Optimizing Recovery prediction after Cardiac Arrest (ORCA)

优化心脏骤停 (ORCA) 后的恢复预测

基本信息

批准号：
10600023
负责人：
Jonathan Elmer
金额：
$ 63.06万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-15 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10600023
关键词：
Academic Medical Centers Address Anoxic Encephalopathy Benchmarking Brain Brain Injuries Caring Cause of Death Clinical Clinical Data Collection Coma Complex Critical Care Data Databases Decision Support Systems Detection Electroencephalography Ensure Event Evolution Failure Family Future Heart Arrest Hospitalization Hour Human Image Information Sciences Injury Knowledge Label Laboratories Learning Machine Learning Methods Modality Modeling Modern Medicine Monitor Neurologic Neurologic Examination Outcome Output Patient Care Patient-Focused Outcomes Patients Pattern Performance Pharmaceutical Preparations Physicians Physiological Physiology Process Prognosis Provider Recommendation Recovery Resolution Resources Sampling Series Specificity Speed Statistical Models Structure Supervision Survival Analysis Survivors System Temperature Test Result Testing Time Training Uncertainty United States Validation Withdrawal Work analytical tool cost data ecosystem deep learning demographics disability evidence based guidelines feature selection improved inclusion criteria innovation insight life-sustaining therapy model building neural neurological recovery novel novel strategies outcome prediction patient population predictive tools prognostic prognostication prospective prospective test random forest risk minimization tool

项目摘要

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.

抽象的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受到严密监测，临床医生仅使用一小部分现有数据来预测恢复潜力，使得神经学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：常规临床监测会产生大量丰富的信息，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来构建一个快速提供可操作的预测知识的系统。我们已经打扫干净，组织和聚合了一个大型、高度多元的生理和临床时间序列数据库超过 1,850 名术后患者的超过 170,000 小时的定量脑电图 (EEG) 特征信息逮捕病人。我们将完善和优化分析工具，以更多地预测该患者群体的康复情况比临床专家更快、更准确。我们将使用创新方法来最大限度地减少偏见风险对由容易犯错的人类提供者创建的结果标签引入的模型进行训练。在本提案的目标 1 中，我们将使用新颖的方法来创建信息丰富且可解释的特征异质临床数据，包括脑电图波形、生命体征、药物和实验室测试结果。我们将使用深度学习来识别可预测结果的可解释且简约的特征集。我们将训练、测试和比较多种分析工具的性能。在目标 2 中，我们将前瞻性地将表现最佳的模型与专家临床医生小组进行比较。自信地识别的模型康复前景接近于零的患者比专家临床医生更敏感或更快速，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挽救生命，允许将适当的资源分配给可能受益的患者，避免长期和困难的护理无法康复的患者，让家人免受不确定性的痛苦。