Sepsis Clinical Decision Support [CDS] Master Enrollment Study Protocol

Sepsis Onset Warning System [SOWS] Master Enrollment Study Protocol

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.

Study Overview

• Status: Enrolling by invitation
• Conditions: Severe Sepsis; Severe Sepsis Without Septic Shock

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.

• Study Type: Observational
• Enrollment (Estimated): 40000

Contacts and Locations

Study Locations

• United States
  • California
    • Irvine, California, United States, 92697
      • University of California, Irvine
  • Georgia
    • Augusta, Georgia, United States, 30912
      • Augusta University Medical School
  • Indiana
    • Indianapolis, Indiana, United States, 46202-5200
      • Indiana University Health
  • Missouri
    • Kansas City, Missouri, United States, 64108
      • University Health/ Truman Medical Center
    • Kansas City, Missouri, United States, 66103
      • University of Kansas Medical Center
  • New Jersey
    • Hackensack, New Jersey, United States, 07601
      • Hackensack University Medical Center
  • North Carolina
    • Raleigh, North Carolina, United States, 27610
      • WakeMed Health
  • Ohio
    • Cincinnati, Ohio, United States, 45221
      • University of Cininnati
    • Cleveland, Ohio, United States, 44109
      • Metrohealth Systems
    • Columbus, Ohio, United States, 43210
      • The Ohio State University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 89 years (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

This protocol has open enrollment to all genders, ages, and health statuses in patients admitted to the hospital or presenting to the ED.

Description

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 Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Retrospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Primary Objective: Severe Sepsis; The primary endpoint for this study is defined as the presence of sufficient data for SOWS training and algorithm development to proceed with subsequent validation. To provide sufficient data subsets (severe sepsis EHR encounters) for training and validation of the Sepsis Onset Warning System algorithm. There will not be any interventions administered.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Severe Sepsis	Identify patients having Severe Sepsis with the use of electronic health data	Within 6 hours from presentation to the emergency department

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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
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

Collaborators and Investigators

• Sponsor: Beckman Coulter, Inc.
• Collaborators: Biomedical Advanced Research and Development Authority
• Investigators: Principal Investigator: Elliott Crouser, MD, Ohio State University

Study record dates

Study Major Dates

• Study Start (Actual): February 15, 2021
• Primary Completion (Estimated): March 30, 2025
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: March 23, 2022
• First Submitted That Met QC Criteria: March 23, 2022
• First Posted (Actual): March 31, 2022

Study Record Updates

• Last Update Posted (Actual): June 21, 2024
• Last Update Submitted That Met QC Criteria: June 20, 2024
• Last Verified: June 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Infections; Systemic Inflammatory Response Syndrome; Inflammation; Sepsis; Toxemia
• Other Study ID Numbers: 2.7.2-1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Data elements will be retrospectively extracted from electronic health records from the respective sites and fed into the algorithm for performance testing. These data elements will not be shared with other researchers

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No