SUDEP Risk Research Articles

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit.

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

Management of a first unprovoked epileptic seizure in adolescence and adulthood.

An epileptic seizure is one of the causes of so-called "transient neurological events" (TNEs). The differential diagnosis of a TNE relies mainly on history and physical examination. Laboratory markers are less frequently useful. After diagnosing an epileptic seizure, a distinction must be made between an acute symptomatic and an unprovoked seizure, since they have different treatments and prognosis. History, physical examination and other examinations (laboratory and imaging) are paramount in this distinction. After the diagnosis of a first unprovoked seizure, an EEG should be requested which may aid in establishing the diagnosis, evaluating the recurrence risk or ascertaining the self-limited nature of the seizure. 3T-MRI with an epilepsy protocol can be considered when CT has not clarified the aetiology. The decision to treat should be discussed with the patient/relatives, taking into account the risk of recurrence, the clinical characteristics (aetiology, seizure type, age, job, epileptic seizure schedule, comorbidities and polymedication), probability of AED side effects, and stigmatization. Nowadays, the chosen regimen is usually monotherapy with a second-generation AED that better suits the patient's characteristics, comorbidities and concurrent medication. Counselling should include first aid, precipitating factors, sport and physical exercise in order to avoid possible driving restrictions, the need for therapy compliance, and risk of recurrence and SUDEP.

Sudden unexpected death in epilepsy: Experience of neurologists in Saudi Arabia

ImportanceSudden unexpected death in epilepsy (SUDEP) may account for up to 17% of all deaths in epilepsy. However, it is unknown if neurologists discuss this risk with patients. ObjectiveThis study aimed to examine the understanding and practices of SUDEP by neurologists in Saudi Arabia. MethodsAn electronic web-based survey was sent to 125 neurologists using the mailing list of the Saudi Neurology Society. The survey questions included respondents’ demographics, frequency of SUDEP discussion, reasons for discussing/not discussing SUDEP, and perceived patient reactions. Respondents’ knowledge of the SUDEP risk factors was examined using 12 items from the currently available literature. Logistic regression analyses were applied to examine the factors that influence the frequency of SUDEP discussions and perceived patient reactions. ParticipantsThe participants were neurologists who had completed postgraduate training, devoted >5% of their time to clinical care, and had at least one patient with epilepsy in their independent neurology clinic. ResultsA total of 60 respondents met the eligibility criteria and completed the surveys. Of them, 25% discussed SUDEP most of the time, 65% sometimes or rarely, and 10% never discussed it. Of those who discussed SUDEP with their patients, 63.3% did it if the patient was at high risk. Poor compliance with antiepileptic drugs (AEDs) was the most common patient factor highlighted (81.7%). The perceived patients’ reactions were variable, with positive reactions (motivation to comply and appreciation) being the most frequent. The majority of respondents (78.3%) had incomplete understanding of the published SUDEP risk factors, with SUDEP knowledge scores ≤2.5 (≤50% of the possible total score). The most identified risk factors were frequent generalized tonic-clonic seizures (83.3%), long duration of epilepsy (53.3%), lack of use or sub-therapeutic levels of AEDs (50%), and AED polytherapy (50%). No association was found between how often SUDEP was discussed and other factors, including training in epilepsy, ≥10 years in practice, seeing ≥100 patients, and having SUDEP cases in the past two years. It was found that patients positively reacted to discussion on SUDEP if neurologists had a good understanding of the SUDEP risk factors (χ2 = 5.773, p = 0.016). ConclusionsNeurologists in Saudi Arabia do not often discuss SUDEP with patients that have epilepsy. Moreover, when they do, they stress a more individualized approach despite having only a limited understanding of the SUDEP risk factors. Our findings suggest that more guidance should be provided to practitioners on how best to counsel their patients about SUDEP.

Loss-of-function variants in Kv 11.1 cardiac channels as a biomarker for SUDEP.

