Automated Drug Delivery in Anesthesia

Neckebroek, Martine; Smet, Tom De; Struys, Michel

doi:10.1007/s40140-012-0004-3

Cited by 30 publications

(9 citation statements)

References 92 publications

(72 reference statements)

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“…Relatedly, most automatic drug delivery research in anesthesia has focused on controlling the hypnotic component of anesthesia through closed-loop controlled titration of propofol, based on measurements from an electroencephalogram (EEG) monitor. A partial overview of works on EEGcontrolled propofol infusion can be found in [Le Guien et al, 2016;Neckebroeak et al, 2013].…”

Section: Scopementioning

confidence: 99%

Models for control of intravenous anesthesia

Soltesz

Heusden

Dumont

2020

Automated Drug Delivery in Anesthesia

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Section: Scopementioning

confidence: 99%

Models for control of intravenous anesthesia

Soltesz

Heusden

Dumont

2020

Automated Drug Delivery in Anesthesia

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“…For the purpose of automating the process of anesthesia, numerous control schemes have been proposed over the last years. [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.…”

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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“…We need to validate Anspec-pro with an already approved device that is based on similar methodology, under the same conditions. On long term, our methodology will enable evaluation of pain by means of mathematical models which can be then included in a fully automated drug delivery for anesthesia system [23][24][25][26][27]. Thus, the secondary objective of this observational trial is frequency exploration of the tissue electrical properties influenced by nociception processes.…”

Section: Introductionmentioning

confidence: 99%

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

Neckebroek

Ghita

et al. 2020

JCM

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Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0–10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r2 = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions.

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Automated Drug Delivery in Anesthesia

Cited by 30 publications

References 92 publications

Models for control of intravenous anesthesia

Models for control of intravenous anesthesia

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

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

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