Control Strategy for Anaesthetic Drug Dosage with Interaction Among Human Physiological Organs Using Optimal Fractional Order PID Controller

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, an efficient control strategy for physiological interaction based anaesthetic drug infusion model is explored using the fractional order (FO) proportional integral derivative (PID) controllers. The dynamic model is composed of several human organs by considering the brain response to the anaesthetic drug as output and the drug infusion rate as the control input. Particle Swarm Optimisation (PSO) is employed to obtain the optimal set of parameters for PID/FOPID controller structures. With the proposed FOPID control scheme much less amount of drug-infusion system can be designed to attain a specific anaesthetic target and also shows high robustness for +/-50% parametric uncertainty in the patient's brain model

CLOSED-LOOP CONTROLLED TOTAL INTRA VENOUS ANAESTHESIA

Anaesthesia is important for both surgery and intensive care and intravenous anaesthetics are widely used to provide rapid onset, stable maintenance, and rapid recovery compared with inhaled anaesthetics. The aim of the project on which this thesis is based was to investigate a reliable and safe methodology for delivering total intravenous anaesthesia using closed-loop control technology and bispectral analysis of human electroencephalogram (EEG) waveform. In comparison with Target Controlled Infusion (TCI), drug effect is measured during drug infusion in closed loop anaesthesia (CLAN). This may provide superior safety, better patient care, and better quality of anaesthesia whilst relieving the clinician of the need to make recurrent and minor alterations to drug administration. However, the development of a CLAN system has been hindered by the Jack of a 'gold standard' for anaesthetic states and difficulties with patient variability in pharmacokinetic and pharmacodynamic modelling, and a new and generic mathematical model of a closed-loop anaesthesia system was developed for this investigation. By using this CLAN model, investigations on pharmacokinetic and pharmacodynamic variability existing in patients were carried out. A new control strategy that combines a Proportional, Integral, Derivative (PID) controller, bispectral analysis of EEG waveform and pharmacokinetic/ pharmacodynamic models was investigated. Based on the mathematical model, a prototype CLAN system, the first CLAN system capable of delivering both hypnotics and analgesics simultaneously for total intravenous anaesthesia, was developed. A Bispectral Index (BIS), derived from power spectral and bispectral analysis on EEG waveform, is used to measure depth of anaesthesia. A supervision system with built-in digital signal processing techniques was developed to compensate the non-linear characteristics inherent in the system while providing a comprehensive protection mechanism for patient safety. The CLAN system was tested in 78125 virtual patients modelled using published data. Investigations on intravenous anaesthesia induction and maintenance with the CLAN system were carried out in various clinical settings on 21 healthy volunteers and 15 patients undergoing surgery. Anaesthesia targets were achieved quickly and well maintained in all volunteers/patients except for 2 patients with clinically satisfactory anaesthesia quality.Derriford Hospita

Advanced Control Strategies for Automatic Drug Delivery to Regulate Anesthesia during Surgery

Ph.DDOCTOR OF PHILOSOPH

Adaptive control and identification for on-line drug infusion in anaesthesia.

Anaesthesia is that part of the medical science profession which ensures that the patient’s body is insensitive to pain and possibly other stimuli during surgical operations. It includes muscle relaxation (paralysis) and unconsciousness, both conditions being crucial for the operating surgeon. Maintaining a steady level of muscle relaxation as well as an acceptable depth of anaesthesia (unconsciousness), while keeping the dosage of administered drugs which induce those effects at a minimum level, have successfully been achieved using automatic control. Fixed gain controllers such as P, PI, and PID strategies can perform well when used in clinical therapy and under certain conditions but on the other hand can lead to poor performances because of the large variability between subjects. This is the reason which led to the consideration of adaptive control techniques which seemed to overcome such problems. Two control strategies falling into the above scheme and including the two newly developed techniques, i.e Proportional-Integral-Plus (PIP) control algorithm, and Generalized Predictive Control algorithm (GPC), are considered under extensive simulation studies using the muscle relaxation process associated with two drugs known as Pancuronium-Bromide and Atracurium. Both models exhibit severe non-linearities as well as time-varying dynamics and delays. Only the strategy corresponding to the GPC algorithm is retained for implementation on a 380Z disk-based microcomputer system, while the muscle relaxation process corresponding to either drugs is simulated on a VIDAC 336 analogue computer. The sensitivity of the algorithm is investigated when patient-to-patient parameter variability is evoked. The study is seen to provide the necessary basis for future clinical implementation of the scheme. Following the satisfactory results obtained under such a real-time environment, the self-adaptive GPC algorithm has been successfully applied in theatre to control Atracurium infusion on humans during surgery. This success later motivated further research work in which simultaneous control of muscle relaxation and anaesthesia (unconsciousness) was achieved. A good multivariable model has been derived and controlled via the multivariable version of the SISO GPC algorithm. The results obtained are very encouraging

Automation of the anesthetic process: New computer-based solutions to deal with the current frontiers in the assessment, modeling and control of anesthesia

