Event-Based control of depth of hypnosis in anesthesia

Merigo, Luca; Beschi, Manuel; Padula, Fabrizio; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio

doi:10.1016/j.cmpb.2017.06.007

Cited by 46 publications

(20 citation statements)

References 47 publications

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“…In this case, feedback control is used instead of an anesthesiologist to frequently adjust the infusion profile or target concentration in closed-loop controllers; however, anesthesiologists are still needed to set the desired DoA or DoH and supervise the patient's state via clinical metrics. The result is a reduction in the anesthesiologist's workload, potentially helping to avoid problems associated with distractions and fatigue, as well as increased safety for the patient due to continuous monitoring, the possibility of lower administered drug dosages with a faster postoperative recovery, and a reduction in the occurrence of drug-induced side effects [229]. For these reasons, fully automated systems may have the potential to one-day outperform manual infusion dosing [230], [231].…”

Section: Automation In Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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Section: Automation In Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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“…The control architecture proposed in this paper is similar to the one described in [40], but here the standard time‐driven PID controller is substituted with the event‐based PIDPlus controller, which was proposed in [41] for the administration of propofol only.…”

Section: Control Architecturementioning

confidence: 99%

“…The main result is an optimal tuning rule that allows an easy implementation of the proposed control scheme in practice, guaranteeing adequate robustness of the closed‐loop system. In particular, we extend the methodology proposed in [41] for the propofol‐only administration to the TISO case by combining a PIDPlus controller [42] with the control architecture that implements the explicit technique for the propofol and remifentanil coadministration successfully tested in [40]. The main contribution of this paper is the determination of optimal tuning rules for the control system.…”

Section: Introductionmentioning

confidence: 99%

Event‐based control tuning of propofol and remifentanil coadministration for general anaesthesia

Merigo

Padula

Latronico

et al. 2020

IET Control Theory & Appl

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In this study, the authors present robust tuning rules for an event-based control architecture for the automatic regulation of the depth of hypnosis in anaesthesia. The authors' control system uses propofol and remifentanil coadministration as control variables and the bispectral index as controlled variable. The control system is based on a PIDPlus controller combined with an event generator that detects significant variations of the BIS signal, thus providing strong filtering of the noise. A fixed ratio between the drug infusions allows the anaesthesiologist to explicitly regulate the opioid-hypnotic balance of the anaesthesia. The tuning rules are developed by solving a min-max optimisation problem that optimises the worst-case scenario over a given data set of patient models. A gain scheduling strategy yields optimal performance in both the induction and the maintenance phases of anaesthesia. Finally, through the Monte Carlo method, they validate the effectiveness of the proposed approach on a general population, and the robustness to the intra-and inter-patient variability for different infusion balances.

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“…[7][8][9] Many automated drug delivery methodologies use the proportional-integral-derivative (PID) architecture. 3,[10][11][12][13][14][15] The main drawback of these controllers is that they cannot anticipate the response of the patient. Moreover they do not have any prior knowledge of the drug metabolism, so the performances are sub-optimal.…”

Section: Introductionmentioning

confidence: 99%

A robust model predictive control framework for the regulation of anesthesia process with Propofol

Ntouskas

Sarimveis

2021

Optim Control Appl Methods

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In this work, we present a robust Model Predictive Control (MPC) strategy based on linear matrix inequalities (LMIs) for the intravenous administration of Propofol, a drug which is used for anesthesia during surgeries. The controller is designed for a population of patients and takes into account constraints on the amount of administered drug and on the drug concentration profile. A detailed compartmental mathematical model available in the literature is adjusted to the available data and provides the future predictions of the process. In the context of the application, only the depth of anesthesia (BIS index) is assumed to be measured-as it is common in practice. The state of the system (drug amount in organs) is estimated in real-time by incorporating a state observer.The derived control scheme, along with the designed state observer are able to deal with major challenges in controlling the depth of anesthesia which are inter-and intra-patient variability, model nonlinearity, and model uncertainty.The controller is able to satisfy the divergent characteristics of the patients of the dataset, while satisfying in parallel all the imposed constraints. Moreover, we show that by considering smaller groups of patients with similar characteristics the corresponding responses are significantly improved.

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Event-Based control of depth of hypnosis in anesthesia

Cited by 46 publications

References 47 publications

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Event‐based control tuning of propofol and remifentanil coadministration for general anaesthesia

A robust model predictive control framework for the regulation of anesthesia process with Propofol

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