Sequential updating of a new dynamic pharmacokinetic model for caffeine in premature neonates.

摘要: Caffeine treatment is widely used in nursing care to reduce the risk of apnoea premature neonates. To check therapeutic efficacy against apnoea, caffeine concentration blood an important indicator. The present study was aimed at building a pharmacokinetic model as basis for medical decision support tool. In proposed model, time dependence physiological parameters introduced describe rapid growth take into account large variability population, Pharmacokinetic embedded population structure. whole inferred within Bayesian framework. update predictions data incoming patient are collected, we propose fast method that can be context. This involves sequential updating (at individual and levels) via stochastic particle algorithm. Our provides better than ones obtained with models previously published. We show, through example, improves (reduce bias length credibility intervals). using body mass studied. It shows how informative contrast data. methodological predict blood, after given if collected on treated neonate.