Advanced multiparametric optimization and control studies for anaesthesia

Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone. Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems. In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs. Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented. All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces

Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy

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

Event-based MPC for propofol administration in anesthesia

Background and Objective : The automatic control of anesthesia is a demanding task mostly due to the presence of nonlinearities, intra- and inter-patient variability and specific clinical requirements to be meet. The traditional approach to achieve the desired depth of hypnosis level is based on knowledge and experience of the anesthesiologist. In contrast to a typical automatic control system, their actions are based on events that are related to the effect of the administrated drug. Thus, it is interesting to build a control system that will be able to mimic the behavior of the human way of actuation, simultaneously keeping the advantages of an automatic system.Methods : In this work, an event-based model predictive control system is proposed and analyzed. The nonlinear patient model is used to form the predictor structure and its linear part is exploited to design the predictive controller, resulting in an individualized approach. In such a scenario, the BIS is the controlled variable and the propofol infusion rate is the control variable. The event generator governs the computation of control action applying a dead-band sampling technique. The proposed control architecture has been tested in simulation considering process noise and unmeasurable disturbances. The evaluation has been made for a set of patients using nonlinear pharmacokinetic/pharmacodynamic models allowing realistic tests scenarios, including inter- and intra-patient variability.Results For the considered patients dataset the number of control signal changes has been reduced of about 55% when compared to the classical control system approach and the drug usage has been reduced of about 2%. At the same time the control performance expressed by the integrated absolute error has been degraded of about 11%.Conclusions : The event-based MPC control system meets all the clinical requirements. The robustness analysis also demonstrates that the event-based architecture is able to satisfy the specifications in the presence of significant process noise and modelling errors related to inter- and intra-patient variability, providing a balanced solution between complexity and performance. (c) 2022 Elsevier B.V. All rights reserved

Autonomous systems in anesthesia : where do we stand in 2020? A narrative review

As most of us are aware, almost every facet of our society is becoming, for better or worse, progressively more technology-dependent. Technological advancement has made autonomous systems, also known as robots, an integral part of our life in several fields, including medicine. The application of robots in anesthesia could be classified into 3 types of robots. The first ones are pharmacological robots. These robots are based on closed-loop systems that allow better-individualized anesthetic drug titration for optimal homeostasis during general anesthesia and sedation. Recent evidence also demonstrates that autonomous systems could control hemodynamic parameters proficiently outperforming manual control in the operating room. The second type of robot is mechanical. They enable automated motorized reproduction of tasks requiring high manual dexterity level. Such robots have been advocated to be more accurate than humans and, thus, could be safer for the patient. The third type is a cognitive robot also known as decision support system. This type of robot is able to recognize crucial clinical situation that requires human intervention. When these events occur, the system notifies the attending clinician, describes relevant related clinical observations, proposes pertinent therapeutic options and, when allowed by the attending clinician, may even administer treatment. It seems that cognitive robots could increase patients' safety. Robots in anesthesia offer not only the possibility to free the attending clinicians from repetitive tasks but can also reduce mental workload allowing them to focus on tasks that require human intelligence such as analytical and clinical approach, lifesaving decision-making capacity, and interpersonal interaction. Nevertheless, further studies have yet to be done to test the combination of these 3 types of robots to maintain simultaneously the homeostasis of multiple biological variables and to test the safety of such combination on a large-scale population

Model predictive control using MISO approach for drug co-administration in anesthesia

In this paper, a model predictive control system for the depth of hypnosis is proposed and analyzed. This approach considers simultaneous co-administration of the hypnotic and analgesic drugs and their effect on the Bispectral Index Scale (BIS). The control scheme uses the nonlinear multiple-input–single-output (MISO) model to predict the remifentanil influence over the propofol hypnotic effect. Then, it exploits a generalized model predictive control algorithm and a ratio between the two drugs in order to provide the optimal dosage for the desired BIS level, taking into account the typical constraints of the process. The proposed approach has been extensively tested in simulation, using a set of patients described by realistic nonlinear pharmacokinetic/pharmacodynamic models, which are representative of a wide population. Additionally, an exhaustive robustness evaluation considering inter- and intra-patient variability has been included, which demonstrates the effectiveness of the analyzed control structure

Advanced Signal Processing and Control in Anaesthesia

This thesis comprises three major stages: classification of depth of anaesthesia (DOA); modelling a typical patient’s behaviour during a surgical procedure; and control of DOAwith simultaneous administration of propofol and remifentanil. Clinical data gathered in theoperating theatre was used in this project. Multiresolution wavelet analysis was used to extract meaningful features from the auditory evoked potentials (AEP). These features were classified into different DOA levels using a fuzzy relational classifier (FRC). The FRC uses fuzzy clustering and fuzzy relational composition. The FRC had a good performance and was able to distinguish between the DOA levels. A hybrid patient model was developed for the induction and maintenance phase of anaesthesia. An adaptive network-based fuzzy inference system was used to adapt Takagi-Sugeno-Kang (TSK) fuzzy models relating systolic arterial pressure (SAP), heart rate (HR), and the wavelet extracted AEP features with the effect concentrations of propofol and remifentanil. The effect of surgical stimuli on SAP and HR, and the analgesic properties of remifentanil were described by Mamdani fuzzy models, constructed with anaesthetist cooperation. The model proved to be adequate, reflecting the effect of drugs and surgical stimuli. A multivariable fuzzy controller was developed for the simultaneous administration of propofol and remifentanil. The controller is based on linguistic rules that interact with three decision tables, one of which represents a fuzzy PI controller. The infusion rates of the two drugs are determined according to the DOA level and surgical stimulus. Remifentanil is titrated according to the required analgesia level and its synergistic interaction with propofol. The controller was able to adequately achieve and maintain the target DOA level, under different conditions. Overall, it was possible to model the interaction between propofol and remifentanil, and to successfully use this model to develop a closed-loop system in anaesthesia

