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Completed

NCT Number: NCT05762237

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

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

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Key information

Conditions

Intraoperative Hypotension General Anesthesia

Sex eligibility

All sexes

Study type

Observational

Primary location

Samsung Medical Center

Seoul, 06351, South Korea

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. 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.

Sponsors and collaborators

Lead sponsor

Samsung Medical Center

Other

Registry information

Official study title

Prediction of Intraoperative Hypotension Using Non-invasive Monitoring Devices: Development of Deep Learning Model

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Mar 9, 2023
Registry last updated
Mar 30, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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