The objective of this study is to evaluate the safety and clinical usefulness of the Deep learning based Early Warning Score (DEWS).
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).
Study Type
OBSERVATIONAL
Enrollment
50,000
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).
In-hospital cardiac arrest
Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS.
Time frame: 3 month
Alarm coincidence
Evaluate the alarm coincidence between DEWS and SPTTS.
Time frame: 3 month
Total alarm count.
Compare the total alarm count between DEWS and SPTTS.
Time frame: 3 month
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