Effect of a Sepsis Prediction Algorithm on Clinical Outcomes
- Conditions
- Severe Sepsis
- Interventions
- Diagnostic Test: InSight
- Registration Number
- NCT03960203
- Lead Sponsor
- Dascena
- Brief Summary
In this clinical outcomes analysis, the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay, and 30-day readmission was evaluated.
- Detailed Description
Materials and Methods: Clinical outcomes evaluation performed on a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals. Mortality, hospital length of stay, and 30-day readmission analysis performed for 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 75147
- All patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities were automatically included for clinical outcomes analysis
- Patients under the age of 18
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Comparator InSight The comparator arm will involve patients monitored by InSight.
- Primary Outcome Measures
Name Time Method In-hospital mortality 1 year Rate of in-hospital mortality based on SIRS criteria
- Secondary Outcome Measures
Name Time Method 30-day readmissions 1 year Rate of patient readmissions within 30 days
Hospital length of stay 1 year Duration of hospital length of stay in days based on SIRS criteria