Clinical risk factors in SUDEP

Sveinsson, Ólafur; Andersson, Tomas; Mattsson, Peter; Carlsson, Sofia; Tomson, Torbjörn

doi:10.1212/wnl.0000000000008741

Cited by 204 publications

(140 citation statements)

References 32 publications

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“…Rates of seizure-related deaths and SUDEP correlate with increased seizure frequency and insufficient engagement with clinicians [28] , [20] , [21] , [22] , [23] , [40] , [41] . Without appropriate medical attention, indicators that point to an increased likelihood of premature mortality may not be identified, restricting the ability to intervene [28] . The risk of mortality may increase owing to poor communication about risk.…”

Section: Discussionmentioning

confidence: 99%

“…The risk of mortality may increase owing to poor communication about risk. This can disempower people and inhibit their ability to make informed decisions on, for example, ASM adherence, alcohol and non-prescription drug consumption, family planning, driving, and sleep habits [28] .…”

Section: Discussionmentioning

confidence: 99%

“…Specific questions were included that related to mental health status, alcohol and drug consumption, sleeping patterns, and changes to seizures [27] , [21] , [22] , [23] . We asked about individual living circumstances and whether respondents lived alone or with someone who could provide first aid [28] .…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating risk to people with epilepsy during the COVID-19 pandemic: Preliminary findings from the COV-E study

Thorpe

Ashby

Hallab

et al. 2021

Epilepsy & Behavior

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Highlights The COVID-19 and Epilepsy (COV-E) global surveys launched in May 2020. UK respondents (n=463) report changes in seizure frequency, mental health and sleep. Discussion of risk, including of SUDEP, was infrequent even before the pandemic. COVID-19 is having far-reaching consequences on people with epilepsy. Our study exemplifies the importance of delivering optimal care to mitigate risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Evaluating risk to people with epilepsy during the COVID-19 pandemic: Preliminary findings from the COV-E study

Thorpe

Ashby

Hallab

et al. 2021

Epilepsy & Behavior

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“…A two-fold increased risk in Sudden Unexpected Death in Epilepsy (SUDEP) has been associated with alcohol dependence ( 26 ). This is not recorded in the datasets we used.…”

Section: Discussionmentioning

confidence: 99%

Alcohol-Specific Mortality in People With Epilepsy: Cohort Studies in Two Independent Population-Based Datasets

et al. 2021

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Objectives: The risk of dying by alcohol-specific causes in people with epilepsy has seldom been reported from population-based studies. We aimed to estimate the relative risk of alcohol-specific mortality in people with epilepsy, and the extent to which problematic alcohol use was previously identified in the patients' medical records.Method: We delineated cohort studies in two population-based datasets, the Clinical Practice Research Datalink (CPRD GOLD) in England (January 01, 2001–December 31, 2014) and the Secure Anonymised Information Linkage (SAIL) Databank in Wales (January 01, 2001–December 31, 2014), linked to hospitalization and mortality records. People with epilepsy were matched to up to 20 persons without epilepsy on gender, age (±2 years) and registered general practice. We identified alcohol-specific death from Office for National Statistics (ONS) records using specified ICD-10 codes. We further identified prescriptions, interventions and hospitalisations related to alcohol use.Results: In the CPRD GOLD, we identified 9,871 individuals in the incident epilepsy cohort and 185,800 in the comparison cohort and, in the SAIL Databank, these numbers were 5,569 and 110,021, respectively. We identified a five-fold increased risk of alcohol-specific mortality in people with epilepsy vs. those without the condition in our pooled estimate across the two datasets (deprivation-adjusted HR 4.85, 95%CI 3.46–6.79).Conclusions: People with epilepsy are at increased risk of dying by an alcohol-specific cause than those without the disorder. It is plausible that serious alcohol misuse could either contribute to the development of epilepsy or it could commence subsequent to epilepsy being diagnosed. Regardless of the direction of the association, it is important that the risk of dying as a consequence of alcohol misuse is accurately quantified in people affected by epilepsy. Systematically-applied, sensitive assessment of alcohol consumption by healthcare professionals, at opportunistic, clinical contacts, with rapid access to quality treatment services, should be mandatory and play a key role in reduction of health harms and mortality.

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“…Though SUDEP cases are heterogeneous, some biomarkers have been found to be associated with the SUDEP risk [6][7][8][9][10]. One such biomarker being actively investigated is postictal generalized EEG suppression (PGES).…”

Section: Introductionmentioning

confidence: 99%

Automated detection of activity onset after postictal generalized EEG suppression

Lamichhane

Kim

Segarra

et al. 2020

BMC Med Inform Decis Mak

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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.

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Clinical risk factors in SUDEP

Cited by 204 publications

References 32 publications

Evaluating risk to people with epilepsy during the COVID-19 pandemic: Preliminary findings from the COV-E study

Evaluating risk to people with epilepsy during the COVID-19 pandemic: Preliminary findings from the COV-E study

Alcohol-Specific Mortality in People With Epilepsy: Cohort Studies in Two Independent Population-Based Datasets

Automated detection of activity onset after postictal generalized EEG suppression

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