Deep Learning Models for Prediction of Intraoperative Hypotension Using Non-invasive Parameters
Prediction of Intraoperative Hypotension Using Non-invasive Monitoring Devices: Development of Deep Learning Model
Study Overview
Status
Status
Conditions
Conditions
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Heejoon Jeong, MD
- Phone Number: 821087282414
- Email: heejoonjeong@skku.edu
Study Locations
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Seoul, Korea, Republic of, 06351
- Samsung Medical Center
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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.
Exclusion Criteria:
- The patient with missing data.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
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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.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Deep learning model's prediction ability on intraoperative hypotension event
Time Frame: through study completion, an average of 3 hour
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Area under the curve the receiver operating characteristic (AUROC) curve for the deep learning model to predict intraoperative hypotension.
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through study completion, an average of 3 hour
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Hyun Joo Ahn, MD, PhD, Samsung Medical Center
Publications and helpful links
General Publications
- Lee 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.
- 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.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- SMC 2022-09-096
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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