Skip to main content
OpenTrials
Not Yet Recruiting

NCT Number: NCT05943938

Comparison of Sepsis Prediction Algorithms

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.

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Conditions

Sepsis Disease Attributes Emergencies Infections Inflammation Pathologic Processes Pathological Conditions, Signs and Symptoms Systemic Inflammatory Response Syndrome

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Emory Healthcare System, Atlanta, Georgia, United States

Loading trial locations.

About this study

The primary goal of this study is to prospectively evaluate three sepsis prediction algorithms that are embedded in the EHR. The models will be deployed in a "shadow" mode, and the results will not be displayed to the treatment team during this study. Two of the algorithms are proprietary algorithms of the EHR provider (Epic). The third algorithm is an internally developed, open-source algorithm.

The algorithms will compute the probability of sepsis at periodic intervals and will continue to run on a patient's data until the patient's discharge, death, or upon initiation of intravenous antibiotics (at which point there is an indirect record of clinical suspicion of an infection).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • All adult patients admitted through the ED

Exclusion criteria

  • None

Treatment and study plan

Epic Sepsis Model Version - 1

Other

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

Other names: Proprietary Epic sepsis algorithm -1

Epic Sepsis Model Version - 2

Other

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

Other names: Proprietary Epic sepsis algorithm -2

Emory Sepsis Model

Other

Emory internal algorithm

Other names: Emory Sepsis Algorithm

Primary outcomes

  1. Patient hospitalization-level area under curve (AUC) for identification of sepsis,

    Time frame: Duration of hospital stay (until discharge or death), an expected average of 30 days

    Definition of Sepsis using the Centers for Disease Control and Prevention (CDC) Adult Sepsis Surveillance.

Secondary outcomes

  1. Sensitivity, specificity, and Positive and Negative Predictive Value of algorithms

    Time frame: Duration of hospital stay (until discharge or death), an expected average of 30 days

    Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

  2. Lead time to antibiotic administration

    Time frame: Duration of hospital stay (until discharge or death), an expected average of 30 days

    The time between the initial deployment of the alert in patients confirmed to have sepsis (ture positives) and the physician's ordering of intravenous antibiotic therapy.

  3. Percent expected increase in unnecessary antibiotics

    Time frame: Duration of hospital stay (until discharge or death), an expected average of 30 days

    Percent of patients who were incorrectly identified as having sepsis (false positives), and received antibiotics.

  4. Number needed to screen

    Time frame: Duration of hospital stay (or death), an expected average of 30 days

    The number of alerts that would need to be processed to find one true positive sepsis.

  5. Number of Total and false alert burden

    Time frame: Duration of hospital stay (until discharge or death), an expected average of 30 days

    The number of Total and false alert burden cumulative across all study patients over the study period

  6. Time-horizon based AUCs

    Time frame: 4 hours, 8 hours, and 24 hours

    AUCs will be calculated at 3 pre-specified time horizons.

  7. Accuracy and calibration by subgroup

    Time frame: Duration of hospital stay (until discharge or death), an expected average of 30 days

    The AUC and calibration curves will be compared by sex and race to ensure predictive accuracy is equal across subgroups.

Study contacts

Contact information is provided by the study sponsor or research team.

Sivasubramanium Bhavani, MD

CONTACT

[email protected]

404-712-2970

Sponsors and collaborators

Lead sponsor

Emory University

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jul 13, 2023
Registry last updated
Jan 7, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.