Optimizing Recovery prediction after Cardiac Arrest (ORCA)

摘要 预测从缺氧性脑损伤和心脏骤停后昏迷中恢复是具有挑战性的。虽然病人 在重症监护病房中对心脏骤停复苏的患者进行密集监测，临床医生只使用极小的子集 可用于预测康复潜力的数据不足，使得神经学预测既缓慢又不准确。 这是现代医学中普遍存在的问题的一个具体例子：常规的临床监测产生了 海量丰富的信息，但缺乏将这些数据转化为有用知识的工具。 该项目将利用逮捕后危重护理、信息科学、统计建模和 机器学习使系统能够快速提供可操作的预测知识。我们已经打扫过了， 组织和汇总了一个大型的、高度多元化的时间序列数据库，包括生理和临床 拥有超过170,000小时定量脑电(EEG)特征的信息，适用于&gt；1,850个岗位- 逮捕病人。我们将改进和优化分析工具，以更多地预测这一患者群体的康复 比临床专家更快速、更准确。我们将使用创新的方法将偏见风险降至最低 培训由易出错的人类提供者创建的结果标签引入的模型。 在本提案的目标1中，我们将使用新的方法来创建信息丰富且可解释的要素 不同的临床数据，包括脑电波形、生命体征、药物和实验室检查结果。我们 将使用深度学习来识别这些预测结果的可解释和简约的特征集。 我们将对多种分析工具的性能进行培训、测试和比较。在目标2中，我们将前瞻性地 将表现最好的模型(S)与一组专业临床医生进行比较。自信地识别出 恢复前景几乎为零的患者比专业临床医生更敏感或更快，可以作为 决策支持系统。提高逮捕后预测的速度和准确性将拯救生命， 允许将适当的资源定向到可能受益的患者，避免长期和困难的护理 不能康复的患者，让家人免于不确定的痛苦。

Abstract Predicting recovery from anoxic brain injury and coma after cardiac arrest is challenging. Although patients resuscitated from cardiac arrest are intensively monitored in critical care units, clinicians use only a tiny subset of available data to predict potential for recovery, making neurological prognostication both slow and imprecise. This is a specific example of a ubiquitous problem in modern medicine: routine clinical monitoring generates vast quantities of rich information, but tools to transform these data to useful knowledge are lacking. This project will leverage expertise in post-arrest critical care, information science, statistical modeling and machine learning to make a system that rapidly delivers actionable prognostic knowledge. We have cleaned, organized and aggregated a large, highly multivariate time series database with physiological and clinical information with over 170,000 hours of quantitative electroencephalographic (EEG) features for >1,850 post- arrest patients. We will refine and optimize analytical tools that predict recovery in this patient population more rapidly and accurately than clinical experts. We will use innovative approaches to minimize risk of bias during training of models introduced by outcome labels created by fallible human providers. In Aim 1 of this proposal, we will use novel approaches to create informative and interpretable features from heterogeneous clinical data including EEG waveforms, vital signs, medications and laboratory test results. We will use deep learning to identify interpretable and parsimonious sets of these features that predict outcome. We will train, test and compare the performance of multiple analytical tools. In Aim 2, we will prospectively compare the best performing model(s) against a panel of expert clinicians. Models that confidently identify patients with near-zero prospect of recovery with greater sensitivity or faster than expert clinicians can serve as decision support systems. Improving the speed and accuracy of post-arrest prognostication will save lives, allow appropriate resources to be directed to patients who are likely to benefit, avoid long and difficult care for patients who cannot recover, and spare families the agony of uncertainty.