Intraoperative HypotensionNCT05762237
Deep Learning Models for Prediction of Intraoperative Hypotension Using Non-invasive Parameters
An observational study of Intraoperative Hypotension and General Anesthesia, run by Samsung Medical Center at 1 site in South Korea.
Trial summary
The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Intraoperative Hypotension, General Anesthesia
- How many people
- 5,175 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Apr 2023to 1 May 2024
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population included patients who underwent inhaled general anesthesia for non-cardiac surgery between June 2016 and August 2017 at Seoul National University Hospital, Seoul, South Korea.
Who may be able to join
- The patients who are included in the open database, VtialDB.
- The patients who underwent inhaled general anesthesia for non-cardiac surgery.
- The patients who have non-invasive monitoring data including blood pressure, electrocardiography, pulse oximetry, bispectral index, and capnography.
Who cannot join
- The patient with missing data.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prediction of Intraoperative Hypotension Using Non-invasive Monitoring Devices: Development of Deep Learning Model
The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.
Read the detailed description
Intraoperative hypotension is associated with various postoperative complications such as acute kidney injury. Therefore, precise prediction and prompt treatment of intraoperative hypotension are important. However, it is difficult to accurately predict intraoperative hypotension based on the anesthesiologists' experience and intuition. Recently, deep learning algorithms using invasive arterial pressure monitoring showed the good predictive ability of intraoperative hypotension. It can help the clinician's decisions. However, most patients undergoing general surgery are monitored by non-invasive parameters. Therefore, the investigators investigate the prediction model for intraoperative hypotension using non-invasive monitoring.
- Conditions
- Intraoperative Hypotension, General Anesthesia
- Keywords
- intraoperative hypotension, noninvasive monitor, deep learning algorithm
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Group | In the open source database (VitalDB, https://vitaldb.net), the patients who underwent general anesthesia with non-invasive monitoring including blood pressure, electrocardiography, pulse oximetry, bispectral index, capnography, and minimal alveolar concentration of inhalation agent. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Deep learning model's prediction ability on intraoperative hypotension event
Area under the curve the receiver operating characteristic (AUROC) curve for the deep learning model to predict intraoperative hypotension.
Measured: through study completion, an average of 3 hour
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Mar 2023
- Study start1 Apr 2023
- Primary completion1 May 2024
- Study completion1 May 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Intraoperative HypotensionStudies of the same condition, open and under way.
- More trials run by Samsung Medical CenterThe 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
- Retrospective
- Enrolment
- 5,175 participants (actual)
Sponsor and investigators
- Lead sponsor
- Samsung Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Hyun Joo Ahn, Samsung Medical Center
- Investigators
- Hyun Joo Ahn, MD, PhD, Principal investigator, Samsung Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05762237
- Sponsor's protocol ID
- SMC 2022-09-096
- Status last verified
- May 2024
- First submitted
- 13 Feb 2023
- Last updated
- 30 Mar 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Lee S, Lee HC, Chu YS, Song SW, Ahn GJ, Lee H, Yang S, Koh SB. Deep learning models for the prediction of intraoperative hypotension. Br J Anaesth. 2021 Apr;126(4):808-817. doi: 10.1016/j.bja.2020.12.035. Epub 2021 Feb 6.
PubMed 33558051Lee HC, Jung CW. Vital Recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices. Sci Rep. 2018 Jan 24;8(1):1527. doi: 10.1038/s41598-018-20062-4.
PubMed 29367620
Study at a glance
From the registry record, last updated by the sponsor on 30 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Intraoperative Hypotension Using Non-invasive Monitoring Devices: Development of Deep Learning Model
- ClinicalTrials.gov ID
- NCT05762237
- Acronym / sponsor ID
- SMC 2022-09-096
- Conditions
- Intraoperative Hypotension, General Anesthesia
- Keywords
- intraoperative hypotension, noninvasive monitor, deep learning algorithm
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 5,175 participants (actual)
- Lead sponsor
- Samsung Medical CenterUniversity, hospital or other
- Study start
- 1 Apr 2023 (actual)
- Primary completion
- 1 May 2024 (actual)
- Study completion
- 1 May 2024 (actual)
- First posted
- 9 Mar 2023 (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 investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.
Detailed description
Intraoperative hypotension is associated with various postoperative complications such as acute kidney injury. Therefore, precise prediction and prompt treatment of intraoperative hypotension are important. However, it is difficult to accurately predict intraoperative hypotension based on the anesthesiologists' experience and intuition. Recently, deep learning algorithms using invasive arterial pressure monitoring showed the good predictive ability of intraoperative hypotension. It can help the clinician's decisions. However, most patients undergoing general surgery are monitored by non-invasive parameters. Therefore, the investigators investigate the prediction model for intraoperative hypotension using non-invasive monitoring.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Group | – | –In the open source database (VitalDB, https://vitaldb.net), the patients who underwent general anesthesia with non-invasive monitoring including blood pressure, electrocardiography, pulse oximetry, bispectral index, capnography, and minimal alveolar concentration of inhalation agent. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population included patients who underwent inhaled general anesthesia for non-cardiac surgery between June 2016 and August 2017 at Seoul National University Hospital, Seoul, South Korea.
Key inclusion criteria (3 listed)
- The patients who are included in the open database, VtialDB.
- The patients who underwent inhaled general anesthesia for non-cardiac surgery.
- The patients who have non-invasive monitoring data including blood pressure, electrocardiography, pulse oximetry, bispectral index, and capnography.
Key exclusion criteria (1 listed)
- The patient with missing data.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Deep learning model's prediction ability on intraoperative hypotension event
Time frame: through study completion, an average of 3 hour
Area under the curve the receiver operating characteristic (AUROC) curve for the deep learning model to predict intraoperative hypotension.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Mar 2025.
- Background: PubMed 33558051Lee S, Lee HC, Chu YS, Song SW, Ahn GJ, Lee H, Yang S, Koh SB. Deep learning models for the prediction of intraoperative hypotension. Br J Anaesth. 2021 Apr;126(4):808-817. doi: 10.1016/j.bja.2020.12.035. Epub 2021 Feb 6.
- Background: PubMed 29367620Lee HC, Jung CW. Vital Recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices. Sci Rep. 2018 Jan 24;8(1):1527. doi: 10.1038/s41598-018-20062-4.
- Individual participant dataSharing planned: Undecided
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Intra-operative Hypotension
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.
Hyun Joo Ahn, MD, PhD principal investigator
Samsung Medical Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Intraoperative Hypotension and General Anesthesia, run by Samsung Medical Center at 1 site in South Korea.
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 3 inclusion and 1 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: finished, as of the sponsor's last update on 30 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Deep learning model's prediction ability on intraoperative hypotension event (time frame: through study completion, an average of 3 hour). 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 NCT05762237, which also lists 2 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 30 Mar 2025.