Clinical Research Directory

Comparison of Hemodynamic Responses During Anesthesia Induction Using Eleveld and Schnider

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.

Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

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.

Comparison of the Factors Affecting PSI and BIS Values in Monitoring Anesthetic Depth During Open-Heart Surgery

Measurement of anesthetic depth has long been a subject of investigation, aiming to titrate anesthetic agents appropriately and to prevent intraoperative awareness and consciousness. Many patients undergoing surgery experience fear and anxiety regarding the possibility of remaining conscious, perceiving pain, and being unable to move during anesthesia. Intraoperative awareness-defined as consciousness during anesthesia with explicit recall afterward-is a distressing condition that can lead to post-traumatic stress disorder. However, aiming for excessively deep anesthesia to avoid the possibility of awareness during surgery is not recommended, as it may result in hemodynamic instability due to the effects of anesthetic agents and may impair postoperative cognitive functions, particularly in the elderly population. Common methods used in monitoring anesthetic depth include observing sweating, lacrimation, pupillary dilation, heart rate variability, and blood pressure. However, some of these are subjective and may not always be reliable indicators. Electroencephalogram (EEG)-based monitors such as the Bispectral Index (BIS) and the Patient State Index (PSI) offer more reliable and objective means of monitoring anesthetic depth. These monitors provide numerical values between 0 (indicating unconsciousness) and 100 (indicating full alertness) based on proprietary algorithms, offering valuable insight into the patient's anesthetic state. "Our aim is to examine BIS and PSI values and to investigate the factors that influence these parameters."