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Amikacin Population Pharmacokinetics in Critically Ill Kuwaiti Patients

Amikacin pharmacokinetic data in Kuwaiti (Arab) intensive care unit (ICU) patients are lacking. Fairly sparse serum amikacin peak and trough concentrations data were obtained from adult Kuwaiti ICU patients. The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorit...

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Detalles Bibliográficos
Autores principales: Matar, Kamal M., Al-lanqawi, Yousef, Abdul-Malek, Kefaya, Jelliffe, Roger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3581152/
https://www.ncbi.nlm.nih.gov/pubmed/23484093
http://dx.doi.org/10.1155/2013/202818
Descripción
Sumario:Amikacin pharmacokinetic data in Kuwaiti (Arab) intensive care unit (ICU) patients are lacking. Fairly sparse serum amikacin peak and trough concentrations data were obtained from adult Kuwaiti ICU patients. The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorithm. The estimations of the developed model were assessed using mean error (ME) as a measure of bias and mean squared error (MSE) as a measure of precision. A total of 331 serum amikacin concentrations were obtained from 56 patients. The mean (±SD) model parameter values found were V (c) = 0.2302 ± 0.0866 L/kg, k (slope) = 0.004045 ± 0.00705 min per unit of creatinine clearance, k (12) = 2.2121 ± 5.506 h(−1), and k (21) = 1.431 ± 2.796 h(−1). The serum concentration data were estimated with little bias (ME = −0.88) and good precision (MSE = 13.08). The present study suggests that amikacin pharmacokinetics in adult Kuwaiti ICU patients are generally rather similar to those found in other patients. This population model would provide useful guidance in developing initial amikacin dosage regimens for such patients, especially using multiple model (MM) dosage design, followed by appropriate Bayesian adaptive control, to optimize amikacin dosage regimens for each individual patient.