Physiology-driven seizure management post-cardiac arrest

心脏骤停后生理驱动的癫痫发作管理

• 批准号: 10533755
• 负责人: EDILBERTO AMORIM DE CERQUEIRA
• 金额: $ 22.57万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10533755
• 关键词: Acceleration; Acute; Acute Brain Injuries; American; Anesthetics; Anticonvulsants; Area; Authorization documentation; Big Data Methods; Biological Markers; Biomedical Technology; Biometry; Brain; Brain Injuries; Brain imaging; California; Caring; Categories; Cause of Death; Cerebrum; Clinical; Clinical Trials; Clinical Trials Design; Collaborations; Coma; Complication; Computing Methodologies; Critical Care; Data; Data Scientist; Data Set; Decision Making; Development; Development Plans; Diagnosis; Diffusion Magnetic Resonance Imaging; Early identification; Educational Curriculum; Electroencephalography; Environment; Epilepsy; Epileptogenesis; Evolution; Feedback; Frequencies; Future; Goals; Heart Arrest; Hospitals; Hour; Human; Hypoxic-Ischemic Brain Injury; Individual; Infusion procedures; Injury; International; Intervention Trial; K-Series Research Career Programs; Knowledge; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Mediating; Mentors; Methods; Modeling; Monitor; Morphology; Neurologic; Neurological outcome; Neurologist; Outcome; Patient Selection; Patients; Performance; Phenotype; Physiology; Positioning Attribute; Prediction of Response to Therapy; Propofol; Publishing; Recovery; Recovery of Function; Recurrence; Refractory; Research; Research Personnel; Resuscitation; San Francisco; Scientist; Seizures; Selection for Treatments; Series; Specificity; Survivors; Techniques; Testing; Therapeutic; Therapeutic Intervention; Time; Titrations; Training; Universities; Validation; Weaning; Work; authority; biomarker driven; brain magnetic resonance imaging; career development; cohort; deep learning; deep learning algorithm; disability; epileptiform; hands-on learning; improved; improved outcome; innovation; insight; magnetic resonance imaging biomarker; multidisciplinary; natural hypothermia; nervous system disorder; neuroimaging; neurological recovery; neurophysiology; outcome prediction; personalized medicine; predictive marker; prevent; prognostication; programs; resilience; response biomarker; skills; technological innovation; treatment effect; treatment response; treatment strategy; trend

PROJECT SUMMARY/ABSTRACT Dr. Edilberto Amorim is a neurologist with subspecialty training in critical care and epilepsy who aims to employ biomedical technology innovations in brain monitoring to personalize treatment for patients with hypoxic- ischemic brain injury post-cardiac arrest. This career development award and its rigorous curriculum will establish Dr. Amorim as a clinician-scientist with independent expertise in: 1) Deep learning applied to physiology time-series, 2) Causal inference for observational data, and 3) Quantitative brain imaging. Every year, more than 500,000 Americans have a cardiac arrest. Brain injury is the number one cause of death for patients surviving initial resuscitation, and refractory seizures and other seizure-like brain activity are diagnosed in up to 50% of patients. Despite being a common complication, outcomes are dismal and current treatment strategies for seizures post-cardiac arrest are limited. Dr. Amorim aims to identify physiology-driven biomarkers of resilience to hypoxic-ischemic brain injury by utilizing state-of-the-art computational methods and a massive EEG and neuroimaging dataset with >1,500 subjects. His central hypothesis is that specific time- dependent changes in spike and accompanying EEG activity during cardiac arrest treatment predict seizure control and, ultimately, neurological recovery. The primary objectives of this proposal are: 1) Identify early longitudinal epileptiform EEG phenotypes predictive of neurological recovery using interpretable and deep learning algorithms; 2) Establish quantitative EEG biomarkers of seizure treatment response to anesthetics; and 3) Estimate the causal effect of rapid seizure treatment with anesthetics in preventing structural brain injury quantified with brain MRI. Dr. Amorim has generated preliminary data to demonstrate the feasibility of modeling EEG phenotypes longitudinally for outcome prediction and has applied quantitative EEG biomarkers to predict degree of brain injury on brain MRI. His primary mentor in this proposal will be Dr. Edward Chang, a neuroscientist and leader in human neurophysiology research. His co-mentors will include Dr. Brandon Westover, an authority in machine learning applied to critical care EEG, and Dr. Donna Ferriero, an accomplished translational and neuroimaging investigator in hypoxic-ischemic brain injury. Additional mentoring in quantitative neuroimaging (Dr. Srikantan Nagarajan) and biostatistics (Dr. Charles McCulloch) will be essential components of his training. These aims are expected to establish early non-invasive predictive biomarkers of neurological recovery and seizure control that may: 1) Guide patient selection for clinical trials enrichment and 2) Serve as target to therapeutic interventions after hypoxic-ischemic brain injury. By leveraging the deep expertise of a cross-disciplinary group of world-class mentors and the unparalleled innovation environments of the University of California, San Francisco and the Bay Area, Dr. Amorim will be ideally positioned to uncover fundamental knowledge about epileptogenesis after acute brain injury as well as spearhead clinical trials focused on improving outcomes meaningful to cardiac arrest patients.

