Equivocal significance of post-ictal generalized EEG suppression as a marker of SUDEP risk

Antiseizure medication and SUDEP - a need for unifying methodology in research

The risk of sudden unexpected death in epilepsy (SUDEP) increases with the frequency of generalized tonic-clonic seizures. Carbamazepine (CBZ) and lamotrigine (LTG) have been suggested to increase the risk. However, the prevailing viewpoint is that the choice of antiseizure medication (ASM) does not influence the occurrence. We have explored the approach to addressing this question in relevant studies to evaluate the validity of the conclusions reached. A systematic search was performed in PubMed to identify all controlled studies on SUDEP risk in individuals on CBZ or LTG. Studies were categorized according to whether idiopathic generalized epilepsy (IGE) or females were considered separately, and whether data were adjusted for seizure frequency. Eight studies on CBZ and six studies on LTG were identified. For CBZ, one study showed a significantly increased risk of SUDEP without adjustment for seizure frequency. Another study found significantly increased risk after statistical adjustment for seizure frequency and one study found increased risk with high blood levels. Five other studies found no increase in risk. For LTG, one study showed a significantly increased risk in patients with IGE as opposed to focal epilepsy, and another study showed a significantly increased risk in females. None of the subsequent studies on LTG and none of the studies on CBZ considered females with IGE separately. Taken together the available studies suggest that LTG, and possibly CBZ, may increase occurrence of SUDEP when used in females with IGE. Additional studies with sub-group analysis of females with IGE are needed.

Asymmetrical cortical surface area decrease in epilepsy patients with postictal generalized electroencephalography suppression

Postictal generalized electroencephalographic suppression is a possible electroencephalographic marker for sudden unexpected death in epilepsy. We aimed to investigate the cortical surface area abnormalities in epilepsy patients with postictal generalized electroencephalographic suppression. We retrospectively included 30 epilepsy patients with postictal generalized electroencephalographic suppression (PGES+), 21 epilepsy patients without postictal generalized electroencephalographic suppression (PGES-), and 30 healthy controls. Surface-based analysis on high-resolution T1-weighted images was conducted and cortical surface areas were compared among the three groups, alongside correlation analyses with seizure-related clinical variables. Compared with PGES- group, we identified reduced surface area in the bilateral insula with more extensive distribution in the right hemisphere in PGES+ group. The reduced right insular surface area was associated with younger seizure-onset age. When compared with healthy controls, PGES- group presented reduced surface area in the left caudal middle frontal gyrus; PGES+ group presented more widespread surface area reductions in the right posterior cingulate gyrus, left postcentral gyrus, middle frontal gyrus, and middle temporal gyrus. Our results suggested cortical microstructural impairment in patients with postictal generalized electroencephalographic suppression. The significant surface area reductions in the insular cortex supported the autonomic network involvement in the pathology of postictal generalized electroencephalographic suppression, and its right-sided predominance suggested the potential shared abnormal brain network for postictal generalized electroencephalographic suppression and sudden unexpected death in epilepsy.

Sudden unexpected death in epilepsy: A critical view of the literature

Sudden unexpected death in epilepsy (SUDEP) is a sudden, unexpected, witnessed or unwitnessed, non-traumatic and non-drowning death, occurring in benign circumstances, in an individual with epilepsy, with or without evidence for a seizure and excluding documented status epilepticus in which postmortem examination does not reveal other causes of death. Lower diagnostic levels are assigned when cases met most or all of these criteria, but data suggested more than one possible cause of death. The incidence of SUDEP ranged from 0.09 to 2.4 per 1000 person-years. Differences can be attributed to the age of the study populations (with peaks in the 20-40-year age group) and the severity of the disease. Young age, disease severity (in particular, a history of generalized TCS), having symptomatic epilepsy, and the response to antiseizure medications (ASMs) are possible independent predictors of SUDEP. The pathophysiological mechanisms are not fully known due to the limited data available and because SUDEP is not always witnessed and has been electrophysiologically monitored only in a few cases with simultaneous assessment of respiratory, cardiac, and brain activity. The pathophysiological basis of SUDEP may vary according to different circumstances that make that particular seizure, in that specific moment and in that patient, a fatal event. The main hypothesized mechanisms, which could contribute to a cascade of events, are cardiac dysfunction (included potential effects of ASMs, genetically determined channelopathies, acquired heart diseases), respiratory dysfunction (included postictal arousal deficit for the respiratory mechanism, acquired respiratory diseases), neuromodulator dysfunction, postictal EEG depression and genetic factors.

Association between postictal EEG suppression, postictal autonomic dysfunction, and sudden unexpected death in epilepsy: Evidence from intracranial EEG

OBJECTIVE

The association between postictal electroencephalogram (EEG) suppression (PES), autonomic dysfunction, and Sudden Unexpected Death in Epilepsy (SUDEP) remains poorly understood. We compared PES on simultaneous intracranial and scalp-EEG and evaluated the association of PES with postictal heart rate variability (HRV) and SUDEP outcome.

