Sepsis Clinical Decision Support [CDS] Master Enrollment Study Protocol

Sepsis Onset Warning System [SOWS] Master Enrollment Study Protocol

Status

Enrolling by invitation

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05304728

Enrollment

40000

Registered

2022-03-31

Start date

2021-02-15

Completion date

2026-12-31

Last updated

2024-06-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Severe Sepsis, Severe Sepsis Without Septic Shock

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 89 Years

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).

Design outcomes

Primary

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

Secondary

Measure	Time frame	Description
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

Countries

United States

Outcome results