R01NS123928
Project Grant
Overview
Grant Description
Advancing SUDEP Risk Prediction Using a Multicenter Case-Control Approach - Project Summary
Patients with epilepsy have a 25-fold elevated risk of sudden death. Sudden Unexpected Death in Epilepsy (SUDEP) is the most common disease-related cause of premature mortality in people with seizure disorders, affecting 1 of every 1000 patients annually. Despite this, mechanisms and risk factors of SUDEP remain largely unknown. Having ongoing generalized tonic-clonic seizures (GTCS) and no nocturnal supervision are the only definite risk factors, and reducing seizures is the only currently available preventive strategy.
Several other clinical factors and potential biomarkers such as prolonged post-ictal generalized EEG suppression (PGES) that follows GTCS, abnormal inter-ictal ECG, and structural brain MRI abnormalities were associated with increased SUDEP risk, but none were rigorously confirmed in a large case-control study.
While limitations of single-center studies in accumulating a sufficient number of cases are well recognized, prospective multicenter studies are also severely limited by the time, expense, and loss of follow-up constraining sample size and power.
To sidestep these limitations, we propose a retrospective multisite case-control study that will screen >40,000 patients from 86 epilepsy monitoring centers worldwide, with a conservative expected total of >185 SUDEP cases and 370 age/sex-matched controls. Employing our comprehensive approaches to identify SUDEP cases combined with novel data harmonization techniques will allow us to:
1) Provide an unprecedentedly large curated dataset of SUDEP,
2) Identify clinical, electrophysiological, and imaging predictors of SUDEP using advanced machine learning methods, and
3) Develop an individualized model to predict SUDEP risk that can be used in clinic.
The proposed study will test the hypothesis that SUDEP cases exhibit different electroclinical and imaging characteristics that can provide an individualized prediction model. We will identify ictal electroclinical and interictal electrophysiological and neuroimaging biomarkers of SUDEP. We will compare markers of seizure severity between SUDEP cases and age/sex-matched living epilepsy patients, including decerebrate or decorticate posturing during GTCS, PGES duration, postictal bradycardia + asystole, and post-convulsive central apnea. Additionally, we will assess putative interictal biomarkers including decreased low-frequency power in ECG heart rate variability and decreased MRI-derived volumes in the right hippocampus/amygdala and brainstem. We will also employ machine-learning techniques to uncover novel biomarkers from interictal electrophysiological data.
Finally, using a Bayesian framework, we will develop an individualized SUDEP risk prediction tool that combines clinical features with measures derived from routine EEG, ECG, and MRI. Our goal is to create a SUDEP case-control dataset to identify clinical risk factors and biomarkers that will help to create a robust model of an individual's SUDEP risk based on measures derived from routine clinical care and testing such as interictal ECG, MRI, and EEG. Such a tool could transform clinical practice, facilitate trials of SUDEP interventions, and ultimately save lives.
Patients with epilepsy have a 25-fold elevated risk of sudden death. Sudden Unexpected Death in Epilepsy (SUDEP) is the most common disease-related cause of premature mortality in people with seizure disorders, affecting 1 of every 1000 patients annually. Despite this, mechanisms and risk factors of SUDEP remain largely unknown. Having ongoing generalized tonic-clonic seizures (GTCS) and no nocturnal supervision are the only definite risk factors, and reducing seizures is the only currently available preventive strategy.
Several other clinical factors and potential biomarkers such as prolonged post-ictal generalized EEG suppression (PGES) that follows GTCS, abnormal inter-ictal ECG, and structural brain MRI abnormalities were associated with increased SUDEP risk, but none were rigorously confirmed in a large case-control study.
While limitations of single-center studies in accumulating a sufficient number of cases are well recognized, prospective multicenter studies are also severely limited by the time, expense, and loss of follow-up constraining sample size and power.
To sidestep these limitations, we propose a retrospective multisite case-control study that will screen >40,000 patients from 86 epilepsy monitoring centers worldwide, with a conservative expected total of >185 SUDEP cases and 370 age/sex-matched controls. Employing our comprehensive approaches to identify SUDEP cases combined with novel data harmonization techniques will allow us to:
1) Provide an unprecedentedly large curated dataset of SUDEP,
2) Identify clinical, electrophysiological, and imaging predictors of SUDEP using advanced machine learning methods, and
3) Develop an individualized model to predict SUDEP risk that can be used in clinic.
The proposed study will test the hypothesis that SUDEP cases exhibit different electroclinical and imaging characteristics that can provide an individualized prediction model. We will identify ictal electroclinical and interictal electrophysiological and neuroimaging biomarkers of SUDEP. We will compare markers of seizure severity between SUDEP cases and age/sex-matched living epilepsy patients, including decerebrate or decorticate posturing during GTCS, PGES duration, postictal bradycardia + asystole, and post-convulsive central apnea. Additionally, we will assess putative interictal biomarkers including decreased low-frequency power in ECG heart rate variability and decreased MRI-derived volumes in the right hippocampus/amygdala and brainstem. We will also employ machine-learning techniques to uncover novel biomarkers from interictal electrophysiological data.
Finally, using a Bayesian framework, we will develop an individualized SUDEP risk prediction tool that combines clinical features with measures derived from routine EEG, ECG, and MRI. Our goal is to create a SUDEP case-control dataset to identify clinical risk factors and biomarkers that will help to create a robust model of an individual's SUDEP risk based on measures derived from routine clinical care and testing such as interictal ECG, MRI, and EEG. Such a tool could transform clinical practice, facilitate trials of SUDEP interventions, and ultimately save lives.
