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

生理波形和电子健康记录数据的机器学习可预测、诊断和治疗手术患者的血流动力学不稳定

基本信息

批准号：
10589931
负责人：
Maxime Cannesson
金额：
$ 72.05万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-07 至 2024-12-31
项目状态：
已结题

项目摘要

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.

项目摘要 /摘要如果一个人能够准确预测患者在何时以及为什么在患者期间发展心脏不稳定（CRI）手术，然后可以进行有效的先发制人治疗以改善术后结果和更多有效地使用医疗保健资源。但是，一旦存在器官损伤，震动的迹象通常会迟到。该建议的目的是根据基于电子健康记录（EHR）数据和高保真生理波形，以预测CRI并使得用于这些开发的术中数据和波形的数据库可自由访问。这是尽管有570万美国人被纳入重症监护病房（ICU），但非常相关一年，每年有4200万多人接受手术。 ICU和正在进行的研究和正在进行的研究在逐步的单元中，建立了体系结构，以收集实时高保真生理波形数据流并将其与来自EHR的患者人口统计数据集成在一起，以构建大型数据集，并得出基于机器学习（ML）分析的可操作的融合参数以及实际显示信息在重症监护环境中，在床边推动临床决策支持（CD）。该提议的目标是将这些ML方法应用于高风险手术的复杂和时间压缩环境中存在更大的患者和疾病变异性，并且可以较短的时间来提供真正的个性化医学方法。该工作将使用已经存在的注释术中数据库启动来自加利福尼亚大学尔湾分校，包括EHR和高保真波形数据。这个手术室数据库已经存在，只需要提取。这些数据将用于初始培训，开发ML模型，然后将对前瞻性收集的加利福尼亚大学进行测试安吉尔斯和匹兹堡大学医学中心数据库。同时，这种方法将使用现有了解从上一步的单元 /重症监护室同伙得出的CRI模式，模仿II数据，加利福尼亚大学尔湾分校的数据以及动物研究，以创建智能警报和图形用户界面基于功能血流动力学监测原理的临床决策支持。下一步将利用术中环境的问题和优势的重点，其中一些可以列出 AS：1）手术前已知的患者特征定义预压力基线，允许功能血液动力学监测应力评估，预处理和其他术前校准，2）高手术大多数阶段的直接观察程度和数据密度允许近距离自治早期对新型治疗算法进行监测和滴定，3）在手术的初始部分定义了阶段（诱导，插管，皮肤切口）允许ML方法构建大型公共关系数据库注册表和4）定义的手术程序和压力源（麻醉诱导，腹腔内空气不足，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。

项目成果

期刊论文数量（52）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Noninvasive Monitoring and Potential for Patient Outcome.

无创监测和患者结果的潜力。

DOI：
10.1053/j.jvca.2019.03.045
发表时间：
2019
期刊：
Journal of cardiothoracic and vascular anesthesia
影响因子：
2.8
作者：
Vacas,Susana;Cannesson,Maxime
通讯作者：
Cannesson,Maxime

Minimizing Measurement Variability in Carotid Ultrasound Evaluations.

DOI：
10.1002/jum.15445
发表时间：
2021-04
期刊：
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
影响因子：
0
作者：
Kenny JS;Cannesson M;Barjaktarevic I
通讯作者：
Barjaktarevic I

Postoperative respiratory failure: An update on the validity of the Agency for Healthcare Research and Quality Patient Safety Indicator 11 in an era of clinical documentation improvement programs.

DOI：
10.1016/j.amjsurg.2019.11.019
发表时间：
2020-07
期刊：
AMERICAN JOURNAL OF SURGERY
影响因子：
3
作者：
Stocking, Jacqueline C.;Utter, Garth H.;Drake, Christiana;Aldrich, J. Matthew;Ong, Michael K.;Amin, Alpesh;Marmor, Rebecca A.;Godat, Laura;Cannesson, Maxime;Gropper, Michael A.;Romano, Patrick S.
通讯作者：
Romano, Patrick S.

Feasibility of computer-assisted vasopressor infusion using continuous non-invasive blood pressure monitoring in high-risk patients undergoing renal transplant surgery.