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Predictive algoRithm for EValuation and Intervention in SEpsis (PREVISE)

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ClinicalTrials.gov Identifier: NCT03235193
Recruitment Status : Recruiting
First Posted : August 1, 2017
Last Update Posted : August 3, 2017
Sponsor:
Collaborator:
Cabell Huntington Hospital
Information provided by (Responsible Party):
Dascena

Brief Summary:
In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).

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

Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 1241 participants
Allocation: Non-Randomized
Intervention Model: Factorial Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Prediction of Severe Sepsis Using a Machine Learning Algorithm
Actual Study Start Date : August 1, 2017
Estimated Primary Completion Date : October 2017
Estimated Study Completion Date : October 2017

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
Experimental: With InSight
Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the CHH electronic health record.
Other: Severe Sepsis Prediction
Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.

Other: Severe Sepsis Detection
Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.

Active Comparator: Without Insight
Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.
Other: Severe Sepsis Detection
Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.




Primary Outcome Measures :
  1. In-hospital mortality [ Time Frame: Through study completion, an average of 30 days ]

Secondary Outcome Measures :
  1. Hospital length of stay [ Time Frame: Through study completion, an average of 30 days ]

Other Outcome Measures:
  1. Hospital readmission [ Time Frame: Through study completion, an average of 30 days ]
  2. ICU length of stay [ Time Frame: Through study completion, an average of 30 days ]


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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:

  • All adult patients visiting the emergency department, or admitted to the participating intensive care unit (ICU) wards of Cabell Huntington Hospital will be eligible.

Exclusion Criteria:

  • All patients younger than 18 years of age will be excluded.

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): NCT03235193


Contacts
Contact: Ritankar Das 8722285332 ritankar@dascena.com

Locations
United States, West Virginia
Cabell Huntington Hospital Recruiting
Huntington, West Virginia, United States, 25701
Contact: Hoyt Burdick         
Sponsors and Collaborators
Dascena
Cabell Huntington Hospital
Investigators
Principal Investigator: Hoyt Burdick Cabell Huntington Hospital