Translational Research Symposium: Current State of SUDEP Research

Abstract

People with epilepsy are at 24-fold increased risk of sudden death compared to the general population. Sudden unexpected death in epilepsy (SUDEP) is defined as the sudden, unexpected, nontraumatic, and nondrowning death of an individual with epilepsy, with or without evidence of a seizure, in which postmortem examination does not reveal a structural or toxicologic cause of death. SUDEP is the leading cause of mortality in epilepsy. The SUDEP rate in general epilepsy is >1:1000 person-years, with rates in some drug-resistant epilepsies and developmental and epileptic encephalopathies exceeding 9:1000 person-years. This review summarizes the current state of the field of SUDEP research, including human cohort studies, genetic risk factors, the use of animal models of epilepsy and SUDEP, cardio-respiratory risk factors, EEG biomarkers during sleep, and the use of wearable devices to reduce SUDEP risk. Understanding of the current state of SUDEP research can help inform clinical care, motivate future research directions, and ultimately reduce the risk of SUDEP in the epilepsy population.

Keywords

SUDEP translational research clinical translational

Introduction

Sudden unexpected death in epilepsy (SUDEP) is defined as the sudden, unexpected, nontraumatic, and nondrowning death of a person with epilepsy, with or without evidence of a seizure, in which postmortem examination does not reveal a structural or toxicologic cause of death.¹ SUDEP represents the leading cause of epilepsy-related mortality and is the second leading neurological cause of years of potential life lost, surpassed only by stroke.² The incidence of SUDEP varies considerably based on epilepsy severity, ranging from approximately 0.22 to 1.2 per 1000 patient-years in people with general epilepsy.³ In populations with drug-resistant epilepsy, incidence rises to 3 to 9 per 1000 patient-years, and patients with frequent generalized tonic-clonic seizures (GTCS) face rates as high as 18 per 1000 patient-years.^4,5 SUDEP results in an estimated 100,000 years of potential life lost annually in the United States alone.²

This review synthesizes current knowledge from clinical registries, genetic studies, animal models, and emerging biomarker research to provide a comprehensive overview of SUDEP pathophysiology and identify opportunities for translating research findings into clinical care.

Using Results of SUDEP Research to Inform Clinical Care

SUDEP remains a complex phenomenon, with no single clinical signature or mechanistic certainty. Foundational research, including animal SUDEP models, has made significant contributions to our understanding of pathophysiological mechanisms, including brainstem spreading depression, the role of serotonin and adenosine, and candidate genes. This work can move the field toward the identification of potential therapeutic targets. However, animal models of SUDEP are limited in their translation to clinical care. Typically, cardiorespiratory arrests in SUDEP animal models occur during seizures, rather than in the postictal state, as is more common in seizure-mediated SUDEP in humans. Animal models do not fully capture the diversity of human epilepsy affected by SUDEP. Clinical research is critical to translate scientific discovery to clinical care.

The seminal MORTEMUS study demonstrates the value of clinical research.⁶ This study advanced our understanding of SUDEP mechanisms by systematically analyzing video-EEG recordings of SUDEP cases in epilepsy monitoring units. The MORTEMUS study found that most SUDEP events occurred at night, following a GTCS, and were characterized by a sequence of early postictal apnea, followed by bradycardia, and then terminal asystole. Notably, the study highlighted that respiratory dysfunction, particularly postictal central apnea, often preceded cardiac arrest. This suggests that interventions targeting respiratory support immediately after seizures could be crucial for prevention. Importantly, the MORTEMUS cohort represented adults admitted to epilepsy monitoring units for surgical evaluations, often weaned off medication. This does not represent the full spectrum of individuals affected by SUDEP. In fact, data suggests that individuals with genetic developmental and epileptic encephalopathies (DEE) may be at greater of SUDEP despite not being represented in the MORTEMUS cohort.^7,8

Clinical registries offer real-world data that are essential for identifying risk factors, informing treatment strategies, and recognizing gaps in care. Registries facilitate large-scale data collection to enhance understanding of disease epidemiology, support risk stratification, assess treatment effectiveness, and pinpoint areas requiring improvement. By enabling the identification of modifiable risk factors and patterns of comorbidity, clinical registries assist in developing targeted interventions and preventative measures. Additionally, they contribute to the creation and validation of risk prediction models, which promote individualized patient counselling and management. Registries establish benchmarks and outcomes to reveal disparities in healthcare delivery or health outcomes across regions and populations, while providing population-level insights into incidence, prevalence, and mortality for a variety of conditions.

