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Implementation and Evaluations of Sepsis Watch

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: NCT03655626
Recruitment Status : Completed
First Posted : August 31, 2018
Last Update Posted : August 1, 2019
Sponsor:
Collaborators:
Data & Society Research Institute
Duke Clinical Research Institute
Information provided by (Responsible Party):
Duke University

Brief Summary:
The purpose of this study is to study the implementation and impact of an early warning system to detect and treat sepsis in the emergency room. We are observing the implementation of a Sepsis Machine Learning Model on all Adult patients. All data (observations field notes, interview recording & transcripts, and survey responses) will be stored on HIPAA-compliant Duke servers behind the Duke firewall, and requiring password-protected user authentication to access. The risk to patients is minimal. The two risks to interviewed clinical staff we have identified involve loss of work time and anonymity.

Condition or disease Intervention/treatment Phase
Sepsis Severe Sepsis Septic Shock Other: Sepsis Watch Not Applicable

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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 32003 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Treatment
Official Title: Implementation and Evaluations of Previously Developed Novel Early Warning System to Detect and Treat Sepsis
Actual Study Start Date : November 5, 2018
Actual Primary Completion Date : July 5, 2019
Actual Study Completion Date : July 5, 2019

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Sepsis

Arm Intervention/treatment
Experimental: Sepsis Watch on Duke University Hospital ED Adults
Patients older than 18 years old at time of presentation to Duke University Hospital emergency department.
Other: Sepsis Watch
The operational intervention comprises of a sepsis machine learning model, custom dashboard to present risk scores, and a rapid response team to monitor patients at-risk of sepsis and deliver sepsis treatment. Sepsis Watch was developed under operational management. The rapid response team will utilize information presented on the dashboard and follow a protocol that will enable them to support the primary teams of hospitalized patients.




Primary Outcome Measures :
  1. Rate of Centers for Medicare and Medicaid Services (CMS) bundle completion for patients with sepsis [ Time Frame: Within 96 hours of emergency department arrival ]
    Proportion of patients with sepsis that complete Center for Medicare and Medicaid Services treatment bundle


Secondary Outcome Measures :
  1. Mean time from ED arrival to sepsis for patients with sepsis [ Time Frame: Within 96 hours of emergency department arrival ]
    Mean time from to ED arrival to sepsis

  2. Average number of patients who develop sepsis per day and month [ Time Frame: Within 96 hours of emergency department arrival ]
    Number of patients daily who meet sepsis phenotype

  3. Average number of patients who develop sepsis and are not treated per day and month [ Time Frame: Within 96 hours of emergency department arrival ]
    Number of patients daily who meet sepsis phenotype who are not treated for sepsis

  4. Mean ED length of stay for patients with sepsis [ Time Frame: Within 96 hours of emergency department arrival ]
    Emergency department length of stay for patients with sepsis

  5. Mean Hospital length of stay for patients with sepsis [ Time Frame: Within 30 days of emergency department arrival ]
    Hospital length of stay for patients with sepsis

  6. Mean Inpatient mortality for patients with sepsis [ Time Frame: Within 30 days of emergency department arrival ]
    Inpatient mortality for patients with sepsis

  7. Mean ICU requirement rate for patients with sepsis [ Time Frame: Within 30 days of emergency department arrival ]
    Intensive care unit requirement rate for patients with sepsis

  8. Mean time from sepsis onset to blood culture [ Time Frame: Within 96 hours of emergency department arrival ]
    Time of sepsis to blood culture order and collection for patients with sepsis

  9. Mean time from sepsis onset to antibiotics [ Time Frame: Within 96 hours of emergency department arrival ]
    Time of sepsis to antibiotic order and administration for patients with sepsis

  10. Mean time from sepsis onset to IV fluids [ Time Frame: Within 96 hours of emergency department arrival ]
    Time of sepsis to IV fluids order and administration for patients with sepsis

  11. Mean time from sepsis onset to lactate [ Time Frame: Within 96 hours of emergency department arrival ]
    Time of sepsis to lactate collection for patients with sepsis

  12. Mean time from sepsis onset to CMS bundle completion [ Time Frame: Within 96 hours of emergency department arrival ]
    Time of sepsis to CMS bundle completion for patients with sepsis

  13. Rate of lactate complete for patients with sepsis [ Time Frame: Within 96 hours of emergency department arrival ]
    Proportion of lactate drawn within 3 hours and potentially re-drawn within 6 hours of sepsis for patients with sepsis

  14. Number of sepsis diagnosis codes across Duke University Hospital patients per month [ Time Frame: Within 30 days of emergency department arrival ]
    Number of billing diagnosis codes for sepsis


Other Outcome Measures:
  1. Number of antibiotic orders in Duke University Hospital emergency department per month [ Time Frame: Within 96 hours of emergency department arrival ]
    Number of antibiotic orders in Duke University Hospital emergency department per month

  2. Number of antibiotic days in Duke University Hospital emergency department per month [ Time Frame: Within 96 hours of emergency department arrival ]
    Number of antibiotic days

  3. Number of blood culture orders in Duke University Hospital emergency department per month [ Time Frame: Within 96 hours of emergency department arrival ]
    Total blood culture orders

  4. Number of lactate orders in Duke University Hospital emergency department per month [ Time Frame: Within 96 hours of emergency department arrival ]
    Total lactate orders

  5. Number of IV fluid orders in Duke University Hospital emergency department per month [ Time Frame: Within 96 hours of emergency department arrival ]
    Total IV fluid orders

  6. Number of vasopressor orders in Duke University Hospital emergency department per month [ Time Frame: Within 96 hours of emergency department arrival ]
    Total vasopressor orders

  7. Number of vasopressor days in Duke University Hospital emergency department per month [ Time Frame: Within 96 hours of emergency department arrival ]
    Number of vasopressor days



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Arrival to Duke University Hospital emergency department pods A, B, and C, or resuscitation bay

Exclusion Criteria:

  • Under 18 years old at time of emergency department arrival

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): NCT03655626


Locations
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United States, North Carolina
Duke University Hospital
Durham, North Carolina, United States, 27710
Sponsors and Collaborators
Duke University
Data & Society Research Institute
Duke Clinical Research Institute
Investigators
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Principal Investigator: Cara O'Brien, MD Duke University Health System
Study Director: Mark Sendak, MD Duke Institute for Health Innovation
Additional Information:
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Responsible Party: Duke University
ClinicalTrials.gov Identifier: NCT03655626    
Other Study ID Numbers: Pro00093721
First Posted: August 31, 2018    Key Record Dates
Last Update Posted: August 1, 2019
Last Verified: July 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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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 Duke University:
Machine Learning
Sepsis
Implementation Science
Health Information Technology
Additional relevant MeSH terms:
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Sepsis
Toxemia
Infection
Systemic Inflammatory Response Syndrome
Inflammation
Pathologic Processes