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Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates
by
Thoma, Yann
, Alrahahleh, Dua’a
, Luig, Melissa
, Halena, Stephanie
, Kim, Hannah Yejin
, Van Daele, Ruth
, Stocker, Sophie
, Alffenaar, Jan-Willem
, Nguyen, Thi
in
Anti-Bacterial Agents - pharmacokinetics
/ Antibiotics
/ Bayes Theorem
/ Creatinine
/ Datasets
/ Demographics
/ Drug dosages
/ Drug Monitoring
/ Forecasting
/ Gestational age
/ Humans
/ Infant, Newborn
/ Intensive care
/ Internal Medicine
/ Medicine
/ Medicine & Public Health
/ Original
/ Original Research Article
/ Patients
/ Pharmacokinetics
/ Pharmacology/Toxicology
/ Pharmacotherapy
/ Software
/ Therapeutic drug monitoring
/ Vancomycin - pharmacokinetics
2024
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Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates
by
Thoma, Yann
, Alrahahleh, Dua’a
, Luig, Melissa
, Halena, Stephanie
, Kim, Hannah Yejin
, Van Daele, Ruth
, Stocker, Sophie
, Alffenaar, Jan-Willem
, Nguyen, Thi
in
Anti-Bacterial Agents - pharmacokinetics
/ Antibiotics
/ Bayes Theorem
/ Creatinine
/ Datasets
/ Demographics
/ Drug dosages
/ Drug Monitoring
/ Forecasting
/ Gestational age
/ Humans
/ Infant, Newborn
/ Intensive care
/ Internal Medicine
/ Medicine
/ Medicine & Public Health
/ Original
/ Original Research Article
/ Patients
/ Pharmacokinetics
/ Pharmacology/Toxicology
/ Pharmacotherapy
/ Software
/ Therapeutic drug monitoring
/ Vancomycin - pharmacokinetics
2024
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Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates
by
Thoma, Yann
, Alrahahleh, Dua’a
, Luig, Melissa
, Halena, Stephanie
, Kim, Hannah Yejin
, Van Daele, Ruth
, Stocker, Sophie
, Alffenaar, Jan-Willem
, Nguyen, Thi
in
Anti-Bacterial Agents - pharmacokinetics
/ Antibiotics
/ Bayes Theorem
/ Creatinine
/ Datasets
/ Demographics
/ Drug dosages
/ Drug Monitoring
/ Forecasting
/ Gestational age
/ Humans
/ Infant, Newborn
/ Intensive care
/ Internal Medicine
/ Medicine
/ Medicine & Public Health
/ Original
/ Original Research Article
/ Patients
/ Pharmacokinetics
/ Pharmacology/Toxicology
/ Pharmacotherapy
/ Software
/ Therapeutic drug monitoring
/ Vancomycin - pharmacokinetics
2024
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Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates
Journal Article
Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates
2024
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Overview
Background and Objective
Pharmacokinetic models can inform drug dosing of vancomycin in neonates to optimize therapy. However, the model selected needs to describe the intended population to provide appropriate dose recommendations. Our study aims to identify the population pharmacokinetic (PopPK) model(s) with the best performance to predict vancomycin exposure in neonates in our hospital.
Methods
Relevant published PopPK models for vancomycin in neonates were selected based on demographics and vancomycin dosing strategy. The predictive performance of the models was evaluated in Tucuxi using a local cohort of 69 neonates. Mean absolute error (MAE), relative bias (rBias) and relative root mean square error (rRMSE) were used to quantify the accuracy and precision of the predictive performance of each model for three different approaches: a priori, a posteriori, and Bayesian forecasting for the next course of therapy based on the previous course predictions. A PopPK model was considered clinically acceptable if rBias was between ± 20 and 95% confidence intervals included zero.
Results
A total of 25 PopPK models were identified and nine were considered suitable for further evaluation. The model of De Cock et al. 2014 was the only clinically acceptable model based on a priori [MAE 0.35 mg/L, rBias 0.8 % (95% confidence interval (CI) − 7.5, 9.1%), and rRMSE 8.9%], a posteriori [MAE 0.037 mg/L, rBias − 0.23% (95% CI − 1.3, 0.88%), and rRMSE 6.02%] and Bayesian forecasting for the next courses [MAE 0.89 mg/L, rBias 5.45% (95% CI − 8.2, 19.1%), and rRMSE 38.3%) approaches.
Conclusions
The De Cock model was selected based on a comprehensive approach of model selection to individualize vancomycin dosing in our neonates.
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