Incidence of Epilepsy and Seizures Over the First 6 Months After a COVID-19 Diagnosis: A Retrospective Cohort Study

BACKGROUND: The relationship between COVID-19 and epilepsy is uncertain. We studied the potential association between COVID-19 and seizures or epilepsy in the six months after infection. METHODS: We applied validated methods to an electronic health records network (TriNetX Analytics) of 81 million people. We closely matched people with COVID-19 infections to those with influenza. In each cohort, we measured the incidence and hazard ratios (HRs) of seizures and of epilepsy. We stratified data by age and by whether the person was hospitalized during the acute infection. We then explored time-varying HRs to assess temporal patterns of seizure or epilepsy diagnoses. RESULTS: We analyzed 860,934 electronic health records. After matching, this yielded two cohorts each of 152,754 patients. COVID-19 was associated with an increased risk of seizures and epilepsy compared to influenza. The incidence of seizures within 6 months of COVID-19 was 0.81% (95% CI, 0.75-0.88; HR compared to influenza 1.55 (1.39-1.74)). The incidence of epilepsy was 0.30% (0.26-0.34; HR compared to influenza 1.87 (1.54-2.28)). The HR of epilepsy after COVID-19 compared to influenza was greater in people who had not been hospitalized and in individuals aged under 16 years. The time of peak HR after infection differed by age and hospitalization status. CONCLUSIONS: The incidence of new seizures or epilepsy diagnoses in the six months following COVID-19 was low overall, but higher than in matched patients with influenza. This difference was more marked in people who were not hospitalized, highlighting the risk of epilepsy and seizures even in those with less severe infection. Children appear at particular risk of seizures and epilepsy after COVID-19 providing another motivation to prevent COVID-19 infection in pediatric populations. That the varying time of peak risk related to hospitalization and age may provide clues as to the underlying mechanisms of COVID-associated seizures and epilepsy

Hippocampal MRS and subfield volumetry at 7T detects dysfunction not specific to seizure focus

Ultra high-field 7T MRI offers sensitivity to localize hippocampal pathology in temporal lobe epilepsy (TLE), but has rarely been evaluated in patients with normal-appearing clinical MRI. We applied multimodal 7T MRI to assess if focal subfield atrophy and deviations in brain metabolites characterize epileptic hippocampi. Twelve pre-surgical TLE patients (7 MRI-negative) and age-matched healthy volunteers were scanned at 7T. Hippocampal subfields were manually segmented from 600μm isotropic resolution susceptibility-weighted images. Hippocampal metabolite spectra were acquired to determine absolute concentrations of glutamate, glutamine, myo-inositol, NAA, creatine and choline. We performed case-controls analyses, using permutation testing, to identify abnormalities in hippocampal imaging measures in individual patients, for evaluation against clinical evidence of seizure lateralisation and neuropsychological memory test scores. Volume analyses identified hippocampal subfield atrophy in 9/12 patients (75%), commonly affecting CA3. 7/8 patients had altered metabolite concentrations, most showing reduced glutamine levels (62.5%). However, neither volume nor metabolite deviations consistently lateralized the epileptogenic hippocampus. Rather, lower subiculum volumes and glutamine concentrations correlated with impaired verbal memory performance. Hippocampal subfield and metabolic abnormalities detected at 7T appear to reflect pathophysiological processes beyond epileptogenesis. Despite limited diagnostic contributions, these markers show promise to help elucidate mnemonic processing in TLE

Reflex cardioinhibitory syncope potentially related to SARS-CoV-2 infection: a case report

Autonomic dysfunction related to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is increasingly described in the literature. We report the case of a 30-year-old male with a background of asthma and migraine who experienced a second episode of SARS-CoV-2 infection characterized by mild respiratory symptoms. Twenty-four days after the symptom onset, he developed acute syncope. A tilt test revealed a neuromediated cardioinhibitory response with asystole (Vasovagal Syncope International Study – VASIS type 2B). The temporal association between SARS-CoV-2 infection and syncope seems to indicate a probable causal relationship, which requires corroboration by future studies

Association of dementia risk with focal epilepsy and modifiable cardiovascular risk factors