The Canadian Pediatric SUDEP Registry found that SUDEP is more common in children than previously thought, with an incidence of about 1.1 per 1000 patient-years.^9,10 The North American SUDEP Registry (NASR) showed that SUDEP can affect individuals across the entire range of epilepsy types, not just those with severe or refractory forms.¹¹ By enrolling over 200 SUDEP cases and collecting detailed epidemiological, genetic, and clinical data, NASR has shown that SUDEP occurs in people with both severe and milder, so-called “benign,” epilepsy syndromes. This finding challenges the previous assumption that SUDEP is limited to the most severe cases, and highlights the need for vigilance and risk assessment in all epilepsy patients. The registry also uncovered important social and clinical factors: SUDEP is more likely in individuals from lower socioeconomic backgrounds.

Population-based data from Sweden have shown that the key risk factor for SUDEP is the frequency of GTCS; however, living arrangements also matter: individuals with frequent GTCS who live alone face a dramatically increased risk.⁵ While most SUDEP cases occur in those with frequent seizures, it can also affect people with less frequent or nocturnal seizures, and sometimes those not on medication. Autopsy data show that SUDEP is often underrecognized, especially in older adults with other medical conditions.

Current SUDEP research faces several important gaps that limit progress toward prevention and individualized care. One major challenge is the limited integration of mechanistic insights from animal studies into precision medicine for patients. While animal models have helped identify possible biological pathways, translating these findings into clinical strategies remains incomplete. There is also an incomplete understanding of the mechanisms underlying SUDEP, which hinders the development of high-certainty preventative strategies and validated biomarkers for risk stratification.

Another gap is the lack of comprehensive data from diverse populations, including children, older adults, and individuals in low- and middle-income countries. Most studies have focused on select groups, leaving questions about how risk factors and outcomes may differ across age, geography, and socioeconomic status. Additionally, SUDEP reporting is not sufficiently standardized, making it difficult to compare findings across studies and regions.

Future directions in SUDEP research include improving the integration of basic science and clinical data to enable precision medicine approaches, developing and validating reliable biomarkers for SUDEP risk, and generating high-quality evidence for preventive interventions. Expanding research to include more diverse populations and standardizing SUDEP reporting practices will also be crucial. Ultimately, bridging these gaps will help clinicians better identify at-risk individuals and tailor interventions to reduce SUDEP incidence.

The Role of Genetics in SUDEP Risk

Genetic factors are known to play a role in SUDEP risk. Genetic variants can include monogenic, oligogenic, or polygenic causes. Most cases of SUDEP fall in the oligogenic or polygenic classifications. These latter causes represent common genetic variations within our genome that individually have a very small effect on disease causation, but in combination can result in a higher burden of variants causing disease symptomatology.

There are numerous genes whose variations cause both neuronal and cardiac phenotypes.⁷ A recent study found that the polygenic risk score (PRS) for longevity was significantly lower in a SUDEP cohort when compared to epilepsy and healthy controls.¹² This finding not only improves risk prediction but also takes us one step closer to understanding the mechanisms underlying SUDEP.

Additionally, inclusion of the longevity PRS improved the ability to predict SUDEP risk when combined with existing clinical risk scores (eg, SUDEP-3¹³). Another study looked at 39 autopsy-confirmed SUDEP cases and performed targeted cardiac and epilepsy panels with a yield of 72%.¹⁴ Thirteen variants of interest (7 from cardiac panel and 6 from epilepsy panel) were predicted pathogenic and below population allele frequency threshold, and present priority targets for future functional studies.

