Severe Sepsis
Conditions
Keywords
Dascena, Patient mortality, Length of stay, Readmissions, Algorithm, Diagnostic, Clinical outcomes
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.
Interventions
DIAGNOSTIC_TESTInSight
Clinical decision support (CDS) system for severe sepsis detection and prediction
Sponsors
Dascena
Study design
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* 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
Exclusion criteria
* Patients under the age of 18
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| In-hospital mortality | 1 year | Rate of in-hospital mortality based on SIRS criteria |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Hospital length of stay | 1 year | Duration of hospital length of stay in days based on SIRS criteria |
| 30-day readmissions | 1 year | Rate of patient readmissions within 30 days |
Outcome results
None listed