Prospective Evaluation of Sepsis Prediction Algorithms in a Multi-Hospital Healthcare System

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.

Description

Eligibility

Age range

18+ years

Sex

All

Healthy volunteers

No

Interventions

• Other
  Epic Sepsis Model Version - 1

  The Epic Sepsis Model (ESM) version 1, a proprietary sepsis prediction model.

• Other
  Epic Sepsis Model Version - 2

  The Epic Sepsis Model (ESM) version 2, a proprietary sepsis prediction model.

• Other
  Emory Sepsis Model

  Emory internal algorithm

Locations (7)