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

NCT03752489 · Status: UNKNOWN · Phase: PHASE2 · Type: INTERVENTIONAL · Enrollment: 51645

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 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.

Conditions

  • Sepsis
  • Severe Sepsis
  • Septic Shock

Interventions

DIAGNOSTIC_TEST

Treatment-specific 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 clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.

DIAGNOSTIC_TEST

InSight

The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.

Sponsors & Collaborators

  • Dascena

    lead INDUSTRY

Principal Investigators

  • Qingqing Mao, PhD · Dascena, Inc.

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
TRIPLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-04-01
Primary Completion
2024-03-31
Completion
2024-03-31

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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 NCT03752489 on ClinicalTrials.gov