METHODS

Convulsive seizures were analyzed in patients with drug-resistant epilepsy at 5 centers. Intracranial PES was quantified using the Hilbert transform. HRV was quantified using root mean square of successive differences of interbeat intervals, low-frequency to high-frequency power ratio, and RR-intervals.

RESULTS

There were 64 seizures from 63 patients without SUDEP and 11 seizures from 6 SUDEP patients. PES occurred in 99% and 87% of seizures on intracranial-EEG and scalp-EEG, respectively. Mean PES duration in intracranial and scalp-EEG was similar. Intracranial PES was regional (<90% of channels) in 46% of seizures; scalp PES was generalized in all seizures. Generalized PES showed greater decrease in postictal parasympathetic activity than regional PES. PES duration and extent were similar between patients with and without SUDEP.

CONCLUSIONS

Regional intracranial PES can be present despite scalp-EEG demonstrating generalized or no PES. Postictal autonomic dysfunction correlates with the extent of PES.

SIGNIFICANCE

Intracranial-EEG demonstrates changes in autonomic regulatory networks not seen on scalp-EEG.

Autonomic dysfunction in epilepsy mouse models with implications for SUDEP research

Epilepsy has a high prevalence and can severely impair quality of life and increase the risk of premature death. Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in drug-resistant epilepsy and most often results from respiratory and cardiac impairments due to brainstem dysfunction. Epileptic activity can spread widely, influencing neuronal activity in regions outside the epileptic network. The brainstem controls cardiorespiratory activity and arousal and reciprocally connects to cortical, diencephalic, and spinal cord areas. Epileptic activity can propagate trans-synaptically or via spreading depression (SD) to alter brainstem functions and cause cardiorespiratory dysfunction. The mechanisms by which seizures propagate to or otherwise impair brainstem function and trigger the cascading effects that cause SUDEP are poorly understood. We review insights from mouse models combined with new techniques to understand the pathophysiology of epilepsy and SUDEP. These techniques include in vivo, ex vivo, invasive and non-invasive methods in anesthetized and awake mice. Optogenetics combined with electrophysiological and optical manipulation and recording methods offer unique opportunities to study neuronal mechanisms under normal conditions, during and after non-fatal seizures, and in SUDEP. These combined approaches can advance our understanding of brainstem pathophysiology associated with seizures and SUDEP and may suggest strategies to prevent SUDEP.

Toward automated prediction of sudden unexpected death in epilepsy

Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.

Serotonin receptor expression in hippocampus and temporal cortex of temporal lobe epilepsy patients by postictal generalized electroencephalographic suppression duration

OBJECTIVE

Prolonged postictal generalized electroencephalographic suppression (PGES) is a potential biomarker for sudden unexpected death in epilepsy (SUDEP), which may be associated with dysfunctional autonomic responses and serotonin signaling. To better understand molecular mechanisms, PGES duration was correlated to 5HT1A and 5HT2A receptor protein expression and RNAseq from resected hippocampus and temporal cortex of temporal lobe epilepsy patients with seizures recorded in preoperative evaluation.

METHODS

Analyses included 36 cases (age = 14-64 years, age at epilepsy onset = 0-51 years, epilepsy duration = 2-53 years, PGES duration = 0-93 s), with 13 cases in all hippocampal analyses. 5HT1A and 5HT2A protein was evaluated by Western blot and histologically in hippocampus (n = 16) and temporal cortex (n = 9). We correlated PGES duration to our previous RNAseq dataset for serotonin receptor expression and signaling pathways, as well as weighted gene correlation network analysis (WGCNA) to identify correlated gene clusters.

RESULTS

In hippocampus, 5HT2A protein by Western blot positively correlated with PGES duration (p = .0024, R2  = .52), but 5HT1A did not (p = .87, R2  = .0020). In temporal cortex, 5HT1A and 5HT2A had lower expression and did not correlate with PGES duration. Histologically, PGES duration did not correlate with 5HT1A or 5HT2A expression in hippocampal CA4, dentate gyrus, or temporal cortex. RNAseq identified two serotonin receptors with expression that correlated with PGES duration in an exploratory analysis: HTR3B negatively correlated (p = .043, R2  = .26) and HTR4 positively correlated (p = .049, R2  = .25). WGCNA identified four modules correlated with PGES duration, including positive correlation with synaptic transcripts (p = .040, Pearson correlation r = .52), particularly potassium channels (KCNA4, KCNC4, KCNH1, KCNIP4, KCNJ3, KCNJ6, KCNK1). No modules were associated with serotonin receptor signaling.

SIGNIFICANCE

Higher hippocampal 5HT2A receptor protein and potassium channel transcripts may reflect underlying mechanisms contributing to or resulting from prolonged PGES. Future studies with larger cohorts should assess functional analyses and additional brain regions to elucidate mechanisms underlying PGES and SUDEP risk.

