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Comparison of Sepsis Prediction Algorithms

NCT05943938 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1200

Last updated 2026-01-07

No results posted yet for this study

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.

Conditions

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

Sponsors & Collaborators

  • Emory University

    lead OTHER

Principal Investigators

  • Sivasubramanium Bhavani, MD · Emory University

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-30
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05943938 on ClinicalTrials.gov