heterogeneous genetic landscape including genes implicated in various pathways and mechanisms. We retrospectively studied the clinical and genetic data of patients with genetic DEE who presented at two tertiary centers in Egypt over a 10-year period. Exome sequencing was used for genetic testing. We report 74 patients from 63 unrelated Egyptian families, with a high rate of consanguinity (58%). The most common seizure type was generalized tonic-clonic (58%) and multiple seizure types were common (55%). The most common epilepsy syndrome was early infantile DEE (50%). All patients showed variable degrees of developmental impairment. Microcephaly, hypotonia, ophthalmological involvement and neuroimaging abnormalities were common. Eighteen novel variants were identified and the phenotypes of five DEE genes were expanded with novel phenotype-genotype associations. Obtaining a genetic diagnosis had implications on epilepsy management in 17 patients with variants in 12 genes. In this study, we expand the phenotype and genotype spectrum of DEE in a large single ethnic cohort of patients. Reaching a genetic diagnosis guided precision management of epilepsy in a significant proportion of patients.

The kappa free light chains index is an accurate diagnostic biomarker for paediatric multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) may occur before the age of 18. Differentiation between paediatric MS (PedMS) and other demyelinating syndromes (ODSs) is challenging. In adult with MS, the kappa free light chain (KFLC) index has proven to be a reliable marker of intrathecal Ig synthesis. OBJECTIVE: To assess the diagnostic value of the KFLC index in a cohort of patients with paediatric-onset, inflammatory disorders of the CNS. METHODS: We included 73 patients and divided them into four groups: PedMS (n = 16), ODS (n = 17), encephalitis and/or inflammatory epilepsy (EE, n = 15), and controls without inflammatory CNS diseases (n = 25). The KFLC index was calculated and compared with the results of the oligoclonal bands determination. RESULTS: The KFLC index was higher in the PedMS group (median (interquartile range (IQR)): 150.9 (41.02-310.6)) than in the ODS (3.37 (2.22-8.11)), the EE (5.53 (2.31-25.81)) and the control group (3.41 (2.27-5.08)), respectively. The best KFLC index cut-off for differentiating between patients with PedMS and controls was 6.83 (sensitivity: 100%; specificity: 92%). A KFLC index over 93.77 indicated that the patient is very likely to have PedMS (sensitivity: 68%; specificity: 100%). CONCLUSION: The KFLC index is a reliable tool for the diagnosis of MS in a paediatric population.

SMC1A epilepsy syndrome: clinical data from a large international cohort.

SMC1A epilepsy syndrome or developmental and epileptic encephalopathy-85 with or without midline brain defects (DEE85, OMIM #301044) is an X-linked neurologic disorder associated with mutations of the SMC1A gene, which is also responsible for about 5% of patients affected by Cornelia de Lange syndrome spectrum (CdLS). Only described in female patients, SMC1A epilepsy syndrome is characterized by the onset of severe refractory epileptic seizures in the first year of life, global developmental delay, a variable degree of intellectual disability, and dysmorphic facial features not typical of CdLS. This was a descriptive observational study for the largest international cohort with this specific disorder. The main goal of this study was to improve the knowledge of the natural history of this phenotype with particular attention to the psychomotor development and the epilepsy data. The analyzed cohort shows normal prenatal growth with the subsequent development of postnatal microcephaly. The incidence of neonatal problems (seizures and respiratory compromise) is considerable (51.4%). There is a significant prevalence of central nervous system (20%) and cardiovascular malformations (20%). Motor skills are generally delayed. The presence of drug-resistant epilepsy is confirmed; the therapeutic role of a ketogenic diet is still uncertain. The significant regression of previously acquired skills following the onset of seizures has been observed. Facial dysmorphisms are variable and no patient shows a classic CdLS phenotype. To sum up, SMC1A variants caused drug-resistant epilepsy in these patients, more than two-thirds of whom were shown to progress to developmental and epileptic encephalopathy. The SMC1A gene variants are all different from each other (apart from a couple of monozygotic twins), demonstrating the absence of a mutational hotspot in the SMC1A gene. Owing to the absence of phenotypic specificity, whole-exome sequencing is currently the diagnostic gold standard.

Very high epilepsy prevalence in rural Southern Rwanda: The underestimated burden of epilepsy in sub-Saharan Africa.

OBJECTIVES: Up to 85% of people living with epilepsy (PwE) reside in low-and middle-income countries. In sub-Saharan Africa, the lifetime prevalence of epilepsy is 16 per 1000 persons. In Northern rural Rwanda, a 47.7 per 1000 prevalence has been reported. As variations in prevalence across geographical areas have been observed, we studied the prevalence in Southern rural Rwanda using the same robust methodology as applied in the North. METHODS: We conducted a three-stage, cross-sectional, door-to-door survey in two rural villages in Southern Rwanda from June 2022 to April 2023. First, trained enumerators administered the validated Limoges questionnaire for epilepsy screening. Second, neurologists examined the persons who had screened positively to confirm the epilepsy diagnosis. Third, cases with an inconclusive assessment were separately reexamined by two neurologists to reevaluate the diagnosis. RESULTS: Enumerators screened 1745 persons (54.4% female, mean age: 24 ± 19.3 years), of whom 304 (17.4%) screened positive. Epilepsy diagnosis was confirmed in 133 (52.6% female, mean age: 30 ± 18.2 years) and active epilepsy in 130 persons. Lifetime epilepsy prevalence was 76.2 per 1000 (95% CI: 64.2-89.7‰). The highest age-specific rate occurred in the 29-49 age group. No gender-specific differences were noted. In 22.6% of the PwE, only non-convulsive seizures occurred. The treatment gap was 92.2%, including a diagnosis gap of 79.4%. CONCLUSION: We demonstrated a very high epilepsy prevalence in Southern rural Rwanda, with over 20% of cases having only non-convulsive seizures, which are often underdiagnosed in rural Africa. In line with previous Rwandan reports, we reiterate the high burden of the disease in the country. Geographic variation in prevalence throughout Africa may result from differences in risk and aetiological factors. Case-control studies are underway to understand such differences and propose adapted health policies for epilepsy prevention.

