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

项目摘要/摘要 如果一个人能准确地预测谁、何时和为什么患者会在 手术，然后给予有效的先发制人的治疗，以改善术后结果等 有效利用医疗资源。但是，一旦器官损伤已经存在，休克的迹象通常会出现在很晚的时候。 该提案的目标是开发、验证和测试基于以下各项的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并使 用于这些发展的术中数据和波形数据库可免费获取。这是 非常相关，因为尽管有570万美国人住进了重症监护病房(ICU) 每年有4200多万人接受手术。先前和正在进行的研究在ICU和 在下位机中搭建了实时高保真生理波形数据采集的体系结构 流，并将它们与EHR中的患者人口统计数据相集成，以构建大型数据集，并从 基于机器学习(ML)分析的可操作融合参数以及实时显示信息 在床边的时间，以推动重症监护环境中的临床决策支持(CDS)。这项提案的目标是 是将这些ML方法应用到复杂和时间紧迫的高风险手术环境中 患者和疾病的变异性更大，提供真正个性化服务的时间更短 医学即将到来。这项工作将使用已有的带注解的术中数据库启动 来自加州大学欧文分校，包括EHR和高保真波形数据。这间手术室 数据库已存在，只需提取即可。这些数据将用于初始培训和 开发ML模型，然后在预期收集的加州大学洛杉矶分校进行测试 洛杉矶和匹兹堡大学医学中心数据库。同时，此方法将使用现有的 从先前的降级病房/重症监护病房队列中获得CRI模式的知识，模拟II数据， 加州大学欧文分校的数据和动物研究创建智能警报和图形用户界面 基于功能性血流动力学监测原则的临床决策支持。下一步将是 利用对术中环境的问题和优势的关注，其中一些可以列出 AS：1)手术前已知的患者特征以确定预应激基线，允许 血流动力学监测应激评估、预适应和其他术前校准，2)高 在大多数手术阶段的直接观察程度和数据密度，允许近距离半自主 早期监测和滴定新的治疗算法，3)在手术的初始阶段确定阶段 (诱导、插管、皮肤切开)允许ML方法建立大型公共关系数据库 登记，以及4)定义手术程序和应激源(麻醉诱导、腹内空气注入、 和其他特定于手术的干预措施)，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.