Modelling, Optimisation and Explicit Model Predictive Control of Anaesthesia Drug Delivery Systems

The contributions of this thesis are organised in two parts. Part I presents a mathematical model for drug distribution and drug effect of volatile anaesthesia. Part II presents model predictive control strategies for depth of anaesthesia control based on the derived model. Closed-loop model predictive control strategies for anaesthesia are aiming to improve patient's safety and to fine-tune drug delivery, routinely performed by the anaesthetist. The framework presented in this thesis highlights the advantages of extensive modelling and model analysis, which are contributing to a detailed understanding of the system, when aiming for the optimal control of such system. As part of the presented framework, the model uncertainty originated from patient-variability is analysed and the designed control strategy is tested against the identified uncertainty. An individualised physiologically based model of drug distribution and uptake, pharmacokinetics, and drug effect, pharmacodynamics, of volatile anaesthesia is presented, where the pharmacokinetic model is adjusted to the weight, height, gender and age of the patient. The pharmacodynamic model links the hypnotic depth measured by the Bispectral index (BIS), to the arterial concentration by an artificial effect site compartment and the Hill equation. The individualised pharmacokinetic and pharmacodynamic variables and parameters are analysed with respect to their influence on the measurable outputs, the end-tidal concentration and the BIS. The validation of the model, performed with clinical data for isoflurane and desflurane based anaesthesia, shows a good prediction of the drug uptake, while the pharmacodynamic parameters are individually estimated for each patient. The derived control design consists of a linear multi-parametric model predictive controller and a state estimator. The non-measurable tissue and blood concentrations are estimated based on the end-tidal concentration of the volatile anaesthetic. The designed controller adapts to the individual patient's dynamics based on measured data. In an alternative approach, the individual patient's sensitivity is estimated on-line by solving a least squares parameter estimation problem.Open Acces

Pharmacogenomics – the key to personalized medicine

Different rates of drug metabolism and the effect of commonly prescribed drugs are often seen in clinical practice. Some of these differences can be predicted if the patient’s genetic profile is known by pharmacogenomic analysis, which done once, provides lifetime benefits. In the United States, adverse drug reactions are the fourth leading cause of death, costing their healthcare system about $136 billion annually. By implementing pharmacogenomic testing early in clinical algorithms, debilitating and potentially life-threatening side-effects can be predicted and avoided, which is particularly important in settings of pain therapy and anesthesia. In St. Catherine Specialty Hospital, this approach is readily advocated for our patients. Through the use of the RightMed panel, 25 genes coding for enzymes and other proteins important for drug function, are analyzed, and a pharmacogenomic-driven approach is taken by selecting the right drug, in the right dose, for the right patient

Pharmacogenomics – the key to personalized medicine

Different rates of drug metabolism and the effect of commonly prescribed drugs are often seen in clinical practice. Some of these differences can be predicted if the patient’s genetic profile is known by pharmacogenomic analysis, which done once, provides lifetime benefits. In the United States, adverse drug reactions are the fourth leading cause of death, costing their healthcare system about $136 billion annually. By implementing pharmacogenomic testing early in clinical algorithms, debilitating and potentially life-threatening side-effects can be predicted and avoided, which is particularly important in settings of pain therapy and anesthesia. In St. Catherine Specialty Hospital, this approach is readily advocated for our patients. Through the use of the RightMed panel, 25 genes coding for enzymes and other proteins important for drug function, are analyzed, and a pharmacogenomic-driven approach is taken by selecting the right drug, in the right dose, for the right patient

Advanced model-based control studies for the induction and maintenance of intravenous anaesthesia

This paper describes strategies toward model-based automation of intravenous anaesthesia employing advanced control techniques. In particular, based on a detailed compartmental mathematical model featuring pharmacokinetic and pharmacodynamics information, two alternative model predictive control strategies are presented: a model predictive control strategy, based on online optimization, the extended predictive self-adaptive control and a multiparametric control strategy based on offline optimization, the multiparametric model predictive control. The multiparametric features to account for the effect of nonlinearity and the impact of estimation are also described. The control strategies are tested on a set of 12 virtually generated patient models for the regulation of the depth of anaesthesia by means of the bispectral index (BIS) using Propofol as the administrated anaesthetic. The simulations show fast response, suitability of dose, and robustness to induce and maintain the desired BIS setpoint

On Automation in Anesthesia

The thesis discusses closed-loop control of the hypnotic and the analgesic components of anesthesia. The objective of the work has been to develop a system which independently controls the intravenous infusion rates of the hypnotic drug propofol and analgesic drug remifentanil. The system is designed to track a reference hypnotic depth level, while maintaining adequate analgesia. This is complicated by inter-patient variability in drug sensitivity, disturbances caused foremost by surgical stimulation, and measurement noise. A commercially available monitor is used to measure the hypnotic depth of the patient, while a simple soft sensor estimates the analgesic depth. Both induction and maintenance of anesthesia are closed-loop controlled, using a PID controller for propofol and a P controller for remifentanil. In order to tune the controllers, patient models have been identified from clinical data, with body mass as only biometric parameter. Care has been taken to characterize identifiability and produce models which are safe for the intended application. A scheme for individualizing the controller tuning upon completion of the induction phase of anesthesia is proposed. Practical aspects such as integrator anti-windup and loss of the measurement signal are explicitly addressed. The validity of the performance measures, most commonly reported in closed-loop anesthesia studies, is debated and a new set of measures is proposed. It is shown, both in simulation and clinically, that PID control provides a viable approach. Both results from simulations and clinical trials are presented. These results suggest that closed-loop controlled anesthesia can be provided in a safe and efficient manner, relieving the regulatory and server controller role of the anesthesiologist. However, outlier patient dynamics, unmeasurable disturbances and scenarios which are not considered in the controller synthesis, urge the presence of an anesthesiologist. Closed-loop controlled anesthesia should therefore not be viewed as a replacement of human expertise, but rather as a tool, similar to the cruise controller of a car

PK-PD modelling of the interaction of propofol and midazolam : implementation and future perspectives

This thesis describes the day to day interaction between propofol and midazolam as encountered in every day practice. The direct interaction of premedication given to patients before surgery has profound implications. The propofol induction dose can be decreased with respect to the target BIS. Besides the interaction mechanisms of propofol and midazolam, the pharmacological backgrounds of propofol-opioid interactions are given. The future perspectives of PK-PD modeling and the use of additional informative techniques are given in the last chapter.UBL - phd migration 201