Knowledge, attitude, and practice of epilepsy in rural Sri Lanka

AbstractKnowledge, attitude, and practice in relation to epilepsy in developing countries appears to be different from that in developed countries. This study was conducted to evaluate knowledge, attitudes, expectations, sociocultural aspects, patient characteristics, disease characteristics, pattern of drug therapy, and outcome of patients with epilepsy in rural Sri Lanka. Data were collected from 207 patients attending an epilepsy clinic. In general the study shows a positive trend in knowledge, expectations and attitude toward epilepsy. Social morbidity is reported from 53.6% indicating that public attitude towards epilepsy needs to be changed. Alternative modes of treatment have been tried by 45.9%, reflecting the cultural beliefs in the society. 75% are on monotherapy and carbamazepine is the most commonly used drug. Seizure control is excellent (no seizures during the preceding 6 months) in 33.8%. Side effects of antiepileptic drugs are reported by 76.3%. Various kinds of medical morbidity have been experienced by 32.9% of patients

Methods for the Detection of Seizure Bursts in Epilepsy

Background: Seizure clusters and “bursts” are of clinical importance. Clusters are reported to be a marker of antiepileptic drug resistance. Additionally, seizure clustering has been found to be associated with increased morbidity and mortality. However, there are no statistical methods described in the literature to delineate bursting phenomenon in epileptic seizures.Methods: We present three automatic burst detection methods referred to as precision constrained grouping (PCG), burst duration constrained grouping (BCG), and interseizure interval constrained grouping (ICG). Concordance correlation coefficients were used to confirm the pairwise agreement between common bursts isolated using these three automatic burst detection procedures. Additionally, three graphical methods were employed to demonstrate seizure bursts: modified scatter plots, staircase plots, and dropline plots. Burst detection procedures are demonstrated on data from continuous intracranial ambulatory EEG monitoring in a patient diagnosed with drug-refractory focal epilepsy.Results: We analyzed 1,569 seizures, from our assigned index patient, captured on ambulatory intracranial EEG monitoring. A total of 31, 32, and 32 seizure bursts were detected by the three quantitative methods (BCG, ICG, and PCG), respectively. The concordance correlation coefficient was ≥0.99 signifying considerably stronger than chance burst detector agreements with one another.Conclusions: Bursting is a quantifiable temporal phenomenon in epilepsy and seizure bursts can be reliably detected using our methodology

Mapping topography and network of brain injury in patients with disorders of consciousness

BackgroundThere is a growing interest in the topography of brain regions associated with disorders of consciousness. This has caused increased research output, yielding many publications investigating the topic with varying methodologies. The objective of this study was to ascertain the topographical regions of the brain most frequently associated with disorders of consciousness.MethodsWe performed a cross-sectional text-mining analysis of disorders of consciousness studies. A text mining algorithm built in the Python programming language searched documents for anatomical brain terminology. We reviewed primary PubMed studies between January 1st 2000 to 8th February 2023 for the search query “Disorders of Consciousness.” The frequency of brain regions mentioned in these articles was recorded, ranked, then built into a graphical network. Subgroup analysis was performed by evaluating the impact on our results if analyses were based on abstracts, full-texts, or topic-modeled groups (non-negative matrix factorization was used to create subgroups of each collection based on their key topics). Brain terms were ranked by their frequency and concordance was measured between subgroups. Graphical analysis was performed to explore relationships between the anatomical regions mentioned. The PageRank algorithm (used by Google to list search results in order of relevance) was used to determine global importance of the regions.ResultsThe PubMed search yielded 24,944 abstracts and 3,780 full texts. The topic-modeled subgroups contained 2015 abstracts and 283 full texts. Text Mining across all document groups concordantly ranked the thalamus the highest (Savage score = 11.716), followed by the precuneus (Savage score = 4.983), hippocampus (Savage score = 4.483). Graphical analysis had 5 clusters with the thalamus once again having the highest PageRank score (PageRank = 0.0344).ConclusionThe thalamus, precuneus and cingulate cortex are strongly associated with disorders of consciousness, likely due to the roles they play in maintaining awareness and involvement in the default mode network, respectively. The findings also suggest that other areas of the brain like the cerebellum, cuneus, amygdala and hippocampus also share connections to consciousness should be further investigated

Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis

Summary Background A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME. Methods We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed – last updated on March 11, 2021 – including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/). Findings  368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68–0·73). Interpretation We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools. Funding MING fonds

Characteristics of Epileptiform Discharge Duration and Interdischarge Interval in Genetic Generalized Epilepsies

We sought to investigate (1) the characteristics of epileptiform discharge (ED) duration and interdischarge interval (IDI) and (2) the influence of vigilance state on the ED duration and IDI in genetic generalized epilepsy (GGE). In a cohort of patients diagnosed with GGE, 24-h ambulatory EEG recordings were performed prospectively. We then tabulated durations, IDI, and vigilance state in relation to all EDs captured on EEGs. We used K-means cluster analysis and finite mixture modeling to quantify and characterize the groups of ED duration and IDI. To investigate the influence of sleep, we calculated the mean, median, and SEM in each population from all subjects for sleep state and wakefulness separately, followed by the Kruskal–Wallis test to compare the groups. We analyzed 4,679 EDs and corresponding IDI from 23 abnormal 24-h ambulatory EEGs. Our analysis defined two populations of ED durations and IDI: short and long. In all populations, both ED durations and IDI were significantly longer in wakefulness. Our results highlight different characteristics of ED populations in GGE and the influence by the sleep–wake cycle

The prolonged ambulatory electroencephalography in genetic generalised epilepsies: characteristics and predictors of prognosis

© 2015 Dr. Udaya Kumara SeneviratneINTRODUCTION: Epilepsy is a common and serious neurological disorder affecting 65 million people worldwide. Idiopathic (genetic) generalised epilepsy (GGE) constitutes 15-20% of patients with epilepsy. The electroencephalogram (EEG) plays a crucial role in the diagnosis and classification of epilepsy. Bilateral, symmetrical and synchronous generalised spike-wave activity is considered to be the electrographic hallmark of GGE. However, atypical EEG and clinical features as well as the value of EEG in predicting long-term prognosis of IGE have not been well studied in the past. This thesis presents the first detailed and systematic study on typical and atypical EEG findings, atypical focal seizure symptoms and prognosis of GGE based on quantified, 24-hour, ambulatory EEG data. AIMS AND METHODS: (a) To quantify the typical EEG abnormalities in GGE, describe the circadian variations of epileptiform discharges and explore the differences among syndromes: I prospectively recruited and studied a cohort of patients diagnosed with GGE and classified into syndromes based on International League against Epilepsy criteria. All patients had 24-hour ambulatory EEG recordings according to a standard protocol. I quantified EEG abnormalities and used analysis of variance test to explore the differences among syndromes. (b) To quantify the atypical EEG abnormalities in GGE and to explore the relationship between atypical EEG findings and clinical variables: I used generalised linear mixed models to explore the influence of clinical variables (syndrome, age, state of arousal, number of antiepileptic drugs, seizure-free duration, epilepsy duration) on the outcome of atypical EEG characteristics. (c) To explore the association between seizure-free duration and EEG parameters: I analysed the EEG predictors of seizure recurrence with stepwise Cox proportional hazards regression model. (d) To evaluate focal seizure symptoms among patients diagnosed with GGE and to explore the association between focal seizure symptoms and focal epileptiform discharges as well as seizure-free duration: I elicited focal seizure symptoms (FSS) using a standardised, validated questionnaire. Chi-square test for independence was used to explore the relationship between focal epielptiform discharges and FSS. Regression analysis was conducted to examine the relationship between the duration of seizure freedom and FSS. RESULTS: A total of 120 patients were recruited, of which 13 had normal ambulatory EEGs. The final cohort consisted of 33.3% males and 66.7% females with mean age of 28.5±10.7 years (range, 13-58). The mean age of seizure onset was 13.3±5.1. (a) The vast majority (96%) of epileptiform discharges are symmetric in amplitude with fronto-central maximum in topography. Two-thirds of discharges occur in sleep. Epileptiform discharges demonstrate circadian patterns with four peaks; before midnight, after midnight, early morning and afternoon. There are significant differences in spike densities among syndromes. In general quantified epileptiform activity is higher in JAE and JME than CAE and GTCSO. (b) 66% of 24-hour EEG recordings show atypical abnormalities, significantly influenced by the state of arousal. (c) Longer generalised paroxysms are associated with shorter duration of seizure freedom in GGE. (d) 52% of patients report focal seizure symptoms. There is no association between focal seizure symptoms and focal epileptiform discharges. However, focal seizure symptoms are associated with shorter seizure-free duration. CONCLUSION: The results demonstrate the value of prolonged EEG as a biomarker of diagnosis and potentially prognosis. Prolonged EEG recordings have demonstrated: 1) There are circadian patterns in the occurrence of epileptifom discharges. 2) Atypical EEG features and FSS are common in GGE. 3) Recognition of these variations is important to avoid misdiagnosis and inappropriate choice of antiepileptic drugs

Bathing epilepsy

AbstractA patient who had complex partial seizures provoked by bathing is reported. All the attacks occurred during or immediately after bathing, irrespective of water temperature. The semiology was suggestive of a seizure focus in the mesial temporal lobe. Though there are some similarities with hot-water epilepsy, this case appears to be a distinct type of reflex epilepsy