Advancing SUDEP risk prediction using a multicenter case-control approach

项目摘要 癫痫患者猝死的风险增加25倍。癫痫猝死 （SUDEP）是癫痫患者过早死亡的最常见疾病相关原因， 每年每1000名患者中就有1人受到影响。尽管如此，SUDEP的机制和危险因素在很大程度上仍然存在 未知持续性全身强直阵挛发作（GTCS）和无夜间监护是唯一的 目前，减少癫痫发作是唯一可用的预防战略。其他几 临床因素和潜在的生物标志物，如长期发作后全身性EEG抑制（PGES）， 随后GTCS、发作间期ECG异常和脑结构MRI异常与增加的 SUDEP风险，但没有一个在一项大型病例对照研究中得到严格证实。虽然单中心的局限性 积累足够数量病例的研究得到了广泛认可，前瞻性多中心研究 还受到时间、费用和随访损失的严重限制，限制了样本量和功效。回避 鉴于这些局限性，我们提出了一项回顾性多地点病例对照研究，将筛选> 40，000例患者， 全球有86个癫痫监测中心，保守预计SUDEP病例总数>185例， 年龄/性别匹配的对照。采用我们的综合方法来识别SUDEP病例， 新的数据协调技术将使我们能够：1）提供一个前所未有的大型策划数据集， SUDEP，2）使用先进的机器识别SUDEP的临床、电生理和成像预测因子 学习方法，以及3）开发可用于临床的个体化模型来预测SUDEP风险。 这项研究将检验SUDEP病例表现出不同的电临床和成像的假设 这些特征可以提供个性化的预测模型。我们将确定发作的电临床和 SUDEP的发作间期电生理学和神经成像生物标志物。我们会比较癫痫发作的症状 SUDEP病例与年龄/性别匹配的存活癫痫患者之间的严重程度，包括去大脑或 GTCS、PGES持续时间、发作后心动过缓+心搏停止和惊厥后中枢性 呼吸暂停此外，我们还将评估假定的发作间期生物标志物，包括： ECG心率变异性和右侧海马/杏仁核的MRI衍生体积减少， 脑干我们还将采用机器学习技术来发现发作间期的新生物标志物， 电生理数据。最后，使用贝叶斯框架，我们将制定个性化的SUDEP风险 一种将临床特征与常规EEG、ECG和MRI测量结果相结合的预测工具。 我们的目标是创建一个SUDEP病例对照数据集，以确定临床风险因素和生物标志物，这将有助于 根据常规临床护理的措施，创建一个关于个体SUDEP风险的稳健模型， 测试，如发作间期ECG，MRI和EEG。这样的工具可以改变临床实践，促进 SUDEP干预，最终挽救生命。

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.