NCT05762237

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
• Phase: —
• Study type: Observational
• Enrollment: 5,175 (actual)
• Sponsor: Samsung Medical Center · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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.

Conditions

• Intraoperative Hypotension
• General Anesthesia

Timeline

• Start date: 2023-04-01
• Primary completion: 2024-05-01
• Completion: 2024-05-01
• First posted: 2023-03-09
• Last updated: 2025-03-30

Locations

1 site across 1 country: South Korea