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

Dascena

Status and phase: Unknown; Phase 2

Conditions: Severe Sepsis; Septic Shock; Sepsis

Treatments: Diagnostic Test: Treatment-specific InSight; Diagnostic Test: InSight

Study type: Interventional

Funder types: Industry

Identifiers

NCT03752489

19-426246

Details and patient eligibility

About

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.

Enrollment: 51,645 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Triple Blind

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51,645 participants in 2 patient groups

Fluid treatment-specific algorithm

Experimental group

Description:

The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight.

Treatment:

Diagnostic Test: Treatment-specific InSight

Standard InSight

Active Comparator group

Description:

The control arm will involve patients monitored with the standard, non-treatment specific version of InSight.

Treatment:

Diagnostic Test: InSight