DEPDC5 was identified as a high SUDEP risk gene in a nonconsanguineous French–Canadian family¹⁵ and was later also found in another whole exome rare-variant analysis comparing 57 SUDEP to 2936 controls. It was one of the most enriched genes, further emphasizing the role of DEPDC5 as a high SUDEP risk gene.¹⁶ In a cohort of 510 patients with a genetic DEE, SUDEP cases contributed to 48% of deaths.⁸ SUDEP rates were highest in the SCN1A-DEEs including patients with Dravet syndrome (DS, 5.9%) and other SCN1A-DEEs (27%). SUDEP cases had variants in SCN1A, SCN2A, SCN8A, and STXBP1. These results indicate the potential for genetic-variant-specific SUDEP risk counseling.

In a study surveying caregivers of SUDEP patients,¹⁷ caregivers reported transient subclinical changes in the weeks or months leading up to SUDEP. This highlights the dynamic nature of risk as an individual fluctuates between periods of higher and lower risk probability. The role of genetics in SUDEP risk has a direct impact on clinical care through development of genotype-guided surveillance protocols including cardiac monitoring, long-term neurocognitive and autonomic assessments, and incorporation of genetics into SUDEP counselling.

Animal Models of Multisystem Pathologies Surrounding Seizures and Preceding SUDEP

When developing a preclinical model of disease, the goal is to have translational value (ie, bench-to-bedside, across various fields of medicine). The model must reproduce the (patho-)physiological phenotype seen in people with the disease. When studying the mechanisms and treatments for electrical diseases of the brain and heart, it is imperative that the model mimics the ion channel expression and function seen in humans. Long QT Syndrome Type-2 (LQT2) is a classically studied cardiac disease due to loss-of-function variants in KCNH2. People with LQT2 develop epileptiform activity, seizures, epilepsy, and SUDEP.^18–21

The cardiac ion channel fingerprints are very different in humans versus mice. The impact that mutant channels have on the mouse cardiac action potential remains controversial, which limits its utility.²² In contrast, the cardiac electrical activation-recovery process is very similar in rabbits and humans. Rabbits are an established model for studying seizures²³ and cardiac arrhythmias.^22,24 Humans and rabbits have similar cardiac ion channel expression patterns, cardiomyocyte action potential morphology, and the action potential and cardiac QT interval are sensitive to I_Kr blockade.^22,24–26

Thus, rabbits are an appropriate species to use as a genetic model of LQT2 with epilepsy. Using CRISPR-Cas9-mediated technology, a frameshift mutation was introduced in one allele of the endogenous rabbit Kcnh2 gene. Kcnh2^(+/mut) rabbits have reduced Kcnh2 and K_v11.1 protein expression. In the heart, Kcnh2^(+/mut) rabbits model the LQT2 human phenotype of QT_c prolongation, arrhythmias, and sudden cardiac death. In the brain, Kcnh2^(+/mut) rabbits reproduce the LQT2 neuronal phenotype of epileptiform activity, seizures, and seizure-mediated sudden death. This model provides a translational model of LQT2 with epilepsy, which facilitates studies to identify antiarrhythmic and antiseizure therapies, as well as evaluate the cardiac safety of existent and novel antiseizure therapies.

Conversely, using genetic animal/cellular models of Dravet syndrome, we reported altered cardiac sodium current, action potential and QT_c prolongation, hyperexcitability, and ventricular fibrillation leading to sudden death.^4,27–29 To translate these results to people, a large multiinstitutional dataset of ECG metrics from people with DS was compiled. Similar to preclinical results, people with DS exhibit cardiac ECG abnormalities, which are known substrates for cardiac arrhythmias.

