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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 50,000 (estimated)
• Sponsor: Seoul National University Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: —

Summary

The objective of this study is to evaluate the safety and clinical usefulness of the 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).

Conditions

• Hospital Rapid Response Team
• Hospital Medical Emergency Team

Interventions

Type	Name	Description
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).

Timeline

• Start date: 2021-08-01
• Primary completion: 2021-12-30
• Completion: 2022-04-30
• First posted: 2021-07-07
• Last updated: 2021-07-07