Model-Informed Bayesian Estimation Improves the Prediction of Morphine Exposure in Neonates and Infants.

Abstract

Pain 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. The 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. Among 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. Model-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.