Towards a Multivariable Model for Controlling the Depth of Anaesthesia using Propofol and Remifentanil

Ionescu, Clara-Mihaela; Nașcu, Ioan; Keyser, Robain De

doi:10.3182/20120829-3-hu-2029.00009

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…In fact, hypnosis and analgesia should be addressed simultaneously due to the synergistic effect between the propofol and the analgesic drug remifentanil. The desirable control architecture should have a multiple-input-multiple-output (MIMO) structure, in order to handle the mutual interaction between control variables (the drugs dosage) and controlled variables (DoH and analgesia) [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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

Pawłowski

Schiavo²,

Latronico³

et al. 2022

Journal of Process Control

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Section: Introductionmentioning

confidence: 99%

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

Pawłowski

Schiavo²,

Latronico³

et al. 2022

Journal of Process Control

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“…An individualised physiological based, patient specific, compartmental model for volatile anaesthesia is presented and developed in [12] and a combined strategy of model predictive control (MPC) and estimation under uncertainty is presented in [13]. For intravenous anaesthesia, robustness tests of MPC for DOA using the EPSAC for a single input single output (SISO) model is presented in [14] , different protocols for the administration of Propofol and Remifentanil (multiple input single output (MISO) model) are evaluated in [15] and in [16] a second output variable is determined, that originates from the effect of Remifetanil and leads to the implementation of a MIMO algorithm.…”

mentioning

confidence: 99%

Advanced Model-Based Control Studies for the Induction and Maintenance of Intravenous Anaesthesia

Nașcu

Krieger

Ionescu

et al. 2015

IEEE Trans. Biomed. Eng.

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Abstract-The paper describes strategies towards modelbased automation of intravenous anaesthesia employing advanced control techniques. In particular, based on a detailed compartmental mathematical model featuring pharmacokinetic and pharmacodynamics information two alternative model predictive control strategies are presented: a model predictive control strategy, based on online optimisation, the Extended Predictive Self Adaptive Control (EPSAC) and a multiparametric control strategy based on offline optimisation, the multi-parametric model predictive control (mp-MPC). The multi-parametric features to account for the effect of nonlinearity and the impact of estimation are also described. The control strategies are tested on a set of 12 virtually generated patient models for the regulation of the depth of anaesthesia (DOA) by means of the Bispectral Index (BIS) using Propofol as the administrated anaestetic. The simulations show fast response, suitability of dose and robustness to induce and maintain the desired BIS setpoint.

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Drug co‐administration in anesthesia using event‐based MPC

Pawłowski,

Schiavo,

Latronico

et al. 2023

Intl J Robust & Nonlinear

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In this article, a predictive event‐based control architecture for multivariable drug infusion in the anesthesia process is presented. The control scheme considers a multiple‐input single‐output process, where the depth of hypnosis is the controlled variable and the infusion rates of propofol and remifentanil, which are co‐administered by imposing a fixed ratio between them, are the control variables. The control system is built on the top of a nonlinear pharmacokinetic/pharmacodynamic model for drug co‐administration that reflects the super‐additive effect of both drugs on the bispectral index scale (BIS), which is a measure of the hypnotic state of the patient. Further, in order to compensate the nonlinear behavior of the system, it exploits an external predictor that is designed to increase the robustness to inter‐/intra‐patient variability. The overall controller parameters are tuned by applying an optimization method on a representative set of virtual patients. The main feature of the proposed control algorithm consists in its asynchronous execution, which can be beneficial in reducing control signal changes due to the noisy measurements of the BIS. The evaluation of the analyzed control architecture through a simulation study reveals that control action variations can be reduced by about 48% on average with respect to its time‐based counterpart. This reduction is obtained at the expense of a lower control accuracy, resulting in a degradation of 18% in terms of integrated absolute error. Results are acceptable from a clinical practice perspective proving that the proposed approach is a feasible alternative to classical time‐based predictive controllers.

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Towards a Multivariable Model for Controlling the Depth of Anaesthesia using Propofol and Remifentanil

Cited by 4 publications

References 23 publications

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

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

Advanced Model-Based Control Studies for the Induction and Maintenance of Intravenous Anaesthesia

Drug co‐administration in anesthesia using event‐based MPC

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