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; 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.

抽象的 此快速轨道的STTR应用程序建议增强，验证和鳞片败血症手表，深度 学习败血症检测和管理系统，使用紧急情况中的数据构建 系（ED）杜克大学医院（DUH）。该提议将扩展和增强 向EDS，一般住院病房和重症监护病房（ICU）设置及跨越 美国多个卫生系统。同时早期诊断和及时处理 败血症可以提高死亡率和发病率，早期发现仍然难以捉摸。败血症 观看Duh Ed中的集成提高了3小时败血症捆绑包的符合性12％ 6小时的败血症捆绑包18％。该系统将严重败血症的死亡率降低15％ 败血性休克的死亡率增加了22％。该建议旨在将败血症的观察转变为 可扩展的解决方案，以在其他卫生系统和ED以外的设置中复制此类结果。 在第一阶段，我们通过回顾两个数据提出外部验证 单独的卫生系统。第1阶段将使我们自动化数据质量检查和摄入 当我们策划至少200,000的数据时，来自不同卫生系统的流程从不同的卫生系统进行 在2年内遇到。我们将向每个的临床医生提供模型预测 医院分析将脓毒症手表整合到临床护理中的潜在影响。在第二阶段， 我们建议从第1阶段的每家医院进行时间验证。这将使我们能够 以不可知论的方式将数据记录的实时摄入到败血症手表中 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1阶段的调查结果来提高每个位置的绩效，同时评估联邦和 集中学习方法，结合了来自不同医院的数据。型号 还将评估使用不同输入集的变化，并将构建模型 三个金标准的败血症定义，包括Sepsis-3，CMS Sep-1败血症和CDC成人 败血症事件。在6个月的时间验证期间，我们还将推广败血症手表 用户界面和工作流程通过寻求每家医院的临床医生的反馈来运行 无声模式。这将使败血症手表可在各种临床工作流程中配置。 第2阶段的优化模型和用户界面应允许败血症无缝地保持 在每家医院的常规临床护理中，然后纳入每个医院 这两个卫生系统，最终进入美国的任何卫生系统。