Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning.
first dose
machine learning
pediatrics
simulations
vancomycin
Journal
Pharmaceutical research
ISSN: 1573-904X
Titre abrégé: Pharm Res
Pays: United States
ID NLM: 8406521
Informations de publication
Date de publication:
Oct 2022
Oct 2022
Historique:
received:
12
05
2022
accepted:
23
07
2022
pubmed:
3
8
2022
medline:
15
10
2022
entrez:
2
8
2022
Statut:
ppublish
Résumé
Vancomycin is one of the antibiotics most used in neonates. Continuous infusion has many advantages over intermittent infusions, but no consensus has been achieved regarding the optimal initial dose. The objectives of this study were: to develop a Machine learning (ML) algorithm based on pharmacokinetic profiles obtained by Monte Carlo simulations using a population pharmacokinetic model (POPPK) from the literature, in order to derive the best vancomycin initial dose in preterm and term neonates, and to compare ML performances with those of an literature equation (LE) derived from a POPPK previously published. The parameters of a previously published POPPK model of vancomycin in children and neonates were used in the mrgsolve R package to simulate 1900 PK profiles. ML algorithms were developed from these simulations using Xgboost, GLMNET and MARS in parallel, benchmarked and used to calculate the ML first dose. Performances were evaluated in a second simulation set and in an external set of 82 real patients and compared to those of a LE. The Xgboost algorithm yielded numerically best performances and target attainment rates: 46.9% in the second simulation set of 400-600 AUC/MIC ratio vs. 41.4% for the LE model (p = 0.0018); and 35.3% vs. 28% in real patients (p = 0.401), respectively). The Xgboost model resulted in less AUC/MIC > 600, thus decreasing the risk of nephrotoxicity. The Xgboost algorithm developed to estimate the initial dose of vancomycin in term or preterm infants has better performances than a previous validated LE and should be evaluated prospectively.
Identifiants
pubmed: 35918452
doi: 10.1007/s11095-022-03351-6
pii: 10.1007/s11095-022-03351-6
doi:
Substances chimiques
Anti-Bacterial Agents
0
Vancomycin
6Q205EH1VU
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2497-2506Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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