Quiescence during burst suppression and postictal generalized EEG suppression are distinct patterns of activity

OBJECTIVE

Periods of low-amplitude electroencephalographic (EEG) signal (quiescence) are present during both anesthetic-induced burst suppression (BS) and postictal generalized electroencephalographic suppression (PGES). PGES following generalized seizures induced by electroconvulsive therapy (ECT) has been previously linked to antidepressant response. The commonality of quiescence during both BS and PGES motivated trials to recapitulate the antidepressant effects of ECT using high doses of anesthetics. However, there have been no direct electrographic comparisons of these quiescent periods to address whether these are distinct entities.

METHODS

We compared periods of EEG quiescence recorded from two human studies: BS induced in 29 healthy adult volunteers by isoflurane general anesthesia and PGES in 11 patients undergoing right unilateral ECT for treatment-resistant depression. An automated algorithm allowed detection of EEG quiescence based on a 10-microvolt amplitude threshold. Spatial, spectral, and temporal analyses compared quiescent epochs during BS and PGES.

RESULTS

The median (interquartile range) voltage for quiescent periods during PGES was greater than during BS (1.81 (0.22) microvolts vs 1.22 (0.33) microvolts, p < 0.001). Relative power was greater for quiescence during PGES than BS for the 1-4 Hz delta band (p < 0.001), at the expense of power in the theta (4-8 Hz, p < 0.001), beta (13-30 Hz, p = 0.04) and gamma (30-70 Hz, p = 0.006) frequency bands. Topographic analyses revealed that amplitude across the scalp was consistently higher for quiescent periods during PGES than BS, whose voltage was within the noise floor.

CONCLUSIONS

Quiescent epochs during PGES and BS have distinct patterns of EEG signals across voltage, frequency, and spatial domains.

SIGNIFICANCE

Quiescent epochs during PGES and BS, important neurophysiological markers for clinical outcomes, are shown to have distinct voltage and frequency characteristics.

Changes of oxygen saturation in patients with pure temporal lobe epilepsy

PURPOSE

Ictal hypoxemia is accepted as one of the mechanisms underlying sudden unexpected death in epilepsy (SUDEP). Although ictal hypoxemia is more common in generalized seizures, it also occurs in focal seizures with or without generalization. In this study, we aimed to show the relationship between clinical and electroencephalographic findings of seizures in patients with temporal lobe epilepsy (TLE) with periictal oxygen saturation.

METHODS

The data of 55 adult patients who were hospitalized in the Video EEG Monitoring Unit (VEMU) and operated on for drug-resistant TLE between January 2017 and December 2020 were examined. Forty-five seizures from 21 patients with ictal peripheral arterial saturation information and that were seizure-free for at least a year during the follow-up were included in the study.

RESULTS

The median patient age was 28 (IQR 25-39.5) years (women: 9, men: 12). Age at epilepsy onset was negatively correlated with saturation at seizure onset. Moreover, the age at VEMU admission was also negatively correlated with saturation at seizure onset and the lowest levels of saturation. The saturation at the end of the seizures and the lowest saturation measured in the periictal period with generalization of EEG were significantly lower than those without generalization. The onset of ictal EEG with the rhythmic theta pattern was significantly associated with the lowest level of saturation (<90%), postictal generalized electroencephalographic suppression (PGES), and the presence of generalization.

CONCLUSION

According to the study, rhythmic ictal theta activity, older age, nocturnal seizure, and generalization in ictal EEG might increase the potential risk of SUDEP. Further studies including a greater number of subjects and different epilepsy syndromes may provide more comprehensive information about potential biomarkers for SUDEP.

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Objective

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls.

Methods

This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve.

Results

The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction.

Conclusions

Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

Duration of postictal impaired awareness after bilateral tonic-clonic seizures: EEG and patient characteristics

OBJECTIVE

Ictal and postictal phenomena that may impact the duration of postictal impaired awareness have not been well studied. Postictal unresponsiveness invariably occurs following bilateral tonic-clonic seizures (BTCS). Bilateral tonic-clonic seizures are a major risk factor for sudden unexpected death in epilepsy (SUDEP). We quantify the effects of seizure characteristics on postictal recovery of awareness following BTCS. Factors include: the total seizure duration, the duration of the tonic phase of a BTCS, presence of postictal generalized EEG suppression (PGES), duration of postictal tonic electromyographic discharge, peri-ictal respiratory dysfunction, patient age, duration of epilepsy, and gender.

METHODS

Fifty-eight patients admitted to the epilepsy monitoring unit with BTCS were studied. Forty-one had unilateral onset temporal seizures. The remainder had bitemporal onsets, extratemporal onsets, undetermined onsets, or were generalized at onset. Following the first BTCS, time to initial recovery of awareness and its possible association with patient and seizure characteristics as well as peri-ictal respiratory dysfunction were evaluated. The presence or absence of postictal agitation was noted.

RESULTS

The severity of respiratory dysfunction and seizure characteristics were not associated with time to initial recovery of awareness. A shorter time to recovery of awareness was significantly associated with a younger age (p = 0.007). Postictal agitation was more common in males (p = 0.023).

