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Individualized closed-loop anesthesia through patient model partitioning

Wahlquist, Ylva LU ; van Heusden, Klaske ; Dumont, Guy A. and Soltesz, Kristian LU orcid (2020) 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society In Annual International Conference of the IEEE Engineering in Medicine and Biology Society p.361-364
Abstract
Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, interpatient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic–pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated... (More)
Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, interpatient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic–pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.

Clinical relevance—The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing. (Less)
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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20)
series title
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
pages
361 - 364
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
conference location
Quebec, Canada
conference dates
2020-07-20 - 2020-07-24
external identifiers
  • scopus:85091027241
  • pmid:33018003
ISSN
2694-0604
DOI
10.1109/EMBC44109.2020.9176452
project
Hemodynamic Stabilization
Anesthesia in Closed Loop
language
English
LU publication?
yes
id
210bfdf2-1359-4eed-a61e-75f1b360e1cf
date added to LUP
2020-04-14 10:07:18
date last changed
2023-12-01 11:26:41
@inproceedings{210bfdf2-1359-4eed-a61e-75f1b360e1cf,
  abstract     = {{Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, interpatient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic–pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.<br/><br/>Clinical relevance—The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing.}},
  author       = {{Wahlquist, Ylva and van Heusden, Klaske and Dumont, Guy A. and Soltesz, Kristian}},
  booktitle    = {{42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20)}},
  issn         = {{2694-0604}},
  language     = {{eng}},
  pages        = {{361--364}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Annual International Conference of the IEEE Engineering in Medicine and Biology Society}},
  title        = {{Individualized closed-loop anesthesia through patient model partitioning}},
  url          = {{https://lup.lub.lu.se/search/files/78755005/soltesz20b.pdf}},
  doi          = {{10.1109/EMBC44109.2020.9176452}},
  year         = {{2020}},
}