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

Pawłowski, A.; Schiavo, Michele; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio

doi:10.1016/j.jprocont.2022.07.007

Cited by 12 publications

(10 citation statements)

References 58 publications

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“…In the analyzed system, T base has been selected as 1 second and matches the BIS monitor data rate. Then, T max has been set to 10 seconds (that is, n=10) because of safety reasons, as this ensures the minimal performance of the closed-loop control system [11]. Indeed, in a previous work [22], it was shown that the sampling period of 10 second is the maximum value that allows the clinical requirements to be met in terms of accuracy and disturbance compensation capability in the presence of measurement noise.…”

Section: Control System Tuningmentioning

confidence: 99%

“…For this reason, most of the closed-loop control systems are focused on the single-input single-output (SISO) problem where only one drug and one controlled variable are considered [3,[8][9][10]. Alternatively, a MISO (multiple-input single output) approach can be implemented, where the control problem takes into account the drugs coadministration and their resulting effect on a single controlled variable [10,11]. In this work we focus on a SISO control problem, that is, the depth of hypnosis (DoH) task, where the dosage of the hypnotic drug propofol is the control variable and the Bispectral Index Scale is the controlled variable.…”

Section: Introductionmentioning

confidence: 99%

“…Disregarding the predictive control approach used, most of the mentioned developments are affected by a significant degradation of the control performance when the process noise is considered [11,17,23,24]. The presence of noise in the control system usually results in a noisy control action that, in many cases, implies a non-optimal use of control resources.…”

Section: Introductionmentioning

confidence: 99%

“…The designed controller has been extensively tested under realistic scenarios, taking into account real noise extracted from clinical test as well as unmeasurable disturbances. The obtained results are compared with the classic MPC control scheme from [11], which has been designed by explicitly taking into account the noise issue in the DoH control problem. Moreover, the robustness to inter-and intra-patient variability is evaluated by applying a Monte Carlo technique to provide a population wide patients distribution.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Event-based MPC for propofol administration in anesthesia

Pawłowski¹,

Schiavo²,

Latronico³

et al. 2023

Computer Methods and Programs in Biomedicine

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Section: Control System Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event-based MPC for propofol administration in anesthesia

Pawłowski¹,

Schiavo²,

Latronico³

et al. 2023

Computer Methods and Programs in Biomedicine

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“…In addition, the models can enable in silico testing of various potentially clinically implementable closed-loop control algorithms, e.g., PID-controller-based approaches [ 29 , 30 , 31 ]. Furthermore, they represent the basis for a number of advanced control approaches, such asrobust control [ 32 , 33 , 34 ], model-predictive control [ 35 , 36 ], fuzzy-rule-based decision system [ 37 ], event-based control [ 38 ], etc. Despite difficulties in objective pain measurement [ 18 , 39 ], a number of articles also consider the inherent MIMO (or MISO) nature of the controlled system, which is due to drug interactions, especially when analgesics (such as remeifentanil) are considered [ 40 , 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Individualized Patient-Oriented Depth-of-Hypnosis Measurement Based on Bispectral Index

Karer

Škrjanc

2022

Sensors

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Total intravenous anesthesia is an anesthesiologic technique where all substances are injected intravenously. The main task of the anesthesiologist is to assess the depth of anesthesia, or, more specifically, the depth of hypnosis (DoH), and accordingly adjust the dose of intravenous anesthetic agents. However, it is not possible to directly measure the anesthetic agent concentrations or the DoH, so the anesthesiologist must rely on various vital signs and EEG-based measurements, such as the bispectral (BIS) index. The ability to better measure DoH is directly applicable in clinical practice—it improves the anesthesiologist’s assessment of the patient state regarding anesthetic agent concentrations and, consequently, the effects, as well as provides the basis for closed-loop control algorithms. This article introduces a novel structure for modeling DoH, which employs a residual dynamic model. The improved model can take into account the patient’s individual sensitivity to the anesthetic agent, which is not the case when using the available population-data-based models. The improved model was tested using real clinical data. The results show that the predictions of the BIS-index trajectory were improved considerably. The proposed model thus seems to provide a good basis for a more patient-oriented individualized assessment of DoH, which should lead to better administration methods that will relieve the anesthesiologist’s workload and will benefit the patient by providing improved safety, individualized treatment, and, thus, alleviation of possible adverse effects during and after surgery.

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Multivariable discrete MPC with exponential cost function on the automation of the anesthesia process

Sandre‐Hernandez,

Ramírez‐Barrios,

Ordaz

et al. 2023

Intl J Robust & Nonlinear

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The automation of the delivery of anesthesia infusion rate in a general medical procedure consists of calculating the adequate infusion to regulate a patient's hypnosis state. In this procedure, the patient model has been considered as a Single‐Input Single‐Output system. However, the anesthesiologist introduces other medications, such as analgesics. Therefore, this contribution considers a closed‐loop formulation with two drugs as input control variables: propofol as an anesthetic, and remifentanil as an analgesic. Then, through the mathematical model description, this paper introduces a multiple‐input and multiple‐output (MIMO) model predictive control (MPC) to regulate the deep of hypnosis (DOH) based on the co‐administration of propofol and remifentanil. Since the control variables must be positive during the procedure, the MPC is formulated as a constrained optimization problem. Quadratic programming solves the optimization problem by designing and developing an exponential cost function, which allows the rapid convergence of the MPC optimization algorithm. Finally, the proposed MPC formulation is tested on 12 patients in silico, simulating a 60‐min general surgery. The simulated results verify the performance of the proposed MPC.

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

Cited by 12 publications

References 58 publications

Event-based MPC for propofol administration in anesthesia

Event-based MPC for propofol administration in anesthesia

Improved Individualized Patient-Oriented Depth-of-Hypnosis Measurement Based on Bispectral Index

Multivariable discrete MPC with exponential cost function on the automation of the anesthesia process

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