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UnknownNCT05256264

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

Status
Unknown
Phase
Study type
Observational
Enrollment
90 (estimated)
Sponsor
Taipei Veterans General Hospital, Taiwan · Other Government
Sex
All
Age
20 Years – 80 Years
Healthy volunteers
Accepted

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.

Detailed 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"

Conditions

Interventions

TypeNameDescription
DRUGKetamineKetamine infusion at different dose to observe EEG DSA changes

Timeline

Start date
2022-03-01
Primary completion
2022-12-31
Completion
2022-12-31
First posted
2022-02-25
Last updated
2022-08-17

Locations

1 site across 1 country: Taiwan

Source: ClinicalTrials.gov record NCT05256264. Inclusion in this directory is not an endorsement.