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

Completed

• Conditions: General Anesthesia; Intraoperative Hypotension

• Registration Number: NCT05762237
• Lead Sponsor: Samsung Medical Center

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.

Study Timeline

• Study First Submitted: 2023-02-13
• Study First Submitted QC: 2023-03-07
• Study First Posted: 2023-03-09
• Study Start: 2023-04-01
• Status Verified: 2024-05
• Primary Completion: 2024-05-01
• Study Completion: 2024-05-01
• Last Update Submitted: 2025-03-25
• Last Update Posted: 2025-03-30

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 5175

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

Samsung Medical Center; 🇰🇷 Seoul, Korea, Republic of