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Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents
Author(s) -
Lv Chunle,
Lu Jiejiu,
Jing Li,
Liu TaoTao,
Chen Ming,
Zhang Ren,
Li Chengxin,
Zhou Siru,
Wei Yinyi,
Chen Yiyu
Publication year - 2021
Publication title -
journal of clinical pharmacy and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.622
H-Index - 73
eISSN - 1365-2710
pISSN - 0269-4727
DOI - 10.1111/jcpt.13363
Subject(s) - predictability , medicine , vancomycin , bayesian probability , mean squared prediction error , statistics , predictive modelling , population , computer science , mathematics , environmental health , biology , bacteria , genetics , staphylococcus aureus
What is known and objectives Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set. Methods The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction‐ and simulation‐based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models. Results Ten published PopPK models were assessed. Prediction‐based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% ( F 20 ) ≥35% and PE% within ±30% ( F 30 ) ≥50%), indicating unsatisfactory predictability. In simulation‐based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models. What is new and conclusion The 10 published models performed unsatisfactorily in prediction‐ and simulation‐based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.