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

Phase 2

• Conditions: Septic Shock; Sepsis; Severe Sepsis

• 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

Study Timeline

• Study First Submitted: 2018-11-21
• Study First Submitted QC: 2018-11-21
• Study First Posted: 2018-11-26
• Status Verified: 2021-09
• Last Update Submitted: 2021-09-17
• Last Update Posted: 2021-09-23
• Study Start: 2022-04-01
• Primary Completion: 2024-03-31
• Study Completion: 2024-03-31

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

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