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NCT05304728 Clinical Trial

Sepsis Clinical Decision Support [CDS] Master Enrollment Study Protocol

Severe Sepsis Clinical Trial

Official title:
Sepsis Onset Warning System [SOWS] Master Enrollment Study Protocol

Clinical Trial Details — Status: Enrolling by invitation

Administrative data
NCT number NCT05304728
Other study ID # 2.7.2-1
Secondary ID
Status Enrolling by invitation
Phase
First received
Last updated
Start date February 15, 2021
Est. completion date December 31, 2026

Study information
Verified date June 2024
Source Beckman Coulter, Inc.
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

This protocol will collect real-world data retrospectively from the electronic health record (EHR) as data obtained from the delivery of routine medical care to develop a machine learning (ML)-based Clinical Decision Support (CDS) system for severe sepsis prediction and detection.

Description:

The purpose of this study is to gather data for the clinical development of the Sepsis Onset Warning System (SOWS) Software as Medical Device (SaMD) product to support a De Novo FDA submission and commercialization in the United States. Product development of SOWS is funded in part with federal funds from the Department of Health and Human Services; Office of the Assistant Secretary for Preparedness and Response; Biomedical Advanced Research and Development Authority. Data will be obtained from passive prospective collection of patient encounter data throughout the duration of the planned study to support the product development life cycle activities associated with developing the Sepsis Onset Warning System (SOWS) for severe sepsis risk detection. Inputs from patient health records in combination with proprietary hematology parameters developed by Beckman Coulter, such as Monocyte Distribution Width (MDW), will be used. The SOWS tool will look to use clinical measurements which are commonly and reliably available in the EHR as structured data elements, such as heart rate, temperature, blood pressure, and laboratory results and account for changes in these values over time.

Recruitment information / eligibility
Status Enrolling by invitation
Enrollment 40000
Est. completion date December 31, 2026
Est. primary completion date March 30, 2025
Accepts healthy volunteers
Gender All
Age group 18 Years to 89 Years
Eligibility Inclusion Criteria: - All races, ages and ethnicities - All patients admitted to the hospital or presenting to the Emergency Department Exclusion Criteria: - Patients not presenting to a hospital setting (e.g. urgent care, outpatient clinic excluded).

Study Design

Related Conditions & MeSH terms

Locations
Country Name City State
United States Augusta University Medical School Augusta Georgia
United States University of Cininnati Cincinnati Ohio
United States MetroHealth Systems Cleveland Ohio
United States The Ohio State University Columbus Ohio
United States Hackensack University Medical Center Hackensack New Jersey
United States Indiana University Health Indianapolis Indiana
United States University of California, Irvine Irvine California
United States University Health/ Truman Medical Center Kansas City Missouri
United States University of Kansas Medical Center Kansas City Missouri
United States WakeMed Health Raleigh North Carolina

Sponsors (2)
Lead Sponsor Collaborator
Beckman Coulter, Inc. Biomedical Advanced Research and Development Authority

Country where clinical trial is conducted

United States, 

Outcome
Type Measure Description Time frame Safety issue
Primary Severe Sepsis Identify patients having Severe Sepsis with the use of electronic health data Within 6 hours from presentation to the emergency department
Secondary Mortality Hospital mortality at hospital for Severe Sepsis patients identified by algorithm using electronic health data as potential benefits for increased early detection of risk of severe sepsis Within 6hours from presentation to the emergency department
Secondary Length of Stay Determine length of stay at hospital for Severe Sepsis patients identified by algorithm using electronic health data as potential benefits for increased early detection of risk of severe sepsis Within 6hours from presentation to the emergency department
Secondary Re-admission Rates Determine potential reduction of hospital readmission rates for Severe Sepsis patients identified by algorithm using electronic health data as potential benefits for increased early detection of risk of severe sepsis Within 6hours from presentation to the emergency department
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