Predicting ECMO NeuroLogICal Injuries using mAchiNe Learning (PELICAN)

使用机器学习预测 ECMO 神经损伤 (PELICAN)

• 批准号: 10719312
• 负责人: Lakshmi Raman
• 金额: $ 53.92万
• 依托单位: UT SOUTHWESTERN 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: 2023-07-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10719312
• 关键词: Accounting; Acids; Acute; Adult; Algorithms; Anticoagulation; Artificial Intelligence; Brain Injuries; COVID-19 pneumonia; Cardiopulmonary; Cardiopulmonary Bypass; Cerebrum; Characteristics; Child; Childhood Injury; Classification; Clinical; Clinical Data; Coagulation Process; Computerized Medical Record; Critical Care; Critical Illness; Critically ill children; Data; Development; Diagnosis; Drops; Early Intervention; Electroencephalography; Elements; Etiology; Event; Extracorporeal Membrane Oxygenation; Failure; Family; Financial cost; Foundations; Future; Goals; Growth; Heart; Hemorrhage; Hour; Impairment; Individual; Inflammation; Injury; Intervention; Intervention Studies; Knowledge; Laboratories; Learning; Legal patent; Life; Lung; Machine Learning; Magnetic Resonance Imaging; Measures; Modeling; Monitor; Morbidity - disease rate; Nervous System Trauma; Neurologic; Neurologic Deficit; Neurological observations; Neurological outcome; Organ; Outcome; Patients; Pattern; Perfusion; Physiological; Pilot Projects; Play; Probability; Process; Quality of life; Research; Risk; Risk Assessment; Risk Factors; Role; Scientist; Societies; Stroke; Survivors; Techniques; Technology; Testing; Time; Training; Transplantation; Update; Variant; X-Ray Computed Tomography; base; cerebral hemodynamics; clinical decision-making; clinically significant; cohort; conventional therapy; deep learning; healing; high risk; improved; individual patient; interest; learning algorithm; learning strategy; machine learning algorithm; modifiable risk; mortality; multidisciplinary; neonate; neuroimaging; neuroprotection; novel; organ injury; patient population; patient subsets; personalized intervention; prediction algorithm; predictive modeling; risk prediction model; tool

Project Summary Extracorporeal Membrane Oxygenation (ECMO) is a form of cardiopulmonary bypass used in critically ill children and adults to support the heart and lungs when conventional therapies fail. More than 79,762 children worldwide have been supported to date, and global use of this tool is expanding. Advances in ECMO and critical care have improved survival of otherwise fatal illnesses, thereby unmasking neurologic injury which itself reduces survival by 50-60% and leads to significant long-term neurologic morbidity. The mechanisms of ECMO-related cerebral injuries are poorly understood. Existing research focuses on evaluating discrete elements, such as underlying illness, coagulation abnormalities, anticoagulation management, or markers of end- organ perfusion as factors associated with brain injury. Prior studies have not considered the temporal and dynamic element of clinical events that may play a large role in the genesis of brain injury, and few have explored which variables could predict significant neurologic injury without the bias of pre-selecting variables of interest. Machine learning is a form of artificial intelligence that employs algorithms to discover patterns in an iterative manner directly from input data: in the context of ECMO, it can identify dynamic patterns and relationships between variables prior to neurologic injury. The long-term goal of this research is to identify modifiable bedside predictors of neurologic impairment and thereby drive the development of early interventions to improve neurologic outcomes of children undergoing ECMO. Towards this goal, we have assembled a multi- disciplinary team with clinical and computational expertise. Our central hypothesis is that a robust risk predictive model for SNI in ECMO patients can be developed based on the physiological and laboratory data routinely collected in real-world clinical settings and that this model can be used to identify parameters of SNI for potential intervention. This proposal will utilize advanced machine learning algorithms to build this prediction model in a large multicenter cohort (0-18 years, n=750). In Aim 1, we will use novel probabilistic machine learning algorithms to train and develop a model to predict SNI by neuroimaging. In Aim 2, we will validate and refine the model from Aim 1 using neuroimaging scores and explore a personalized anytime query algorithm that predicts the timing and type of SNI. This collaborative proposal between clinical and computational scientists will lay the groundwork for a neuroprotective interventional study by identifying modifiable risk factors to improve the tragically high neurologic morbidity and mortality in ECMO survivors.

项目概要 体外膜肺氧合 (ECMO) 是体外循环的一种形式，用于 当传统疗法失败时，为危重儿童和成人提供心脏和肺部支持。 迄今为止，全球已有超过 79,762 名儿童获得了支持，并且该工具在全球范围内的使用 扩大。 ECMO 和重症监护的进步提高了致命性患者的生存率 疾病，从而揭露神经损伤，神经损伤本身会使生存率降低 50-60%，并导致 导致显着的长期神经系统发病率。 ECMO相关脑损伤的机制 人们了解甚少。现有的研究侧重于评估离散元素，例如 潜在疾病、凝血异常、抗凝治疗或终末期标志物 器官灌注作为与脑损伤相关的因素。之前的研究没有考虑到 临床事件的时间和动态元素可能在大脑的起源中发挥重要作用 很少有人探索哪些变量可以预测严重的神经损伤而无需 预先选择感兴趣变量的偏差。机器学习是人工智能的一种形式 它采用算法直接从输入数据中以迭代方式发现模式： ECMO 背景下，它可以在实施之前识别变量之间的动态模式和关系 神经损伤。 这项研究的长期目标是确定神经系统疾病的可修改床边预测因素 损伤，从而推动早期干预措施的发展，以改善神经系统 接受 ECMO 的儿童的结果。为了实现这一目标，我们组建了一个多 具有临床和计算专业知识的学科团队。我们的中心假设是，一个稳健的 ECMO 患者 SNI 的风险预测模型可以根据生理和 在现实世界的临床环境中常规收集的实验室数据，并且可以使用该模型 确定潜在干预的 SNI 参数。本提案将利用先进的机器 学习算法在大型多中心队列（0-18 岁，n=750）中构建此预测模型。 在目标 1 中，我们将使用新颖的概率机器学习算法来训练和开发模型 通过神经影像学预测 SNI。在目标 2 中，我们将使用以下方法验证和完善目标 1 的模型 神经影像评分并探索预测时间的个性化随时查询算法 和 SNI 类型。临床和计算科学家之间的这项合作提案将奠定 通过识别可改变的危险因素来为神经保护干预研究奠定基础 改善 ECMO 幸存者的高神经系统发病率和死亡率。