271203 Individualized Physiologically Based Modeling and Model Predictive Control of Volatile Anesthesia
The modeling and control of anesthesia is believed to benefit the safety of the patient undergoing surgery and provide anesthetists and researchers with valuable insights. It is expected that mathematical modeling and optimized control could (i) pave the way for personalized health care, taking into account the individual patient characteristics for optimal and flexible drug infusion rates, (ii) provide the anesthetist with more time to focus on critical issues, and (iii) guarantee the safety of the patient minimizing side-effects.
This work presents a physiologically based, patient-specific, compartmental model for volatile anesthesia, consisting of a multiple blood and tissue compartmental model, adjusted to the weight, height, sex and age of the patient (Krieger et al. 2011). The predicted hypnotic effect and cardiovascular and respiratory depression is directly correlated to the arterial anesthetic concentration via an effect site concentration and the Hill-equation and (Gentilini et al. 2001). The individual patient characteristics in the pharmacodynamic equations are included via an initial estimate based on the physiologic variables of the patient and updated during the course of anesthesia as a function of the obtained measurements, such as the mean arterial blood pressure.
The derived model is used as the underlying model for model predictive control (MPC) of anesthesia. MPC is the method of choice for the biomedical system of anesthesia, as it can handle multiple-input multiple-output (MIMO) systems and safety constraints on the drug infusion rates, measured physiological variables and drug concentrations. Via applying multi-parametric programming for MPC, a fast and patient specific calculation of the optimal control inputs (Dua & Pistikopoulos 2005), e.g. the drug infusion rates and the inspired concentration of the volatile anesthetic agent is assured.
Dua, P. & Pistikopoulos, E.N., 2005. Modelling and control of drug delivery systems. Computers and Chemical Engineering, 29, p.2290-2296.
Gentilini, A.L. et al., 2001. Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane. IEEE Transactions on Biomedical Engineering, 48(8), p.874-889.
Krieger, A. et al., 2011. A novel physiologically based compartmental model for volatile anaesthesia, 21st European Symposium on Computer Aided Process Engineering. Elsevier, p. 1495 – 1499
See more of this Group/Topical: Topical 7: Biomedical Applications of Chemical Engineering