Solving Sepsis: Early Identification and Prompt Management Using Machine Learning

解决脓毒症：利用机器学习进行早期识别和及时管理

• 批准号: 10384254
• 负责人: Manesh R Patel
• 金额: $ 27.58万
• 依托单位: COHERE-MED, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10384254
• 关键词: Accident and Emergency department; Adoption; Adult; Area; Benchmarking; Caring; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical; Country; Data; Data Analyses; Data Element; Data Set; Detection; Diffusion; Early Diagnosis; Early identification; Economic Burden; Electronic Health Record; Ensure; Event; Feedback; General Ward; Geographic Locations; Geography; Gold; Health; Health system; Hospital Mortality; Hospital Units; Hospitals; Hour; Ingestion; Inpatients; Intensive Care Units; Intervention; Learning; Location; Machine Learning; Measurement; Modeling; Morbidity - disease rate; Outcome; Output; Patient-Focused Outcomes; Performance; Pharmaceutical Preparations; Phase; Positioning Attribute; Process; Publishing; Records; Running; Sepsis; Septic Shock; Site; Small Business Technology Transfer Research; State Hospitals; Subgroup; Surface; System; Technology; Time; United States; University Hospitals; Update; Validation; Variant; Vendor; adjudicate; base; clinical care; commercialization; comorbidity; cost; data ingestion; data quality; deep learning; demographics; design; economic impact; economic outcome; experience; flexibility; implementation barriers; implementation facilitators; improved; iterative design; machine learning model; mortality; patient population; predictive modeling; quality assurance; ward

Abstract This fast-track STTR application proposes to enhance, validate, and scale Sepsis Watch, a deep learning sepsis detection and management system built using data from the Emergency Department (ED) Duke University Hospital (DUH). The proposal will extend and enhance Sepsis Watch to EDs, general inpatient wards, and intensive care unit (ICU) settings across multiple health systems in the United States. While early diagnosis and prompt treatment of sepsis can improve mortality and morbidity, early detection has remained elusive. The Sepsis Watch integration in the DUH ED improved compliance with the 3-hour sepsis bundle by 12% and the 6-hour sepsis bundle by 18%. The system reduced mortality for severe sepsis by 15% and mortality for septic shock by 22%. This proposal seeks to transform Sepsis Watch into a scalable solution to replicate such results at other health systems and in settings beyond the ED. In Phase I, we propose external validation through a retrospective analysis of data from two separate health systems. Phase 1 will let us automate data quality checks and ingestion processes at scale from different health systems as we curate data from at least 200,000 encounters over a 2-year period. We will present model predictions to clinicians from each hospital to analyze potential impact of integrating Sepsis Watch into clinical care. In Phase II, we propose conducting temporal validation at each hospital from Phase I. This will allow us to design real-time ingestion of data records into Sepsis Watch in a manner that is agnostic to electronic health record (EHR) vendor systems. We will optimize the machine learning model using Phase 1 findings to improve performance at each location while assessing federated and centralized learning approaches that incorporate data from different hospitals. Models variations that utilize different sets of inputs will also be assessed and models will be built to three gold-standard sepsis definitions, including Sepsis-3, CMS SEP-1 sepsis, and CDC Adult Sepsis Event. During the 6-month temporal validation we will also generalize the Sepsis Watch user-interface and workflow by seeking feedback from clinicians at each hospital as it is run in silent mode. This will allow Sepsis Watch to be configurable to various clinical workflows. The optimized model and user-interface in Phase 2 should allow Sepsis Watch to be seamlessly integrated into routine clinical care in each hospital and then into other hospitals within each of the two health systems and eventually to any health system in the US.

摘要 这个快速通道STTR应用程序建议增强，验证和扩展脓毒症观察，一个深入的 学习脓毒症检测和管理系统建立使用的数据从紧急 杜克大学医院（DUH）（艾德）部门。该提案将扩大和加强 脓毒症监测适用于急诊室、普通住院病房和重症监护病房（ICU）， 美国的多个医疗系统。早期诊断和及时治疗 脓毒症可以提高死亡率和发病率，但早期发现仍然是难以捉摸的。脓毒症 DUH艾德中的手表集成将3小时脓毒症捆绑治疗的依从性提高了12% 6小时脓毒症组减少了18%该系统将严重败血症的死亡率降低了15% 感染性休克的死亡率降低了22%该提案旨在将脓毒症观察转变为 可扩展的解决方案，在其他卫生系统和ED以外的环境中复制这些结果。 在第一阶段，我们建议通过回顾性分析两个项目的数据进行外部验证 独立的卫生系统。第1阶段将让我们自动化数据质量检查和摄取 当我们从至少20万个不同的卫生系统中收集数据时， 在两年的时间里相遇。我们将向临床医生提供模型预测， 医院分析将脓毒症观察整合到临床护理中的潜在影响。在第二阶段， 我们建议从第一阶段开始在每家医院进行时间验证。这将使我们能够 以不可知的方式将数据记录实时摄取设计到脓毒症观察中， 电子健康记录（EHR）供应商系统。我们将优化机器学习模型 使用第1阶段的调查结果来提高每个地点的性能， 集中学习方法，将来自不同医院的数据合并。模型 还将评估利用不同输入集的变化，并建立模型， 三种金标准脓毒症定义，包括脓毒症-3、CMS SEP-1脓毒症和CDC成人 败血症事件。在6个月的时间验证期间，我们还将概括败血症观察 用户界面和工作流程，在运行时寻求每家医院临床医生的反馈 静音模式。这将允许Sepsis Watch可配置为各种临床工作流程。 第2阶段的优化模型和用户界面应允许败血症观察无缝 整合到每家医院的常规临床护理中，然后再整合到每家医院的其他医院中， 这两个卫生系统，并最终在美国的任何卫生系统。