Abstract
The goal of target-controlled delivery of intravenous (IV) anesthetics is the achievement and maintenance of a suitable depth of hypnosis (DOH) in a fast and safe manner, where DOH is associated with a certain effect site (i.e. brain) drug concentration. Nowadays, the delivery of anesthetic drugs is performed by target-controlled infusion (TCI) pumps adjusting the delivery rate using an algorithm based on pharmacokinetic (PK) models having no feedback. However, the inaccuracy of concentration prediction using this PK model for certain individuals can be up to 100%. In this chapter, we show that the precision of anesthesia delivery can definitely be improved by realising a feedback loop with sensors able to provide measurements of the anesthetic concentration in body fluids in real time. We present two possible approaches for building the control feedback loop using plasma concentration measurements: one representing the classic method in pharmacokinetics based on Bayesian inference and another one being an example of classic method in control theory based on Kalman filter. The first one performs real-time re-estimation of PK model parameters with each new measurement, while the latter one estimates the offset values for drug concentration correction. The adjusted concentration values are further used to compute the personalised delivery rate using the classic TCI algorithm. To validate the algorithms’ robustness, we simulate measurements covering the maximum space of possible values using inter- and intra-patient variability of the statistical Eleveld’s (Eleveld, Proost, Cortinez, Absalom, Struys, Anesth Analg 118(6):1221–1237, 2014, [8]) PK model. This allows one to disturb the system to its extreme before testing it on patients. We provide the robustness analysis of these algorithms with respect to realistic measurement periods and delays.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbas H, Jiang Z, Jang K, Liang J, Dixit S, Mangharam R (2016) Computer aided clinical trials for implantable cardiac devices. In: SES 2016: Symposium F-2: modeling, design and safety analysis in physiological closed-loop systems
Bailey JM, Haddad WM (2005) Drug dosing control in clinical pharmacology. IEEE Control Syst 25(2):35–51
Beal S, Sheiner L, Boeckmann A, Bauer R (1989–2009) NONMEM user’s guides
Burchum JR, Rosenthal LD, Jones BO, Neumiller JJ, Lehne RA (2016) Lehne’s pharmacology for nursing care
d Silva MM, Mendona T, Wigren T (2010) Online nonlinear identification of the effect of drugs in anaesthesia using a minimal parameterization and BIS measurements. In: Proceedings of the 2010 American control conference, pp 4379–4384
De Smet T, Struys MMRF, Greenwald S, Mortier EP, Shafer SL (2007) Estimation of optimal modeling weights for a Bayesian-based closed-loop system for propofol administration using the bispectral index as a controlled variable: a simulation study. Anesth Analg 105(6):1629–1638
Dumont GA, Ansermino JM (2013) Closed-loop control of anesthesia: a primer for anesthesiologists. Anesth Analg 117(5):1130–1138
Eleveld D, Proost J, Cortinez L, Absalom A, Struys M (2014) A general purpose pharmacokinetic model for propofol. Anesth Analg 118(6):1221–1237
Gentilini A, Frei CW, Glattfedler AH, Morari M, Sieber TJ, Wymann R, Schnider TW, Zbinden AM (2001) Multitasked closed-loop control in anesthesia. IEEE Eng Med Biol Mag 20(1):39–53
Kivlehan F, Garay F, Guo J, Chaum E, Lindner E (2012) Toward feedback-controlled anesthesia: voltammetric measurement of propofol (2,6-diisopropylphenol) in serum-like electrolyte solutions. Anal Chim Acta 84(18):7670–7676
Kotsovolis G, Komninos G (2009) Awareness during anesthesia: how sure can we be at the patient is sleeping indeed? Hippokratia 2(13):83–89
Langmaier J, Garay F, Kivlehan F, Chaum E, Lindner E (2011) Electrochemical quantification of 2,6-diisopropylphenol (propofol). Anal Chim Acta 704(1–2):63–67
Liu N, Chazot T, Hamada S, Landais A, Boichut N, Dussaussoy C, Trillat B, Beydon L, Samain E, Sessler DI, Fischler M (2011) Closed-loop coadministration of propofol and remifentanil guided by bispectral index: a randomized multicenter study. Anesth Analg 12(3):546–557
Mandel JE, Sarraf E (2012) The variability of response to propofol is reduced when a clinical observation is incorporated in the control: a simulation study. Anesth Analg 114(6):1221–1229
Marsh B, White M, Morton N, Kenny GNC (1991) Pharmacokinetic model driven infusion of propofol in children. Br J Anaesth 67(1):41–48
Orchestra Base Primea TCI system. https://www.fresenius-kabi.de/orchestrabaseprimea.htm
Orser BA, Mazer CD, Baker AJ (2008) Awareness during anaesthesia. Can Med Assoc J 2(178):185–188
Rampil IR (1998) A primer for EEG signal processing in anesthesia. Anesthesiology 89(4):980–1002
Schnider T, Minto C, Gambus PL, Andresen C, Goodale D, Shafer S, Youngs E (1998) The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers. Anesthesiology 88(5):1170–1182
Schnider TW, Minto CF, Shafer SL, Gambus PL, Andresen C, Goodale DB, Youngs EJ (1999) The influence of age on propofol pharmacodynamics. Anesthesiology 90(6):1502–1516
Shafer SL, Gregg KM (1992) Algorithms to rapidly achieve and maintain stable drug concentrations at the site of drug effect with a computer-controlled infusion pump. J Pharmacokinet Biopharm 20(2):147–169
Silva MM (2014) Nonlinear modeling and feedback control of drug delivery in anesthesia. Doctoral thesis
Silva MM, Medvedev A, Wigren T, Mendona T (2015) Modeling the effect of intravenous anesthetics: a path toward individualization. IEEE Design Test 32(5):17–26
Simalatsar A, Guidi M, Buclin T (2016) Cascaded PID controller for anaesthesia delivery. In: 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 533–536
Stradolini F, Kilic T, Taurino I, De Micheli G, Boero G (2018) Cleaning strategy for carbon-based electrodes: long-term propofol monitoring in human serum. Sens Actuators B B(269):304–313
Stradolini F, Tuoheti A, Ros PM, Demarchi D, Carrara S (2017) Raspberry pi based system for portable and simultaneous monitoring of anesthetics and therapeutic compounds. In: 2017 New generation of CAS (NGCAS), pp 101–104
Zhusubaliyev ZT, Medvedev A, Silva MM (2013) Bifurcation analysis for PID-controller tuning based on a minimal neuromuscular blockade model in closed-loop anesthesia. In: 52nd IEEE conference on decision and control, pp 115–120
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Simalatsar, A., Guidi, M., Roduit, P., Buclin, T. (2019). Methods for Personalised Delivery Rate Computation for IV Administered Anesthetic Propofol. In: Liò, P., Zuliani, P. (eds) Automated Reasoning for Systems Biology and Medicine. Computational Biology, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-17297-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-17297-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17296-1
Online ISBN: 978-3-030-17297-8
eBook Packages: Computer ScienceComputer Science (R0)