Hospital Rapid Response TeamNCT04951973
Deep Learning Based Early Warning Score in Rapid Response Team Activation
An observational study of Hospital Rapid Response Team and Hospital Medical Emergency Team, run by Seoul National University Hospital.
Trial summary
The objective of this study is to evaluate the safety and clinical usefulness of the Deep learning based Early Warning Score (DEWS). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hospital Rapid Response Team, Hospital Medical Emergency Team
- What is being tested?
- Deep Learning Based Early Warning Score (DEWS)
- How many people
- 50,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2021to 30 Apr 2022
- How long will I take part?
- 3 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: Patients admitted to general ward
Who may be able to join
- Patients admitted to general ward and monitored by in-hospital rapid response system
Who cannot join
- patients admitted to pediatric ward
- patients in emergency room, intensive care unit, and operating room
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Comparison of Deep Learning Based Early Warning Score and Conventional Screening System in Rapid Response Team Activation in General Ward Patients
The objective of this study is to evaluate the safety and clinical usefulness of the Deep learning based Early Warning Score (DEWS).
Read the 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).
- Being tested
- Deep Learning Based Early Warning Score (DEWS)
- Keywords
- deep learning based early warning score, rapid response team, in-hospital cardiac arrest
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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).
What is being measured
The primary outcome is the main question the study is designed to answer.
In-hospital cardiac arrest
Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS.
Measured: 3 month
Secondary outcomes (2)
Alarm coincidence
Evaluate the alarm coincidence between DEWS and SPTTS.
Measured: 3 month
Total alarm count.
Compare the total alarm count between DEWS and SPTTS.
Measured: 3 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jul 2021
- Study start1 Aug 2021 (estimated)
- Primary completion30 Dec 2021 (estimated)
- Study completion30 Apr 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Hospital Rapid Response TeamStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50,000 participants (estimated)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Korea Health Industry Development Institute, VUNO Inc., Inha University Hospital, Mediplex Sejong Hospital, Incheon, Sejong General Hospital, Dong-A University
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04951973
- Sponsor's protocol ID
- DEWS_2021
- Status last verified
- Jun 2021
- First submitted
- 27 Jun 2021
- Last updated
- 7 Jul 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 29945914Cho 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.
PubMed 32205618Cho 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.
PubMed 37670324
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of Deep Learning Based Early Warning Score and Conventional Screening System in Rapid Response Team Activation in General Ward Patients
- ClinicalTrials.gov ID
- NCT04951973
- Acronym / sponsor ID
- DEWS_2021
- Keywords
- deep learning based early warning score, rapid response team, in-hospital cardiac arrest
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 50,000 participants (estimated)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Collaborators
- Korea Health Industry Development Institute, VUNO Inc., Inha University Hospital, Mediplex Sejong Hospital, Incheon, Sejong General Hospital, Dong-A University
- Study start
- 1 Aug 2021 (estimated)
- Primary completion
- 30 Dec 2021 (estimated)
- Study completion
- 30 Apr 2022 (estimated)
- First posted
- 7 Jul 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Months
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients admitted to general ward
Key inclusion criteria (1 listed)
- Patients admitted to general ward and monitored by in-hospital rapid response system
Key exclusion criteria (2 listed)
- patients admitted to pediatric ward
- patients in emergency room, intensive care unit, and operating room
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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). | Diagnostic test | – |
Primary outcome
In-hospital cardiac arrest
Time frame: 3 month
Compare the predictability of in-hospital cardiac arrest between DEWS and SPTTS.
2 secondary outcomes
Alarm coincidence
Time frame: 3 month
Total alarm count.
Time frame: 3 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2021.
- Background: PubMed 29945914Kwon 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.
- Background: PubMed 32205618Cho 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.
- Derived from this study: PubMed 37670324Cho 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
An observational study of Hospital Rapid Response Team and Hospital Medical Emergency Team, run by Seoul National University Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yeon Joo Lee, MD (82-31-787-7082, yjlee1117@snubh.org), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
In-hospital cardiac arrest (time frame: 3 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04951973, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jul 2021.