Comparison of Sepsis Prediction Algorithms

Sepsis Clinical Trial

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05943938
• Other study ID #: STUDY00005958
• Status: Not yet recruiting
• Start date: August 2024
• Est. completion date: December 2024

Study information

• Verified date: May 2024
• Source: Emory University
• Contact: Sivasubramanium Bhavani, MD
• Phone: 404-712-2970
• Email: sivasubramanium.bhavani[@]emory.edu
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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:

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).

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 1200
• Est. completion date: December 2024
• Est. primary completion date: December 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• All adult patients admitted through the ED

Exclusion Criteria:

• None

Related Conditions & MeSH terms

• Sepsis
• Toxemia

Intervention

Other:
• Epic Sepsis Model Version - 1
The Epic Sepsis Model (ESM) version 1, a proprietary sepsis prediction model.
• Epic Sepsis Model Version - 2
The Epic Sepsis Model (ESM) version 2, a proprietary sepsis prediction model.
• Emory Sepsis Model
Emory internal algorithm

Locations

Country	Name	City	State
United States	Emory Healthcare System	Atlanta	Georgia
United States	Emory Hospital	Atlanta	Georgia
United States	Emory Midtown Hospital	Atlanta	Georgia
United States	Emory Saint Joseph's Hospital	Atlanta	Georgia
United States	Emory Decatur Hospital	Decatur	Georgia
United States	Emory Johns Creek Hospital	Johns Creek	Georgia
United States	Emory Hillandale Hospital	Lithonia	Georgia

Sponsors (1)

Lead Sponsor	Collaborator
Emory University

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Patient hospitalization-level area under curve (AUC) for identification of sepsis,	Definition of Sepsis using the Centers for Disease Control and Prevention (CDC) Adult Sepsis Surveillance.	Duration of hospital stay (until discharge or death), an expected average of 30 days
Secondary	Sensitivity, specificity, and Positive and Negative Predictive Value of algorithms	Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).	Duration of hospital stay (until discharge or death), an expected average of 30 days
Secondary	Lead time to antibiotic administration	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.	Duration of hospital stay (until discharge or death), an expected average of 30 days
Secondary	Percent expected increase in unnecessary antibiotics	Percent of patients who were incorrectly identified as having sepsis (false positives), and received antibiotics.	Duration of hospital stay (until discharge or death), an expected average of 30 days
Secondary	Number needed to screen	The number of alerts that would need to be processed to find one true positive sepsis.	Duration of hospital stay (or death), an expected average of 30 days
Secondary	Number of Total and false alert burden	The number of Total and false alert burden cumulative across all study patients over the study period	Duration of hospital stay (until discharge or death), an expected average of 30 days
Secondary	Time-horizon based AUCs	AUCs will be calculated at 3 pre-specified time horizons.	4 hours, 8 hours, and 24 hours
Secondary	Accuracy and calibration by subgroup	The AUC and calibration curves will be compared by sex and race to ensure predictive accuracy is equal across subgroups.	Duration of hospital stay (until discharge or death), an expected average of 30 days