Predicting ECMO NeuroLogICal Injuries using mAchiNe Learning (PELICAN)

项目摘要 体外膜氧合(ECMO)是一种用于临床的体外循环 当常规治疗失败时，危重儿童和成人需要支持心脏和肺。 到目前为止，全世界已有79,762名儿童得到了支持，这一工具的全球使用情况是 正在扩张。ECMO和重症监护的进展提高了原本致命的存活率 疾病，从而揭露神经损伤，它本身就降低了50%-60%的存活率，并导致 严重的长期神经系统疾病。ECMO相关脑损伤机制的研究进展 人们对此了解甚少。现有的研究集中在评估离散元素，如 潜在疾病、凝血异常、抗凝治疗或终末期标记物 与脑损伤相关的因素的器官灌注。以前的研究没有考虑到 临床事件的时间和动态因素可能在脑的起源中发挥重要作用 损伤，很少有人探索哪些变量可以预测严重的神经损伤 预先选择感兴趣的变量的偏差。机器学习是人工智能的一种形式 它使用算法以迭代的方式直接从输入数据中发现模式： ECMO的上下文，它可以识别动态模式和变量之间的关系 神经损伤。 这项研究的长期目标是确定神经学的可修改床边预测因子。 并因此推动早期干预措施的发展，以改善神经学 儿童接受体外反搏的结局。为了实现这一目标，我们召集了多个 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的风险预测模型可根据生理学和 实验室数据通常在真实世界的临床环境中收集，并且该模型可以使用 为潜在的干预确定SNI的参数。这项提议将利用先进的机器 在一个大的多中心队列中(0-18岁，n=750)建立这种预测模型的学习算法。 在目标1中，我们将使用新的概率机器学习算法来训练和开发一个模型 通过神经成像预测SNI。在目标2中，我们将验证和改进目标1中的模型 神经成像评分并探索预测时间的个性化随时查询算法 和SNI的类型。临床和计算科学家之间的这项合作提案将 通过确定可修改的危险因素来进行神经保护性干预研究的基础 改善ECMO幸存者悲剧性的高神经发病率和死亡率。

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.