Machine Learning-Based Model for Individualized Drug Dose Prediction for Propofol

Not yet recruiting

• Conditions: Anesthesiology; Anesthesiology Management

• Registration Number: NCT06703879
• Lead Sponsor: General Hospital of Ningxia Medical University

Brief Summary

The goal of this observational study is to develop an individualized prediction model for drug dosage during propofol-induced loss of consciousness in anticipation of advances in research in this area. An appropriate delivery model to reduce perianesthesia complications in patients, especially in outpatient painless endoscopy patients. The main question it aims to answer is:

What type of machine learning algorithm should be used to build a drug dose prediction model that is suitable for patient awareness of anesthesia induction? 1000 participants routinely with propofol induced anesthesia loss of consciousness included in this study.

Detailed Description

Study Methods:

（1）Case selection： Patients requiring elective surgical treatment in the Cardiovascular and Cerebrovascular Disease Hospital of General Hospital of Ningxia Medical University.

Inclusion criteria: Patients were gender-neutral and aged ≥18 years.

（2) Clinical study protocol： Patients were admitted to the operating room to establish intravenous access, connected to ECG monitoring, EEG monitoring, 4L/min mask oxygenation, and real-time video recording of the entire anesthesia induction process using a video recorder for postoperative integration of various data. Propofol was pumped in at 100 mg/kg/h, and the anesthesiologist with propofol assessed the degree of sedation of the patient until the patient was deeply sedated and the MOAA/S score was 0, and the pumping of propofol was stopped.

(3) Observation indicators： Demographic information and general preoperative data were collected, including patients' gender, age, weight, height, ASA classification, body mass index (BMI), and past medical history (hypertension, coronary heart disease, diabetes mellitus, respiratory disease, stroke).

Systolic blood pressure, diastolic blood pressure, mean arterial pressure, heart rate, finger pulse oximetry (SPO2), electroencephalogram waveforms, the dosage of propofol administered from the start of anesthesia induction to MOAA/S = 0, and the time of administration of propofol were recorded for all the basic and extended monitoring parameters, respectively, after the patient was admitted to the operating room and at the time of the 0 score of the MOAA/S score.

(4) Evaluation methods of each item Depth of anesthesia assessment: MOAA/S depth of sedation was used for assessment, and MOAA/S score of 4-5 was classified as mild sedation, which indicated that the response to calling names in normal tone was sensitive or slow; 2-3 was classified as moderate sedation, which indicated that there was a response to calling names loudly or repeatedly, or there was a response to slight pushing and vibration; ≤1 was classified as deep sedation, which indicated that there was a response to pain stimulation; 0 was classified as general anesthesia, which indicated that there was a response to pain stimulation; 0 was classified as general anesthesia, which indicated that there was a response to pain stimulation. ≤1 is classified as deep sedation, indicating a response to painful stimuli; 0 is classified as general anesthesia, indicating no response to painful stimuli.

EEG feature extraction: use python MNE (https://mne.tools/stable/index.html) module package for analysis, MNE can read most common physiological signals in raw data format, the specific methods are as follows: ① Data extraction: import data → electrode positioning → reject useless electrodes → re-reference → filter → drop sampling rate → run ICA → batch processing → segmentation and baseline correction → interpolation of bad conductance and rejection of bad conductance → removal of noise components → rejection of bad segments; ② Data division: the data of the required EEG monitoring points are divided to include wakefulness and sedation; ③ Feature extraction: time domain, amplitude square root value, kurtosis, skewness, burst suppression rate/minute, frequency domain, and entropy index are obtained by Fourier transform.

(5) Proposed machine learning algorithm: regression analysis is a predictive modeling technique that works from a set of data to determine quantitative relational equations between certain variables, statistically test these relational equations between the variables, and identify the variables that have a significant effect from among the multiple variables that affect a particular variable.

Study Timeline

• Status Verified: 2024-11
• Study First Submitted: 2024-11-18
• Study Start: 2024-11-20
• Study First Submitted QC: 2024-11-21
• Last Update Submitted: 2024-11-21
• Study First Posted: 2024-11-25
• Last Update Posted: 2024-11-25
• Primary Completion: 2025-03-30
• Study Completion: 2025-06-30

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

• Inpatients requiring elective surgery or outpatient painless endoscopic surgery； Induction of anesthesia with propofol； Age≥18 years old； ASA classifications I-III

Exclusion Criteria

• Propofol allergy； Contraindication to anesthesia; Declined to participate in the study

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Drug dosage of propofol for inducing loss of consciousness in patients	through study completion, an average of six months	Propofol was pumped at 100 mg/kg/h. The anesthesiologist assessed the patient's sedation level with the MOAA/S scale until the patient was deeply sedated and the MOAA/S score was less than or equal to 1, and pumping of propofol was stopped.; Propofol Continuous Pumping Volume Recorded(mg). Record propofol pumping duration.

Secondary Outcome Measures

Name	Time	Method
Relevant factors that can influence the intravenous dose of propofol	through study completion, an average of six months	Demographic information and general preoperative data were collected, including the patient's sex, age, weight, height, ASA classification, body mass index (BMI), and past medical history (hypertension, coronary artery disease, diabetes mellitus, respiratory disease, stroke).
Patient's peri-anesthetic vital signs	through study completion, an average of six months	Systolic blood pressure, diastolic blood pressure, mean arterial pressure, heart rate, and finger pulse oximetry (SPO2) were recorded after the patient was admitted to the operating room and at a MOAA/S score of 0, respectively.
Characteristics of the patient's perianesthesia electroencephalogram	through study completion, an average of six months	EEG waveforms were recorded during the patient's perianesthesia period using an EEG monitor to mark the EEG characteristics during wakefulness and MOAA/S less than or equal to 1.

Trial Locations

Locations (1)

General Hospital of Ningxia Medical University; 🇨🇳 Yinchuan, Ningxia, China