Models for control of intravenous anesthesia

2013

In this paper a new estimation procedure for the parameters of the PK/PD model for the simultaneous administration of propofol and remifentanil introduced in [1] is presented. This model has the advantage of being parsimoniously parameterized, which allows a simple parameter estimation procedure, based on the patient's step response. It is shown that the parameter estimates obtained in this way provide good results when used to tune the positive control law introduced in [2].

Pharmacology, 2012

2012 20th Telecommunications Forum (TELFOR), 2012

Manual or open-loop administration does not take into account patient's individual dose or dose-response relationship; hence they represent sub-optimal solutions for optimizing individual drug titration. This may lead to underor over-sedation, increasing the time on mechanical ventilation, the length of intensive care unit (ICU) stay and mortality. Model based predictive control can mitigate with this problem, improving the efficiency of drug delivery and patient safety. A multiple-input single-output (MISO) patient model is identified and validated in this paper. The inputs are two drugs commonly used for general anesthesia, propofol and remifentanil, and the output is the Bispectral Index (BIS). Wavelet time-frequency analysis was used to filter the measured signals. The parameters of the interaction model which relates the effect-site concentrations of these drugs to BIS are identified based on least-squares algorithm, using data from real-life clinical tests.

2006

This thesis investigates the design and performance of a controller for the maintenance of anesthesia during surgery. The controller is designed to be robustly stable for a large population of patients. Even though anesthetic drugs are amongst the most dangerous drugs used in today's clinical setting, anesthesia procedures are known to be very safe. Hence, the impact of automation in anesthesia in terms of patients' safety cannot be clearly established. However, there are a number of significant clinical advantages to be gained by closing the loop: 1. Recent evidences suggest that most patients undergoing anesthesia procedures are overdosed. This is one of the main reasons for patients' discomfort and slow recovery. Literature suggests that closedloop systems can significantly reduce drug consumption and lessen recovery times, thus improving the patient outcome while reducing drug-associated costs and bed occupancy. Using a closed-loop controller would allow for an infusion-type titration that provides smoother transitions, thus avoiding the respiratory and hemodynamic depression observed in a bolus-based manual regimen. 3. Closed-loop controllers are also particularly well-suited for solving complex optimization problems. The profound synergy that exists between intravenous anesthetics and opioids could then be fully exploited. This could be a significant factor contributing to a reduction in drug usage and the improvement of patients' comfort. This project is particularly challenging. In particular: 1. There is no accepted measure of depth of anesthesia. Hence, it is necessary to work at the conceptual and sensor levels in order to define adequate feedback measures. 2. Drug effect modeling suffers from many shortcomings. In particular, published studies are often not in agreement regarding model parameters. 3. Uncertainty of dose/response models is daunting. Measuring this uncertainty is necessary in order to ensure stability of the control design. While the anesthesia closed-loop concept has already been investigated in the past, no breakthrough has yet been achieved. We feel it is necessary to investigate the anesthesia system from a control engineering perspective. This thesis is divided into two distinct parts. Part A contains the first 4 chapters and presents a thorough introduction to clinical anesthesia. The main concepts, terminology and issues are covered, including anesthesia monitors and basic pharmacology principles. A review of the prior closed-loop control attempts is presented in Chapter 4. Part B contains the chapters 5 to 8. In these chapters, we investigate a new sensor technology to quantify both cortical and autonomic activity. This technology is used to derive drug effect models, from which uncertainty bounds are derived. Based on this uncertainty analysis, we derive robustly stable controllers achieving clinically adequate performances. Finally, we invite the readers to refer to Chapter 9 for a complete synopsis and summary of this thesis.

IFAC-PapersOnLine, 2020

Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that interindividual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work.

