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

General Anesthesia Clinical Trial

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

Clinical Trial Summary

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

Clinical Trial 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 Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT05762237
Study type Observational
Source Samsung Medical Center
Contact Heejoon Jeong, MD
Phone 821087282414
Email heejoonjeong@skku.edu
Status Recruiting
Phase
Start date April 1, 2023
Completion date September 30, 2023
View Details
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