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

Laure Ponthier1,2, Pauline Ensuque2, Alexandre Destere1,3

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

Pharmaceutical Research
|August 2, 2022
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Machine learning accurately predicts optimal vancomycin initial doses for neonates. The Xgboost algorithm demonstrated superior performance over existing methods, reducing nephrotoxicity risk.

Keywords:
first dosemachine learningpediatricssimulationsvancomycin

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Area of Science:

  • Pharmacology
  • Neonatal Medicine
  • Machine Learning

Background:

  • Vancomycin is a critical antibiotic for neonates, but optimal initial dosing remains debated.
  • Continuous infusion offers benefits, yet consensus on initial dosage is lacking.

Purpose of the Study:

  • To develop a machine learning (ML) algorithm for determining optimal vancomycin initial doses in neonates.
  • To compare the ML algorithm's performance against a literature equation (LE).

Main Methods:

  • Population pharmacokinetic (POPPK) model parameters were used for Monte Carlo simulations.
  • Xgboost, GLMNET, and MARS ML algorithms were developed and benchmarked.
  • Performance was evaluated using simulation data and real patient data.

Main Results:

  • The Xgboost algorithm showed superior target attainment rates compared to the LE model in simulations and real patients.
  • Xgboost resulted in fewer instances of AUC/MIC > 600, indicating a reduced risk of nephrotoxicity.

Conclusions:

  • The developed Xgboost algorithm for vancomycin dosing in neonates outperforms a previously validated LE.
  • Prospective evaluation of this ML-based dosing strategy is recommended.