Clinical trial NCT05256264: Machine-learning Algorithm to Differentiate Intraoperative Ketamine Dosing Based on Electroencephalographic Density Spectrum Array Analysis
Official title: Machine-learning Algorithm to Differentiate Intraoperative Ketamine Dosing Based on Electroencephalographic Density Spectrum Array Analysis
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Electroencephalographic Density Spectrum Array Analysis
Density Spectrum Array
Ketamine
Multimodal General Anesthesia
Keywords: General anesthesia
Ketamine
Density Spectrum Array
Electroencephalogram
Multimodal General Anesthesia
Machine learning
Lead sponsor: Taipei Veterans General Hospital, Taiwan
Sponsor class: OTHER_GOV
First posted: 2022-02-25
Enrollment: 90
Sex: ALL
Minimum age (years): 20
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients admitted for spine surgery under the department of orthopedic surgery at VGHTPE, a tertiary hospital in Taiwan, will be selected.

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"

Eligibility criteria: Inclusion Criteria:

1. Patients undergoing elective spine surgery
2. Aged 20-80 years old

Exclusion Criteria:

1. Pregnancy
2. Presence of major brain disorders: stroke, epilepsy, Parkinson's disease, etc.
3. Any of the following major organ disorders:

   1. Chronic pulmonary disease (Clinically diagnosed severe chronic obstructive pulmonary disease or FEV1/FVC \<70% or FEV1 \<50%)
   2. Heart failure (NYHA III or IV)
   3. Chronic renal failure (eGFR\<60ml/min/1.73m2)
4. Ongoing sepsis or infection
5. Ongoing of history of CNS-acting medications/substances use (e.g.: sedatives, hypnotics, stimulants, etc.)

Primary outcomes:
measure: Ketamine dosing based on EEG DSA
timeFrame: 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.
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: Taipei Veterans General Hospital
state: Beitou
Study officials:
affiliation: Taipei Veterans General Hospital, Taiwan
name: Chien-Kun Ting, MD, PhD
role: STUDY_CHAIR
References: