Deep Learning Models for Prediction of Intraoperative Hypotension Using Non-invasive Parameters

March 25, 2025 updated by: Hyun Joo Ahn, Samsung Medical Center

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.

Study Overview

Status

Completed

Conditions

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 Type

Observational

Enrollment (Actual)

5175

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

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.

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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 the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
through study completion, an average of 3 hour

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Samsung Medical Center

Investigators

  • Principal Investigator: Hyun Joo Ahn, MD, PhD, Samsung Medical Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 1, 2023

Primary Completion (Actual)

May 1, 2024

Study Completion (Actual)

May 1, 2024

Study Registration Dates

First Submitted

February 13, 2023

First Submitted That Met QC Criteria

March 7, 2023

First Posted (Actual)

March 9, 2023

Study Record Updates

Last Update Posted (Actual)

March 30, 2025

Last Update Submitted That Met QC Criteria

March 25, 2025

Last Verified

May 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SMC 2022-09-096

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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