Individualized PID tuning for maintenance of general anesthesia with propofol and remifentanil coadministration

Schiavo, Michele; Padula, Fabrizio; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio

doi:10.1016/j.jprocont.2021.12.004

Cited by 21 publications

(10 citation statements)

References 31 publications

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“…Nevertheless, the conclusion can be mitigated considering that the PID used for the comparison has been optimized on a patient table while in the more recent paper [20] the authors proposed to optimize the PID for each patient characteristic, and thus for each PK model. Since this controller was longer to implement and to test, though, the one from [19] has been used.…”

Section: Resultsmentioning

confidence: 99%

“…Since this controller was longer to implement and to test, though, the one from [19] has been used. In [20] the authors explain that this individualized approach allows the controller to reduce undershoot. However similar conclusions should be obtained with the more recent PID version since it is inherent to this architecture which does not handle the PD uncertainties.…”

Section: Resultsmentioning

confidence: 99%

“…This simulation is used to test the controllers on a wide range of patient profiles. The performance criteria are those proposed in [29] and also used in [19], [20], and [21]. They are listed below: The involved optimization problems are solved using CASADI software [30] with IPOPT solver.…”

Section: B Simulation Setupmentioning

confidence: 99%

“…This idea has been then formalized in [17] and [18] where an H ∞ and an MPC controller have been respectively tested with clinical trials and simulations. More recently in [19], [20], and [21] the authors have used the idea of fixing the ratio between drug flow rates to propose a PID and an MPC controller. To assess, the robustness of those last controllers, uncertainties have been introduced in the model and Monte-Carlo simulations have been performed.…”

Section: Introductionmentioning

confidence: 99%

“…From a control point of view, this is the most challenging part of the anesthesia process since the patient drug reaction is the most uncertain. Compared to the recent literature on the topic, [19], [20] and [21], the method presented in this paper does not assume a fixed ratio between drug flow, the balance between the drugs is managed through the optimization process. The robustness of the method is tested using Monte-Carlo simulations and results are compared to those obtained by the PID controller presented in [19].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Data-based Pharmacodynamic Modeling for BIS and Mean Arterial Pressure Prediction during General Anesthesia

Aubouin--Pairault

Fiacchini

Dang

2023

2023 European Control Conference (ECC)

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In this paper, a multi-model predictive control approach is used to automate the co-administration of propofol and remifentanil from bispectral index measurement during general anesthesia. To handle the parameter uncertainties in the non-linear output function, multiple Extended Kalman Filters are used to estimate the state of the system in parallel. The best model is chosen using a model-matching criterion and used in a non-linear MPC to compute the next drug rates. The method is compared with a conventional non-linear MPC approach and a PID from the literature. The robustness of the controller is evaluated using Monte-Carlo simulations on a wide population introducing uncertainties in the models. Both simulation setup and controller codes are accessible in open source for further use. Our preliminary results show the potential interest in using a multi-model method to handle parameter uncertainties.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: B Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data-based Pharmacodynamic Modeling for BIS and Mean Arterial Pressure Prediction during General Anesthesia

Aubouin--Pairault

Fiacchini

Dang

2023

2023 European Control Conference (ECC)

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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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Online identification of pharmacodynamic parameters for closed-loop anesthesia with model predictive control

Aubouin–Pairault,

Fiacchini,

Dang

2024

Computers & Chemical Engineering

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Individualized PID tuning for maintenance of general anesthesia with propofol and remifentanil coadministration

Cited by 21 publications

References 31 publications

Data-based Pharmacodynamic Modeling for BIS and Mean Arterial Pressure Prediction during General Anesthesia

Data-based Pharmacodynamic Modeling for BIS and Mean Arterial Pressure Prediction during General Anesthesia

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

Online identification of pharmacodynamic parameters for closed-loop anesthesia with model predictive control

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