Hospital Medical Emergency Team, Hospital Rapid Response Team
Conditions
Keywords
deep learning based early warning score, rapid response team, in-hospital cardiac arrest
Brief 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).
Interventions
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).
Sponsors
Study design
Eligibility
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| In-hospital cardiac arrest | 3 month | Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Alarm coincidence | 3 month | Evaluate the alarm coincidence between DEWS and SPTTS. |
| Total alarm count. | 3 month | Compare the total alarm count between DEWS and SPTTS. |