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Predictive algoRithm for EValuation and Intervention in SEpsis

NCT03235193 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 2296

Last updated 2021-09-21

No results posted yet for this study

Summary

In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).

Conditions

  • Sepsis
  • Septic Shock
  • Severe Sepsis

Interventions

OTHER

Severe Sepsis Prediction

Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.

OTHER

Severe Sepsis Detection

Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.

Sponsors & Collaborators

  • Cabell Huntington Hospital

    collaborator OTHER

  • Dascena

    lead INDUSTRY

Principal Investigators

  • Hoyt Burdick · Cabell Huntington Hospital

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
FACTORIAL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2017-07-01
Primary Completion
2017-08-30
Completion
2017-08-30

Countries

  • United States

Study Locations

More Related Trials

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 NCT03235193 on ClinicalTrials.gov