SIGNIFICANCE

Focal seizures may impair awareness by active inhibition of subcortical arousal mechanisms. Focal seizures progressing to bilateral tonic-clonic seizures (BTCS) result in further widespread cerebral dysfunction impacting postictal awareness. MRI studies show accelerated brain aging in patients with temporal lobe epilepsy. Our findings suggest that patient age, as a surrogate marker for the lifetime burden of seizures, results in a progressive worsening in time to recovery after BTCS by an increasing negative impact on networks involved in arousal.

A Peri-Ictal EEG-Based Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP) Derived From Brain Network Analysis

Sudden unexpected death in epilepsy (SUDEP) is the leading seizure-related cause of death in epilepsy patients. There are no validated biomarkers of SUDEP risk. Here, we explored peri-ictal differences in topological brain network properties from scalp EEG recordings of SUDEP victims. Functional connectivity networks were constructed and examined as directed graphs derived from undirected delta and high frequency oscillation (HFO) EEG coherence networks in eight SUDEP and 14 non-SUDEP epileptic patients. These networks were proxies for information flow at different spatiotemporal scales, where low frequency oscillations coordinate large-scale activity driving local HFOs. The clustering coefficient and global efficiency of the network were higher in the SUDEP group pre-ictally, ictally and post-ictally (p < 0.0001 to p < 0.001), with features characteristic of small-world networks. These results suggest that cross-frequency functional connectivity network topology may be a non-invasive biomarker of SUDEP risk.

OUP accepted manuscript

Sudden unexpected death in epilepsy is the most catastrophic outcome of epilepsy. Each year there are as many as 1.65 cases of such death for every 1000 individuals with epilepsy. Currently, there are no methods to predict or prevent this tragic event, due in part to a poor understanding of the pathologic cascade that leads to death following seizures. We recently identified enhanced seizure-induced mortality in four inbred strains from the genetically diverse Collaborative Cross mouse population. These mouse models of sudden unexpected death in epilepsy provide a unique tool to systematically examine the physiological alterations during fatal seizures, which can be studied in a controlled environment and with consideration of genetic complexity. Here, we monitored the brain oscillations and heart functions before, during, and after non-fatal and fatal seizures using a flurothyl-induced seizure model in freely moving mice. Compared with mice that survived seizures, non-survivors exhibited significant suppression of brainstem neural oscillations that coincided with cortical epileptic activities and tachycardia during the ictal phase of a fatal seizure. Non-survivors also exhibited suppressed delta (0.5–4 Hz)/gamma (30–200 Hz) phase-amplitude coupling in cortex but not in brainstem. A connectivity analysis revealed elevated synchronization of cortex and brainstem oscillations in the delta band during fatal seizures compared with non-fatal seizures. The dynamic ictal oscillatory and connectivity features of fatal seizures provide insights into sudden unexpected death in epilepsy and may suggest biomarkers and eventual therapeutic targets.

The role of convulsive seizures in SUDEP

Convulsive seizures are the most consistently reported risk factor for SUDEP. However, the precise mechanisms by which convulsive seizures trigger fatal cardiopulmonary changes are still unclear. Additionally, it is not clear why some seizures cause death when most do not. This article reviews the physiologic changes that occur during and after convulsive seizures and how these may contribute to SUDEP. Seizures activate specific cortical and subcortical regions that can cause potentially lethal cardiorespiratory changes. Clinical factors, including sleep state, medication treatment and withdrawal, positioning and posturing during seizures, and underlying structural or genetic conditions may also affect specific aspects of seizures that may contribute to SUDEP. While seizure control, either through medication or surgical treatment, is the primary intervention that reduces SUDEP risk, unfortunately, seizures cannot be fully controlled despite maximal treatment in a significant proportion of people with epilepsy. Thus specific interventions to prevent adverse seizure-related cardiopulmonary consequences are needed. The potential roles of repositioning/stimulation after seizures, oxygen supplementation, cardiopulmonary resuscitation and clinical treatment options in reducing SUDEP risk are explored. Ultimately, understanding of these factors may lead to interventions that could reduce or prevent SUDEP.

Improving prediction of sudden unexpected death in epilepsy: From SUDEP-7 to SUDEP-3

OBJECTIVE

Sudden unexpected death in epilepsy (SUDEP) is a significant cause of mortality in epilepsy. The aim of this study is to evaluate the validity of the SUDEP-7 inventory and its components as tools for predicting SUDEP risk, and to develop and validate an improved inventory.

METHODS

The study included 28 patients who underwent video-electroencephalography (EEG) monitoring and later died of SUDEP, and 56 age- and sex-matched control patients with epilepsy. The SUDEP-7 score, its individual components, and an alternative inventory were examined as predictors of SUDEP.

