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Deep Learning Based Early Warning Score in Rapid Response Team Activation

NCT04951973 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 50000

Last updated 2021-07-07

No results posted yet for this study

Summary

The objective of this study is to evaluate the safety and clinical usefulness of the Deep learning based Early Warning Score (DEWS).

Conditions

  • Hospital Rapid Response Team
  • Hospital Medical Emergency Team

Interventions

DIAGNOSTIC_TEST

Deep Learning Based Early Warning Score (DEWS)

DEWS use 4 vital signs (systolic blood pressure, HR, respiratory rate, and body temperature) to predict in-hospital cardiac arrest. Deep-learning approach facilitates learning the relationship between the vital signs and cardiac arrest to achieve the high sensitivity and low false-alarm rate of the track-and-trigger system (TTS).

Sponsors & Collaborators

  • Korea Health Industry Development Institute

    collaborator OTHER_GOV

  • VUNO Inc.

    collaborator INDUSTRY

  • Inha University Hospital

    collaborator OTHER

  • Mediplex Sejong Hospital, Incheon

    collaborator UNKNOWN

  • Sejong General Hospital

    collaborator OTHER

  • Dong-A University

    collaborator OTHER

  • Seoul National University Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-08-01
Primary Completion
2021-12-30
Completion
2022-04-30

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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 NCT04951973 on ClinicalTrials.gov