Importance: Epilepsy has been associated with cognitive impairment and potentially dementia in older individuals. However, the extent to which epilepsy may increase dementia risk, how this compares with other neurological conditions, and how modifiable cardiovascular risk factors may affect this risk remain unclear. Objective: To compare the differential risks of subsequent dementia for focal epilepsy compared with stroke and migraine as well as healthy controls, stratified by cardiovascular risk. Design, Setting, and Participants: This cross-sectional study is based on data from the UK Biobank, a population-based cohort of more than 500 000 participants aged 38 to 72 years who underwent physiological measurements and cognitive testing and provided biological samples at 1 of 22 centers across the United Kingdom. Participants were eligible for this study if they were without dementia at baseline and had clinical data pertaining to a history of focal epilepsy, stroke, or migraine. The baseline assessment was performed from 2006 to 2010, and participants were followed up until 2021. Exposures: Mutually exclusive groups of participants with epilepsy, stroke, and migraine at baseline assessment and controls (who had none of these conditions). Individuals were divided into low, moderate, or high cardiovascular risk groups based on factors that included waist to hip ratio, history of hypertension, hypercholesterolemia, diabetes, and smoking pack-years. Main Outcomes and Measures: Incident all-cause dementia; measures of executive function; and brain total hippocampal, gray matter, and white matter hyperintensity volumes. Results: Of 495 149 participants (225 481 [45.5%] men; mean [SD] age, 57.5 [8.1] years), 3864 had a diagnosis of focal epilepsy only, 6397 had a history of stroke only, and 14 518 had migraine only. Executive function was comparable between participants with epilepsy and stroke and worse than the control and migraine group. Focal epilepsy was associated with a higher risk of developing dementia (hazard ratio [HR], 4.02; 95% CI, 3.45 to 4.68; P < .001), compared with stroke (HR, 2.56; 95% CI, 2.28 to 2.87; P < .001), or migraine (HR, 1.02; 95% CI, 0.85 to 1.21; P = .94). Participants with focal epilepsy and high cardiovascular risk were more than 13 times more likely to develop dementia (HR, 13.66; 95% CI, 10.61 to 17.60; P < .001) compared with controls with low cardiovascular risk. The imaging subsample included 42 353 participants. Focal epilepsy was associated with lower hippocampal volume (mean difference, −0.17; 95% CI, −0.02 to −0.32; t = −2.18; P = .03) and lower total gray matter volume (mean difference, −0.33; 95% CI, −0.18 to −0.48; t = −4.29; P < .001) compared with controls. There was no significant difference in white matter hyperintensity volume (mean difference, 0.10; 95% CI, −0.07 to 0.26; t = 1.14; P = .26). Conclusions and Relevance: In this study, focal epilepsy was associated with a significant risk of developing dementia, to a greater extent than stroke, which was magnified substantially in individuals with high cardiovascular risk. Further findings suggest that targeting modifiable cardiovascular risk factors may be an effective intervention to reduce dementia risk in individuals with epilepsy

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

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 (48·5%) 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 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93 ·7%) in the internal-validation dataset and 0 ·95 (0·92–0·98, sensitivity 97 ·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). 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

Impact of the COVID-19 pandemic on people with epilepsy: Findings from the US arm of the COV-E study

Objectives: As part of the COVID-19 and Epilepsy (COV-E) global study, we aimed to understand the impact of COVID-19 on the medical care and well-being of people with epilepsy (PWE) in the United States, based on their perspectives and those of their caregivers. Methods: Separate surveys designed for PWE and their caregivers were circulated from April 2020 to July 2021; modifications in March 2021 included a question about COVID-19 vaccination status. Results: We received 788 responses, 71% from PWE (n = 559) and 29% (n = 229) from caregivers of persons with epilepsy. A third (n = 308) of respondents reported a change in their health or in the health of the person they care for. Twenty-seven percent (n = 210) reported issues related to worsening mental health. Of respondents taking ASMs (n = 769), 10% (n = 78) reported difficulty taking medications on time, mostly due to stress causing forgetfulness. Less than half of respondents received counseling on mental health and stress. Less than half of the PWE reported having discussions with their healthcare providers about sleep, ASMs, and potential side effects, while a larger proportion of caregivers (81%) reported having had discussions with their healthcare providers on the same topics. More PWE and caregivers reported that COVID-19-related measures caused adverse impact on their health in the post-vaccine period than during the pre-vaccine period, citing mental health issues as the primary reason. Significance: Our findings indicate that the impact of the COVID-19 pandemic in the US on PWE is multifaceted. Apart from the increased risk of poor COVID-19 outcomes, the pandemic has also had negative effects on mental health and self-management. Healthcare providers must be vigilant for increased emotional distress in PWE during the pandemic and consider the importance of effective counseling to diminish risks related to exacerbated treatment gaps

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

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 (48·5%) 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 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). 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