Sepsis Clinical Decision Support [CDS] Master Enrollment Study Protocol

N/AEnrolling By InvitationNCT05304728

Beckman Coulter, Inc.40,000 enrolled

Overview

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.

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

40,000

Conditions

Severe Sepsis Severe Sepsis Without Septic Shock

Eligibility

Sex: ALLMin age: 18 YearsMax age: 89 Years

Medical Language ↔ Plain English

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).

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

Outcomes

Primary Outcomes

Severe Sepsis

Identify patients having Severe Sepsis with the use of electronic health data

Time frame: Within 6 hours from presentation to the emergency department

Secondary Outcomes

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

Time frame: 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

Time frame: 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

Time frame: Within 6hours from presentation to the emergency department

Data from ClinicalTrials.gov

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