Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment

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 fluid treatment-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, reductions in in-hospital mortality.

Official Title
-----------------
Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment

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

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


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: All adults above age 18 who are a member of one of the clinical subpopulations studied in this trial are eligible to participate in the study. Exclusion Criteria: Under age 18

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: Fluid treatment-specific algorithm<br>The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight. | Diagnostic Test: Treatment-specific 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 clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.<br>|
| Active Comparator: Standard InSight<br>The control arm will involve patients monitored with the standard, non-treatment specific version of InSight. | Diagnostic Test: InSight<br>* The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.<br>|

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

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| In-hospital SIRS-based mortality | Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay | Through study completion, an average of 8 months | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Dascena, machine learning, fluid administration, clustering algorithm, mortality, diagnostic