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Predictive algoRithm for EValuation and Intervention in SEpsis

Not Applicable
Completed
Conditions
Severe Sepsis
Septic Shock
Sepsis
Interventions
Other: Severe Sepsis Prediction
Other: Severe Sepsis Detection
Registration Number
NCT03235193
Lead Sponsor
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).

Detailed Description

Not available

Recruitment & Eligibility

Status
COMPLETED
Sex
All
Target Recruitment
2296
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.

Study & Design

Study Type
INTERVENTIONAL
Study Design
FACTORIAL
Arm && Interventions
GroupInterventionDescription
With InSightSevere Sepsis PredictionHealthcare 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.
With InSightSevere Sepsis DetectionHealthcare 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.
Without InsightSevere Sepsis DetectionHealthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.
Primary Outcome Measures
NameTimeMethod
In-hospital mortalityThrough study completion, an average of 30 days
Secondary Outcome Measures
NameTimeMethod
Hospital length of stayThrough study completion, an average of 30 days

Trial Locations

Locations (1)

Cabell Huntington Hospital

🇺🇸

Huntington, West Virginia, United States

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