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 meas...