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This paper introduces a relation between body mass index and fat tissue properties, thereby addressing a significant gap in conventional pharmacokinetic models for non-lean patients affecting patient outcome from long-term anesthesia. We use a Trust-Region algorithm to approximate the risk of drug trapping in fat cells, correlating the body mass index with the ratio of porosity to permeability in fat tissues. By iterative optimum search algorithms, we obtain approximations of a nonlinear function and compare with classical search methods. By providing a model for comorbidity risk associated with body mass index for general anesthesia, we enhance the expected closed-loop performance of any regulatory algorithm making use of this additional information.

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A Trust-Region Algorithm for Identification of BMI-Related Fat Tissue Properties

  • 会议论文

  • 发表日期：

    2025年

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  • A. R. Ynineb,
  • E. Yumuk,
  • G. Ben Othman,
  • H. Farbakhsh,
  • D. Copot,
  • R. De Keyser,
  • S. Ladaci,
  • C. M. Ionescu

摘要

This paper introduces a relation between body mass index and fat tissue properties, thereby addressing a significant gap in conventional pharmacokinetic models for non-lean patients affecting patient outcome from long-term anesthesia. We use a Trust-Region algorithm to approximate the risk of drug trapping in fat cells, correlating the body mass index with the ratio of porosity to permeability in fat tissues. By iterative optimum search algorithms, we obtain approximations of a nonlinear function and compare with classical search methods. By providing a model for comorbidity risk associated with body mass index for general anesthesia, we enhance the expected closed-loop performance of any regulatory algorithm making use of this additional information.