Optimizing Recovery prediction after Cardiac Arrest (ORCA)

摘要 预测心脏骤停后缺氧性脑损伤和昏迷的恢复具有挑战性。虽然患者 从心脏骤停中复苏的患者在重症监护室中受到集中监测，临床医生仅使用一小部分 可用数据来预测恢复的可能性，使得神经系统的预测既缓慢又不精确。 这是现代医学中普遍存在的问题的一个具体例子： 大量丰富的信息，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重症监护，信息科学，统计建模和 机器学习，以建立一个快速提供可操作的预测知识的系统。我们打扫过了， 组织和汇总了一个大型的，高度多变量的时间序列数据库， 超过170，000小时的定量脑电图（EEG）特征信息， 逮捕病人我们将进一步完善和优化预测该患者人群恢复的分析工具， 比临床专家更快更准确。我们将使用创新的方法，以尽量减少偏倚的风险， 训练由易犯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从 异质临床数据，包括EEG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特征的可解释和简约集。 我们将培训，测试和比较多种分析工具的性能。在目标2中，我们预期 将表现最好的模型与专家临床医生小组进行比较。模型可以自信地识别 康复前景接近于零的患者，比专家临床医生更敏感或更快 决策支持系统。提高逮捕后鉴定的速度和准确性将挽救生命， 允许将适当的资源用于可能受益的患者，避免长期和困难的护理， 无法康复的病人，并使家庭免受不确定性的痛苦。

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.