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

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

Automatic control of the depth of anesthesia-clinical results

This paper presents clinical results of the implementation of an automatic controller previously designed by the authors for the BIS level of patients subject to general anesthesia. Since the controller has a state feedback component, an observer is introduced in order to estimate state

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

An open source patient simulator for design and evaluation of computer based multiple drug dosing control for anesthetic and hemodynamic variables

We are witnessing a notable rise in the translational use of information technology and control systems engineering tools in clinical practice. This paper empowers the computer based drug dosing optimization of general anesthesia management by means of multiple variables for patient state stabilization. The patient simulator platform is designed through an interdisciplinary combination of medical, clinical practice and systems engineering expertise gathered in the last decades by our team. The result is an open source patient simulator in Matlab/Simulink from Mathworks(R). Simulator features include complex synergic and antagonistic interaction aspects between general anesthesia and hemodynamic stabilization variables. The anesthetic system includes the hypnosis, analgesia and neuromuscular blockade states, while the hemodynamic system includes the cardiac output and mean arterial pressure. Nociceptor stimulation is also described and acts as a disturbance together with predefined surgery profiles from a translation into signal form of most commonly encountered events in clinical practice. A broad population set of pharmacokinetic and pharmacodynamic (PKPD) variables are available for the user to describe both intra- and inter-patient variability. This simulator has some unique features, such as: i) additional bolus administration from anesthesiologist, ii) variable time-delays introduced by data window averaging when poor signal quality is detected, iii) drug trapping from heterogeneous tissue diffusion in high body mass index patients. We successfully reproduced the clinical expected effects of various drugs interacting among the anesthetic and hemodynamic states. Our work is uniquely defined in current state of the art and first of its kind for this application of dose management problem in anesthesia. This simulator provides the research community with accessible tools to allow a systematic design, evaluation and comparison of various control algorithms for multi-drug dosing optimization objectives in anesthesia

MPC for Propofol Anesthesia: the Noise Issue

The design of automatic control systems for general anesthesia is a challenging task due to the severe safety requirements and process constraints. This is even more complex when model-based control techniques are used due to the significant variability of the process model. Additionally, issues like noisy measurements and interference also influence the control system overall performance. In this context, adequate filtering and control system sampling period selection should be analyzed to test their influence on the controller. In this paper, an MPC system for the depth of hypnosis, where the BIS signal is used as a controlled variable, is analyzed. The main purpose is to test and evaluate how the process noise affects the performance of the control system. The analysis is performed in a simulation study using a dataset of virtual patients representative of a wide population. Results show that a satisfactory performance is obtained when the noise is explicitly taken into account in the controller tuning procedure for a specific sampling period

Closed-Loop Control of Anaesthetic Effect

Archivo disponible en la web de la revista, Open Access, en la siguiente URL: https://www.intechopen.com/books/pharmacology/closed-loop-control-of-anesthetic-effect Se puede referenciar de la siguiente manera: Santiago Torres, Juan A. Méndez, Héctor Reboso, José A. Reboso and Ana León (2012). Closed-Loop Control of Anaesthetic Effect, Pharmacology, Dr. Luca Gallelli (Ed.), InTech, DOI: 10.5772/37609. Available from: https://www.intechopen.com/books/pharmacology/closed-loop-control-of-anesthet

PID control of depth of hypnosis in anesthesia for propofol and remifentanil coadministration

Tese de mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasThe purpose of general anesthesia is to deeply sedate a person so that they lose consciousness, sensitivity, and body reflexes, and so that surgeries can be safely performed without the patient feeling pain or discomfort during the procedure. General anesthesia is a combination of the effect of three components, namely hypnosis, analgesia, and neuromuscular blockade. Each component is regulated through the action of a specific drug, or through the combined effect of two or more drugs. In recent years there have been many advances in the field of automatic control systems for drug delivery during anesthesia, which can be implemented using a wide variety of controllers and process variables. The reason behind these advances is that an automatic control system can provide several benefits, such as a reduction in the anesthesiologist's workload, a reduction in the amount of medication used (which implies a faster and better recovery time for the patient in the postoperative phase), and, in fact, a more robust performance with fewer episodes of over- or under-dosing of the drug. A proportional-integral-derivative controller (PID) continuously calculates the error value that is the difference between the desired value and the measured process variable and applies a correction that is based on proportional, integral and derivative terms. In this dissertation, a specific PID control system for propofol and remifentanil is proposed to regulate the hypnosis component during anesthesia using the bispectral index (BIS) as the process variable. Infusion rates of both drugs are also controlled. The adjustment of the PID parameters, so that the BIS was closer to what was expected, was done using a genetic algorithm. The implementation of the control system was done in Simulink in order to simulate a surgery. The simulation scheme includes the patient models for both drugs, a disturbance profile, and two different PID controllers for the two phases of anesthesia - induction and maintenance. Aspects such as noise in the BIS signal and artifacts were taken into account in the system and a suitable noise filter was applied in the control algorithm. In addition, a ratio between the infusion rates of propofol and remifentanil has been introduced to allow the anesthesiologist to choose the appropriate opioid-hypnotic balance In the end, a performance analysis of the control system was made based on seven performance indices (namely the integrated absolute error, the settling time, the median performance error, the median absolute performance error, the wobble, and the above and below recommended BIS values). Although there are many types of control systems for the automatic control of hypnosis depth described in the literature, these are not usually used in clinical practice. Therefore, it is important to continue research to produce robust and user-friendly systems that integrate clinicians' clinical knowledge and meet their actual needs