Deep Learning Based Early Warning Score in Rapid Response Team Activation
NCT ID: NCT04951973
Last Updated: 2021-07-07
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
50000 participants
OBSERVATIONAL
2021-08-01
2022-04-30
Brief Summary
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Detailed Description
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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).
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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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).
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* patients in emergency room, intensive care unit, and operating room
18 Years
ALL
No
Sponsors
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Korea Health Industry Development Institute
OTHER_GOV
VUNO Inc.
INDUSTRY
Inha University Hospital
OTHER
Mediplex Sejong Hospital, Incheon
UNKNOWN
Sejong General Hospital
OTHER
Dong-A University
OTHER
Seoul National University Hospital
OTHER
Responsible Party
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Central Contacts
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References
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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.
Cho KJ, Kim JS, Lee DH, Lee SM, Song MJ, Lim SY, Cho YJ, Jo YH, Shin Y, Lee YJ. Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards. Crit Care. 2023 Sep 5;27(1):346. doi: 10.1186/s13054-023-04609-0.
Other Identifiers
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DEWS_2021
Identifier Type: -
Identifier Source: org_study_id
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