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Deep Learning Based Early Warning Score in Rapid Response Team Activation

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04951973
Recruitment Status : Unknown
Verified June 2021 by Seoul National University Hospital.
Recruitment status was:  Not yet recruiting
First Posted : July 7, 2021
Last Update Posted : July 7, 2021
Sponsor:
Collaborators:
Korea Health Industry Development Institute
VUNO
Inha University Hospital
Mediplex Sejong Hospital, Incheon
Sejong Hospital, Bucheon
Dong-A University
Information provided by (Responsible Party):
Seoul National University Hospital

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

Condition or disease Intervention/treatment
Hospital Rapid Response Team Hospital Medical Emergency Team Diagnostic Test: Deep Learning Based Early Warning Score (DEWS)

Detailed Description:

SPTTS is the representative trigger tracking system. In addition to the conventional SPTTS, DEWS will be calculated at each time point by the previously developed algorithm. SPTTS and DEWS will be shown simulataneously on the screening board. The rapid response team performs the rescue activity as before, using both SPTTS and DEWS simultaneously.

The alarm threshold setting of DEWS will be changed to 70 points, 75 points, and 80 points every month.

The primary and secondary outcomes will be evaluated to compare SPTTS and DEWS (based on each threshold).

Study Design
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Study Type : Observational [Patient Registry]
Estimated Enrollment : 50000 participants
Observational Model: Cohort
Time Perspective: Prospective
Target Follow-Up Duration: 3 Months
Official Title: Comparison of Deep Learning Based Early Warning Score and Conventional Screening System in Rapid Response Team Activation in General Ward Patients
Estimated Study Start Date : August 1, 2021
Estimated Primary Completion Date : December 30, 2021
Estimated Study Completion Date : April 30, 2022
Groups and Cohorts
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Intervention Details:
  • 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).

Outcome Measures
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Primary Outcome Measures :
  1. In-hospital cardiac arrest [ Time Frame: 3 month ]
    Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS.


Secondary Outcome Measures :
  1. Alarm coincidence [ Time Frame: 3 month ]
    Evaluate the alarm coincidence between DEWS and SPTTS.

  2. Total alarm count. [ Time Frame: 3 month ]
    Compare the total alarm count between DEWS and SPTTS.


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
Patients admitted to general ward
Criteria

Inclusion Criteria:

  • Patients admitted to general ward and monitored by in-hospital rapid response system

Exclusion Criteria:

  • patients admitted to pediatric ward
  • patients in emergency room, intensive care unit, and operating room
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04951973


Contacts
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Contact: Yeon Joo Lee, MD 82-31-787-7082 yjlee1117@snubh.org

Sponsors and Collaborators
Seoul National University Hospital
Korea Health Industry Development Institute
VUNO
Inha University Hospital
Mediplex Sejong Hospital, Incheon
Sejong Hospital, Bucheon
Dong-A University
More Information
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
Publications:

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Responsible Party: Seoul National University Hospital
ClinicalTrials.gov Identifier: NCT04951973    
Other Study ID Numbers: DEWS_2021
First Posted: July 7, 2021    Key Record Dates
Last Update Posted: July 7, 2021
Last Verified: June 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Seoul National University Hospital:
deep learning based early warning score
rapid response team
in-hospital cardiac arrest
Additional relevant MeSH terms:
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Emergencies
Disease Attributes
Pathologic Processes