Machine-learning Algorithm to Differentiate Intraoperative Ketamine Dosing Based on Electroencephalographic Density Spectrum Array Analysis

Ketamine Clinical Trial

Official title: Machine-learning Algorithm to Differentiate Intraoperative Ketamine Dosing Based on Electroencephalographic Density Spectrum Array Analysis

Status Recruiting

Clinical Trial Summary

Ketamine is widely used in the setting of multimodal general anesthesia, and the Electroencephalographic density spectral array (DSA) monitoring has been implemented in the practice of anesthesia. The purpose of this study is to investigate the dose-response EEG changes in patients during the perioperative period when ketamine is used. With the application of machine-learning algorithm, we aim to interpret the ketamine dosing precisely and accurately, based on the DSA obtained.

Clinical Trial Description

In this single-blinded randomized controlled trial, we enroll 90 patients undergoing major spine surgery, and candidates are divided into 3 groups: A (control group using sevoflurane only), B (sevoflurane + 3µg/kg ketamine infusion), C (sevoflurane + 6µg/kg/min ketamine infusion). Throughout the perioperative period, under standardized perioperative care, each patient will be monitored with EEG DSA. The data obtained will be utilized for machine-learning in the development of a algorithm to interpret the precise dosing of ketamine in respect of its effect on the EEG DSA. This study is also anticipated to boost the development of dose-response algorithm of other agents, promoting the advancement of the concept of "multimodal general anesthesia" ;

Related Conditions & MeSH terms

• Ketamine

• NCT number: NCT05256264
• Study type: Observational
• Source: Taipei Veterans General Hospital, Taiwan
• Contact: Hui Zen Hee
• Phone: +886928507736
• Email: huizen.hee@gmail.com
• Status: Recruiting
• Start date: March 1, 2022
• Completion date: December 31, 2022