ObjectiveTo compare the frequency and impact on the channel function of KCNH2 variants in SUDEP patients with epilepsy controls comprising patients older than 50 years, a group with low SUDEP risk, and establish loss‐of‐function KCNH2 variants as predictive biomarkers of SUDEP risk.MethodsWe searched for KCNH2 variants with a minor allele frequency of <5%. Functional analysis in Xenopus laevis oocytes was performed for all KCNH2 variants identified.Results KCNH2 variants were found in 11.1% (10/90) of SUDEP individuals compared to 6.0% (20/332) of epilepsy controls (p = 0.11). Loss‐of‐function KCNH2 variants, defined as causing >20% reduction in maximal amplitude, were observed in 8.9% (8/90) SUDEP patients compared to 3.3% (11/332) epilepsy controls suggesting about threefold enrichment (nominal p = 0.04). KCNH2 variants that did not change channel function occurred at a similar frequency in SUDEP (2.2%; 2/90) and epilepsy control (2.7%; 9/332) cohorts (p > 0.99). Rare KCNH2 variants (<1% allele frequency) associated with greater loss of function and an ~11‐fold enrichment in the SUDEP cohort (nominal p = 0.03). In silico tools were unable to predict the impact of a variant on function highlighting the need for electrophysiological analysis.InterpretationThese data show that loss‐of‐function KCNH2 variants are enriched in SUDEP patients when compared to an epilepsy population older than 50 years, suggesting that cardiac mechanisms contribute to SUDEP risk. We propose that genetic screening in combination with functional analysis can identify loss‐of‐function KCNH2 variants that could act as biomarkers of an individual’s SUDEP risk.

Teaching NeuroImage: Increasing SPECTations for Ictal SPECT in Epilepsy Surgical Evaluation

We report a 15-year-old boy with chromosome 19p13.3 microdeletion, intellectual disability, colobomas, and drug-resistant epilepsy. He had near daily seizures characterized by eyelid fluttering, behavioral arrest, and unawareness with occasional progression to generalized tonic activity. Brain MRI showed diffuse white matter hyperintensities with a predilection for subcortical U-fibers. Video-EEG monitoring showed multifocal interictal discharges but a consistent ictal onset grossly in the left posterior temporal and occipital regions but with up to 10 seconds of electroclinical delay. Brain PET was unremarkable, but ictal SPECT (figures 1 and 2) revealed a discrete functional lesion in the left ventral occipital cortex. Here, ictal SPECT delineated the 3D borders of the seizure onset zone in the deep occipital cortex. This information can be used to further refine the epileptogenic zone (EZ) in a targeted stereo-EEG investigation to optimize surgical outcome1 and decrease SUDEP risk.2

Sometimes, More Is More: Antiseizure Medication polytherapy Is Associated With Decreased SUDEP Risk.

Pharmacologic Treatment and SUDEP risk: A Nationwide, Population-Based, Case-Control Study Sveinsson O, Andersson T, Mattsson P, et al. Neurology. 2020;95(18):e2509-e2518. doi:10.1212/WNL.0000000000010874Objective:We conducted a nationwide case–control study in Sweden to test the hypothesis that antiepileptic drugs (AEDs) mono- or polytherapy, adherence, antidepressants, neuroleptics, β-blockers, and statins are associated with sudden unexpected death in epilepsy (SUDEP) risk.Methods:Included were 255 SUDEP cases and 1148 matched controls. Information on clinical factors and medications came from medical records and the National Patient and Prescription Registers. The association between SUDEP and medications was assessed by odds ratios (ORs) with 95% CIs adjusted for potential risk factors including type of epilepsy, living conditions, comorbidity, and frequency of generalized tonic–clonic seizures (GTCS).Results:Polytherapy, especially taking 3 or more AEDs, was associated with a substantially reduced risk of SUDEP (OR: 0.31, 95% CI: 0.14-0.67). Combinations including lamotrigine (OR: 0.55, 95% CI: 0.31-0.97), valproic acid (OR: 0.53, 95% CI: 0.29-0.98), and levetiracetam (OR: 0.49, 95% CI: 0.27-0.90) were associated with reduced risk. No specific AED was associated with increased risk. Regarding monotherapy, although numbers were limited, the lowest SUDEP risk was seen in users of levetiracetam (0.10, 95% CI: 0.02-0.61). Having nonadherence mentioned in the medical record was associated with an OR of 2.75 (95% CI: 1.58-4.78). Statin use was associated with a reduced SUDEP risk (OR: 0.34, 95% CI: 0.11-0.99) but selective serotonin reuptake inhibitor use was not.Conclusion:These results provide support for the importance of medication adherence and intensified AED treatment for patients with poorly controlled GTCS in the effort to reduce SUDEP risk and suggest that comedication with statins may reduce risk.

