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Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning

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Abstract

Introduction

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.

Materials and methods

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.

Results

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.

Conclusion

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.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors acknowledge the kind contribution of the children and their parents

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Authors and Affiliations

  1. Pharmacology & Transplantation, University Limoges, INSERM U1248 P&T, 2 rue du Pr Descottes, F-87000, Limoges, France

    Laure Ponthier, Alexandre Destere, Pierre Marquet, Marc Labriffe & Jean-Baptiste Woillard

  2. Department of Pediatrics, University Hospital of Limoges, Limoges, France

    Laure Ponthier & Pauline Ensuque

  3. Department of Pharmacology and Toxicology, University Hospital of Nice, Nice, France

    Alexandre Destere

  4. Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France

    Pierre Marquet, Marc Labriffe & Jean-Baptiste Woillard

  5. Pediatric Pharmacology, Department of Biological Pharmacology, Saint-Louis University Hospital, Assistance Publique - Hôpitaux de Paris, Saint-Louis, France

    Evelyne Jacqz-Aigrain

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  1. Laure Ponthier
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Contributions

LP & JBW: made substantial contributions to the conception, design of the work; acquisition, analysis, interpretation of data and drafted the work, PE : made substantial contributions to the acquisition and analysis, PM and ML made substantial contributions to the interpretation of data and EJA made substantial contributions to the acquisition, interpretation of data and draft. AD built the shiny application.

Corresponding author

Correspondence to Jean-Baptiste Woillard.

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Ponthier, L., Ensuque, P., Destere, A. et al. Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning. Pharm Res 39, 2497–2506 (2022). https://doi.org/10.1007/s11095-022-03351-6

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  • DOI: https://doi.org/10.1007/s11095-022-03351-6

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