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.
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 Type
OBSERVATIONAL
Enrollment
1,200
Establishment of a database of clinical characteristics of propofol-induced loss of consciousness in patients with complete clinical information Acquisition of basic perioperative monitoring data and extended monitoring data: 1,000 patients aged ≥18 years who needed to undergo surgical treatment were included (according to the machine learning diagnosis results, if the poor model fit was due to the small sample size, the necessary number of samples could be continued to be collected), and perioperative monitoring and management of the patients was performed, and a video recorder was used to videotape the whole anesthesia induction process in real time, so as to facilitate the postoperative integration of various data. The basic characteristics of the patients and the perioperative monitoring characteristics were extracted from the surgical anesthesia recording system, including gender, age, height, weight, blood pressure, ASA classification, electroencephalographic parameters, the a
General Hospital of Ningxia Medical University
Yinchuan, Ningxia, China
Drug dosage of propofol for inducing loss of consciousness in patients
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.
Time frame: through study completion, an average of six months
Relevant factors that can influence the intravenous dose of propofol
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).
Time frame: through study completion, an average of six months
Patient's peri-anesthetic vital signs
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.
Time frame: through study completion, an average of six months
Characteristics of the patient's perianesthesia electroencephalogram
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.
Time frame: through study completion, an average of six months
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