Interictal 18F-FDG brain PET metabolism in patients with postictal EEG suppression

ObjectivePostictal generalized suppression (PGES) may be associated with SUDEP risk. We aimed to study metabolic changes on 18Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in patients with focal to bilateral (generalized) seizures (GTCS) and PGES on stereoelectroencephalography (SEEG). MethodsWe analyzed interictal brain metabolism in a group of 19 patients with widespread postictal suppression (PGES+) associated with SEEG-recorded GTCS. This group was compared to 25 patients without widespread suppression (PGES−) as defined by SEEG, matched for epilepsy localization and lateralization. Frequency of GTCS was observed to be higher in the PGES+ group (high risk group for SUDEP). Analysis of metabolic data was performed by statistical parametric mapping (SPM) on the whole-brain, and principal component analysis (PCA) on AAL (automated anatomical labeling) atlas. ResultsStatistical parametric mapping showed right temporal pole hypometabolism in the PGES+ group (T-score = 3.90; p < 0.001; k = 185), in comparison to the PGES− group. Principal component analysis showed association between the metabolic values of certain regions of interest and PGES+/PGES− groups, confirmed by a significant difference (p < 0.05) in the values of the right dorsal temporal pole and of the left temporal pole between the two groups. Principal component analysis showed two dimensions significantly related to the PGES+/PGES− partition, involving the following regions: right temporal pole, right parahippocampal gyrus, right Rolandic operculum, bilateral paracentral lobule, right precuneus, right thalamus, right caudate and pallidum, bilateral cerebellum, left temporal pole, left Heschl’s gyrus, left calcarine region, and left caudate, with loss of connection in PGES+ patients. Metabolic differences were independent of epilepsy localization and lateralization and persisted after correction for GTCS frequency. SignificanceInterictal metabolic changes within a predominantly right-sided network involving temporal lobe and connected cortical and subcortical structures were seen in patients with frequent GTCS presenting widespread postictal suppression.

Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment

BackgroundSudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997).MethodsThis work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011).ResultsThe machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity.ConclusionThe methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

Automated detection of activity onset after postictal generalized EEG suppression

BackgroundSudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization.MethodsWe address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classifies whether it consists of the end of PGES or not. Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. The features that we have used are computationally inexpensive, making it suitable for real-time implementations and low-power solutions. The reference labels for classification are based on annotations by trained clinicians identifying the end of PGES in an EEG recording.ResultsWe evaluated our classification model on an independent test dataset from 34 epileptic patients and obtained an AUreceiver operating characteristic (ROC) (area under the curve) of 0.84. We found that inclusion of multiple EEG channels is important for better classification results, possibly owing to the generalized nature of PGES. Of among the channels included in our analysis, the central EEG channels were found to provide the best discriminative representation for the detection of the end of PGES.ConclusionAccurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES—otherwise difficult to detect due to EEG noise and artifacts—using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.

Heart rate variability in frontal lobe epilepsy: Association with SUDEP risk.

