Last reviewed · How we verify

NCT04951973

Deep Learning Based Early Warning Score in Rapid Response Team Activation

Status unknown Last updated 7 July 2021
What this trial tests

trial testing Deep Learning Based Early Warning Score (DEWS) in Hospital Rapid Response Team in 50,000 participants. Status unknown.

Timeline
1 August 2021
Primary endpoint
30 December 2021
30 April 2022

Quick facts

Lead sponsorSeoul National University Hospital
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment50,000
Start date1 August 2021
Primary completion30 December 2021
Estimated completion30 April 2022

Drugs / interventions tested

Conditions studied

Sponsor

Seoul National University Hospital

Who can join

18 and older, any sex, with Hospital Rapid Response Team or Hospital Medical Emergency Team. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

  1. 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.
    Cho KJ, Kim JS, Lee DH, Lee SM, et al · · 2023 · cited 20× · PMID 37670324 · DOI 10.1186/s13054-023-04609-0

Verify or expand the search:

Other recruiting trials for Hospital Rapid Response Team

Currently open trials in the same condition.

Other Seoul National University Hospital trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT04951973.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing