300 research outputs found

    Cost-effectiveness of cenobamate for focal seizures in people with drug-resistant epilepsy

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    OBJECTIVES: To estimate the cost-effectiveness of add-on cenobamate in the UK when used to treat drug-resistant focal seizures in adults who are not adequately controlled with at least two prior antiseizure medication, including at least one used adjunctively. METHODS: We estimated the cost per quality-adjusted life-year (QALY) for cenobamate compared to brivaracetam, eslicarbazepine, lacosamide and perampanel in the UK National Health Service over a lifetime time horizon. We used a Markov cohort structure to determine response to treatment, using pooled data from three long-term studies of cenobamate. A network meta-analysis informed the likelihood of response to therapy with brivaracetam, eslicarbazepine, lacosamide and perampanel relative to cenobamate. Once individuals discontinued treatment, they transitioned to subsequent treatment health states, including other antiseizure medicines, surgery, and vagus nerve stimulation. Costs included treatment, administration, routine monitoring, event management and adverse events. Published evidence and expert opinion informed the likelihood of response to subsequent treatments, associated adverse events, and costs. Utility data was based on short-form, six dimensions utility. Discounting was applied at 3.5% per annum as per National Institute for Health and Care Excellence guidance. Uncertainty was explored through deterministic and probabilistic sensitivity analyses. RESULTS: In the base case, cenobamate led to cost savings of 拢51,967 (compared to brivaracetam), 拢21,080 (compared to eslicarbazepine), 拢33,619 (compared to lacosamide), and 拢28,296 (compared to perampanel) and increased QALYs of 1.047 (compared to brivaracetam), 0.598 (compared to eslicarbazepine), 0.776 (compared to lacosamide), and 0.703 (compared to perampanel) per individual over a lifetime time horizon. Cenobamate also dominated the four drugs across most sensitivity analyses. Differences were due to reduced seizure frequency with cenobamate relative to comparators. SIGNIFICANCE: Cenobamate improved QALYs and was less costly than brivaracetam, eslicarbazepine, lacosamide and perampanel. Therefore, cenobamate may be considered as a cost-effective adjunctive antiseizure medication for people with drug-resistant focal seizures

    Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

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    Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics

    Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

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    BACKGROUND: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. METHODS: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. FINDINGS: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48路5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0路92 (95% CI 0路91-0路94, sensitivity 85路0%, specificity 93路7%) in the internal-validation dataset and 0路95 (0路92-0路98, sensitivity 97路5%, specificity 82路4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0路94, 0路93-0路96, sensitivity 88路2%, specificity 95路3%). INTERPRETATION: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy. FUNDING: The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre

    Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

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    Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48路5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0路92 (95% CI 0路91鈥0路94, sensitivity 85路0%, specificity 93路7%) in the internal-validation dataset and 0路95 (0路92鈥0路98, sensitivity 97路5%, specificity 82路4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0路94, 0路93鈥0路96, sensitivity 88路2%, specificity 95路3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy

    Implementing WHO's Intersectoral Global Action Plan for epilepsy and other neurological disorders in Southeast Asia: a聽proposal

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    Summary: The World Health Assembly approved the Intersectoral Global Action Plan for epilepsy and neurological disorders. Member states, including those in Southeast Asia, must now prepare to achieve IGAP's strategic targets by embracing novel approaches and strengthening existing policies and practices. We propose and present evidence to support four such processes. The opening course should engage all stakeholders to develop people-centric instead of outcome-centric approaches. Rather than caring for convulsive epilepsy alone, as currently done, primary care providers should also be skilled in diagnosing and treating focal and non-motor seizures. This could reduce the diagnostic gap as over half of epilepsies present with focal seizures. Currently, primary care providers lack knowledge and skills to manage focal seizures. Technology-enabled aids can help overcome this limitation. Lastly, there is need to add newer 鈥渆asy to use鈥 epilepsy medicines to Essential Medicines lists in light of emerging evidence for better tolerability, safety and user-friendliness

    TMS-evoked EEG potentials demonstrate altered cortical excitability in migraine with aura

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    Migraine is associated with altered sensory processing, that may be evident as changes in cortical responsivity due to altered excitability, especially in migraine with aura. Cortical excitability can be directly assessed by combining transcranial magnetic stimulation with electroencephalography (TMS-EEG). We measured TMS evoked potential (TEP) amplitude and response consistency as these measures have been linked to cortical excitability but were not yet reported in migraine. We recorded 64-channel EEG during single-pulse TMS on the vertex interictally in 10 people with migraine with aura and 10 healthy controls matched for age, sex and resting motor threshold. On average 160 pulses around resting motor threshold were delivered through a circular coil in clockwise and counterclockwise direction. Trial-averaged TEP responses, frequency spectra and phase clustering (over the entire scalp as well as in frontal, central and occipital midline electrode clusters) were compared between groups, including comparison to sham-stimulation evoked responses. Migraine and control groups had a similar distribution of TEP waveforms over the scalp. In migraine with aura, TEP responses showed reduced amplitude around the frontal and occipital N100 peaks. For the migraine and control groups, responses over the scalp were affected by current direction for the primary motor cortex, somatosensory cortex and sensory association areas, but not for frontal, central or occipital midline clusters. This study provides evidence of altered TEP responses in-between attacks in migraine with aura. Decreased TEP responses around the N100 peak may be indicative of reduced cortical GABA-mediated inhibition and expand observations on enhanced cortical excitability from earlier migraine studies using more indirect measurements

