Trials / Completed
CompletedNCT03960203
Effect of a Sepsis Prediction Algorithm on Clinical Outcomes
Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission
- Status
- Completed
- Phase
- N/A
- Study type
- Interventional
- Enrollment
- 75,147 (actual)
- Sponsor
- Dascena · Industry
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Accepted
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.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| DIAGNOSTIC_TEST | InSight | Clinical decision support (CDS) system for severe sepsis detection and prediction |
Timeline
- Start date
- 2017-01-01
- Primary completion
- 2018-06-01
- Completion
- 2018-06-01
- First posted
- 2019-05-22
- Last updated
- 2019-05-24
Source: ClinicalTrials.gov record NCT03960203. Inclusion in this directory is not an endorsement.