WONOEP 2022: Neurotechnology for the diagnosis of epilepsy.

Epilepsy's myriad causes and clinical presentations ensure that accurate diagnoses and targeted treatments remain a challenge. Advanced neurotechnologies are needed to better characterize individual patients across multiple modalities and analytical techniques. At the XVIth Workshop on Neurobiology of Epilepsy: Early Onset Epilepsies: Neurobiology and Novel Therapeutic Strategies (WONOEP 2022), the session on "advanced tools" highlighted a range of approaches, from molecular phenotyping of genetic epilepsy models and resected tissue samples to imaging-guided localization of epileptogenic tissue for surgical resection of focal malformations. These tools integrate cutting edge research, clinical data acquisition, and advanced computational methods to leverage the rich information contained within increasingly large datasets. A number of common challenges and opportunities emerged, including the need for multidisciplinary collaboration, multimodal integration, potential ethical challenges, and the multistage path to clinical translation. Despite these challenges, advanced epilepsy neurotechnologies offer the potential to improve our understanding of the underlying causes of epilepsy and our capacity to provide patient-specific treatment.

Potential merits and flaws of large language models in epilepsy care: A critical review.

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.

Delays to care in infantile epileptic spasms syndrome: Racial and ethnic inequities.

OBJECTIVE: Non-Hispanic (NH) Black children are less likely to receive a standard treatment course for infantile epileptic spasms syndrome (IESS) than White/NH children at pediatric tertiary care epilepsy centers in the United States. However, if inequities exist in time to diagnosis is unknown. Diagnostic delays as little as 1 week can be associated with worse developmental outcomes. METHODS: Diagnostic delays were evaluated in a retrospective cohort of 100 children with new onset IESS between January 2019 and May 2022. RESULTS: Children with Black, Indigenous, and People of Color (BIPOC) caregivers were more likely to experience clinically significant delays in referral from first provider to neurologist, when compared to White/NH children, even after controlling for other demographic and clinical variables (odds ratio = 4.98, confidence interval = 1.24-19.94, p = .023). SIGNIFICANCE: Disproportionate diagnostic delays place BIPOC children at risk of adverse developmental and epilepsy outcomes. Further interventional prospective and qualitative studies are needed to address inequities in care.

Prediction tools and risk stratification in epilepsy surgery.

OBJECTIVE: This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2). METHODS: We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3-4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%-70%), and low (ESGS = 2, SFS = 0-1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures. RESULTS: The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05). SIGNIFICANCE: ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.

Long-term physical and psychiatric morbidities and mortality of untreated, deferred, and immediately treated epilepsy.

OBJECTIVE: In Australia, 30% of newly diagnosed epilepsy patients were not immediately treated at diagnosis. We explored health outcomes between patients receiving immediate, deferred, or no treatment, and compared them to the general population. METHODS: Adults with newly diagnosed epilepsy in Western Australia between 1999 and 2016 were linked with statewide health care data collections. Health care utilization, comorbidity, and mortality at up to 10 years postdiagnosis were compared between patients receiving immediate, deferred, and no treatment, as well as with age- and sex-matched population controls. RESULTS: Of 603 epilepsy patients (61% male, median age = 40 years) were included, 422 (70%) were treated immediately at diagnosis, 110 (18%) received deferred treatment, and 71 (12%) were untreated at the end of follow-up (median = 6.8 years). Immediately treated patients had a higher 10-year rate of all-cause admissions or emergency department presentations than the untreated (incidence rate ratio [IRR] = 2.0, 95% confidence interval [CI] = 1.4-2.9) and deferred treatment groups (IRR = 1.7, 95% CI = 1.0-2.8). They had similar 10-year risks of mortality and developing new physical and psychiatric comorbidities compared with the deferred and untreated groups. Compared to population controls, epilepsy patients had higher 10-year mortality (hazard ratio = 2.6, 95% CI = 2.1-3.3), hospital admissions (IRR = 2.3, 95% CI = 1.6-3.3), and psychiatric outpatient visits (IRR = 3.2, 95% CI = 1.6-6.3). Patients with epilepsy were also 2.5 (95% CI = 2.1-3.1) and 3.9 (95% CI = 2.6-5.8) times more likely to develop a new physical and psychiatric comorbidity, respectively. SIGNIFICANCE: Newly diagnosed epilepsy patients with deferred or no treatment did not have worse outcomes than those immediately treated. Instead, immediately treated patients had greater health care utilization, likely reflecting more severe underlying epilepsy etiology. Our findings emphasize the importance of individualizing epilepsy treatment and recognition and management of the significant comorbidities, particularly psychiatric, that ensue following a diagnosis of epilepsy.