- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04951973
Deep Learning Based Early Warning Score in Rapid Response Team Activation
Comparison of Deep Learning Based Early Warning Score and Conventional Screening System in Rapid Response Team Activation in General Ward Patients
Study Overview
Status
Intervention / Treatment
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 Type
Enrollment (Anticipated)
Contacts and Locations
Study Contact
- Name: Yeon Joo Lee, MD
- Phone Number: 82-31-787-7082
- Email: yjlee1117@snubh.org
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
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
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
In-hospital cardiac arrest
Time Frame: 3 month
|
Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS.
|
3 month
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Alarm coincidence
Time Frame: 3 month
|
Evaluate the alarm coincidence between DEWS and SPTTS.
|
3 month
|
Total alarm count.
Time Frame: 3 month
|
Compare the total alarm count between DEWS and SPTTS.
|
3 month
|
Collaborators and Investigators
Publications and helpful links
General Publications
- Kwon JM, Lee Y, Lee Y, Lee S, Park J. An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest. J Am Heart Assoc. 2018 Jun 26;7(13):e008678. doi: 10.1161/JAHA.118.008678.
- Cho KJ, Kwon O, Kwon JM, Lee Y, Park H, Jeon KH, Kim KH, Park J, Oh BH. Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System. Crit Care Med. 2020 Apr;48(4):e285-e289. doi: 10.1097/CCM.0000000000004236.
Study record dates
Study Major Dates
Study Start (ANTICIPATED)
Primary Completion (ANTICIPATED)
Study Completion (ANTICIPATED)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (ACTUAL)
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- DEWS_2021
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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