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: 10623375
• 负责人: Manesh R Patel
• 金额: $ 91.28万
• 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 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10623375
• 关键词: Solving; Sepsis; Early; Identification; Prompt

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 这个快速通道 STTR 应用程序旨在增强、验证和扩展脓毒症观察，这是一个深度的 学习使用紧急情况数据构建的脓毒症检测和管理系统 科室 (ED) 杜克大学医院 (DUH)。该提案将扩展和加强 脓毒症观察 急诊科、普通住院病房和重症监护病房 (ICU) 环境 美国的多个卫生系统。在早期诊断、及时治疗的同时 脓毒症可以提高死亡率和发病率，但早期发现仍然难以实现。败血症 DUH ED 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 小时败血症组减少 18%。该系统将严重败血症的死亡率降低了 15% 感染性休克死亡率降低 22%。该提案旨在将脓毒症观察转变为 可扩展的解决方案，可在其他卫生系统和急诊室以外的环境中复制此类结果。 在第一阶段，我们建议通过对两个项目的数据进行回顾性分析来进行外部验证 独立的卫生系统。第一阶段将让我们自动化数据质量检查和摄取 当我们整理来自至少 200,000 个数据时，会大规模处理来自不同卫生系统的数据 2年期间的遭遇。我们将向各个领域的临床医生展示模型预测 医院分析将脓毒症观察纳入临床护理的潜在影响。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 以不可知的方式设计将数据记录实时摄取到脓毒症观察中 电子健康记录 (EHR) 供应商系统。我们将优化机器学习模型 使用第一阶段的调查结果来提高每个地点的绩效，同时评估联合和 整合来自不同医院的数据的集中学习方法。型号 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 Sepsis-3、CMS SEP-1 脓毒症和 CDC Adult 败血症事件。在 6 个月的时间验证期间，我们还将概括脓毒症观察 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。这将使脓毒症观察能够针对各种临床工作流程进行配置。 第 2 阶段的优化模型和用户界面应该允许脓毒症观察无缝地 纳入每家医院的常规临床护理，然后纳入每个医院的其他医院 这两个卫生系统，并最终适用于美国的任何卫生系统。