Automatic Control of General Anesthesia: New Developments and Clinical Experiments

L’anestesia generale è uno stato di coma farmacologicamente indotto, temporaneo e reversibile. Il suo obiettivo consiste nel provocare la perdita totale della coscienza e nel sopprimere la percezione del dolore. Essa costituisce un aspetto fondamentale per la medicina moderna in quanto consente di praticare interventi chirurgici invasivi senza causare ansia e dolore al paziente. Nella pratica clinica dell’anestesia totalmente endovenosa questi effetti vengono generalmente ottenuti mediante la somministrazione simultanea del farmaco ipnotico propofol e del farmaco analgesico remifentanil. Il dosaggio di questi farmaci viene gestito dal medico anestesista basandosi su linee guida farmacologiche e monitorando la risposta clinica del paziente. Recenti sviluppi nelle tecniche di elaborazione dei segnali fisiologici hanno consentito di ottenere degli indicatori quantitativi dello stato anestetico del paziente. Tali indicatori possono essere utilizzati come segnali di retroazione per sistemi di controllo automatico dell'anestesia. Lo sviluppo di questi sistemi ha come obiettivo quello di fornire uno strumento di supporto per l'anestesista. Il lavoro presentato in questa tesi è stato svolto nell'ambito del progetto di ricerca riguardante il controllo automatico dell'anestesia attivo presso l'Università degli Studi di Brescia. Esso è denominato ACTIVA (Automatic Control of Total IntraVenous Anesthesia) ed è il risultato della collaborazione tra il Gruppo di Ricerca sui Sistemi di Controllo dell’Università degli Studi di Brescia e l’Unità Operativa Anestesia e Rianimazione 2 degli Spedali Civili di Brescia. L’obiettivo del progetto ACTIVA consiste nello sviluppo teorico, nell’implementazione e nella validazione clinica di strategie di controllo innovative per il controllo automatico dell’anestesia totalmente endovenosa. Nel dettaglio, in questa tesi vengono inizialmente presentati i risultati sperimentali ottenuti con strutture di controllo basate sull'algoritmo PID e PID ad eventi per la somministrazione di propofol e remifentanil. Viene poi presentato lo sviluppo teorico e la validazione clinica di strutture di controllo predittivo basate su modello. Successivamente vengono presentati i risultati di uno studio in simulazione riguardante una soluzione di controllo innovativa che consente all'anestesista di regolare esplicitamente il bilanciamento tra propofol e remifentanil. Infine, vengono presentati gli sviluppi teorici ed i relativi studi in simulazione riguardanti soluzioni di controllo personalizzate per le fasi di induzione e mantenimento dell'anestesia.General anesthesia is a state of pharmacologically induced, temporary and reversible coma. Its goal is to cause total loss of consciousness and suppress the perception of pain. It constitutes a fundamental aspect of modern medicine as it allows invasive surgical procedures to be performed without causing anxiety and pain to the patient. In the clinical practice of total intravenous anesthesia, these effects are generally obtained by the simultaneous administration of the hypnotic drug propofol and of the analgesic drug remifentanil. The dosing of these drugs is managed by the anesthesiologist on the basis of pharmacological guidelines and by monitoring the patient's clinical response. Recent developments in physiological signal processing techniques have introduced the possibility to obtain quantitative indicators of the patient's anesthetic state. These indicators can be used as feedback signals for automatic anesthesia control systems. The development of these systems aims to provide a support tool for the anesthesiologist. The work presented in this thesis has been carried out in the framework of the research project concerning the automatic control anesthesia at the University of Brescia. The project is called ACTIVA (Automatic Control of Total IntraVenous Anesthesia) and is the result of the collaboration between the Research Group on Control Systems of the University of Brescia and the Anesthesia and Intensive Care Unit 2 of the Spedali Civili di Brescia. The objective of the ACTIVA project consists in the theoretical development, implementation, and clinical validation of innovative control strategies for the automatic control of total intravenous anesthesia. In detail, in this thesis the experimental results obtained with control structures based on the PID and on event-based PID controllers for the administration of propofol and remifentanil are initially presented. The theoretical development and clinical validation of model predictive control strategies is then proposed. Next, the results of a simulation study regarding an innovative control solution that allows the anesthesiologist to explicitly adjust the balance between propofol and remifentanil are given. Finally, the theoretical developments and the relative simulation studies concerning personalized control solutions for induction and maintenance phases of anesthesia are explained