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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05762237

Enrollment

5175

Registered

2023-03-09

Start date

2023-04-01

Completion date

2024-05-01

Last updated

2025-03-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

General Anesthesia, Intraoperative Hypotension

Keywords

intraoperative hypotension, noninvasive monitor, deep learning algorithm

Brief summary

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
Deep learning model's prediction ability on intraoperative hypotension event	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.

Countries

South Korea

Outcome results