Biomedical Informatics Tools for Applied Perioperative Physiology

应用围手术期生理学的生物医学信息学工具

• 批准号: 10612383
• 负责人: Maxime Cannesson
• 金额: $ 60.81万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612383
• 关键词: Academia; Acceleration; Admission activity; Adopted; Agreement; Algorithms; American; Anesthesia procedures; Anesthesiology; Animal Model; Area; Attention; Biological; Biological Sciences; Calibration; California; Cause of Death; Central venous pressure; Certified registered nurse anesthetist; Characteristics; Clinical; Clinical Medicine; Communities; Complex; Critical Care; Data; Data Collection; Data Set; Databases; Detection; Development; Documentation; Early Diagnosis; Electrocardiogram; Electronic Health Record; Event; Future; Gender; Goals; Hand; Health Personnel; Heart Arrest; Hospitals; Human; Incidence; Industry; Ingestion; Inpatients; Intensive Care Units; Israel; Knowledge; Lead; Los Angeles; Machine Learning; Measures; Medical center; Medicine; Melons; Minority Groups; Modeling; Monitor; Morbidity - disease rate; Operating Rooms; Operative Surgical Procedures; Patient Care; Patient Monitoring System; Patient-Focused Outcomes; Patients; Pattern; Perioperative; Physiologic Monitoring; Physiological; Physiology; Plumbing; Population Heterogeneity; Postoperative Period; Procedures; Process; Provider; Registries; Research; Research Personnel; Resources; Series; Shock; Source; Stress; System; Technology; Therapeutic Intervention; Time; Training; Twin Multiple Birth; Universities; Validation; Work; acute care; algorithm development; analytical tool; biomedical informatics; cardiovascular collapse; clinical decision support; clinical decision-making; clinically relevant; cloud based; computer science; data archive; data integration; data streams; deep learning; density; electronic health database; human data; human subject; improved; informatics tool; information display; interest; interoperability; large datasets; learning algorithm; machine learning algorithm; monitoring device; mortality; multidimensional data; novel; predictive modeling; predictive signature; predictive tools; pressure; public database; relational database; sex; skills; stressor; tool

Project Summary / Abstract Even though US hospitals have widely adopted electronic health record (EHR) documentation of patient care, interoperability of these systems remains an issue, leading to challenges in data integration. In the operating room (OR) setting, during surgery, physiological waveforms (arterial pressure, EKG, SpO2, central venous pressure, etc.) represent a large source of information used by clinical monitors to extract and display information in order for healthcare providers to make clinical decisions. Integration and synchronization of high-quality EHR and physiological waveform data in large datasets of surgical patients would allow machine learning and deep learning approaches to plumb these datasets for clinically relevant signatures that would promote advanced OR patient monitoring systems to define present state, predict state trajectory, suggest effective counter measures to minimize patients decompensated states, and define the usefulness and efficacy of new monitoring devices. The objective of this proposal is to focus the resources of an interdisciplinary team from academia (University of California Los Angeles (UCLA), University of California Irvine (UCI), and Carnegie Mellon University Computer Sciences), industry (Edwards Lifesciences Critical Care), and clinical medicine (anesthesiology, surgery, and critical care at UCLA, UCI, Beth Israel, and University of Pittsburgh Medical Center) to create, develop, and organize large surgical datasets combining EHR and high fidelity physiological waveform data, to make these datasets freely accessible, and to develop new predictive/forecasting monitoring systems for the surgical patients. The study will begin with the development of a machine learning algorithm to predict cardiovascular collapse during surgery. This algorithm development will be based on physiological signatures predictive of cardiovascular collapse identified in the animal models of shock. The study hypothesis is that the combination of two separate OR databases containing EHR and physiological waveforms will allow for training and development of monitoring solutions, predictive and/or prescriptive analytics tools, clinical decision support, and validate them on an independent, external validation database. The surgical setting is relevant because although 5.7 million Americans are admitted annually to an Intensive Care Unit, more than 50 million undergo surgery. OR databases are unique in medicine because: 1) Changes occur quickly and the lead-time before an event is compressed; 2) Knowledge of baseline/pre-stress status of surgical patients allows normalization, calibration, and markedly enhances prediction; 3) Continuous and immediate presence of dense skilled acute care practitioners allows faster implementation of complex treatment algorithms in the OR; and 4) Defined stages, procedures, and stressors allow building large common relational database registries. By helping to focus the provider's attention on significant events and changes in the patient's state and by suggesting physiological interpretations of that state, such systems will permit early detection of complex problems and provide guidance on therapeutic interventions improving patient outcomes.

