The Performance of an Artificial Neural Network Model in Predicting the Early Distribution Kinetics of Propofol in Morbidly Obese and Lean Subjects.

Journal: Anesthesia and analgesia
PMID: 33079873

Abstract

BACKGROUND: Induction of anesthesia is a phase characterized by rapid changes in both drug concentration and drug effect. Conventional mammillary compartmental models are limited in their ability to accurately describe the early drug distribution kinetics. Recirculatory models have been used to account for intravascular mixing after drug administration. However, these models themselves may be prone to misspecification. Artificial neural networks offer an advantage in that they are flexible and not limited to a specific structure and, therefore, may be superior in modeling complex nonlinear systems. They have been used successfully in the past to model steady-state or near steady-state kinetics, but never have they been used to model induction-phase kinetics using a high-resolution pharmacokinetic dataset. This study is the first to use an artificial neural network to model early- and late-phase kinetics of a drug.

Authors

  • Jerry Ingrande
    From the Department of Anesthesiology.
  • Rodney A Gabriel
    Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.
  • Julian McAuley
    Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California.
  • Karolina Krasinska
    Stanford University Mass Spectrometry Laboratory, Stanford, California.
  • Allis Chien
    Stanford University Mass Spectrometry Laboratory, Stanford, California.
  • Hendrikus J M Lemmens
    Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California.

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