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Subpopulation-Specific Sepsis Identification Using Machine Learning

NCT03644940 · Status: WITHDRAWN · Phase: PHASE2 · Type: INTERVENTIONAL

Last updated 2021-09-23

No results posted yet for this study

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 subpopulation-optimized 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, in-hospital SIRS-based mortality. The secondary endpoints will be in-hospital severe sepsis/shock-coded mortality, SIRS-based hospital length of stay, and severe sepsis/shock-coded hospital length of stay.

Conditions

  • Sepsis
  • Severe Sepsis
  • Septic Shock

Interventions

DIAGNOSTIC_TEST

CustomSight

Subpopulation-specific clinical decision support (CDS) system for severe sepsis detection

Sponsors & Collaborators

Principal Investigators

  • Ritankar Das, MSc · Dascena

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
TRIPLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-12-31
Primary Completion
2021-07-31
Completion
2021-07-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03644940 on ClinicalTrials.gov