Significant advancements have been made in the development of novel methodologies for anesthesia management, nociception monitoring, and system control. These innovations bridge various disciplines, including biomedical engineering, clinical medicine, and control theory, fostering knowledge transfer between research and clinical practice.
Several innovative methodologies were developed to enhance anesthesia monitoring, control, and patient care:
• Nociception/Antinociception Index: Novel approach for the pain/analgesia indices using the AnspecPro device was developed and validated against commercial devices (Medasense, Medstorm). This allows for accurate measurement of nociceptive responses to surgical stimuli during anesthesia, ensuring better control and patient safety.
• Multivariable Patient Models: Advanced multivariable models were created to capture the interaction of hypnotic and opioid drugs (Propofol and Remifentanil) with hypnosis (Bispectral Index) and analgesia. Moreover, a novel system modeling technique to adapt patient model parameters under conditions of uncertainty were proposed, addressing the challenges of poor identifiability in clinical data due to ethical and safety constraints. These models improve drug dosing strategies and enhance the accuracy of patient-specific anesthesia management.
• Augmented Patient Models: An augmented pharmacokinetic (PK) model that accounts for non-uniform drug uptake and clearance as a nonlinear function of body mass index (BMI), addressing drug trapping risks in long-term anesthesia. The model is validated through both open-loop and closed-loop control, optimizing drug dosages to minimize overdosing and protect vital organs such as the liver and kidneys.
• Model predictive control with integrated disturbance (surgical) profiles: A novel method in multi-drug control optimization involves the development and integration of surgical disturbance profiles—tailored to major, moderate, and minor surgeries—into a Model Predictive Control (MPC) strategy. This approach mimics closely the surgical stimuli to provide information to the controller and minimize intraoperative uncertainty, thereby enhancing patient safety and reducing variability in response.
