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

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

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

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

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

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management