Recent Funded Projects
Advancing SUDEP risk prediction using a multicenter case-control approach
PI: Orrin Devinsky MD; Samden D. Lhatoo MD, FRCP; Daniel Friedman, MD
This study will combine and harmonize data from 86 epilepsy centers to study a retrospective cohort who succumbed to Sudden Unexpected Death in Epilepsy (SUDEP) to identify clinical, electrophysiological and imaging predictors of SUDEP and develop an individualized model using machine learning methods to predict SUDEP risk. In addition to understanding SUDEP mechanisms through studying a large number of SUDEP cases it will also identify the development and validation of an individual risk calculator which could make a big difference with adult patients trying to gain independence.
Cardiac and Autonomic Pathological Markers for Arrhythmias and Sudden Unexpected Death in Epilepsy Patients
PI: David Auerbach PhD
Patients with epilepsy are at high risk of sudden death from SUDEP. We are not able to predict a patient’s risk of SUDEP. Patients without known SUDEP risk factors account for the largest number of SUDEP cases. In contrast to prior risk factor association studies that are not linked to a proposed cause for SUDEP, this study will develop pathological markers for a proposed SUDEP mechanism. Analytics that are validated in the cardiac field, but are new to the epilepsy/SUDEP field will identify the clinical populations at risk of, and the triggers for, cardiac-mediated SUDEP.
Defining breathing network neuromodulatory approaches for prevention of Sudden Unexpected Death in Epilepsy (SUDEP)
PI: Nuria Lacuey-Lecumberri MD, PhD
Sudden Unexpected Death in Epilepsy (SUDEP) usually occurs in patients with frequent convulsive seizures due to seizure-induced breathing failure. There are currently no technologies or devices available for directly preventing death in patients at high risk. The goal of this project is to improve the overall understanding of breathing control by identifying specific brain areas that are most important for breathing function and stimulation techniques that can be used to prevent seizure-induced breathing failure. The outcome of this research will pave the way for deep brain stimulation devices for breathing rescue as a targeted SUDEP prevention strategy.
An informatics framework for SUDEP risk marker identification and risk assessment
PI: Licong Cui, PhD
The main goal of this project is to develop an informatics approach for automated extraction of SUDEP risk markers from multimodal clinical data to enable individualized SUDEP risk assessment. Success of this study will enable systematic SUDEP risk assessment based on known and putative factors and communication of such risk factors to patients with epilepsy. Ultimately, this study can lead to evidence-based SUDEP risk assessment tools that help clinicians and patients manage potentially modifiable risks, leading to overall reduced SUDEP mortality and improved epilepsy patient care.
The role of central CO2 chemosensitivity in postictal respiratory depression and SUDEP
PI: George B Richerson, MD, PhD
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. In many cases, death follows a period of severe seizure-induced respiratory depression, for which the cause is not understood. The proposed research is relevant to public health and the NIH’s mission because a better understanding of the role played by central CO2 chemosensation in the pathogenesis of seizure-induced respiratory depression is expected to lead to the identification of novel candidate biomarkers for SUDEP risk and to the development of new preventive treatments for SUDEP.
Isolating SUDEP risk conferred by genomic co-variation in candidate SUDEP genes
PI: Alica Goldman, MD, PhD
This pilot project aims to evaluate the components of genetic risk in SUDEP in SCN1A gene related epilepsy and in patients with febrile seizure phenotype. Goal of this project is to develop critically needed bioinformatic tools for genomic SUDEP risk stratification. Results will have an immediate impact on patients with epilepsy due to SCN1A gene pathogenic variation and they will inform future bioinformatics and statistical design when analyzing SUDEP risk in patients with the clinically very common febrile seizure phenotype as well as when evaluating causality in existing collections of SUDEP cases.
