title: Sequential updating of a new dynamic pharmacokinetic model for caffeine in premature neonates.
abstract: OBJECTIVE
  Caffeine treatment is widely used in nursing care to reduce the risk of apnoea in premature neonates. To check the therapeutic efficacy of the treatment against apnoea, caffeine concentration in blood is an important indicator. The present study was aimed at building a pharmacokinetic model as a basis for a medical decision support tool.
  
  
  METHODS
  In the proposed model, time dependence of physiological parameters is introduced to describe rapid growth of neonates. To take into account the large variability in the population, the pharmacokinetic model is embedded in a population structure. The whole model is inferred within a Bayesian framework. To update caffeine concentration predictions as data of an incoming patient are collected, we propose a fast method that can be used in a medical context. This involves the sequential updating of model parameters (at individual and population levels) via a stochastic particle algorithm.
  
  
  RESULTS
  Our model provides better predictions than the ones obtained with models previously published. We show, through an example, that sequential updating improves predictions of caffeine concentration in blood (reduce bias and length of credibility intervals). The update of the pharmacokinetic model using body mass and caffeine concentration data is studied. It shows how informative caffeine concentration data are in contrast to body mass data.
  
  
  CONCLUSIONS
  This study provides the methodological basis to predict caffeine concentration in blood, after a given treatment if data are collected on the treated neonate.
authors.affiliation: Institut National de l'Environnement Industriel et des Risques, Verneuil-en-Halatte, France. sandrine.micallef@ineris.fr
authors.forename: Sandrine
authors.initials: S
authors.lastname: Micallef
authors.forename: Billy
authors.initials: B
authors.lastname: Amzal
authors.forename: Véronique
authors.initials: V
authors.lastname: Bach
authors.forename: Karen
authors.initials: K
authors.lastname: Chardon
authors.forename: Pierre
authors.initials: P
authors.lastname: Tourneux
authors.forename: Frédéric Y
authors.initials: FY
authors.lastname: Bois
mesh_terms: D001499:Bayes Theorem; D002110:Caffeine / Q000493:pharmacokinetics*; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature
chemical_list: D002110:Caffeine