RESULTS

SUDEP-7 scores were significantly higher among SUDEP patients compared with controls, both at time of admission (p = 0.024) and most recent follow-up (p = 0.016). SUDEP-7 scores declined only among controls, who demonstrated reduced seizure frequency. Seizure freedom after epilepsy surgery was also associated with survival. Several components of the SUDEP-7 inventory were independently associated with higher risk of SUDEP, including more than three generalized tonic-clonic (GTC) seizures (p = 0.002), one or more GTC seizures (p = 0.001), or one or more seizures of any type within the last year (p = 0.013), and intellectual disability (p = 0.031). In stepwise regression models, SUDEP-7 scores did not enhance the prediction of SUDEP over either GTC seizure frequency or seizure frequency alone. A novel SUDEP-3 inventory comprising GTC seizure frequency, seizure frequency, and intellectual disability (p < 0.001) outperformed the SUDEP-7 inventory (p = 0.010) in predicting SUDEP.

SIGNIFICANCE

Our findings demonstrate the limitations of the SUDEP-7 inventory. We propose a new three-item SUDEP-3 inventory, which predicts SUDEP better than the SUDEP-7.

Proteomics and Transcriptomics of the Hippocampus and Cortex in SUDEP and High-Risk SUDEP Patients

OBJECTIVE

To identify the molecular signaling pathways underlying sudden unexpected death in epilepsy (SUDEP) and high-risk SUDEP compared to control patients with epilepsy.

METHODS

For proteomics analyses, we evaluated the hippocampus and frontal cortex from microdissected postmortem brain tissue of 12 patients with SUDEP and 14 with non-SUDEP epilepsy. For transcriptomics analyses, we evaluated hippocampus and temporal cortex surgical brain tissue from patients with mesial temporal lobe epilepsy: 6 low-risk and 8 high-risk SUDEP as determined by a short (<50 seconds) or prolonged (≥50 seconds) postictal generalized EEG suppression (PGES) that may indicate severely depressed brain activity impairing respiration, arousal, and protective reflexes.

RESULTS

In autopsy hippocampus and cortex, we observed no proteomic differences between patients with SUDEP and those with non-SUDEP epilepsy, contrasting with our previously reported robust differences between epilepsy and controls without epilepsy. Transcriptomics in hippocampus and cortex from patients with surgical epilepsy segregated by PGES identified 55 differentially expressed genes (37 protein-coding, 15 long noncoding RNAs, 3 pending) in hippocampus.

CONCLUSION

The SUDEP proteome and high-risk SUDEP transcriptome were similar to those in other patients with epilepsy in hippocampus and cortex, consistent with diverse epilepsy syndromes and comorbid conditions associated with SUDEP. Studies with larger cohorts and different epilepsy syndromes, as well as additional anatomic regions, may identify molecular mechanisms of SUDEP.

Automated Analysis of Risk Factors for Postictal Generalized EEG Suppression

Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES.

Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES.

Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1–169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6–26.39, p = 0.0003), 7.17 (CI 1.29–39.76, p = 0.02), and 4.77 (CI 1.25–18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations.

Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies.

Cardiac Autonomic Dysfunction and Risk of Sudden Unexpected Death in Epilepsy

OBJECTIVE

We aimed to test whether patients who died of sudden unexpected death in epilepsy (SUDEP) had an abnormal cardiac autonomic response to sympathetic stimulation by hyperventilation.

METHODS

We conducted a retrospective, observational, case-control study of a group of patients who died of SUDEP and controls who were matched to the patients for epilepsy type, drug resistance, sex, age at EEG recording, age at onset of epilepsy, and duration of epilepsy. We analyzed the heart rate (HR) and HR variability (HRV) at rest and during and after hyperventilation performed during the patient's last EEG recording before SUDEP. In each group, changes over time in HRV indexes were analyzed with linear mixed models.

RESULTS

Twenty patients were included in each group. In the control group, the HR increased and the root mean square of successive RR-interval differences (RMSSD) decreased during the hyperventilation and then returned to the baseline values. In the SUDEP group, however, the HR and RMSSD did not change significantly during or after hyperventilation. A difference in HR between the end of the hyperventilation and 4 minutes after its end discriminated well between patients with SUDEP and control patients (area under the receiver operating characteristic curve 0.870, sensitivity 85%, specificity 75%).

CONCLUSION

Most of patients with subsequent SUDEP have an abnormal cardiac autonomic response to sympathetic stimulation through hyperventilation. An index reflecting the change in HR on hyperventilation might be predictive of the risk of SUDEP and could be used to select patients at risk of SUDEP for inclusion in trials assessing protective measures.

Postictal Death Is Associated with Tonic Phase Apnea in a Mouse Model of Sudden Unexpected Death in Epilepsy

OBJECTIVE

Sudden unexpected death in epilepsy (SUDEP) is an unpredictable and devastating comorbidity of epilepsy that is believed to be due to cardiorespiratory failure immediately after generalized convulsive seizures.

METHODS

We performed cardiorespiratory monitoring of seizure-induced death in mice carrying either a p.Arg1872Trp or p.Asn1768Asp mutation in a single Scn8a allele-mutations identified from patients who died from SUDEP-and of seizure-induced death in pentylenetetrazole-treated wild-type mice.