Frontal lobe epilepsy (FLE) may impair autonomic heart rate modulation. Decreased heart rate variability (HRV) may enhance risk of sudden death. Our objective was to describe whole day and wakefulness/sleep HRV parameters from FLE patients in comparison with those of healthy controls and correlate HRV parameters to SUDEP-7 scores. Ten patients with FLE and 15 healthy controls underwent a 24-hour electrocardiogram holter. The SUDEP-7 score was calculated for patients. Subgroups were identified according to active epilepsy, number of generalized seizures, cognitive deficit, medication load, and time-length of epilepsy. Time-domain SDNN, SDNNi, SDANN, rMSDD, and pNN50 and frequency-domain LF, HF, and LF/HF parameters were analyzed. Wilcoxon and Spearman correlation tests were used. A P<.05 was considered significant. Patients SDNN, SDNNi, rMSSD, and pNN50 were decreased in 24-hour recordings. Although a tendency for a protective effect of sleep was seen for both patients and controls, intragroup comparisons of sleeping/waking states revealed a significant increase in sleep rMSSD (P=.046) and pNN50 (P=.041) only for controls. All 24-hour time-domain parameters and LF were inversely and significantly correlated to SUDEP-7, particularly SDANN (ρ=-0.896, P=.00019), known to deteriorate with diminished physical activity and decreased in patients with more generalized seizures. Wakefulness parameters did not correlate to SUDEP-7, whereas correlations to sleep parameters were very strong, particularly with rMSSD (ρ=-0.945, P=.00012). Cognitive deficit was associated with decreased pNN50, sleep pNN50, and LH. HRV is impaired in patients with FLE. Low HRV scores are associated with increased risk for SUDEP as measured by the SUDEP-7 score.

Living safely with epilepsy: a key learning review.

This review focusses on counselling to reduce the risks of seizures in epilepsy patients. Risk reduction, whilst maximising the independence of persons with epilepsy, is a core element of epilepsy practice. The importance of the issue is reflected by the inclusion of risk assessment and reduction in the recent International League Against Epilepsy Curriculum for epileptology. This article addresses the key elements of epilepsy risk and provides practical guidance for counselling and risk reduction. A review of key publications was performed focusing on: (1) the risk of injury associated with seizures; (2) safety issues related to seizures and lifestyle; (3) SUDEP risk and avoidance; and (4) assessing individual risks. Risk is common in epilepsy and is multifactorial. Clinicians should be aware of the risk factors associated with different epilepsy features, such as seizure type, as well as those related to lifestyle and common indoor and outdoor activities. In addition, transcultural differences should be considered in risk assessment and counselling, e.g. regarding SUDEP or even driving, for which regulations vary across nations. Assessment of individual risk is evolving through a better understanding of risk factors and methodologies which can improve communication and empower patients to help identify and manage their individual risks.

Natural history of generalized motor seizures: A retrospective analysis

PurposeThis study aims to characterize the natural history of generalized motor seizures through longitudinal stratification of patient-reported clinical seizures into high, medium and low rates of generalized motor seizures (also known as generalized tonic–clonic seizures or GTCs). MethodsFrom 2007 to 2018, 1.4 million seizures were recorded by 12,402 SeizureTracker.com users that met inclusion/exclusion criteria. The number of GTCs per year since the first seizure diary entry was calculated for each user and categorized as: Low (0 GTCs/year), Medium (1–2 GTCs/year), or High (>3 GTCs/year) GTC rates. ResultsKaplan–Meier survival curves for the time until exiting the initial category were computed. There was a global difference between risk groups (p < 0.01). Further pairwise log rank tests revealed a difference between each pair of risk groups (p < 0.01). At 3 years, 40.8% of people initially presenting with high GTC rates remained in their initial category, while 77.3% of people initially presenting with low GTC rates remained in their initial category. ConclusionA patient with a low rate of GTCs is likely to remain at low risk for future GTCs, whereas higher GTC rate patients (at least one GTC/year) may leave their initial risk stratification. Thus, yearly re-assessment may be prudent when considering risk of further GTCs. Given the association between higher yearly rates of GTCs with increased SUDEP risk and morbidity in epilepsy, further validation of these findings is important for prognostication.