Awardee
Funding Goals
(1) TO SUPPORT EXTRAMURAL RESEARCH FUNDED BY THE NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE (NINDS) INCLUDING: BASIC RESEARCH THAT EXPLORES THE FUNDAMENTAL STRUCTURE AND FUNCTION OF THE BRAIN AND THE NERVOUS SYSTEM, RESEARCH TO UNDERSTAND THE CAUSES AND ORIGINS OF PATHOLOGICAL CONDITIONS OF THE NERVOUS SYSTEM WITH THE GOAL OF PREVENTING THESE DISORDERS, RESEARCH ON THE NATURAL COURSE OF NEUROLOGICAL DISORDERS, IMPROVED METHODS OF DISEASE PREVENTION, NEW METHODS OF DIAGNOSIS AND TREATMENT, DRUG DEVELOPMENT, DEVELOPMENT OF NEURAL DEVICES, CLINICAL TRIALS, AND RESEARCH TRAINING IN BASIC, TRANSLATIONAL AND CLINICAL NEUROSCIENCE. THE INSTITUTE IS THE LARGEST FUNDER OF BASIC NEUROSCIENCE IN THE US AND SUPPORTS RESEARCH ON TOPICS INCLUDING BUT NOT LIMITED TO: DEVELOPMENT OF THE NERVOUS SYSTEM, INCLUDING NEUROGENESIS AND PROGENITOR CELL BIOLOGY, SIGNAL TRANSDUCTION IN DEVELOPMENT AND PLASTICITY, AND PROGRAMMED CELL DEATH, SYNAPSE FORMATION, FUNCTION, AND PLASTICITY, LEARNING AND MEMORY, CHANNELS, TRANSPORTERS, AND PUMPS, CIRCUIT FORMATION AND MODULATION, BEHAVIORAL AND COGNITIVE NEUROSCIENCE, SENSORIMOTOR LEARNING, INTEGRATION AND EXECUTIVE FUNCTION, NEUROENDOCRINE SYSTEMS, SLEEP AND CIRCADIAN RHYTHMS, AND SENSORY AND MOTOR SYSTEMS. IN ADDITION, THE INSTITUTE SUPPORTS BASIC, TRANSLATIONAL AND CLINICAL STUDIES ON A NUMBER OF DISORDERS OF THE NERVOUS SYSTEM INCLUDING (BUT NOT LIMITED TO): STROKE, TRAUMATIC INJURY TO THE BRAIN, SPINAL CORD AND PERIPHERAL NERVOUS SYSTEM, NEURODEGENERATIVE DISORDERS, MOVEMENT DISORDERS, BRAIN TUMORS, CONVULSIVE DISORDERS, INFECTIOUS DISORDERS OF THE BRAIN AND NERVOUS SYSTEM, IMMUNE DISORDERS OF THE BRAIN AND NERVOUS SYSTEM, INCLUDING MULTIPLE SCLEROSIS, DISORDERS RELATED TO SLEEP, AND PAIN. PROGRAMMATIC AREAS, WHICH ARE PRIMARILY SUPPORTED BY THE DIVISION OF NEUROSCIENCE, ARE ALSO SUPPORTED BY THE DIVISION OF EXTRAMURAL ACTIVITIES, THE DIVISION OF TRANSLATIONAL RESEARCH, THE DIVISION OF CLINICAL RESEARCH, THE OFFICE OF TRAINING AND WORKFORCE DEVELOPMENT, THE OFFICE OF PROGRAMS TO ENHANCE NEUROSCIENCE WORKFORCE DEVELOPMENT, AND THE OFFICE OF INTERNATIONAL ACTIVITIES. (2) TO EXPAND AND IMPROVE THE SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. TO UTILIZE THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM, TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
New York,
New York
100164852
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 355% from $683,618 to $3,107,895.
New York University was awarded
SUDEP Risk Prediction Study: Multicenter Case-Control Approach
Project Grant R01NS123928
worth $3,107,895
from the National Institute of Neurological Disorders and Stroke in August 2021 with work to be completed primarily in New York New York United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.853 Extramural Research Programs in the Neurosciences and Neurological Disorders.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 8/6/25
Period of Performance
8/15/21
Start Date
7/31/26
End Date
Funding Split
$3.1M
Federal Obligation
$0.0
Non-Federal Obligation
$3.1M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01NS123928
Transaction History
Modifications to R01NS123928
Additional Detail
Award ID FAIN
R01NS123928
SAI Number
R01NS123928-4207610335
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NQ00 NIH National Institute of Neurological Disorders and Stroke
Funding Office
75NQ00 NIH National Institute of Neurological Disorders and Stroke
Awardee UEI
M5SZJ6VHUHN8
Awardee CAGE
3D476
Performance District
NY-12
Senators
Kirsten Gillibrand
Charles Schumer
Charles Schumer
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Institute of Neurological Disorders and Stroke, National Institutes of Health, Health and Human Services (075-0886) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,187,002 | 83% |
| Office of the Director, National Institutes of Health, Health and Human Services (075-0846) | Health research and training | Grants, subsidies, and contributions (41.0) | $251,591 | 17% |
Modified: 8/6/25