Deep Learning Based Early Warning Score in Rapid Response Team Activation

NCT04951973 | Unknown

• 1 other identifier: DEWS_2021
• Study Type: observational
• Target: 50,000
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

deep learning based early warning score; rapid response team; in-hospital cardiac arrest

Outcome Measures

Primary Outcomes (1)

• In-hospital cardiac arrest

  Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS.

  3 month

Secondary Outcomes (2)

• Alarm coincidence

  3 month

• Total alarm count.

  3 month

Interventions

Deep Learning Based Early Warning Score (DEWS) DIAGNOSTIC_TEST

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

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients admitted to general ward

You may qualify if:

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

You may not qualify if:

• patients admitted to pediatric ward
• patients in emergency room, intensive care unit, and operating room

Sponsors & Collaborators

• Seoul National University Hospital: lead
• Korea Health Industry Development Institute: collaborator
• VUNO Inc.: collaborator
• Inha University Hospital: collaborator
• Mediplex Sejong Hospital, Incheon: collaborator
• Sejong General Hospital: collaborator
• Dong-A University: collaborator

Related Publications (3)

• 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.

  PMID: 29945914 BACKGROUND

• 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.

  PMID: 32205618 BACKGROUND

• 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.

  PMID: 37670324 DERIVED

Central Study Contacts

Yeon Joo Lee, MD

CONTACT

82-31-787-7082; yjlee1117@snubh.org

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 27, 2021
• First Posted: July 7, 2021
• Study Start: August 1, 2021
• Primary Completion: December 30, 2021
• Study Completion: April 30, 2022
• Last Updated: July 7, 2021

Record last verified: 2021-06

Data Sharing

• IPD Sharing: Will not share