Prospective Validation of Neurophysiologic Outcome Prediction in Acute Brain Injury

急性脑损伤神经生理结果预测的前瞻性验证

• 批准号: 10584338
• 负责人: Aaron F Struck
• 金额: $ 81.38万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584338
• 关键词: Acute; Acute Brain Injuries; Address; Anticonvulsants; Artificial Intelligence; Binding; Bioinformatics; Biological Markers; Brain; Brain Injuries; Caring; Cerebrum; Cessation of life; Chronic; Chronic Phase; Clinical; Clinical Data; Clinical Trials; Collaborations; Computerized Medical Record; Critical Illness; Data; Data Collection; Databases; Development; Electroencephalography; Epilepsy; Epileptogenesis; Equipoise; Future; General Hospitals; Geography; Guidelines; Hospitalization; Hospitals; Image; Injury; Learning; Machine Learning; Massachusetts; Measures; Modeling; Monitor; Neurological outcome; Observational Study; Outcome; Pathologic; Patient-Focused Outcomes; Patients; Pattern; Periodicals; Pharmaceutical Preparations; Phase; Process; Prognosis; Property; Psychological reinforcement; Resources; Retrospective Studies; Risk; Risk Factors; Sample Size; Secondary Prevention; Seizures; Site; Techniques; Time; Translating; Uncertainty; Universities; Update; Validation; Wisconsin; Workload; acquired epilepsy; bioinformatics infrastructure; brain electrical activity; care outcomes; clinical care; clinically actionable; clinically significant; cohort; deep learning model; detector; disability; epileptiform; functional improvement; improved; improved outcome; large scale data; machine learning algorithm; machine learning model; multimodality; neurological recovery; neurophysiology; novel; outcome prediction; predictive modeling; prevent; prospective; public database; radiological imaging; risk prediction; signal processing; tool

PROJECT SUMMARY Acute brain injury causes over 163,000 deaths in the US annually and leaves many more patients with long- term disability. Preventing secondary brain injury is critical to improving neurologic outcomes in these patients. Pathological brain electrical activity (measured via EEG) following acute brain injury contributes to long-term disability via seizures in the acute phase and epilepsy in the chronic phase. EEG is the primary tool for monitoring aberrant brain activity, yet it is underutilized due to uncertainty regarding the clinical significance of EEG findings and the high workload associated with interpreting high volumes of EEG data. Our group has made progress toward addressing these short-comings by developing three novel machine learning algorithms: 1) a seizure forecasting model for hospitalized patients (“2HELPS2B”); 2) a model that measures the Burden of Epileptiform Activity (EA—seizures and highly epileptiform patterns such as lateralized periodic discharges) to predict neurologic outcomes (“BEACON”); and 3) A model that uses EA in the acute phase of brain injury to predict the risk of developing epilepsy. This proposal is the culminating step required to translate preliminary studies into actionable clinical tools. In this prospective multicenter observational study, we will collect clinical and EEG data on 3000 patients with acute brain injury to further develop and validate these models. Our study leverages an existing multicenter bioinformatics infrastructure and established collaborations between Yale University, University of Wisconsin, and Massachusetts General Hospital, to create the scale and quality of data needed to address three specific aims: SA1: Develop and prospectively validate an automated in-hospital seizure-forecasting model for use in acute brain injury, based on our previously developed 2HELPS2B score—termed auto-2HELPS2B; SA2: Prospectively validate the BEACON model for the impact of prolonged epileptiform activity on functional and clinical outcomes in critical illness at discharge, 3 months, 6 months, 1-year, and 2-years, and estimate the optimal anti-seizure drug administration strategy (indications and intensity of drugs) to mitigate detrimental effects of EA on functional and clinical outcomes; SA3: Prospectively validate EAs as a biomarker of 1- and 2- year epilepsy risk after acute brain injury, evaluate effects of anti-seizure drugs on acute phase EAs and subsequent development of epilepsy, and combine EEG with radiographic and clinical information to further improve our current epilepsy forecasting risk model. Upon completing these aims, we will have 1. A large and representative database of high-quality EEG and clinical data on brain-injured patients with 2-year of outcomes data. 2. Validated EEG tools to guide care in determining acute seizure risk, prognosis for neurological recovery, and the likelihood of developing epilepsy.

项目摘要 在美国，急性脑损伤每年导致超过163，000人死亡，并使更多的患者长期处于... 残疾术语预防继发性脑损伤对于改善这些患者的神经功能结局至关重要。 急性脑损伤后病理性脑电活动（通过EEG测量）有助于长期 急性期癫痫发作和慢性期癫痫导致残疾。脑电图是主要的工具， 监测异常的大脑活动，但由于不确定性的临床意义， EEG结果和与解释大量EEG数据相关的高工作量。 我们的团队通过开发三种新型机器， 学习算法：1）用于住院患者的癫痫发作预测模型（“2 HELPS 2B”）; 2） 测量癫痫样活动的负荷（EA-癫痫发作和高度癫痫样模式，如偏侧化 周期性放电）以预测神经学结果（“BEACON”）;以及3）在急性脑缺血中使用EA的模型。 脑损伤的早期阶段来预测癫痫的风险。 该提案是将初步研究转化为可操作的临床工具所需的最终步骤。在 这项前瞻性多中心观察性研究，我们将收集3000例患者的临床和EEG数据， 急性脑损伤，以进一步开发和验证这些模型。我们的研究利用了现有的多中心 生物信息学基础设施和耶鲁大学，威斯康星州大学， 和马萨诸塞州总医院，以创建所需的数据的规模和质量，以解决三个具体的 目标：SA 1：开发并前瞻性验证一种自动化的院内死亡预测模型，用于 急性脑损伤，基于我们先前开发的2 HELPS 2B评分-称为auto-2 HELPS 2B; SA 2： 普罗维登斯验证了BEACON模型对癫痫样活动延长对功能和 出院、3个月、6个月、1年和2年时危重病的临床结局，并估计 最佳抗癫痫药物给药策略（药物适应症和强度），以减轻有害的 EA对功能和临床结局的影响; SA 3：Prostaglandin验证EA作为1-和2-的生物标志物 评估抗癫痫药物对急性期癫痫发作的影响， 癫痫随后发展，并将联合收割机与放射学和临床信息结合，以进一步 改善我们目前的癫痫风险预测模型。 完成这些目标后，我们将有1。一个大型的、有代表性的高质量脑电图数据库， 脑损伤患者的临床数据和2年结局数据。2.经验证的EEG工具可指导护理， 确定急性癫痫发作风险、神经恢复的预后以及发展癫痫的可能性。