Cardiorespiratory Dysfunction Leading to SUDEP

Respiratory dysfunction represents a critical mechanism underlying SUDEP. Seizures disrupt breathing and these mechanisms can be modeled in animals to advance understanding and prevention strategies. Clinical studies reveal that even nonfatal seizures, particularly those of focal temporal origin, significantly disrupt breathing and oxygen/carbon dioxide homeostasis.^30,31 Approximately 33% of focal seizures cause hypoxemia, with 14% reaching severe levels (SpO₂ < 80%), while 37% result in hypercapnia.^30,32 Ictal central apnea occurs in approximately 40% of seizures, predominantly in temporal focal seizures,³³ and postconvulsive central apnea (PCCA) develops in 22% of both focal and generalized seizures, with the latter showing particularly strong association with SUDEP risk.³⁴

Multiple Kcna1 (K_v1.1) knockout mouse models, ranging from global deletions to region-specific conditional knockouts, have been developed to investigate SUDEP mechanisms and identify the neural circuits involved.^35,36 Interictal respiratory variability is correlated with postictal hypoxemia severity in patients.³⁷ Global and pan-neuronal knockout models demonstrate increased baseline respiratory variability along with decreased spontaneous and post-sigh apnea incidence.^36,38 Interestingly, K_v1.1 knock-out mice on a different genetic background exhibited increased apnea.³⁹

Video-EEG monitoring of fatal and near-fatal events across multiple genetic models (Kcna1, Scn8a, Scn1a, and Depdc5) reveals that respiratory failure consistently precedes cardiac arrest.^40–42 The terminal cascade typically involves GTCS with tonic hindlimb extension, postictal generalized EEG suppression (PGES), terminal apnea, progressive bradycardia, and ultimately terminal asystole.⁴⁰ Notably, corticolimbic Kcna1 conditional knockout mice uniquely exhibit terminal apnea with postictal onset (rather than ictal), more closely mirroring SUDEP in the MORTEMUS study and emphasizing that forebrain-driven mechanisms are important to SUDEP pathophysiology in this model.^35,43

These findings suggest that respiratory measures including hypercapnic ventilatory response (HCVR), respiratory variability, and PCCA may serve as clinical SUDEP risk biomarkers.^32,34,37 However, critical questions remain regarding whether a unified mechanism explains all SUDEP cases, how seizures specifically disrupt brainstem respiratory networks, the roles of specific neuronal subpopulations, why some apneas prove transient while others become fatal, and how sex differences influence vulnerability to these catastrophic outcomes.^44–46 Collectively, these studies emphasize that seizure-related respiratory arrest is a primary driver of SUDEP and that forebrain-driven mechanisms are critical to SUDEP pathophysiology, highlighting the necessity of integrating animal model findings with clinical data to develop targeted preventive interventions.

Sleep EEG Biomarkers and SUDEP Risk Prediction

Established SUDEP risk factors include frequent GTCS, lack of seizure freedom, inadequate treatment escalation in refractory epilepsy, and absence of nocturnal supervision.³ Prior work revealed potential biomarker candidates such as postictal generalized EEG suppression (PGES), various network connectivity metrics, structural MRI and fMRI alterations, and PCCA but they lacked sufficient sensitivity or consistency across patients to be clinically actionable.^47–50

Given that many SUDEP events occur during sleep,^51,52 sleep physiology offers an underexplored window into risk mechanisms. Using overnight EEG data from the Center for SUDEP Research, sleep architecture and quantitative sleep EEG metrics were examined across SUDEP cases, high-risk patients, and low-risk or nonepilepsy controls. Key parameters included sleep stage distribution, spindle characteristics, and slow wave activity (SWA)—a well-established measure of sleep homeostasis. While sleep efficiency and stage proportions did not differ meaningfully between groups, striking abnormalities emerged in SWA dynamics. In contrast to the expected overnight decline in SWA reflecting dissipation of sleep pressure, SUDEP and high-risk individuals demonstrated little to no SWA decay, and in some cases even paradoxical increases.⁵³ These findings suggest a loss of normal sleep homeostasis, potentially leading to chronic sleep debt—an independent contributor to all-cause mortality.

