Bayesian network to optimize the first dose of antibiotics: application to amikacin. (October 2016)

• Record Type: Journal Article
• Authors: Debeurme, Guillaume; Ducher, Michel; Jean-bart, Elodie; Goutelle, Sylvain; Bourguignon, Laurent
• Is Part Of: International journal of pharmacokinetics. Volume 1:Number 1(2016)
• Page Start: 35
• Page End: 42
• Publication Date: 2016-10
• Subjects: amikacin -- aminoglycosides -- Bayesian network -- forecasting -- machine learning -- model -- nonparametric -- pharmacokinetics -- statistic; Pharmacokinetics -- Periodicals; 615.705
• DOI: 10.4155/ipk.16.3
• Languages: English
• ISSNs: 2053-0846

Abstract

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.