Hemorrhage and Thrombosis in Extracorporeal Life Support

体外生命支持中的出血和血栓形成

• 批准号: 10708435
• 负责人: Adeel Abbasi
• 金额: $ 17.95万
• 依托单位: OCEAN STATE RESEARCH INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10708435
• 关键词: Amyloid beta-Protein; Bioinformatics; Biology; Blood Vessels; Cardiopulmonary; Centers of Research Excellence; Cerebral Ischemia; Cerebral hemisphere hemorrhage; Cerebrum; Clinical; Critical Illness; Data Set; Failure; Goals; Hemorrhage; Hemostatic function; Ischemic Stroke; Life; Machine Learning; Morbidity - disease rate; Near-Infrared Spectroscopy; Neurologic; Oxygen saturation measurement; Patients; Play; Refractory; Research; Risk Factors; Role; Stroke; Techniques; Testing; Thrombosis; Time; base; improved outcome; innovation; machine learning model; modifiable risk; mortality; novel; novel marker

Extracorporeal life support (ECLS) rescues critically ill patients with refractory cardiopulmonary failure. Hemorrhage and thrombosis are the most significant causes of morbidity and mortality in ECLS; stroke and intracerebral hemorrhage (ICH) are the most devastating among them. The use of ECLS has exploded, however studies to date have been unable to predict these complications. Amyloid beta (Al3) plays a central role in cerebral hemostasis and may contribute to neurologic complications during ECLS. Cerebral oximetry via near-infrared spectroscopy (NIRS) can detect cerebral ischemia. The long-term goal of the Pl's research is to harness rigorous bioinformatic techniques to identify and validate novel markers of hemorrhage and thrombosis in ECLS to improve outcomes. The Pl's central hypothesis is that machine learning will identify key and modifiable risk factors for bleeding and thrombosis and that incorporating innovative neurophenotyping (Al3 levels and NIRS) will enhance network prediction for neurological complications. The Pl will test this hypothesis by pursuing the following three aims: Aim 1: Generate and cross-validate machine learning models to predict hemorrhage (including ICH) within ELSO and collaborative datasets Aim 2: Generate and cross-validate machine learning models to predict thrombosis (including ischemic stroke) within ELSO and collaborative datasets Hypothesis: Novel networks of patient-, clinical-, and time-based factors will emerge as strong predictors of hemorrhage (Aim 1), and thrombosis (Aim 2), respectively.

体外生命支持(ECLS)抢救患有难治性心肺衰竭的危重患者。 出血和血栓形成是ECLS发病率和死亡率的最主要原因；中风和 脑出血(ICH)是其中最具破坏性的疾病。ECLS的使用出现了爆炸性增长， 然而，到目前为止，研究还无法预测这些并发症。淀粉样β蛋白(Al3)在 在脑出血中发挥作用，并可能导致ECLS期间的神经并发症。脑血氧仪 通过近红外光谱(NIRS)可以检测到脑缺血。人民大会党的长期目标 研究是利用严格的生物信息学技术来识别和验证新的标记 ECLS中的出血和血栓形成以改善预后。PL的中心假设是机器 学习将确定出血和血栓形成的关键和可修改的危险因素，并将 创新的神经表型(Al3水平和近红外光谱)将增强神经病学的网络预测 并发症。公共关系委员会将通过追求以下三个目标来检验这一假设： 目标1：生成并交叉验证机器学习模型以预测出血(包括 Ich)在ELSO和协作数据集中 目标2：生成和交叉验证机器学习模型以预测血栓形成(包括 缺血性中风)在ELSO和协作数据集中 假设：基于患者、临床和时间因素的新网络将成为强有力的预测因素 出血(目标1)和血栓形成(目标2)。