Model structures, uncertainty and pharmacometric covariate modeling

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Abstract: The conventional series interconnection of a mammillary compartment model for pharmacodynamics (PK) and a Hill sigmoid for pharmacodynamics (PD) forms the basis for a vast majority of model-based feedback controller designs for automatic delivery of anesthetic drugs. Considered, and in some cases clinically evaluated, controllers range from PID to MPC. While to some extent physiologically motivated, and well-established within the pharmacology literature, using the models in a clinical setting comes with several challenges. One challenge lies in data available for system identification being limited in excitation, for patient safety and ethical reasons. Another challenge is variability in response dynamics between patients. This is typically approached through pharmacometric covariate modeling, that aims to model the PKPD parameters as explicit functions of known covariates such as patient age, weight, etc. Despite numerous published covariate models for popular anesthetic drugs, there are no models that fit data as closely as implicitly assumed by many proposed control synthesis strategies. There are several plausible contributors to this situation: the pre-determined search space of covariate functions might limit model quality; EEGmonitor artifacts and unmeasured disturbances; inter-patient variability of response dynamics caused by for example change in hemodynamic state; structural mismatch between actual response dynamics and assumed PK-PD model. In this talk we look at some published clinical data and argue that closed-loop control of anesthesia is much more a data-driven modeling challenge, than a controller synthesis challenge. Worthwhile questions to ask are what model structures provide an adequate balance between under-fitting of data, identifiability of parameters form the same data, and adequacy for model-based controller synthesis. There is unlikely one simple answer, but a large and increasing amount of modeling data is available to investigate these questions.
Period2022 aug. 22
Evenemangstitel6th IEEE CSS Conference on Control Technology and Applications (CCTA 2022)
Typ av evenemangKonferens
PlatsTrieste, ItalienVisa på karta

Ämnesklassifikation (UKÄ)

  • Reglerteknik
  • Farmakologi och toxikologi