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RCT of Sepsis Machine Learning Algorithm

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03882476
Recruitment Status : Unknown
Verified March 2019 by Dascena.
Recruitment status was:  Not yet recruiting
First Posted : March 20, 2019
Last Update Posted : March 25, 2019
Sponsor:
Collaborator:
University of California, San Francisco
Information provided by (Responsible Party):
Dascena

Brief Summary:
The focus of this study will be to conduct a prospective, multi-center randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a machine-learning algorithm will be applied to EHR data for the detection of sepsis. For patients determined to have a high risk of sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, in-hospital SIRS-based mortality.

Condition or disease Intervention/treatment Phase
Sepsis Severe Sepsis Septic Shock Diagnostic Test: InSight Phase 2

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 51645 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Triple (Participant, Care Provider, Investigator)
Primary Purpose: Diagnostic
Official Title: Randomized Controlled Trial of a Machine Learning Algorithm for Early Sepsis Detection
Estimated Study Start Date : January 1, 2020
Estimated Primary Completion Date : February 28, 2021
Estimated Study Completion Date : February 28, 2021

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Sepsis

Arm Intervention/treatment
Experimental: Experimental
The experimental arm will involve patients monitored by InSight.
Diagnostic Test: InSight
Clinical decision support (CDS) system for sepsis detection

No Intervention: Control
The control arm will have no intervention and will involve patients with the usual standard of care.



Primary Outcome Measures :
  1. In-hospital SIRS-based mortality [ Time Frame: Through study completion, an average of eight months ]
    Rate of mortality attributed to patients meeting two or more SIRS criteria at some point during their stay



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

Inclusion Criteria:

  • During the study period, all patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities will automatically be enrolled in the trial, until the enrollment target for the study is met

Exclusion Criteria:

  • Patients under the age of 18

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03882476


Contacts
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Contact: Ritankar Das, MSc 872-228-5332 ritankar@dascena.com
Contact: Chris Barton, MD 415-206-5762 Chris.Barton@ucsf.edu

Sponsors and Collaborators
Dascena
University of California, San Francisco
Investigators
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Principal Investigator: Ritankar Das, MSc Dascena
Publications:
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Responsible Party: Dascena
ClinicalTrials.gov Identifier: NCT03882476    
Other Study ID Numbers: 19-494662
First Posted: March 20, 2019    Key Record Dates
Last Update Posted: March 25, 2019
Last Verified: March 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Dascena:
Dascena
patient mortality
machine learning
algorithm
diagnostic
Additional relevant MeSH terms:
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Sepsis
Toxemia
Infection
Systemic Inflammatory Response Syndrome
Inflammation
Pathologic Processes