Sepsis Clinical Decision Support [CDS] Master Enrollment Study Protocol
- Conditions
- Severe Sepsis Without Septic ShockSevere Sepsis
- Registration Number
- NCT05304728
- Lead Sponsor
- Beckman Coulter, Inc.
- Brief 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.
- Detailed 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 & Eligibility
- Status
- ENROLLING_BY_INVITATION
- Sex
- All
- Target Recruitment
- 40000
- All races, ages and ethnicities
- All patients admitted to the hospital or presenting to the Emergency Department
- Patients not presenting to a hospital setting (e.g. urgent care, outpatient clinic excluded).
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Severe Sepsis Within 6 hours from presentation to the emergency department Identify patients having Severe Sepsis with the use of electronic health data
- Secondary Outcome Measures
Name Time Method Mortality Within 6hours from presentation to the emergency department 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
Length of Stay Within 6hours from presentation to the emergency department 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
Re-admission Rates Within 6hours from presentation to the emergency department 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
Trial Locations
- Locations (10)
University of California, Irvine
🇺🇸Irvine, California, United States
Augusta University Medical School
🇺🇸Augusta, Georgia, United States
Indiana University Health
🇺🇸Indianapolis, Indiana, United States
University Health/ Truman Medical Center
🇺🇸Kansas City, Missouri, United States
University of Kansas Medical Center
🇺🇸Kansas City, Missouri, United States
Hackensack University Medical Center
🇺🇸Hackensack, New Jersey, United States
WakeMed Health
🇺🇸Raleigh, North Carolina, United States
University of Cininnati
🇺🇸Cincinnati, Ohio, United States
MetroHealth Systems
🇺🇸Cleveland, Ohio, United States
The Ohio State University
🇺🇸Columbus, Ohio, United States
University of California, Irvine🇺🇸Irvine, California, United States