Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical Patients

项目总结/摘要 如果人们能够准确地预测谁，何时以及为什么患者在治疗期间发生心肺不稳定（CRI）， 手术，然后可以给予有效的先发制人的治疗，以改善术后结果， 有效利用医疗资源。但是一旦器官损伤已经存在，休克的迹象往往会出现得很晚。 该提案的目标是开发、验证和测试实时术中风险预测工具， 电子健康记录（EHR）数据和高保真生理波形来预测CRI， 可免费访问用于这些开发的术中数据和波形数据库。这是 这是非常重要的，因为尽管570万美国人被送进了重症监护室（ICU）， 每年有超过4200万人接受手术。既往和正在ICU进行的研究， 在降压单元中建立了实时采集高保真生理波形数据的体系结构 数据流，并将其与EHR中的患者人口统计数据整合，以构建大型数据集， 基于机器学习（ML）分析的可操作融合参数以及真实的显示信息 在床边的时间，以推动临床决策支持（CDS）在重症监护环境。这项提案的目的是 将这些ML方法应用于高风险手术的复杂和时间压缩环境， 存在更大的患者和疾病变异性，并且可以在更短的时间内提供真正个性化的 医学接近。这项工作将开始使用一个已经存在的注释术中数据库 包括EHR和高保真波形数据。这个手术室 数据库已存在，只需要提取。这些数据将用于初始培训， 开发ML模型，然后在加州洛杉矶大学前瞻性收集的数据上进行测试。 Angeles和匹兹堡大学医学中心数据库。同时，这种方法将利用现有的 从既往降压病房/重症监护病房队列中获得的CRI模式知识，MIMIC II数据， 加州尔湾大学的数据和动物研究，以创建智能报警和图形用户界面， 基于功能性血流动力学监测原理的临床决策支持。下一步将 重点关注术中环境的问题和优势，其中一些可以列出 作为：1）术前已知患者特征，以定义预应力基线，允许功能 血流动力学监测应力评价、预处理和其他术前校准，2）高 手术大部分阶段的直接观察程度和数据密度，允许密切的半自主 早期监测和滴定新的治疗算法，3）手术初始部分的定义阶段 （诱导，插管，皮肤切开）允许ML方法构建大型通用关系数据库 登记，和4）定义的外科手术和应激源（麻醉诱导，腹腔内空气吹入， 和其他手术特定干预），这将改变CRI对测量变量的影响。

Project Summary / Abstract If one could accurately predict who, when and why patients develop cardiorespiratory instability (CRI) during surgery, then effective preemptive treatments could be given to improve postoperative outcome and more effectively use healthcare resources. But signs of shock often occur late once organ injury is already present. The goal of this proposal is to develop, validate, and test real-time intraoperative risk prediction tools based on electronic health record (EHR) data and high-fidelity physiological waveforms to predict CRI and make the databases of intraoperative data and waveforms used for these developments freely accessible. This is extremely relevant because although 5.7 million Americans are admitted to an Intensive Care Units (ICU) in one year, more than 42 millions undergo surgery annually. Previous and ongoing studies conducted in the ICU and in the step down unit have built the architecture to collect real-time high-fidelity physiological waveform data streams and integrate them with patient demographics from the EHR to build large data sets, and derive actionable fused parameters based on machine learning (ML) analytics as well as display information in real time at the bedside to drive clinical decision support (CDS) in the critical care setting. The goal of this proposal is to apply these ML approaches to the complex and time compressed environment of high-risk surgery where greater patient and disease variability exist and shorter period of time is available to deliver truly personalized medicine approaches. The work will be initiated using an already existing annotated intraoperative database from the University of California Irvine including EHR and high-fidelity waveform data. This operating room database already exists and needs only to be extracted. This data will be used for the initial training and development of the ML model that will then be tested on prospectively collected University of California Los Angeles and University of Pittsburgh Medical Center databases. Simultaneously, this approach will use existing knowledge of CRI patterns derived from previous step down unit / intensive care unit cohorts, MIMIC II data, University of California Irvine data, and animal studies to create smart alarms and graphic user interface for clinical decision support based on functional hemodynamic monitoring principles. The next step will then leverage the focus on the issues and strengths of the intraoperative environment, some of which can be listed as: 1) Known patients characteristics before surgery to define pre-stress baseline, allowing functional hemodynamic monitoring stress evaluations, preconditioning, and other preoperative calibrations, 2) High degree of direct observation and data density during most phases of surgery allowing close semi-autonomous monitoring and titration of novel treatment algorithms early, 3) Defined stages in the initial part of surgery (induction, intubation, skin incision) allowing ML approaches to build large common relational database registries, and 4) Defined surgical procedure and stressors (anesthesia induction, intra-abdominal air insufflation, and other surgery-specific interventions), which will alter the impact of CRI on measured variables.