ArticleTherapeutic drug monitoring2020
Model-Informed Bayesian Estimation Improves the Prediction of Morphine Exposure in Neonates and Infants.
Article in Therapeutic drug monitoring, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 3 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Systemic opioid regimens for postoperative pain in neonates.The Cochrane database of systematic reviews · 2023Pooled it
- Systemic opioids versus other analgesics and sedatives for postoperative pain in neonates.The Cochrane database of systematic reviews · 2023Pooled it
- Systemic opioid regimens for postoperative pain in neonates.The Cochrane database of systematic reviews · 2023Pooled it
- The Coming of Age of Model-Informed Precision Therapeutics for Children.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2025Article
- Evaluation of Neonatal and Paediatric Vancomycin Pharmacokinetic Models and the Impact of Maturation and Serum Creatinine Covariates in a Large Multicentre Data Set.Clinical pharmacokinetics · 2023Article
- Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review.Frontiers in pediatrics · 2021Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPain control in infants is an important clinical concern, with potential long-term adverse neurodevelopmental effects. Intravenous morphine is routinely administered for postoperative pain management; however, its dose-concentration-response relationship in neonates and infants has not been well characterized. Although the current literature provides dosing guidelines for the average infant, it fails to control for the large unexplained variability in morphine clearance and response in individual patients. Bayesian estimation can be used to control for some of this variability. The authors aimed to evaluate morphine pharmacokinetics (PKs) and exposure in critically ill neonates and infants receiving standard-of-care morphine therapy and compare a population-based approach to the model-informed Bayesian techniques.
methodsThe PKs and exposure of morphine and its active metabolites were evaluated in a prospective opportunistic PK study using 221 discarded blood samples from 57 critically ill neonates and infants in the neonatal intensive care unit. Thereafter, a population-based PK model was compared with a Bayesian adaptive control strategy to predict an individual's PK profile and morphine exposure over time.
resultsAmong the critically ill neonates and infants, morphine clearance showed substantial variability with a 40-fold range (ie, 2.2 to 87.1, mean 23.7 L/h/70 kg). Compared with the observed morphine concentrations, the population-model based predictions had an R of 0.13, whereas the model-based Bayesian predictions had an R of 0.61.
conclusionsModel-informed Bayesian estimation is a better predictor of morphine exposure than PK models alone in critically ill neonates and infants. A large variability was also identified in morphine clearance. A further study is warranted to elucidate the predictive covariates and precision dosing strategies that use morphine concentration and pain scores as feedbacks.
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