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

Study Overview
=================
Brief 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

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

Conditions
-----------------
Electroencephalographic Density Spectrum Array Analysis, Density Spectrum Array, Ketamine, Multimodal General Anesthesia

Intervention / Treatment
-----------------
* Drug: Ketamine


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Patients undergoing elective spine surgery Aged 20-80 years old Exclusion Criteria: Pregnancy Presence of major brain disorders: stroke, epilepsy, Parkinson's disease, etc. Any of the following major organ disorders: Chronic pulmonary disease (Clinically diagnosed severe chronic obstructive pulmonary disease or FEV1/FVC <70% or FEV1 <50%) Heart failure (NYHA III or IV) Chronic renal failure (eGFR<60ml/min/1.73m2) Ongoing sepsis or infection Ongoing of history of CNS-acting medications/substances use (e.g.: sedatives, hypnotics, stimulants, etc.)

Ages Eligible for Study
-----------------
Minimum Age: 20 Years
Maximum Age: 80 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Control group<br>Sevoflurane | |
| Low dose ketamine group<br>Sevoflurane + 3µg/kg/min ketamine infusion | Drug: Ketamine<br>* Ketamine infusion at different dose to observe EEG DSA changes<br>* Other names: Ketamine Hydrochloride;|
| High dose ketamine group<br>Sevoflurane + 6µg/kg/min ketamine infusion | Drug: Ketamine<br>* Ketamine infusion at different dose to observe EEG DSA changes<br>* Other names: Ketamine Hydrochloride;|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Ketamine dosing based on EEG DSA | The DSA is obtained from raw EEG by fast Fourier transformation. By interpreting the DSA, we can obtain the trends and changes of power within different frequencies over time, in respect to different ketamine dosing. | DSA EEG is placed before anesthesia starts, recorded throughout the operative procedure until emergence and patient is extubated. The saved EEG DSA data will be retrieved by the end of the day. | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
General anesthesia, Ketamine, Density Spectrum Array, Electroencephalogram, Multimodal General Anesthesia, Machine learning