Gram Type Infection-Specific Sepsis Identification Using Machine Learning

Study Overview
=================
Brief Summary
-----------------
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a Gram type infection-specific algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.

Official Title
-----------------
Gram Type Infection-Specific Sepsis Identification Using Machine Learning

Conditions
-----------------
Sepsis, Severe Sepsis, Septic Shock

Intervention / Treatment
-----------------
* Diagnostic Test: InSight


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: All adults above age 18 who are a member of one of the three subpopulations studied in this trial (patients with Gram-positive infection, patients with Gram-negative infection, and patients with mixed Gram-positive and Gram-negative infection) are eligible to participate in the study. Exclusion Criteria: Under age 18 No record of Gram infection

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Triple


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Gram type infection-specific algorithm<br>The experimental arm will involve patients monitored by the Gram type infection-customized version of InSight. | Diagnostic Test: InSight<br>* The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between various Gram-type infections.<br>|
| No Intervention: Standard treatment protocol<br>The control arm will involve patients treated with the regular diagnosis and treatment protocol for gram-type infection, where fluid cultures are run to determine infection type. | |

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in time to antibiotic administration | Change in time period between diagnosis of Gram infection and administration of antibiotics to treat infection | Through study completion, an average of 8 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in administration of unnecessary antibiotics | Changes in amount of secondary antibiotics administered | Through study completion, an average of 8 months | 
| Change in administration of unnecessary antibiotics | Changes in total hours spent on antibiotics | Through study completion, an average of 8 months | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Dascena, Gram infection, antibiotic administration, machine learning, algorithm, diagnostic