Bayesian network to optimize the first dose of antibiotics: application to amikacin-Reference-Cited by-同舟云学术

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

Published:2016-10 Issue:1 Volume:1 Page:35-42
ISSN:2053-0846
Container-title:International Journal of Pharmacokinetics
language:en
Short-container-title:International Journal of Pharmacokinetics

Author:

Debeurme Guillaume¹,Ducher Michel¹,Jean-bart Elodie¹,Goutelle Sylvain¹²³,Bourguignon Laurent¹²³

Affiliation:

1. Hospices Civils de Lyon, Groupement Hospitalier de Gériatrie, Hôpital Antoine Charial, Service Pharmaceutique, 40 avenue de la Table de Pierre, 69340 Francheville, France

2. Laboratoire de Biométrie et Biologie Evolutive, CNRS UMR 5558, Université de Lyon, 69622 Villeurbanne, France

3. Université Lyon 1, ISPB – Faculté de Pharmacie de Lyon, 8 avenue Rockefeller, 69373 Lyon cedex 08, France

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.

Publisher

Future Science Ltd

Link

http://www.future-science.com/doi/pdf/10.4155/ipk.16.3

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