European Journal of Control, 2005

Control technology has been applied to a wide variety of industrial and domestic applications to improve performance, safety and efficiency. Anesthesia, a critical aspect of clinical and emergency medicine, has not yet benefited from such technological advances. The lack of dedicated feedback sensors, and the large inter-and intra-patient variability in terms of patients' response to drug administration, have seriously limited the effectiveness and reliability of closed-loop controllers in clinical settings. However, recent advances in sensing devices, along with robust nonlinear control theories, have generated new hopes that the gap between manual and automated control of anesthesia can finally be bridged. This paper addresses the pharmacological principles of clinical anesthesia in a context appropriate for control engineers. Concepts and terminology, monitoring issues, as well as drug dose vs. response relationships, are covered.

Computational and Mathematical Methods in Medicine, 2016

Maintaining the depth of hypnosis (DOH) during surgery is one of the major objectives of anesthesia infusion system. Continuous administration of Propofol infusion during surgical procedures is essential but increases the undue load of an anesthetist in operating room working in a multitasking setup. Manual and target controlled infusion (TCI) systems are not good at handling instabilities like blood pressure changes and heart rate variability arising due to interpatient variability. Patient safety, large interindividual variability, and less postoperative effects are the main factors to motivate automation in anesthesia. The idea of automated system for Propofol infusion excites the control engineers to come up with a more sophisticated and safe system that handles optimum delivery of drug during surgery and avoids postoperative effects. In contrast to most of the investigations with linear control strategies, the originality of this research work lies in employing a nonlinear cont...

Wasit Journal for Pure sciences

The safe and personalized administration of anesthetic drugs is a significant concern in clinical practice, and automated control of anesthesia can address this issue by reducing human error, such as under- or over-dosing. This has the added benefit of allowing anesthesiologists to focus on more critical tasks and emergency management. The advantages of automated anesthesia delivery are not limited to anesthesiologists alone, as patients also benefit from the personalized and safe administration of drugs. This article offers a concise overview of the latest developments in closed-loop anesthesia delivery control systems. These systems include a range of elements such as monitoring depth of anesthesia, patient modeling, control techniques, safety systems, and clinical trial validation. Although anesthesia control has undergone significant changes over the years, a fully integrated system remains elusive. To move towards personalized patient care, it is important to assess the c...

IFAC Proceedings Volumes, 2008

Anesthesia process is to maintain a triad of hypnosis, analgesia and neuromuscular blockade by infusing several drugs which are specific for each state. This work focuses on controlling the hypnosis with RTDA (Robustness, Set-point tracking, Disturbance rejection, Aggressiveness) controller by regulation of propofol using Bispectral Index (BIS) as primary controlled variable. One of the main advantages of RTDA controller is its intuitive tuning parameters when compared to PID and MPC controllers. For the controller design, a fourthorder nonlinear pharmacokinetic-pharmacodynamic representation is used for the hypnosis dynamics of patients. Nominal values for pharmacokinetics and pharmacodynamics were taken from the literature. Then the performance of the RTDA controller is compared with the performances of the PID and MPC controllers. Robust performance of these controllers is tested for a selected range of patients by considering variability in parameters of the patient model. Also studied are the relative performances with respect to different set-points in BIS, and disturbances in BIS signal. Numerical simulations show that the RTDA controller provides better performance compared to the other two controllers.

2008 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008

Monitoring and controlling the depth of anesthesia during a surgery is really important since over dosing and under dosing can be dangerous for the patients. The model which is used for describing the relationship between input anesthetic agents and output patient endpoint variables is pharmacokineticpharmacodynamic model which included the most significant covariates such as age and weight. Bispectral index (BIS) is one of the best criteria for evaluating the depth of Anesthesia. In this research we used BIS as a patient endpoint and Propofol as an anesthetic agent. As there is a large variety between the patients, we need a controller which should be robust against the disturbances and because the anesthesia process is nonlinear and contains a time delay, model predictive controllers (MPCs) seems act very well for it. In this paper we tried to use a constrained generalized predictive control (GPC) method for controlling the depth of Anesthesia. For comparison a PID controller has been designed. The results showed that the performance of GPC with or without presence of the noise and disturbance was much better than PID controller and also it was more robust.