Individualized closed-loop anesthesia through patient model partitioning
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/210bfdf2-1359-4eed-a61e-75f1b360e1cf
- author
- Wahlquist, Ylva LU ; van Heusden, Klaske ; Dumont, Guy A. and Soltesz, Kristian LU
- organization
- publishing date
- 2020-08
- 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}}, }