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Life sciences · Observational Study
ClinicalTrials.gov · October 26, 2026
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Observational Study.
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Registry record from ClinicalTrials.gov (NCT05943938). This is a study registration, not published results. Lead sponsor: Emory University. Recruitment status: NOT_YET_RECRUITING. Study type: OBSERVATIONAL. Enrollment: 1200 participants (ESTIMATED). Conditions: Sepsis. Interventions: OTHER: Epic Sepsis Model Version - 1; OTHER: Epic Sepsis Model Version - 2; OTHER: Emory Sepsis Model. Primary outcome measures: Patient hospitalization-level area under curve (AUC) for identification of sepsis, , Duration of hospital stay (until discharge or death), an expected average of 30 days. Brief summary: Sepsis is a severe response to infection resulting in organ dysfunction and often leading to death. More than 1.5 million people get sepsis every year in the U.S., and 270,000 Americans die from sepsis annually. Delays in the diagnosis of sepsis lead to increased mortality. Several clinical decision support algorithms exist for the early identification of sepsis. The research team will compare the performance of three sepsis prediction algorithms to identify the algorithm that is most accurate and clinically actionable. The algorithms will run in the background of the electronic health record (EHR) and the predictions will not be revealed to patients or clinical staff. In this current evaluation study, the algorithms will not affect any part of a patient's care. The algorithms will be deployed across the Emory healthcare system on data from all patients presenting to the emergency department.