项目摘要/摘要 尽管美国医院已经广泛采用了病人护理的电子健康记录(EHR)文档， 这些系统的互操作性仍然是一个问题，导致数据集成方面的挑战。在运营中 房间(OR)设置，术中生理波形(动脉压、EKG、SpO2、中心静脉 压力等)代表临床监护仪用来提取和显示信息的大量信息源 以便医疗保健提供者做出临床决策。高质量电子病历的整合与同步 而手术患者大数据集中的生理波形数据将允许机器学习和深度 从这些数据集中提取临床相关特征的学习方法将促进高级OR 患者监控系统可定义当前状态、预测状态轨迹、建议有效的应对措施 以最大限度地减少患者的失代偿状态，并确定新的监测设备的有用性和有效性。 这项提议的目标是集中学术界(大学)的跨学科小组的资源 加州大学洛杉矶分校(UCLA)、加州大学欧文分校(UCI)和卡内基梅隆大学计算机 科学)、工业(爱德华兹生命科学重症监护)和临床医学(麻醉学、外科和 加州大学洛杉矶分校、加州大学洛杉矶分校、贝丝以色列分校和匹兹堡大学医学中心的重症监护)来创建、开发和 组织结合EHR和高保真生理波形数据的大型手术数据集，以使这些 免费获取数据集，并为外科手术开发新的预测/预测监测系统 病人。这项研究将从开发一种预测心血管疾病的机器学习算法开始 在手术过程中晕倒。这一算法的开发将基于生理特征预测 在休克的动物模型中发现了心血管衰竭。研究假设是这种组合 包含EHR和生理波形的两个单独的OR数据库将允许进行训练和 开发监测解决方案、预测性和/或说明性分析工具、临床决策支持以及 在独立的外部验证数据库上对它们进行验证。手术环境是相关的，因为尽管 每年有570万美国人住进重症监护病房，其中超过5000万人接受手术。 或数据库在医学上是独一无二的，因为：1)变化发生得很快，事件发生前的提前期是 2)手术患者的基线/预应激状态的知识允许正常化、校准、 并显著提高预见性；3)持续、即时地提供密集、熟练的急救护理 从业者允许在OR中更快地实施复杂的治疗算法；以及4)定义的阶段， 过程和压力源允许构建大型公共关系数据库注册表。通过帮助关注 提供者对患者状态的重大事件和变化的关注，并通过建议生理学 对于这种状态的解释，这类系统将能够及早发现复杂问题并提供指导 关于改善患者预后的治疗干预。

项目成果

Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.

医学信息学手术室生命体征和事件存储库 (MOVER)：一个公共访问的手术室数据库。

• DOI: 10.1093/jamiaopen/ooad084
• 发表时间: 2023
• 期刊: JAMIA open
• 影响因子: 2.1
• 作者: Samad,Muntaha;Angel,Mirana;Rinehart,Joseph;Kanomata,Yuzo;Baldi,Pierre;Cannesson,Maxime
• 通讯作者: Cannesson,Maxime

Preoperative Point-of-Care Ultrasound to Identify Frailty and Predict Postoperative Outcomes: A Diagnostic Accuracy Study.

• DOI: 10.1097/aln.0000000000004064
• 发表时间: 2022-02-01
• 期刊: ANESTHESIOLOGY
• 影响因子: 8.8
• 作者: Canales, Cecilia;Mazor, Einat;Coy, Heidi;Grogan, Tristan R.;Duval, Victor;Raman, Steven;Cannesson, Maxime;Singh, Sumit P.
• 通讯作者: Singh, Sumit P.