Bayesian network to optimize the first dose of antibiotics: application to amikacin | Zendy

Guillaume Debeurme | Zendy; Michel Ducher | Zendy; E. Jean-Bart | Zendy; Sylvain Goutelle | Zendy; Laurent Bourguig | Zendy

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Bayesian network to optimize the first dose of antibiotics: application to amikacin

Author(s) -

Guillaume Debeurme,

Michel Ducher,

E. Jean-Bart,

Sylvain Goutelle,

Laurent Bourguig

Publication year - 2016

Publication title -

international journal of pharmacokinetics

Language(s) - English

Resource type - Journals

eISSN - 2053-0854

pISSN - 2053-0846

DOI - 10.4155/ipk.16.3

Subject(s) - amikacin , pharmacokinetics , bayesian network , body weight , bayesian probability , medicine , computer science , antibiotics , mathematics , statistics , nuclear medicine , pharmacology , chemistry , biochemistry

Objective: To construct and validate a network to predict the first dose of amikacin. Methods: Anthropometric and therapeutic data were recorded for 120 patients. Bayesian network (BN) was built to predict the dose to achieve a fixed target peak concentration of 64 mg/l. In 40 subjects, doses predicted with the BN (BND) and based on body weight (BWD) were compared with adjusted doses calculated using a pharmacokinetic software (MM-USCPACK; BID). Results: The calculated dose differed by <20% from the ideal dose in 62.5% of the patients with the BN and in 43.8% of the patients with the BW. Conclusion: BN is a promising approach to optimize the prediction of the first dose.

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