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：
10337430
负责人：
Jonathan Elmer
金额：
$ 65.39万
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/10337430
关键词：
Academic Medical Centers Address Anoxic Encephalopathy Benchmarking Brain Brain Injuries Cardiopulmonary Resuscitation 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 Hour Human Image Information Sciences Injury Knowledge Label Laboratories 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 base cost data ecosystem deep learning demographics disability evidence based guidelines improved inclusion criteria innovation insight life-sustaining therapy neurological recovery novel novel strategies outcome prediction patient population predictive tools prognostic prognostication prospective prospective test random forest relating to nervous system 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康复是具有挑战性的。虽然病人从心脏骤停中复苏的人在重症监护单元中进行了深入监控，临床医生仅使用一个小子集可用数据以预测恢复的潜力，使神经系统预后既缓慢又不精确。这是现代医学中普遍存在问题的特定例子：常规临床监测会产生大量丰富的信息，但是缺乏将这些数据转换为有用知识的工具。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制造一个可以快速提供可行的预后知识的系统。我们已经打扫过有组织和汇总的高度多元时间序列数据库，具有生理和临床超过170,000小时的定量脑电图（EEG）特征的信息> 1,850 逮捕患者。我们将完善和优化分析工具，以预测该患者人群的恢复比临床专家快速，准确。我们将使用创新的方法来最大程度地降低偏见的风险由易犯错的人类提供者创建的结果标签引入的模型培训。在本提案的目标1中，我们将使用新颖的方法来创建信息异质临床数据，包括EEG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这些预测结果的这些特征的可解释和简约的集合。我们将训练，测试和比较多种分析工具的性能。在AIM 2中，我们将前景将最佳性能模型与专家临床医生小组进行比较。自信地识别的模型比专家临床医生更快或更快地恢复恢复前景的患者可以作为决策支持系统。提高预防后预后的速度和准确性将挽救生命，允许将适当的资源引向可能受益的患者，避免长期和困难的护理无法康复的患者，并避免家庭带来不确定性的痛苦。