Sepsis, Septic Shock, Severe Sepsis
Conditions
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).
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 CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
Sponsors
Cabell Huntington Hospital
Dascena
Study design
Allocation
NON_RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| In-hospital mortality | Through study completion, an average of 30 days |
Secondary
| Measure | Time frame |
|---|---|
| Hospital length of stay | Through study completion, an average of 30 days |
Other
| Measure | Time frame |
|---|---|
| Hospital readmission | Through study completion, an average of 30 days |
| ICU length of stay | Through study completion, an average of 30 days |
Countries
United States
Outcome results
None listed