SUDEP in adults and children

Sudden unexpected death in epilepsy (SUDEP) represents an important cause of death in patients with epilepsy and it exceeds the expected rate of sudden death in the general population by nearly 24 times. We searched the electronic databases (Cochrane, EMBASE, Scopus, Medline, Pubmed) for studies related to etiology and risk stratification of SUDEP including data on Takotsubo cardiomyopathy (TKC) following seizures resulting in death or near death.: SUDEP is more common among males in the fourth decade of life. Risk for SUDEP is increased by early onset of seizures, low IQ, generalised tonic clonic seizures, nocturnal seizures and seizure frequency. Nonadherance to antiepileptic medications, absence of therapeutic drug level monitoring, presence of neuropathological lesions on imaging and certain subgroups like Dravet syndrome increase its risk. The risk for premature death in patients undergoing temporal lobe resection for drug resistant epilepsy decreased over time but remained above the standard population. Prolonged postictal electroencephalographic suppression was a risk factor for SUDEP in patients with generalised seizures which may indicate a cerebral electrical shutdown. Documented ictal/postictal hypoventilation, laryngeal spasm and cardiac rhythm abnormalities prior to SUDEP may suggest central apnea, neurogenic pulmonary edema, cardiac arrhythmia, or a combination of the above as a cause. Seizure triggered TKC does not seem to play a major role in the pathogenesis of SUDEP.

Enhancement of central norepinephrinergic neurotransmission contributes to inhibition of seizure-induced respiratory arrest by targeting the beta-1 adrenergic receptor (β1-AR) locating in the cardiomyocytes in the DBA/1 mouse SUDEP model

Sudden unexpected death of epilepsy (SUDEP) is the key cause of of death in patients with epilepsy. Due to the complicated pathogenesis of SUDEP, however, the exact mechanism of SUDEP remains elusive. Currently, although it is recognized that the seizure-induced respiratory arrest (S-IRA) may be a main cause for SUDEP, other factors resulting in SUDEP can not be excluded e.g arrhythmias. Our previous findings indicated that the incidence of seizure-induced respiratory arrest S-IRA and SUDEP evoked by acoustic stimulation or pentetrazol (PTZ) injection was significantly reduced by atomoxetine, a norepinephrine reuptake inhibitor (NRI), suggesting that noradrenergic neurotransmission modulates S-IRA and SUDEP. Given that norepinephrine acts on the central and peripheral target to modulate respiratory and circulation function by targeting adrenergic receptor α and beta (a-AR and β-AR) and the arrhythmias can be contributed to SUDEP. Meanwhile, to further test whether cardiac factors are implicated in S-IRA and SUDEP, we choose esmolol hydrochloride, a selective antagonist of beta-1 adrenergic receptor (β1-AR) to test it in our models. Our findings demonstrated that the lower incidence of S-IRA and SUDEP evoked by acoustic stimulation or PTZ in DBA/1 mice by administration with atomoxetine was significantly reversed by intraperitoneal (IP) of esmolol hydrochloride. Importantly, the data of electrocardiogram (ECG) showed that the cardiac arrhythmia evoked by acoustic stimulation including the ventricular tachycardia, ventricular premature beat and atrioventricular block and administration of atomoxetine significantly reduced theses arrhythmias and the incidence of S-IRA and SUDEP in our models. Thus, the dysfunction of respiratory and circulation may be implicated in the pathogenesis of S-IRA and SUDEP hand in hand and enhancing central norepinephrinergic neurotransmission contributes to inhibition of seizure-induced respiratory arrest by targeting β1-AR locating in the cardiomyocytes. Our findings will show a new light on decoding the pathogenesis of SUDEP. Keywords: sudden unexpected death in epilepsy (SUDEP); seizure-induced respiratory arrest S-IRA); esmolol hydrochloride (Esmolol); Electrocardiogram (ECG); locus coeruleus (LC); cardiac arrhythmia; pentetrazol (PTZ)

Scn1a gene reactivation after symptom onset rescues pathological phenotypes in a mouse model of Dravet syndrome

Dravet syndrome is a severe epileptic encephalopathy caused primarily by haploinsufficiency of the SCN1A gene. Repetitive seizures can lead to endurable and untreatable neurological deficits. Whether this severe pathology is reversible after symptom onset remains unknown. To address this question, we generated a Scn1a conditional knock-in mouse model (Scn1a Stop/+) in which Scn1a expression can be re-activated on-demand during the mouse lifetime. Scn1a gene disruption leads to the development of seizures, often associated with sudden unexpected death in epilepsy (SUDEP) and behavioral alterations including hyperactivity, social interaction deficits and cognitive impairment starting from the second/third week of age. However, we showed that Scn1a gene re-activation when symptoms were already manifested (P30) led to a complete rescue of both spontaneous and thermic inducible seizures, marked amelioration of behavioral abnormalities and normalization of hippocampal fast-spiking interneuron firing. We also identified dramatic gene expression alterations, including those associated with astrogliosis in Dravet syndrome mice, that, accordingly, were rescued by Scn1a gene expression normalization at P30. Interestingly, regaining of Nav1.1 physiological level rescued seizures also in adult Dravet syndrome mice (P90) after months of repetitive attacks. Overall, these findings represent a solid proof-of-concept highlighting that disease phenotype reversibility can be achieved when Scn1a gene activity is efficiently reconstituted in brain cells.

