Sepsis, Septic Shock, Severe Sepsis
Conditions
Keywords
InSight, Dascena
Brief summary
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a large, diverse patient cohort. In this prospective study, the ability of InSight to predict severe sepsis patients is investigated. Specifically, InSight is compared with a well established severe sepsis detector in the UCSF electronic health record (EHR).
Interventions
Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
Upon receiving information from the severe sepsis detector in the UCSF electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
Sponsors
University of California, San Francisco
Dascena
Study design
Allocation
RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* All adult patients admitted to the participating units will be eligible.
Exclusion criteria
* All patients younger than 18 years of age will be excluded.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Hospital length of stay | Through study completion, an average of 45 days |
Secondary
| Measure | Time frame |
|---|---|
| In-hospital mortality | Through study completion, an average of 45 days |
Other
| Measure | Time frame |
|---|---|
| ICU length of stay | Through study completion, an average of 45 days |
Countries
United States
Outcome results
None listed