RESULTS

The primary cause of seizure-induced death for all mice was apnea, as (1) apnea began during a seizure and continued for tens of minutes until terminal asystole, and (2) death was prevented by mechanical ventilation. Fatal seizures always included a tonic phase that was coincident with apnea. This tonic phase apnea was not sufficient to produce death, as it also occurred during many nonfatal seizures; however, all seizures that were fatal had tonic phase apnea. We also made the novel observation that continuous tonic diaphragm contraction occurred during tonic phase apnea, which likely contributes to apnea by preventing exhalation, and this was only fatal when breathing did not resume after the tonic phase ended. Finally, recorded seizures from a patient with developmental epileptic encephalopathy with a previously undocumented SCN8A likely pathogenic variant (p.Leu257Val) revealed similarities to those of the mice, namely, an extended tonic phase that was accompanied by apnea.

INTERPRETATION

We conclude that apnea coincident with the tonic phase of a seizure, and subsequent failure to resume breathing, are the determining events that cause seizure-induced death in Scn8a mutant mice. ANN NEUROL 2021;89:1023-1035.

Automated detection of activity onset after postictal generalized EEG suppression

BACKGROUND

Sudden 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.

METHODS

We 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.

RESULTS

We 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.

CONCLUSION

Accurate 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.

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5–4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5–1.5 Hz signal and amplitude of 30–50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier—a hidden Markov model—was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

Glial Modulation of Electrical Rhythms in a Neuroglial Network Model of Epilepsy

OBJECTIVE

An important EEG-based biomarker for epilepsy is the phase-amplitude cross-frequency coupling (PAC) of electrical rhythms; however, the underlying pathways of these pathologic markers are not always clear. Since glial cells have been shown to play an active role in neuroglial networks, it is likely that some of these PAC markers are modulated via glial effects.

METHODS

We developed a 4-unit hybrid model of a neuroglial network, consisting of 16 sub-units, that combines a mechanistic representation of neurons with an oscillator-based Cognitive Rhythm Generator (CRG) representation of glial cells-astrocytes and microglia. The model output was compared with recorded generalized tonic-clonic patient data, both in terms of PAC features, and state classification using an unsupervised hidden Markov model (HMM).

RESULTS

The neuroglial model output showed PAC features similar to those observed in epileptic seizures. These generated PAC features were able to accurately identify spontaneous epileptiform discharges (SEDs) as seizure-like states, as well as a postictal-like state following the long-duration SED, when applied to the HMM machine learning algorithm trained on patient data. The evolution profile of the maximal PAC during the SED compared well with patient data, showing similar association with the duration of the postictal state.

CONCLUSION

The hybrid neuroglial network model was able to generate PAC features similar to those observed in ictal and postictal epileptic states, which has been used for state classification and postictal state duration prediction.

SIGNIFICANCE

Since PAC biomarkers are important for epilepsy research and postictal state duration has been linked with risk of sudden unexplained death in epilepsy, this model suggests glial synaptic effects as potential targets for further analysis and treatment.

Causes and Effects Contributing to Sudden Death in Epilepsy and the Rationale for Prevention and Intervention

Sudden unexpected death in epilepsy (SUDEP) claims the lives of one in every thousand epileptic patients each year. Autonomic, cardiac, and respiratory pieces to a mechanistic puzzle have not yet been completely assembled. We propose a single sequence of causes and effects that unifies disparate and competitive concepts into a single algorithm centered on ictal obstructive apnea. Based on detailed animal studies that are sometimes impossible in humans, and striking parallels with a growing body of clinical examples, this framework (1) accounts for the autonomic, cardiac, and respiratory data to date by showing the causal relationships between specific elements, and (2) highlights specific kinds of data that can be used to precisely classify various patient outcomes. The framework also justifies a “near miss” designation to be applied to any cases with evidence of obstructive apnea even, and perhaps especially, in individuals that do not require resuscitation. Lastly, the rationale for preventative oxygen therapy is demonstrated. With better mechanistic understanding of SUDEP, we suggest changes for detection and classification to increase survival rates and improve risk stratification.

The postictal state - What do we know?

This narrative review provides a broad and comprehensive overview of the most important discoveries on the postictal state over the past decades as well as recent developments. After a description and definition of the postictal state, we discuss postictal sypmtoms, their clinical manifestations, and related findings. Moreover, pathophysiological advances are reviewed, followed by current treatment options.

Treatments for the prevention of Sudden Unexpected Death in Epilepsy (SUDEP)

BACKGROUND

This is an updated version of the original Cochrane Review, published in 2016, Issue 7. Sudden Unexpected Death in Epilepsy (SUDEP) is defined as sudden, unexpected, witnessed or unwitnessed, non-traumatic or non-drowning death of people with epilepsy, with or without evidence of a seizure, excluding documented status epilepticus and in whom postmortem examination does not reveal a structural or toxicological cause for death. SUDEP has a reported incidence of 1 to 2 per 1000 patient-years and represents the most common epilepsy-related cause of death. The presence and frequency of generalised tonic-clonic seizures (GTCS), male sex, early age of seizure onset, duration of epilepsy, and polytherapy are all predictors of risk of SUDEP. The exact pathophysiology of SUDEP is currently unknown, although GTCS-induced cardiac, respiratory, and brainstem dysfunction appears likely. Appropriately chosen antiepileptic drug treatment can render around 70% of patients free of all seizures. However, around one-third will remain drug-resistant despite polytherapy. Continuing seizures place patients at risk of SUDEP, depression, and reduced quality of life. Preventative strategies for SUDEP include reducing the occurrence of GTCS by timely referral for presurgical evaluation in people with lesional epilepsy and advice on lifestyle measures; detecting cardiorespiratory distress through clinical observation and seizure, respiratory, and heart rate monitoring devices; preventing airway obstruction through nocturnal supervision and safety pillows; reducing central hypoventilation through physical stimulation and enhancing serotonergic mechanisms of respiratory regulation using selective serotonin reuptake inhibitors (SSRIs); and reducing adenosine and endogenous opioid-induced brain and brainstem depression.

