Solving Sepsis: Early Identification and Prompt Management Using Machine Learning

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.