    Magnetic resonance imaging findings in Kenyans and South Africans with active convulsive epilepsy: an observational study

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    Objective: Focal epilepsy is common in low- and middle-income countries. The frequency and nature of possible underlying structural brain abnormalities have, however, not been fully assessed. Methods: We evaluated the possible structural causes of epilepsy in 331 people with epilepsy (240 from Kenya and 91 from South Africa) identified from community surveys of active convulsive epilepsy. Magnetic resonance imaging (MRI) scans were acquired on 1.5-Tesla scanners to determine the frequency and nature of any underlying lesions. We estimated the prevalence of these abnormalities using Bayesian priors (from an earlier pilot study) and observed data (from this study). We used a mixed-effect modified Poisson regression approach with the site as a random effect to determine the clinical features associated with neuropathology. Results: MRI abnormalities were found in 140 of 240 (modeled prevalence鈥=鈥59%, 95% confidence interval [CI]: 53%鈥64%) of people with epilepsy in Kenya, and in 62 of 91 (modeled prevalence鈥=鈥65%, 95% CI: 57%鈥73%) in South Africa, with a pooled modeled prevalence of 61% (95% CI: 56%鈥66%). Abnormalities were common in those with a history of adverse perinatal events (15/23 [65%, 95% CI: 43%鈥84%]), exposure to parasitic infections (83/120 [69%, 95% CI: 60%鈥77%]) and focal electroencephalographic features (97/142 [68%, 95% CI: 60%鈥76%]), but less frequent in individuals with generalized electroencephalographic features (44/99 [44%, 95% CI: 34%鈥55%]). Most abnormalities were potentially epileptogenic (167/202, 82%), of which mesial temporal sclerosis (43%) and gliosis (34%) were the most frequent. Abnormalities were associated with co-occurrence of generalized non-convulsive seizures (relative risk [RR]鈥=鈥1.12, 95% CI: 1.04鈥1.25), lack of family history of seizures (RR鈥=鈥0.91, 0.86鈥0.96), convulsive status epilepticus (RR鈥=鈥1.14, 1.08鈥1.21), frequent seizures (RR鈥=鈥1.12, 1.04鈥1.20), and reported use of anti-seizure medication (RR鈥=鈥1.22, 1.18鈥1.26). Significance: MRI identified pathologies are common in people with epilepsy in Kenya and South Africa. Mesial temporal sclerosis, the most common abnormality, may be amenable to surgical correction. MRI may have a diagnostic value in rural Africa, but future longitudinal studies should examine the prognostic role

    Association of Mortality and Risk of Epilepsy With Type of Acute Symptomatic Seizure After Ischemic Stroke and an Updated Prognostic Model

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    IMPORTANCE: Acute symptomatic seizures occurring within 7 days after ischemic stroke may be associated with an increased mortality and risk of epilepsy. It is unknown whether the type of acute symptomatic seizure influences this risk. OBJECTIVE: To compare mortality and risk of epilepsy following different types of acute symptomatic seizures. DESIGN, SETTING, AND PARTICIPANTS: This cohort study analyzed data acquired from 2002 to 2019 from 9 tertiary referral centers. The derivation cohort included adults from 7 cohorts and 2 case-control studies with neuroimaging-confirmed ischemic stroke and without a history of seizures. Replication in 3 separate cohorts included adults with acute symptomatic status epilepticus after neuroimaging-confirmed ischemic stroke. The final data analysis was performed in July 2022. EXPOSURES: Type of acute symptomatic seizure. MAIN OUTCOMES AND MEASURES: All-cause mortality and epilepsy (at least 1 unprovoked seizure presenting >7 days after stroke). RESULTS: A total of 4552 adults were included in the derivation cohort (2547 male participants [56%]; 2005 female [44%]; median age, 73 years [IQR, 62-81]). Acute symptomatic seizures occurred in 226 individuals (5%), of whom 8 (0.2%) presented with status epilepticus. In patients with acute symptomatic status epilepticus, 10-year mortality was 79% compared with 30% in those with short acute symptomatic seizures and 11% in those without seizures. The 10-year risk of epilepsy in stroke survivors with acute symptomatic status epilepticus was 81%, compared with 40% in survivors with short acute symptomatic seizures and 13% in survivors without seizures. In a replication cohort of 39 individuals with acute symptomatic status epilepticus after ischemic stroke (24 female; median age, 78 years), the 10-year risk of mortality and epilepsy was 76% and 88%, respectively. We updated a previously described prognostic model (SeLECT 2.0) with the type of acute symptomatic seizures as a covariate. SeLECT 2.0 successfully captured cases at high risk of poststroke epilepsy. CONCLUSIONS AND RELEVANCE: In this study, individuals with stroke and acute symptomatic seizures presenting as status epilepticus had a higher mortality and risk of epilepsy compared with those with short acute symptomatic seizures or no seizures. The SeLECT 2.0 prognostic model adequately reflected the risk of epilepsy in high-risk cases and may inform decisions on the continuation of antiseizure medication treatment and the methods and frequency of follow-up