OBJECTIVES

To assess the effectiveness of interventions in preventing SUDEP in people with epilepsy by synthesising evidence from randomised controlled trials of interventions and cohort and case-control non-randomised studies.

SEARCH METHODS

For the latest update we searched the following databases without language restrictions: Cochrane Register of Studies (CRS Web, 4 February 2019); MEDLINE (Ovid, 1946 to 1 February 2019); SCOPUS (1823 to 4 February 2019); PsycINFO (EBSCOhost, 1887 to 4 January 2019); CINAHL Plus (EBSCOhost, 1937 to 4 February 2019); ClinicalTrials.gov (5 February 2019); and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP, 5 February 2019). We checked the reference lists of retrieved studies for additional reports of relevant studies and contacted lead study authors for any relevant unpublished material. We identified any grey literature studies published in the last five years by searching: Zetoc database; ISI Proceedings; International Bureau for Epilepsy (IBE) congress proceedings database; International League Against Epilepsy (ILAE) congress proceedings database; abstract books of symposia and congresses, meeting abstracts, and research reports.

SELECTION CRITERIA

We aimed to include randomised controlled trials (RCTs), quasi-RCTs, and cluster-RCTs; prospective non-randomised cohort controlled and uncontrolled studies; and case-control studies of adults and children with epilepsy receiving an intervention for the prevention of SUDEP. Types of interventions included: early versus delayed pre-surgical evaluation for lesional epilepsy; educational programmes; seizure-monitoring devices; safety pillows; nocturnal supervision; selective serotonin reuptake inhibitors (SSRIs); opiate antagonists; and adenosine antagonists.

DATA COLLECTION AND ANALYSIS

We aimed to collect data on study design factors and participant demographics for included studies. The primary outcome of interest was the number of deaths from SUDEP. Secondary outcomes included: number of other deaths (unrelated to SUDEP); change in mean depression and anxiety scores (as defined within the study); clinically important change in quality of life, that is any change in quality of life score (average and endpoint) according to validated quality of life scales; and number of hospital attendances for seizures.

MAIN RESULTS

We identified 1277 records from the databases and search strategies. We found 10 further records by searching other resources (handsearching). We removed 469 duplicate records and screened 818 records (title and abstract) for inclusion in the review. We excluded 785 records based on the title and abstract and assessed 33 full-text articles. We excluded 29 studies: eight studies did not assess interventions to prevent SUDEP; eight studies were review articles, not clinical studies; five studies measured sensitivity of devices to detect GTCS but did not directly measure SUDEP; six studies assessed risk factors for SUDEP but not interventions for preventing SUDEP; and two studies did not have a control group. We included one cohort study and three case-control studies of serious to critical risk of bias. The 6-month prospective cohort study observed no significant effect of providing patients with SUDEP information on drug compliance and quality of life, anxiety and depression levels. The study was too short and with no deaths observed in either group to determine a protective effect. Two case control studies reported a protective effect for nocturnal supervision against SUDEP. However due to significant heterogeneity, the results could not be combined in meta-analysis. One study of 154 SUDEP cases and 616 controls reported an unadjusted odds ratio (OR) of 0.34 (95% CI 0.22 to 0.53; P < 0.0001). The same study demonstrated the protective effect was independent of seizure control, suggesting that nocturnal supervision is not just a surrogate marker of seizure control. The second case-control study of 48 SUDEP cases and 220 controls reported an unadjusted OR of 0.08 (95% CI 0.02 to 0.27; P < 0.0001). The third case-control study of residential care centre patients who were already receiving physical checks more than 15 minutes apart throughout the night did not report any protective effect for additional nocturnal supervision (physical checks < 15 minutes apart; use of listening devices; dormitory setting; and use of bed sensors). However the same study did ascertain a difference between centres: the residential centre with the lowest level of supervision had the highest incidence of SUDEP. The case-control studies did not report on quality of life or depression and anxiety scores.

AUTHORS' CONCLUSIONS

We found limited, very low-certainty evidence that supervision at night reduces the incidence of SUDEP. Further research is required to identify the effectiveness of other current interventions - for example seizure detection devices, safety pillows, SSRIs, early surgical evaluation, educational programmes, and opiate and adenosine antagonists - in preventing SUDEP in people with epilepsy.