The current trend in automating the anesthetic process focuses on developing a system for fully controlling the different variables involved in anesthesia. To this end, several challenges need to be addressed first. The main objective of this thesis is to propose new solutions that provide answers to the current problems in the field of assessing, modeling and controlling the anesthetic process. Undoubtedly, the main handicap to the development of a comprehensive proposal lies in the absence of a reliable measure of analgesia. This thesis proposes a novel fuzzy-logic-based scheme to evaluate the impact of including a new variable in a decision-making process. This scheme is validated by way of a preliminary analysis of the Analgesia Nociception Index (ANI) monitor on analgesic drug titration. Furthermore, the capacity of the ANI monitor to provide information to replicate the decisions of the experts in different clinical situations is studied. To this end, different artificial intelligence-based algorithms are used: specifically, the suitability of this index is evaluated against other variables commonly used in clinical practice. Regarding the modeling of anesthesia, this thesis presents an adaptive model that allows characterizing the pharmacological interaction effects between the hypnotic and analgesic drug on the depth of hypnosis. In addition, the proposed model takes into account both inter- and intra-patient variabilities observed in the response of the subjects. Finally, this work presents the synthesis of a robust optimal PID controller for regulating the depth of hypnosis by considering the effect of the uncertainties derived from the patient's pharmacological response. Moreover, a study is conducted on the limitations introduced when using a PID controller versus the development of higher order solutions under the same clinical and technical considerations

Control strategy for anaesthetic drug dosage with interaction among human physiological organs using optimal fractional order PID controller

In this paper, an efficient control strategy for physiological interaction based anaesthetic drug infusion model is explored using the fractional order (FO) proportional integral derivative (PID) controllers. The dynamic model is composed of several human organs by considering the brain response to the anaesthetic drug as output and the drug infusion rate as the control input. Particle Swarm Optimisation (PSO) is employed to obtain the optimal set of parameters for PID/FOPID controller structures. With the proposed FOPID control scheme much less amount of drug-infusion system can be designed to attain a specific anaesthetic target and also shows high robustness for ±50% parametric uncertainty in the patient’s brain mode

Dose-response-time data analysis

The traditional approach to pharmacodynamic modelling relies on knowledge about the pharmacokinetics. A prerequisite for obtaining kinetic information is reliable exposure data. However, in several therapeutic areas, exposure data are unavailable including when the drug response precedes the systemic exposure (for example pulmonary drug administration) and when the drug is locally administered (for example ophthalmics). Dose-response-time (DRT) data analysis provides an alternative to exposure-driven pharmacodynamic modelling when exposure data are sparse or lacking. In DRT modelling, the response data are assumed to contain enough information about the drug kinetics, whereby a biophase model can be developed and act as the driver of the pharmacological response. The following work presents the fundamental principles of DRT modelling. This include the entire procedure of identifying a DRT model, encompassing the assessment of the biophase function and the pharmacodynamic model, extensions to cover population variations, identifiability analysis, parameter estimation, and model validation. To demonstrate the utility of the technique, two extensive pre-clinical DRT studies of the interaction between nicotinic acid (NiAc) and free fatty acids (FFA) are presented. The first study covered the response behaviour following intravenous and oral NiAc dosing in both normal (lean) and diseased (obese) rats. The second study extended the models of the first study to incorporate insulin as a driver of the FFA response. Moreover, data from chronic trials were analysed with the aim to quantitatively understand the adaptive behaviours associated with long-term NiAc treatments. The aim of this work is to answer the questions of when and how to use DRT data analysis, and what the limitations of the method are. The DRT models of the first study were successfully fitted to all response-time courses in lean rats, with high precision in the parameter estimates (relative standard errors (RSE) < 25%), visual predictive check (VPC) and individual plots that captured the population and subject trends, and "-shrinkages of less than 10%. The model for the obese rats were less precise, with specific parameters being practically non-identifiable (with, for example, RSE 250%). The results for both lean and obese rats were generally consistent with those of an exposure-driven reference model, albeit with less precision and accuracy in the parameter estimates. Finally, the model was able to describe non-linear biophase kinetics, present at high oral dosages of NiAc. The DRT models of the second study were able to capture the response-time courses for insulin and FFA on a population and individual level, and for both lean and obese rats. However, many parameters were uncertain (with RSE of, for example, 30-50%) and some were practically non-identifiable (with RSE of > 100%). The estimates were generally less precise and more inaccurate than those obtained in an exposure-driven reference model. Yet, most parameter estimates of the DRT models were within one standard deviation from those of the exposure-driven model. The final model was used to predict steady-state FFA exposures following repeated NiAc dosing for a range of different infusion protocols. The optimal dosing regimens consisted of infusions and wash-out periods were the wash-outs were 2h longer than the infusions. These predictions were consistent with those made by the exposure-driven model. Albeit, the DRT model predicted a slightly lower optimal reduction of FFA exposure. It is important to recognise that DRT analyses introduce bias and variability in the parameter estimates. To obtain reliable results, it is advisable to have rich pharmacodynamic data, covering drug administration at different routes, rates, and schedules. With these issues taken into account, the technique still performed well in the two extensive studies presented in this work. In conclusion, DRT data analysis is a modelling technique used in situations when exposure data are unavailable. The method is versatile and can describe a range of different pharmacological behaviours. Precision and accuracy is lost when comparing to an exposure driven pharmacodynamic modelling approach. Thus, DRT modelling is not to be considered as a replacement of the gold-standard pharmacokinetic-pharmacodynamic framework, but rather as a compliment when exposure data are unavailable