Neural correlate of reduced respiratory chemosensitivity during chronic epilepsy

While central autonomic cardiorespiratory dysfunction underlies sudden unexpected death in epilepsy (SUDEP), the specific neural mechanisms that lead to SUDEP remain to be determined. Here we took an advantage of single cell neuronal Ca2+ imaging and intrahippocampal kainic acid (KA)-induced chronic epilepsy in mice to investigate progressive changes in key cardiorespiratory brainstem circuits during chronic epilepsy. Following induction of status epilepticus (SE), we observed that the adaptive ventilatory responses to hypercapnia were reduced in mice with chronic epilepsy for 5 weeks post-SE. These changes were paralleled by reduced chemosensitivity of neurons in the retrotrapezoid nucleus (RTN), an important centre for respiratory chemosensitivity. Over the same period, chemosensory responses of the presympathetic RVLM neurons showed a slower decrease. Mice with chronic epilepsy were more sensitive to chemoconvulsants and exhibited a greatly reduced latency to seizure induction compared to naive mice. This enhanced sensitivity to seizures, which invade the RTN, puts the chemosensory circuits at further risk and increases the chances of terminal apnoea. Our findings establish a dysfunctional breathing phenotype with its RTN neuronal correlate in mice with chronic epilepsy and suggests a functional non-invasive biomarker test, based on respiratory chemosensitivity, to identify people with epilepsy at risk of SUDEP.

Clinical Genetics of Inherited Arrhythmogenic Disease in the Pediatric Population

Sudden death is a rare event in the pediatric population but with a social shock due to its presentation as the first symptom in previously healthy children. Comprehensive autopsy in pediatric cases identify an inconclusive cause in 40–50% of cases. In such cases, a diagnosis of sudden arrhythmic death syndrome is suggested as the main potential cause of death. Molecular autopsy identifies nearly 30% of cases under 16 years of age carrying a pathogenic/potentially pathogenic alteration in genes associated with any inherited arrhythmogenic disease. In the last few years, despite the increasing rate of post-mortem genetic diagnosis, many families still remain without a conclusive genetic cause of the unexpected death. Current challenges in genetic diagnosis are the establishment of a correct genotype–phenotype association between genes and inherited arrhythmogenic disease, as well as the classification of variants of uncertain significance. In this review, we provide an update on the state of the art in the genetic diagnosis of inherited arrhythmogenic disease in the pediatric population. We focus on emerging publications on gene curation for genotype–phenotype associations, cases of genetic overlap and advances in the classification of variants of uncertain significance. Our goal is to facilitate the translation of genetic diagnosis to the clinical area, helping risk stratification, treatment and the genetic counselling of families.

Interventions to Improve Safer Sleep Practices in Families With Children Considered to Be at Increased Risk for Sudden Unexpected Death in Infancy: A Systematic Review

Background: Advice to families to follow infant care practices known to reduce the risks of Sudden Unexpected Death in Infancy (SUDI) has led to a reduction in deaths across the world. This reduction has slowed in the last decade with most deaths now occurring in families experiencing social and economic deprivation. A systematic review of the literature was commissioned by the National Child Safeguarding Practice Review Panel in England. The review covered three areas: interventions to improve engagement with support services, parental decision-making for the infant sleep environment, and interventions to improve safer sleep practices in families with infants considered to be at risk of SUDI.Aim: To describe the safer sleep interventions tested with families with infants at risk of SUDI and investigate what this literature can tell us about what works to reduce risk and embed safer sleep practices in this group.Methods: Eight online databases were systematically searched in December 2019. Intervention studies that targeted families with infants (0–1 year) at increased risk of SUDI were included. Studies were limited to those from Western Europe, North America or Australasia, published in the last 15 years. The Quality Assessment Tool for Studies with Diverse Designs was applied to assess quality. Data from included studies were extracted for narrative synthesis, including mode of delivery using Michie et al.'s Mode of Delivery Taxonomy.Results: The wider review returned 3,367 papers, with 23 intervention papers. Five types of intervention were identified: (1) infant sleep space and safer sleep education programs, (2) intensive or targeted home visiting services, (3) peer educators/ambassadors, (4) health education/raising awareness interventions, (5) targeted health education messages using digital media.Conclusion: Influencing behavior in families with infants at risk of SUDI has traditionally focused on “getting messages across,” with interventions predominantly using education and awareness raising mechanisms. This review found evidence of interventions moving from “information giving” to “information exchange” models using personalized, longer term relationship-building models. This shift may represent an improvement in how safer sleep advice is implemented in families with infants at risk, but more robust evidence of effectiveness is required.Systematic Review Registration:https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/901091/DfE_Death_in_infancy_review.pdf, identifier: CRD42020165302.

Significant ketoacidosis at autopsy: a single-centre systematic review

AimTo examine the value of vitreous beta-hydroxybutyrate and serum acetone in the investigation of sudden unexpected death.MethodsCoroners’ autopsy reports from a provincial UK city, with a population of approximately 900 000, over a 24-month period with significant ketoacidosis were studied. Demographic features, medical history, anatomical and histological findings, and biochemical parameters, including renal function, vitreous glucose, serum and vitreous alcohol, were analysed.ResultsForty-two cases (28 males and 14 females) were identified; 55% had a history of alcohol and/or substance misuse, and mental health problems, particularly depression and anxiety, and 16% were diabetic. In all, 50% of subjects had alcoholic ketoacidosis (AKA), 19% had diabetic ketoacidosis (DKA) and 12% had a history of both diabetes and alcohol abuse. In 19% of cases, an exact cause of ketoacidosis was established. In AKA, the subjects typically had low vitreous glucose and low or undetected blood alcohol levels. All of the subjects with raised vitreous glucose levels had DKA.ConclusionKetoacidosis is relatively common and should be considered as a cause of sudden death, especially in alcoholic patients and patients with diabetes with no clear cause of death at autopsy.

CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering

<abstract> <p>Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.</p> </abstract>