Parallel investigations in the K_v1.1-null mouse model of SUDEP revealed analogous disturbances, including reduced sleep efficiency, blunted diurnal oscillations in SWA, and impaired homeostatic responses to prior wakefulness. Additional analyses showed that interictal epileptiform discharges during NREM sleep were associated with diminished SWA slope decay, providing a mechanistic link between epileptic activity and disrupted sleep regulation.⁵⁴

The clinical implications are substantial. Sleep EEG features may offer objective biomarkers to stratify SUDEP risk and inform individualized counseling. Obstructive sleep apnea (OSA) is commonly comorbid with epilepsy and can itself be a disruptor of sleep homeostasis.⁵⁵ CPAP treatment may restore normal sleep dynamics and hence patients should routinely be evaluated for OSA in epilepsy clinics. Furthermore, emerging evidence suggests that pharmacologic agents capable of normalizing sleep homeostasis,⁵⁶ including dual orexin receptor antagonists, may reduce SUDEP vulnerability. Future research should validate these sleep-based biomarkers in larger cohorts and assess whether interventions that restore sleep homeostasis can mitigate SUDEP risk.

Using Implantable and Wearable Devices to Better Understand and Reduce SUDEP Risk

Population based studies consistently demonstrate that the strongest clinical risk factors for SUDEP are nocturnal focal-to-bilateral and GTCS in people who live alone.⁵⁷ There are currently no devices that have been validated for SUDEP monitoring for use in real-time or acute emergencies. However, seizure detection devices, predominantly using one (or more) modalities of accelerometry, heart rate, electrodermal activity, or electroencephalogram (EEG) recordings can be used to detect nocturnal tonic-clonic seizures with promising accuracy and low false alarm rates. Multimodal algorithms appear to perform the best across all performance metrics.⁵⁸ However, these devices are often expensive, require ongoing subscription, and are stigmatising for users.⁵⁹

Implantable or subscalp EEG devices for seizure have the advantage of being more accurate, resistant to motion or muscle associated with seizures, and are helpful for seizure detection and quantification. However, these devices are expensive and require surgery.

Nonwearable devices such as below the mattress sensors and video-based detection can provide a wealth of information on whole body motion and breathing. They can also be used in complex epilepsy syndromes such as DEE, as this may improve tolerability.

As data becomes readily available with the increase in home smart technology, this enables us to study longitudinal and temporal patterns of how these markers of seizure frequency, patterns, and autonomic function may vary over time, providing mechanistic clues into SUDEP.

Improved seizure detection devices can also more accurately document tonic-clonic seizure frequency and help to more accurately ascertain known risk factors for SUDEP such as medication adherence, sleep quality, stress, attending neurology and medical appointments, mental health or substance disorder support, and having an up-to-date seizure emergency plan. This information could be important in developing and implementing risk mitigation strategies.

Combined with advances in artificial intelligence, devices and software can go beyond 2-dimentional risk stratification combining all forms of clinical, physiological, imaging, and lifestyle meta-data, for more personalized monitoring and interventions.

Conclusions

The evidence presented in this review has direct implications for clinical practice. First, SUDEP risk counseling should be incorporated into routine epilepsy care, as recommended by the American Academy of Neurology and American Epilepsy Society.³ Clinicians should consider concurrent cardiac and respiratory monitoring during standard EEG, and out-of-hospital monitoring with wearable devices. Genetic testing should be considered, especially in patients with DEE, as variants in genes such as SCN1A, SCN8A, and DEPDC5 confer elevated SUDEP risk and may inform targeted surveillance protocols.⁸ Finally, patients should be evaluated for modifiable risk factors including nocturnal supervision, obstructive sleep apnea, and medication adherence.^53,57

Future research priorities include validation of emerging biomarkers such as sleep EEG abnormalities and respiratory dysfunction measures, development of reliable seizure detection devices for home use, and integration of genetic risk scores into clinical risk stratification tools. Animal models continue to provide mechanistic insights, but translating these findings to human populations remains challenging. Importantly, declining SUDEP incidence trends observed in multiple cohorts suggest that improved epilepsy care may already be reducing mortality.⁶⁰ Continued investment in SUDEP research, combined with implementation of evidence-based prevention strategies, offers the potential to significantly reduce this leading cause of epilepsy-related death.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Justin M. Ryan

Quratulain Zulfiqar-Ali

David S. Auerbach

Edward Glasscock

Rama Maganti

Shobi Sivathamboo

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