作者
Bo-Hao Tang, Jin-Yuan Zhang, Karel Allegaert, Guo-Xiang Hao, Bu-Fan Yao, Stephanie Leroux, Alison H Thomson, Ze Yu, Fei Gao, Yi Zheng, Yue Zhou, Edmund V Capparelli, Valerie Biran, Nicolas Simon, Bernd Meibohm, Yoke-Lin Lo, Remedios Marques, Jose-Esteban Peris, Irja Lutsar, Jumpei Saito, Evelyne Jacqz-Aigrain, John van den Anker, Yue-E Wu, Wei Zhao
发表日期
2023/8
期刊
Clinical Pharmacokinetics
卷号
62
期号
8
页码范围
1105-1116
出版商
Springer International Publishing
简介
Background and Objective
High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0–24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions.
Methods
C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0–24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0–24. An external dataset was used for predictive performance evaluation.
Results
Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates …
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BH Tang, JY Zhang, K Allegaert, GX Hao, BF Yao… - Clinical Pharmacokinetics, 2023