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Abstract
Target-controlled infusion (TCI) constitutes a clinically available alternative to manually administering the infusion rate of the anesthetic drug propofol. In TCI, a drug infusion profile is optimized to track a reference trajectory of blood plasma or effect site (brain cortex) drug concentration, or a corresponding clinical effect. TCI is a pure feed-forward openloop strategy, fully reliant on an underlying dynamic patient model. We show how TCI dosing of propofol—to achieve a desired depth of hypnosis—can be posed as a QP problem. We design this QP problem based on a nominal pharmacological propofol model by Eleveld et al. Then, we investigate how interpatient variability, described as mixed effects of a particular distribution within the Eleveld model, affects TCI performance. Based on the Mahalanobis distance, we sample from probability quantiles of the mixed-effect model and evaluate the TCI designed for the nominal patient across these samples. The main outcome is that performance, in terms of achieved hypnotic depth, deteriorates to what is at the limit of clinical acceptance already when considering only 1 % of the most likely patients drawn from the uncertainty model. This is under the—for the TCI system unrealistically favorable—assumption that there is no uncertainty in the relation between effect site concentration, and clinical effect. The conclusion from the arising results is that the benefit of propofol TCI over manual dosing is unclear, even within the model that the TCI system was designed for.
Original language | English |
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Title of host publication | Proceedings of the 22nd European Control Conference |
Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Pages | 3027-3032 |
ISBN (Electronic) | 978-390714410-7 |
DOIs | |
Publication status | Published - 2024 |
Event | 22nd European Control Conference, ECC 2024 - Stockholm, Sweden Duration: 2024 Jun 25 → 2024 Jun 28 https://ecc24.euca-ecc.org |
Conference
Conference | 22nd European Control Conference, ECC 2024 |
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Abbreviated title | ECC24 |
Country/Territory | Sweden |
City | Stockholm |
Period | 2024/06/25 → 2024/06/28 |
Internet address |
Subject classification (UKÄ)
- Control Engineering
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- 1 Finished
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Learning pharmacometric model structures from data
Soltesz, K. (Researcher), Bernhardsson, B. (PI), Sundell, J. (Researcher) & Wahlquist, Y. (Researcher)
2022/04/01 → 2024/03/31
Project: Research