    Prognosis of adults and children following a first unprovoked seizure

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    BackgroundEpilepsy is clinically defined as two or more unprovoked epileptic seizures more than 24 hours apart. Given that, a diagnosis of epilepsy can be associated with significant morbidity and mortality, it is imperative that clinicians (and people with seizures and their relatives) have access to accurate and reliable prognostic estimates, to guide clinical practice on the risks of developing further unprovoked seizures (and by definition, a diagnosis of epilepsy) following single unprovoked epileptic seizure.Objectives1. To provide an accurate estimate of the proportion of individuals going on to have further unprovoked seizures at subsequent time points following a single unprovoked epileptic seizure (or cluster of epileptic seizures within a 24-hour period, or a first episode of status epilepticus), of any seizure type (overall prognosis). 2. To evaluate the mortality rate following a first unprovoked epileptic seizure.Search methodsWe searched the following databases on 19 September 2019 and again on 30 March 2021, with no language restrictions. The Cochrane Register of Studies (CRS Web), MEDLINE Ovid (1946 to March 29, 2021), SCOPUS (1823 onwards), ClinicalTrials.gov, the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). CRS Web includes randomized or quasi-randomized, controlled trials from PubMed, Embase, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform (ICTRP), the Cochrane Central Register of Controlled Trials (CENTRAL), and the Specialized Registers of Cochrane Review Groups including Epilepsy. In MEDLINE (Ovid) the coverage end date always lags a few days behind the search date.Selection criteriaWe included studies, both retrospective and prospective, of all age groups (except those in the neonatal period (Data collection and analysisTwo review authors conducted the initial screening of titles and abstracts identified through the electronic searches, and removed non-relevant articles. We obtained the full-text articles of all remaining potentially relevant studies, or those whose relevance could not be determined from the abstract alone and two authors independently assessed for eligibility. All disagreements were resolved through discussion with no need to defer to a third review author. We extracted data from included studies using a data extraction form based on the checklist for critical appraisal and data extraction for systematicreviews of prediction modelling studies (CHARMS). Two review authors then appraised the included studies, using a standardised approach based on the quality in prognostic studies (QUIPS) tool, which was adapted for overall prognosis (seizure recurrence). We conducted a meta-analysis using Review Manager 2014, with a random-effects generic inverse variance meta-analysis model, which accounted for any between-study heterogeneity in the prognostic effect. We then summarised the meta-analysis by the pooled estimate (the average prognostic factor effect), its 95% confidence interval (CI), the estimates of I虏 and Tau虏 (heterogeneity), and a 95% prediction interval for the prognostic effect in a single population at three various time points, 6 months, 12 months and 24 months. Subgroup analysis was performed according to the ages of the cohorts included; studies involving all ages, studies that recruited adult only and those that were purely paediatric.Main resultsFifty-eight studies (involving 54 cohorts), with a total of 12,160 participants (median 147, range 31 to 1443), met the inclusion criteria for the review. Of the 58 studies, 26 studies were paediatric studies, 16 were adult and the remaining 16 studies were a combination of paediatric and adult populations. Most included studies had a cohort study design with two case-control studies and one nested case-control study. Thirty-two studies (29 cohorts) reported a prospective longitudinal design whilst 15 studies had a retrospective design whilst the remaining studies were randomised controlled trials. Nine of the studies included presented mortality data following a first unprovoked seizure. For a mortality study to be included, a proportional mortality ratio (PMR) or a standardised mortality ratio (SMR) had to be given at a specific time point following a first unprovoked seizure. To be included in the meta-analysis a study had to present clear seizure recurrence data at 6 months, 12 months or 24 months. Forty-six studies were included in the meta-analysis, of which 23 were paediatric, 13 were adult, and 10 were a combination of paediatric and adult populations. A meta-analysis was performed at three time points; six months, one year and two years for all ages combined, paediatric and adult studies, respectively. We found an estimated overall seizure recurrence of all included studies at six months of 27% (95% CI 24% to 31%), 36% (95% CI 33% to 40%) at one year and 43% (95% CI 37% to 44%) at two years, with slightly lower estimates for adult subgroup analysis and slightly higher estimates for paediatric subgroup analysis. It was not possible to provide a summary estimate of the risk of seizure recurrence beyond these time points as most of the included studies were of short follow-up and too few studies presented recurrence rates at a single time point beyond two years. The evidence presented was found to be of moderate certainty.Authors' conclusionsDespite the limitations of the data (moderate-certainty of evidence), mainly relating to clinical and methodological heterogeneity we have provided summary estimates for the likely risk of seizure recurrence at six months, one year and two years for both children and adults. This provides information that is likely to be useful for the clinician counselling patients (or their parents) on the probable risk of further seizures in the short-term whilst acknowledging the paucity of long-term recurrence data, particularly beyond 10 years
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