Pharmacodynamic modeling of propofol-induced general anesthesia in young adults
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ChakravartyEtAl_HIPOCT2017_PDmodelPropofolHuman.pdf
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Author(s)
Chakravarty, Sourish
Nikolaeva, Ksenia
Kishnan, Devika
Flores, Francisco J.
Purdon, Patrick Lee
Brown, Emery Neal
Date Issued
December 2017
Journal
2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chakravarty, Sourish et al. "Pharmacodynamic modeling of propofol-induced general anesthesia in young adults." 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), November 2017, Bethesda, Maryland, USA, Institute of Electrical and Electronics Engineers (IEEE), December 2017. © 2017 IEEE
Version
Author's final manuscript
Abstract
Target controlled infusion (TCI) of intraveneous anesthetics can assist clinical practitioners to provide improved care for General Anesthesia (GA). Pharmacoki-netic/Pharmacodynamic (PK/PD) models help in relating the anesthetic drug infusion to observed brain activity inferred from electroencephalogram (EEG) signals. The parameters in popular population PK/PD models for propofol-induced GA (Marsh and Schnider models) are either verified based on proprietary functions of the EEG signal which are difficult to correlate with the neurophysiological models of anesthesia, or the marker itself needs to be estimated simultaneously with the PD model. Both these factors make these existing paradigms challenging to apply in real-time context where a patient-specific tuning of parameters is desired. In this work, we propose a simpler EEG marker from frequency domain description of EEG and develop two corresponding PK/PD modeling approaches which differ in whether they use existing population-level PK models (approach 1) or not (approach 2). We use a simple deterministic parameter estimation approach to identify the unknown PK/PD model parameters from an existing human EEG data-set. We infer that both approaches 1 and 2 yield similar and reasonably good fits to the marker data. This work can be useful in developing patient-specific TCI strategies to induce GA.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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DOI of Published Version
https://doi.org/10.1109/hic.2017.8227580
