Predictive algoRithm for EValuation and Intervention in SEpsis
Not Applicable
Completed
- Conditions
- Severe SepsisSeptic ShockSepsis
- Interventions
- Other: Severe Sepsis PredictionOther: 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
Group Intervention Description With InSight Severe Sepsis Prediction 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. With InSight Severe Sepsis Detection 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. Without Insight Severe Sepsis Detection Healthcare 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
Name Time Method In-hospital mortality Through study completion, an average of 30 days
- Secondary Outcome Measures
Name Time Method Hospital length of stay Through study completion, an average of 30 days
Trial Locations
- Locations (1)
Cabell Huntington Hospital
🇺🇸Huntington, West Virginia, United States