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Gram Type Infection-Specific Sepsis Identification Using Machine Learning

NCT03734484 · Status: WITHDRAWN · Phase: PHASE2 · Type: INTERVENTIONAL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 Gram type infection-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, time to antibiotic administration. The secondary endpoint will be reduction in the administration of unnecessary antibiotics, which includes reductions in secondary antibiotics and reductions in total time on antibiotics.

Conditions

  • Sepsis
  • Severe Sepsis
  • Septic Shock

Interventions

DIAGNOSTIC_TEST

InSight

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 various Gram-type infections.

Sponsors & Collaborators

  • Dascena

    lead INDUSTRY

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
2022-05-01
Primary Completion
2022-11-30
Completion
2023-03-01

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Entities

Diseases

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 NCT03734484 on ClinicalTrials.gov