Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
Phase 2
- Conditions
- Septic ShockSepsisSevere Sepsis
- Interventions
- Diagnostic Test: InSightDiagnostic Test: Treatment-specific InSight
- Registration Number
- NCT03752489
- Lead Sponsor
- Dascena
- 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 51645
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
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Standard InSight InSight The control arm will involve patients monitored with the standard, non-treatment specific version of InSight. Fluid treatment-specific algorithm Treatment-specific InSight The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight.
- Primary Outcome Measures
Name Time Method In-hospital SIRS-based mortality Through study completion, an average of 8 months Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay
- Secondary Outcome Measures
Name Time Method