项目摘要/摘要 埃迪尔贝托·阿莫林博士是一名神经科医生，接受过重症监护和癫痫方面的专门培训，他的目标是聘请 脑监测方面的生物医学技术创新为低氧患者提供个性化治疗- 心脏骤停后的缺血性脑损伤。这一职业发展奖及其严格的课程将 将阿莫林博士确立为临床医生-科学家，具有独立的专业知识：1)深度学习应用于 生理时间序列，2)观测数据的因果推断，3)定量脑成像。每个 去年，超过50万美国人心脏骤停。脑损伤是人类死亡的头号原因。 在最初的复苏、难治性癫痫发作和其他癫痫样脑活动中幸存下来的患者 在高达50%的患者中被确诊。尽管是一种常见的并发症，但结果是令人沮丧的和当前的 心脏骤停后癫痫的治疗策略有限。阿莫林博士的目标是识别由生理因素驱动的 利用最先进的计算方法和方法研究缺氧缺血性脑损伤的恢复力生物标记物 拥有1,500名受试者的海量脑电和神经成像数据集。他的中心假设是特定的时间- 心脏骤停治疗期间棘波和伴随脑电活动的依赖性变化可预测癫痫发作 控制，并最终实现神经康复。这项建议的主要目标是：1)及早确定 用可解释和深度预测神经康复的纵向癫痫样脑电表型 2)建立癫痫发作治疗对麻醉药反应的定量脑电生物标志物； 3)评估麻醉药快速惊厥治疗预防结构性脑损伤的因果效应 脑部MRI对损伤程度进行量化。阿莫林博士已经生成了初步数据来证明 对脑电表型进行纵向建模以预测结果，并应用了定量脑电生物标志物 目的：在头颅MRI上预测颅脑损伤程度。他在这项提议中的主要导师将是张博士，他是一名 神经学家和人类神经生理学研究的领导者。他的联合导师将包括布兰登博士 Westover，一位将机器学习应用于重症监护EEG的权威，以及Donna Ferriero博士，一位 完成了缺氧缺血性脑损伤的翻译和神经影像研究。其他内容 定量神经成像(Srikantan Nagarajan博士)和生物统计学(Charles McCulloch博士)方面的指导将 是他训练的重要组成部分。这些目标有望建立早期非侵入性预测 神经康复和癫痫控制的生物标志物可以：1)指导临床试验的患者选择 2)作为缺氧缺血性脑损伤后治疗干预的靶点。通过 利用由世界级导师和无与伦比的 加州大学旧金山分校和旧金山湾区的创新环境，阿莫林博士将 非常适合揭示急性脑损伤后癫痫发生的基本知识，以及 先锋临床试验的重点是改善对心脏骤停患者有意义的结果。