Adenosine kinase and adenosine receptors A1R and A2AR in temporal lobe epilepsy and hippocampal sclerosis and association with risk factors for SUDEP

The "adenosine hypothesis of SUDEP" (sudden unexpected death in epilepsy) predicts that a seizure-induced adenosine surge combined with impaired metabolic clearance can foster lethal apnea or cardiac arrest. Changes in adenosine receptor density and adenosine kinase (ADK) occur in surgical epilepsy patients. Our aim was to correlate the distribution of ADK and adenosine A2A and A1 receptors (A2A R and A1 R) in surgical tissue from patients with temporal lobe epilepsy and hippocampal sclerosis (TLE/HS) with SUDEP risk factors. In 75 cases, patients were stratified into high-risk (n=16), medium-risk (n=11) and low-risk (n=48) categories according to the frequency of generalized seizures before surgery. Using whole-slide scanning Definiens image analysis we quantified the labeling index (LI) for ADK, A2A R, and A1 R in seven regions of interest: temporal cortex, temporal lobe white matter, CA1, CA4, dentate gyrus, subiculum, and amygdala and relative to glial and neuronal densities with glial fibrillary acidic protein (GFAP) and neuronal nuclear antigen (NeuN). A1 R showed predominant neuronal, A2A R astroglial, and ADK nuclear labeling in all regions but with significant variation. Compared with the low-risk group, the high-risk group had significantly lower A2A R LI in the temporal cortex. In HS cases with severe neuronal cell loss and gliosis predominantly in the CA1 and CA4 regions, significantly higher A1 R was present in the amygdala in high-risk than in low-risk cases. There was no significant difference in neuronal loss or gliosis between the risk groups or differences for ADK labeling. Reduced cortical A2A R suggests glial dysfunction and impaired adenosine modulation in response to seizures in patients at higher risk for SUDEP. Increased neuronal A1 R in the high-risk group could contribute to periictal amygdala dysfunction in SUDEP.

Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach.

BackgroundSudden unexpected death in epilepsy (SUDEP) is second only to stroke in neurological events resulting in years of potential life lost. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a period of suppressed brain activity often occurring after generalized tonic-clonic seizure, a most significant risk factor for SUDEP. Therefore, PGES has been considered as a potential biomarker for SUDEP risk. Automatic PGES detection tools can address the limitations of labor-intensive, and sometimes inconsistent, visual analysis. A successful approach to automatic PGES detection must overcome computational challenges involved in the detection of subtle amplitude changes in EEG recordings, which may contain physiological and acquisition artifacts.ObjectiveThis study aimed to present a random forest approach for automatic PGES detection using multichannel human EEG recordings acquired in epilepsy monitoring units.MethodsWe used a combination of temporal, frequency, wavelet, and interchannel correlation features derived from EEG signals to train a random forest classifier. We also constructed and applied confidence-based correction rules based on PGES state changes. Motivated by practical utility, we introduced a new, time distance–based evaluation method for assessing the performance of PGES detection algorithms.ResultsThe time distance–based evaluation showed that our approach achieved a 5-second tolerance-based positive prediction rate of 0.95 for artifact-free signals. For signals with different artifact levels, our prediction rates varied from 0.68 to 0.81.ConclusionsWe introduced a feature-based, random forest approach for automatic PGES detection using multichannel EEG recordings. Our approach achieved increasingly better time distance–based performance with reduced signal artifact levels. Further study is needed for PGES detection algorithms to perform well irrespective of the levels of signal artifacts.

Genetic variants in incident SUDEP cases from a community-based prospective cohort with epilepsy

ObjectiveSudden unexpected death in epilepsy (SUDEP) is a leading cause of epilepsy-related mortality in young adults. It has been suggested that SUDEP may kill over 20 000 people with epilepsy...

