BIS-EEG

This prospective, single-center, randomized interventional study aims to investigate the effects of commonly used adjunct anesthetic drugs on the Bispectral Index (BIS) and electroencephalographic (EEG) activity during total intravenous anesthesia. BIS monitoring is widely used to estimate the depth of anesthesia. However, previous studies suggest that certain anesthetic drugs, particularly dexmedetomidine and esketamine, may alter BIS values independently of the patient's actual level of consciousness. As a result, BIS readings may not always accurately reflect anesthetic depth when multiple drugs are administered together. Adult patients undergoing elective laparoscopic cholecystectomy under standardized propofol-remifentanil anesthesia will be randomly assigned to receive one of the following adjunct drugs: midazolam, dexmedetomidine, esketamine, or no additional study drug (control group). Continuous BIS values and raw EEG signals will be recorded throughout the study period. The primary objective is to compare changes in BIS values among different adjunct anesthetic drugs under otherwise similar anesthetic conditions. The study will also explore corresponding EEG characteristics associated with these changes. The findings are expected to improve understanding of drug-related differences in processed EEG monitoring and provide data for future development of more accurate anesthesia depth assessment methods.

This randomized prospective study aims to compare the effects of two target-controlled infusion models, the Schnider and Eleveld models, on anesthetic depth and hemodynamic responses during anesthesia induction in adult patients undergoing spinal surgery. Patients scheduled for spinal surgery will be randomized into either the Schnider or Eleveld group. Before induction, standard monitoring will be applied, including electrocardiography, peripheral oxygen saturation, invasive arterial blood pressure monitoring, heart rate monitoring, and bispectral index monitoring. Anesthesia induction will be performed with fentanyl 2 mcg/kg, rocuronium 0.6 mg/kg, and propofol administered by target-controlled infusion with an effect-site target concentration of 3 mcg/mL according to the allocated pharmacokinetic model. Hemodynamic parameters and bispectral index values will be recorded before induction and at the 1st, 3rd, 5th, and 10th minutes after induction. Additional parameters, including time to BIS below 40, time to delta activity, burst suppression duration, total propofol dose during the first 5 minutes, need for additional propofol, hemodynamic response to intubation, vasopressor requirement within the first 10 minutes, and use of esmolol, will also be documented. The primary aim is to evaluate whether the Schnider and Eleveld models differ in terms of induction-related hemodynamic stability and anesthetic depth during the early induction period.

The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states. While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.