Risks and predictive biomarkers of sudden unexpected death in epilepsy patient

PURPOSE OF REVIEW

The current review updates our knowledge regarding sudden unexpected death in epilepsy patient (SUDEP) risks, risk factors, and investigations of putative biomarkers based on suspected mechanisms of SUDEP.

RECENT FINDINGS

The overall incidence of SUDEP in adults with epilepsy is 1.2/1000 patient-years, with surprisingly comparable figures in children in recently published population-based studies. This risk was found to decrease over time in several cohorts at a rate of -7% per year, for unknown reasons. Well established risk factors include frequency of generalized tonic-clonic seizures, while adding antiepileptic treatment, nocturnal supervision and use of nocturnal listening device appear to be protective. In contrast, recent data failed to demonstrate the predictive value of heart rate variability, periictal cardiorespiratory dysfunction, and postictal generalized electroencephalography suppression. Preliminary findings suggest that brainstem and thalamic atrophy may be associated with a higher risk of SUDEP. Novel experimental and human data support the primary role of generalized tonic-clonic seizure-triggered respiratory dysfunction and the likely contribution of altered brainstem serotoninergic neurotransmission, in SUDEP pathophysiology.

SUMMARY

Although significant progress has been made during the past year in the understanding of SUDEP mechanisms and investigation of numerous potential biomarkers, we are still missing reliable predictors of SUDEP beyond the well established clinical risk factors.

Let's talk SUDEP

Sudden unexplained death in epilepsy (SUDEP) is a devastating complication of epilepsy which was under-recognized in the recent past despite its clear importance. In this review, we examine the definition of SUDEP, revise current pathophysiological theories, discuss risk factors and preventative measures, disclose tools for appraising the SUDEP risk, and last but not least dwell upon announcing and explaining the SUDEP risk to the patients and their caretakers. We aim to aid the clinicians in their responsibility of knowing SUDEP, explaining the SUDEP risk to their patients in a reasonable and sensible way and whenever possible, preventing SUDEP. Future studies are definitely needed to increase scientific knowledge and awareness related to this prioritized topic with malign consequences.

Decreasing SUDEP incidence in a tertiary epilepsy center between 1981 and 2016: Effects of better patient supervision

OBJECTIVE

The objective of this study was to investigate the incidence of sudden unexpected death in epilepsy (SUDEP) in a tertiary epilepsy center in the years 1981-2016 with an emphasis on patient supervision and nursing intervention in different departments.

METHODS

We identified 14 SUDEP cases (probable, definite, definite plus). Patient-years (PY) and incidence were calculated for the periods of six years for the general epileptology wards (adults and children) and, in addition, for the epilepsy monitoring unit (EMU) since 1990.

RESULTS

The incidence of SUDEP showed a decreasing trend over time (r = -0.81; p = 0.053, two-sided; Pearson correlation coefficient). This is especially true in children (no SUDEP occurred in pediatric general epileptology since 1992). Additionally, in the EMU (314 PY since the start of 1990), no SUDEP occurred. Sudden unexpected death in epilepsy incidence was highest (6.8/1000 PY) in the early time periods (1981-1992) and lowest (1.7/1000 PY) in the later time periods (1999-2010). In the general epileptology wards (3579 PY), the overall incidence was 3.9 per 1000 PY (95% confidence interval (CI): 2.1-6.6).

CONCLUSIONS

We assume that the decreased SUDEP incidence is an effect of better supervision by the use of technical means (e.g., video cameras, pulse oximeters, seizure detection systems) and rooming-in of parents or family.

Dead in the Night: Sleep-Wake and Time-Of-Day Influences on Sudden Unexpected Death in Epilepsy

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related death in patients with refractory epilepsy. Convergent lines of evidence suggest that SUDEP occurs due to seizure induced perturbation of respiratory, cardiac, and electrocerebral function as well as potential predisposing factors. It is consistently observed that SUDEP happens more during the night and the early hours of the morning. The aim of this review is to discuss evidence from patient cases, clinical studies, and animal research which is pertinent to the nocturnality of SUDEP. There are a number of factors which might contribute to the nighttime predilection of SUDEP. These factors fall into four categories: influences of (1) being unwitnessed, (2) lying prone in bed, (3) sleep-wake state, and (4) circadian rhythms. During the night, seizures are more likely to be unwitnessed; therefore, it is less likely that another person would be able to administer a lifesaving intervention. Patients are more likely to be prone on a bed following a nocturnal seizure. Being prone in the accouterments of a bed during the postictal period might impair breathing and increase SUDEP risk. Sleep typically happens at night and seizures which emerge from sleep might be more dangerous. Lastly, there are circadian changes to physiology during the night which might facilitate SUDEP. These possible explanations for the nocturnality of SUDEP are not mutually exclusive. The increased rate of SUDEP during the night is likely multifactorial involving both situational factors, such as being without a witness and prone, and physiological changes due to the influence of sleep and circadian rhythms. Understanding the causal elements in the nocturnality of SUDEP may be critical to the development of effective preventive countermeasures.