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

NCT05762237 | Completed DMC

• 1 other identifier: SMC 2022-09-096
• Study Type: observational
• Target: 5,175
• Locations: 1 country
• Sites: 1

Brief Summary

The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.

Conditions

Intraoperative Hypotension; General Anesthesia

Keywords

intraoperative hypotension; noninvasive monitor; deep learning algorithm

Outcome Measures

Primary Outcomes (1)

• 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.

  through study completion, an average of 3 hour

Study Arms (1)

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.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• The patient with missing data.

Sponsors & Collaborators

• Samsung Medical Center: lead

Study Sites (1)

Samsung Medical Center

Seoul, Seoul, 06351, South Korea

Location

Related Publications (2)

• 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.

  PMID: 33558051 BACKGROUND

• 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.

  PMID: 29367620 BACKGROUND

Study Officials

• Hyun Joo Ahn, MD, PhD

  Samsung Medical Center

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: February 13, 2023
• First Posted: March 9, 2023
• Study Start: April 1, 2023
• Primary Completion: May 1, 2024
• Study Completion: May 1, 2024
• Last Updated: March 30, 2025

Record last verified: 2024-05

Locations

KR (1)