Genetics in epilepsy, Dravet and SUDEP: A systematic review

1.1. Introduction:Epilepsy is a neurologic condition and its patients have higher mortality rates than healthy individuals. One of the most frequent death causes in epilepsy is SUDEP. Moreover, another cause in epilepsy, although rare, is Dravet Syndrome and it’s generally diagnosed in children in their first year of life. In around 70% of the patients there is a genetic inheritance involved and several genes have been described as related to SUDEP and Dravet Syndrome which the most prevalent were DNA and belonged to the voltage-gated channel coding. 1.2. Objectives:Identify genes related do Dravet Syndrome, SUDEP and Epilepsy in literature, analyze the convergence of results, and discuss the differences that were found, contributing to the study of genetic mapping. 1.3. Methodology:Meta-analysis realized through a bibliographical research in the electronic data banks such as PUBMED, NCBI, Scholar Google and Scielo with the descriptors Epilepsy; Epilepsies, Myoclonic; Polymorphism, Single Nucleotide; MicroRNAs; Death, Sudden. The languages used to filter the articles were English, Spanish and Portuguese. The year was a filtering factor, selecting only articles published in the last 20 years. 1.4. Results: About forty (40) genes were found to be SUDEP related and the most relevant was KCNH2, appearing more frequently than other genes. Additionally, in Epilepsy without risk for SUDEP twenty-eight (28) DNA genes were found, which the most frequently mentioned was HCN2, appearing six (6) times. Among Dravet syndrome patients, only two (2) genes were described: SCN1A and SCN9A, both of them were SUDEP risk related. In epilepsy nineteen (19) microRNA genes were found in five (5) different articles. 1.5. Project: It was identified genes related to Dravet Syndrome, SUDEP and Epilepsy in literature and was noted that some genes appeared more frequently than others. Among the DNA related genes, the most prevalent were around those belonging to the voltage-gated channel coding, being them sodium, hydrogen and potassium. The results demonstrate that 25% of all articles mentioned SCN1A; 17,5% mentioned HCN2 and 15% mentioned KCNH2. Together these findings provide a basis for further investigation around the role of ionic channels in SUDEP and DS. On the other hand, regarding to MicroRNA linked genes data were scarce. Only five articles described changes on SUDEP and neither described changes compatible with DS. There was no most prevalent mRNA gene. 1.6. Conclusion: A range of studies associated DNA mutations with Epilepsy, DS and SUDEP. However, MicroRNA research could not provide concrete evidence on its influence in the development of the epilepsy and epilepsy related conditions. The identification of possible etiological factors can allow a correct and early diagnosis, providing better prognosis and therapeutic outcome, besides it can evaluate the recurrence of the disease in the family. Still, further prospective studies in larger cohorts are required to further define the genetic predisposition to SUDEP, DS and Epilepsy.

Update on the Genetic Basis of Sudden Unexpected Death in Epilepsy.

Epilepsy is a common neurological disorder associated with increased morbidity and mortality. Sudden unexpected death in epilepsy, also known as SUDEP, is the main cause of death in patients with epilepsy. SUDEP has an incidence of 1.2 per 1000 person-years in adults and 0.2 per 1000 person-years in children. SUDEP accounts for 8–17% of deaths in patients with epilepsy. It is commonly associated with a history of generalized tonic-clonic seizures, and its risk may be increased by other factors such as postictal electroencephalographic suppression, prone sleeping position, altered heart rate variability, conduction abnormalities, gender, or antiepileptic medications. Recently, electrocardiograms, electroencephalograms, and imaging markers have helped clinicians stratify SUDEP risk and identify patients in need of close monitoring. However, the pathophysiology of SUDEP is likely multifactorial and still unknown. Improving the knowledge of SUDEP incidence, risk factors, and biomarkers can help design and implement effective prevention strategies.

