473 research outputs found

    Advanced multiparametric optimization and control studies for anaesthesia

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    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

    Modelling, Optimisation and Explicit Model Predictive Control of Anaesthesia Drug Delivery Systems

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    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

    Exploratory mathematical frameworks and design of control systems for the automation of propofol anesthesia

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    A variety of automatic control systems are increasingly being deployed to assist clinicians to monitor patient functioning and enhance healthcare delivery during surgical procedures. This article deals with the mathematical design framework of closed-loop infusion schemes for propofol delivery in general anesthesia. The main emphasis of this research series is to come up with a better-performing control system which could handle the clinical concerns of automation-based anesthesia without compromise of safety. Also, the research is geared at studying the performance of these plausible control-based automatic drug infusion patterns in a computer environment prior to actual clinical implementation. The study advances the design of effective model-based predictive control (MPC) strategies familiar to engineers in the process industries, as well as a preliminary design of a proportional–integral–derivative (PID) controller. The consideration of the traditional PID controller is followed by two linear MPC strategies and a nonlinear one. These varieties of closed-loop infusion strategies were designed in order to make well-informed comparison and assessment of the promising method(s) of control for the sought clinical application. The successive linearization technique is being applied in novelty to anesthesia in this work. The results indicate that the MPC controllers show great promise for adoption for automated drug delivery in anesthesia delivering better performance. This sets the pace for future investigations which may assess, via pseudo-clinical in silico studies, the deployment of the controllers

    General Anesthesia as a Multimodal Individualized Clinical Concept

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    In this book, a series of modern multimodal monitoring techniques during general anesthesia are presented, with a focus on patient-oriented anesthesia based on the individual needs of each patient reflected in the degree of hypnosis, the nociception–antinociception balance, and neuromuscular transmission. Moreover, a series of secondary implications for hemodynamic status, post-anesthetic recovery, and patient satisfaction are highlighted

    Exploring the pharmacodynamics of multidrug combinations and using the advances in technology to individualise anaesthetic drug titration

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    In current practice, pharmacokinetic-dynamic (PK/PD) models are frequently used to describe the combined relationship between the time course of drug plasma concentrations (PK) and the time independent relationship between the drug concentration at the receptor site and the clinical effect (PD). This thesis contributes to the knowledge in anaesthetic pharmacology and explores the dose-response relationships of propofol and sevoflurane (with and without the coadministration of remifentanil) in greater detail using PK/PD models. Our studies show that PK/PD models are useful in clinical practice. The concept of neural inertia could have an influence on these models, but is still controversial in humans and it does not break down the essence and applicability of these PK/PD models. Subsequently, we used these models to compare the pharmacodynamics of propofol and sevoflurane (with and without remifentanil) at both a population level as well as at an individual level. This comparison let us describe potency ratios between both hypnotics which is very helpful for anaesthetist when switching between these drugs for any reason during a case. We applied the same PK/PD models and similar potency ratios in clinical practice using the SmartPilot® View, a drug advisory system, to guide anaesthetic drug titration, and we assessed its clinical utility. Finally, we evaluated a novel method to analyse the cerebral drug effect on the EEG using Artificial Intelligence in order to explore the feasibility of whether a single index can quantify the hypnotic effect in a drug-independent way

    Strain threshold for ventilator-induced lung injury

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    Introduction Unphysiological lung strain (tidal volume/functional residual capacity, TV/FRC) may cause ventilator-induced lung injury (VILI) [1]. Whether VILI develops proportionally to the applied strain or only above a critical threshold remains unknown. Methods In 20 healthy, mechanically ventilated pigs, FRC and lung weight were measured by computed tomography. Animals were then ventilated for up to 54 hours with a TV set to produce a predetermined strain. At the end, lung weight was measured with a balance. VILI was defi ned as fi nal lung weight exceeding the initial one. Results Lung weight either did not increase at all (no-VILI group; lung weight change \u201373 \ub1 42 g, n = 9) or markedly augmented (VILI group; 264 \ub1 80 g, n = 11). In the two groups, strain was 1.38 \ub1 0.68 and 2.16 \ub1 0.50 (P <0.01), respectively. VILI occurred only when lung strain reached or exceeded a critical threshold, between 1.5 and 2.1 (Figure 1). Conclusions In animals with healthy lungs VILI only occurs when lung strain exceeds a critical threshold. Reference 1. Gattinoni L, Carlesso E, Cadringher P, et al.: Physical and biological triggers of ventilator-induced lung injury and its prevention [review]. Eur Respir J 2003, 22(Suppl 47):15s-25s

    Cardiac cycle efficiency as prognostic index in ICUs

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    We implement a beam steering system based on a directly-modulated unseeded R-SOA, allowing the distribution of 2.4 GHz 64QAM OFDMA signals with 2048-subcarriers satisfying IEEE 802.16e specifications
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