Advancing SUDEP risk prediction using a multicenter case-control approach

使用多中心病例对照方法推进 SUDEP 风险预测

• 批准号: 10463739
• 负责人: Orrin Devinsky
• 金额: $ 62.82万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10463739
• 关键词: Address; Affect; Amygdaloid structure; Bayesian Modeling; Biological Markers; Bradycardia; Brain Stem; Cardiopulmonary; Case-Control Studies; Central Sleep Apnea; Characteristics; Clinic; Clinical; Counseling; Data; Data Collection; Data Set; Development; Disease; Electrocardiogram; Electroencephalography; Electrophysiology (science); Epilepsy; Exhibits; Factor Analysis; Frequencies; Functional disorder; Goals; Heart Arrest; Hippocampus (Brain); Image; Individual; Intervention; Knowledge; Machine Learning; Magnetic Resonance Imaging; Measures; Methods; Modeling; Monitor; Multicenter Studies; National Institute of Neurological Disorders and Stroke; Outcome; Patients; Performance; Persons; Physiological; Posture; Premature Mortality; Prevention strategy; Prospective Studies; Records; Research; Retrospective cohort; Risk; Risk Factors; Sample Size; Scalp structure; Seizures; Selection Bias; Severities; Sudden Death; Supervision; Techniques; Testing; Time; Tonic - clonic seizures; United States National Institutes of Health; awake; base; biomarker performance; brain magnetic resonance imaging; candidate marker; case control; clinical care; clinical practice; clinical risk; cost; data harmonization; deep learning; epidemiology study; follow-up; heart rate variability; high risk; innovation; machine learning method; magnetic resonance imaging biomarker; magnetic resonance imaging/electroencephalography; neuroimaging marker; novel; novel marker; personalized predictions; personalized risk prediction; potential biomarker; predictive modeling; prospective; random forest; research clinical testing; risk prediction; risk prediction model; screening; sex; statistical and machine learning; sudden unexpected death in epilepsy; tool

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.

项目总结