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.

项目摘要 体外膜氧合（ECMO）是一种心肺旁路的形式 当常规疗法失败时，患病的儿童和成人以支持心脏和肺部。 迄今为止，全世界有超过79,762名儿童得到支持，该工具的全球使用是 扩展。 ECMO和重症监护的进步提高了其他致命的生存 疾病，从而揭露了神经系统损伤，该神经系统损伤本身将生存率降低50-60％，并导致 大量的长期神经系统发病率。 ECMO相关脑损伤的机制 知之甚少。现有研究重点是评估离散元素，例如 潜在的疾病，凝血异常，抗凝管理或末端的标志 器官灌注是与脑损伤相关的因素。先前的研究没有考虑 临床事件的时间和动态元素可能在大脑的起源中起重要作用 受伤，很少有人探索哪些变量可以预测明显的神经损伤而无需 感兴趣的预选变量的偏见。机器学习是人工智能的一种形式 该算法直接从输入数据直接以迭代方式发现模式：在 ECMO的上下文，它可以识别变量之前的动态模式和关系 神经损伤。 这项研究的长期目标是确定神经系统的可修改床头预测指标 损害，从而推动发展早期干预措施以改善神经系统 经历ECMO的儿童的结果。为了实现这一目标，我们组装了一个 具有临床和计算专业知识的纪律团队。我们的中心假设是一个健壮的 可以根据生理和 实验室数据通常在现实世界临床环境中收集，并且可以使用该模型 确定SNI的参数以进行潜在干预。该建议将利用高级机器 学习在大型多中心队列中构建此预测模型的算法（0-18岁，n = 750）。 在AIM 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 通过神经影像预测SNI。在AIM 2中，我们将使用AIM 1验证和完善模型 神经影像学分数并探索一个个性化的任何时间查询算法，以预测时间安排 和SNI的类型。临床科学家和计算科学家之间的此协作提议将提出 通过确定可修改的危险因素的神经保护性研究的基础 改善ECMO幸存者中悲惨的高神经系统发病率和死亡率。