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| Name | Class |
|---|---|
| Korea Health Industry Development Institute | OTHER_GOV |
| VUNO Inc. | INDUSTRY |
| Inha University Hospital | OTHER |
| Mediplex Sejong Hospital, Incheon |
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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).
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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). |
| Measure | Description | Time Frame |
|---|---|---|
| In-hospital cardiac arrest | Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS. | 3 month |
| Measure | Description | Time Frame |
|---|---|---|
| Alarm coincidence | Evaluate the alarm coincidence between DEWS and SPTTS. | 3 month |
| Total alarm count. | Compare the total alarm count between DEWS and SPTTS. |
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Inclusion Criteria:
Exclusion Criteria:
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Patients admitted to general ward
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yeon Joo Lee, MD | Contact | 82-31-787-7082 | yjlee1117@snubh.org |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29945914 | Background | 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. | |
| 32205618 | 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. |
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| Type | Date | Date Unknown |
|---|---|---|
| Release | Jul 25, 2023 | |
| Reset | Mar 7, 2024 |
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| Release Date | Unrelease Date | Unrelease Date Unknown | Reset Date | MCP Release Number |
|---|---|---|---|---|
| Jul 25, 2023 | Mar 7, 2024 |
| UNKNOWN |
| Sejong General Hospital | OTHER |
| Dong-A University | OTHER |
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| 3 month |
| 37670324 | Derived | 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. |