Unravelling the mysteries of sudden unexpected death in epilepsy

IntroductionSudden unexpected death in epilepsy (SUDEP) is the most frequent cause of premature death in epileptic patients. Most SUDEP events occur at night and frequently go unnoticed; the exact pathophysiological mechanisms of this phenomenon therefore remain undetermined. Nevertheless, most cases of SUDEP are attributed to an infrequent yet extremely severe complication of epileptic seizures. DevelopmentWe conducted a systematic literature search on PubMed. Our review article summarises scientific evidence on the classification, pathophysiological mechanisms, risk factors, biomarkers, and prevention of SUDEP. Likewise, we propose new lines of research and critically analyse findings that are relevant to clinical practice. ConclusionsCurrent knowledge suggests that SUDEP is a heterogeneous phenomenon caused by multiple factors. In most cases, however, SUDEP is thought to be due to postictal cardiorespiratory failure triggered by generalised tonic-clonic seizures and ultimately leading to cardiac arrest. The underlying pathophysiological mechanism involves multiple factors, ranging from genetic predisposition to environmental factors. Risk of SUDEP is higher in young adults with uncontrolled generalised tonic-clonic seizures. However, patients apparently at lower risk may also experience SUDEP. Current research focuses on identifying genetic and neuroimaging biomarkers that may help determine which patients are at high risk for SUDEP. Antiepileptic treatment is the only preventive measure proven effective to date. Night-time monitoring together with early resuscitation may reduce the risk of SUDEP.

Age-related phenotype and biomarker changes in SSADH deficiency.

ObjectiveSuccinic Semialdehyde Dehydrogenase (SSADH) deficiency is a disorder of elevated gamma‐amino butyric acid (GABA) and gamma hydroxybutyric acid (GHB) and a complex neuropsychiatric profile. Adult reports suggest worsening epilepsy and high SUDEP risk.MethodsSubjects with confirmed SSADH deficiency were recruited into a longitudinal study. Plasma thyroid hormone and total GABA/GHB were quantified by standard clinical chemistry methodologies and mass spectrometry, respectively.ResultsA total of 133 subjects with SSADH deficiency are enrolled in the registry; 49 participated in the longitudinal study. The age range of the population is 8 weeks to 63 years (median 7.75 year; 44% male). There is a significant difference in proportions among the age groups in subjects affected with hypotonia, compulsive behavior, sleep disturbances, and seizures. Epilepsy is present in 50% of the total population, and more prevalent in subjects 12 years and older (P = 0.001). The median age of onset for absence seizures was 2 years, and 12 years for generalized tonic‐clonic seizures (P < 0.01). The SUDEP rate in adults was 12% (4/33). There was a significant age‐dependent negative correlation between GABA and T3 levels.InterpretationThere is an age‐dependent association with worsening of epilepsy, behavioral disturbances including obsessive‐compulsive behavior, and sleep disturbances with age in SSADH deficiency. There is a high risk of SUDEP. We have observed more absence seizures in younger patients, compared to tonic‐clonic in the older cohort, which correlates with age‐related changes in GABA and GHB concentration and thyroid function, as well as the natural history of seizures in the murine model.

Dravet syndrome in South African infants: Tools for an early diagnosis

PurposeDravet syndrome (DS) is a well-described, severe genetic epileptic encephalopathy with an increased risk of SUDEP. The incidence and genetic architecture of DS in African patients is virtually unknown, largely due to lack of awareness and unavailability of genetic testing. The clinical benefits of the available precision medicine approaches to treatment emphasise the importance of an early, correct diagnosis. We investigated the genetic causes and clinical features of DS in South African children to develop protocols for early, cost-effective diagnosis in the local setting. MethodWe selected 22 South African children provisionally diagnosed with clinical DS for targeted resequencing of DS-associated genes. We sought to identify the clinical features most strongly associated with SCN1A-related DS, using the DS risk score and clinical co-variates under various statistical models. ResultsDisease-causing variants were identified in 10 of the 22 children: nine SCN1A and one PCDH19. Moreover, we showed that seizure onset before 6 months of age and a clinical DS risk score of >6 are highly predictive of SCN1A-associated DS. Clinical reassessment resulted in a revised diagnosis in 10 of the 12 variant-negative children. ConclusionThis first genetic study of DS in Africa confirms that de novo SCN1A variants underlie disease in the majority of South African patients. Affirming the predictive value of seizure onset before 6 months of age and a clinical DS risk score of >6 has significant practical implications for the resource-limited setting, presenting simple diagnostic criteria which can facilitate early correct treatment, specialist consultation and genetic testing.