Evaluation of a Bayesian Method of Amikacin Dosing in Intensive Care Unit Patients with Normal or Impaired Renal Function

Lacarelle, Bruno; Granthil, C.; Manelli, J.C.; Bruder, Nicolas; François, G.; Cano, J. P.

doi:10.1097/00007691-198706000-00005

Cited by 20 publications

(16 citation statements)

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“…This is the reason they supply a precise and reliable estimate of the kinetic parameters despite the reduced number of patients available for the analysis. In contrast to previously published studies (8,13), ately predict plasma serum concentrations and urinary drug elimination.…”

Section: Discussionmentioning

confidence: 63%

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A population approach to the forecasting of amikacin plasma and urinary levels using a prescribed dosage regimen

Joubert¹,

Bressolle²,

Gouby³

et al. 1999

Eur. J. Drug Metab. Pharmacokinet.

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We retrospectively analyzed amikacin pharmacokinetics in 19 critically ill patients who received amikacin intravenously. Fourteen subjects (577 serum amikacin concentrations, 167 urine measurements) were studied to obtain data for population modeling, while 5 patients (267 serum amikacin concentrations, 68 urine measurements) were studied for the assessment of predictive performance. The population analysis was performed using serum and urine amikacin measurements; the renal clearance of amikacin was expressed as a function of creatinine clearance. A two-compartment model was fitted to the population data by using NONMEM. The population characteristics of the pharmacokinetic parameters (fixed and random effects) were estimated using the FOCE method. The population pharmacokinetic parameters with the interindividual variability (CV%) were as follows: slope (0.254, 126%) and intercept (3 l/h, 59.6%) of the linear model which relate the amikacin renal clearance to the creatinine clearance, initial volume of distribution (17.1 l, 22.2%), intercompartment clearance (5.22 l/h, 104%), steady state volume of distribution (55.2 l, 64.1%) and urinary elimination (67.5%, 36.3%). The Bayesian approach developed in this study accurately predicts amikacin concentrations in serum and urine and allows for the estimation of amikacin pharmacokinetic parameters, minimizing the risk of bias in the prediction.

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Section: Discussionmentioning

confidence: 63%

“…Dosage adjustment resulted in the minimization of clinical toxicity, and in improvement in the rates of achieving therapeutic concentrations and in patients' clinical outcome. Several methods using the Bayesian approach to adjust arnikacin doses have been described elsewhere (8)(9)(10)(11)(12)(13)(14).…”

Section: Methodsmentioning

confidence: 99%

A population approach to the forecasting of amikacin plasma and urinary levels using a prescribed dosage regimen

Joubert¹,

Bressolle²,

Gouby³

et al. 1999

Eur. J. Drug Metab. Pharmacokinet.

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“…Wide interpatient variability in aminoglycoside pharmacokinetics has led to a variety of methods for individualising therapy (Abel & Aronoff 1984;Bottger et al 1988;Burton et al 1985Burton et al , 1986Chow et al 1978;Chrystyn 1988;Denaro & Ravenscroft 1989;Garraffo et al 1989;Godley et al 1988;Hatton et al 1984;Katz & Argenio 1986;Lacarelle et al 1987;Lazurko et al 1981;McClellan & Farringer 1989;Murray et al 1986;Robinson et al 1984;Rodvold & Blum 1987;Russell et al 1978;Sheiner & Stuart 1981). Recent literature has suggested that haematology/oncology patients may also show variation in aminoglycoside pharmacokinetics (Higa & Murray 1987;Manny & Hutson 1986).…”

Section: Discussionmentioning

confidence: 99%

Aminoglycoside Forecasting in Neutropenic Patients with Cancer

Kosirog

Rospond

Destache

et al. 1993

Clinical Pharmacokinetics

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To validate the population pharmacokinetic parameters of aminoglycoside disposition in patients with cancer, a retrospective evaluation of predictive performance of a Bayesian program was performed in 155 patients from 1986 to 1989 who received amikacin, gentamicin or tobramycin. Each patient received 1 of the 3 drugs and had initial drug concentration determination, with a second set of drug concentrations drawn < or = 14 days after the initial dose. Predictions of 64 amikacin, 144 gentamicin and 102 tobramycin concentrations were generated using 1-compartment model pharmacokinetic parameters, serum creatinine values, patients' dosage history and demographic data. The mean (+/- SD) observed (and predicted) serum concentrations for amikacin were 18.9 +/- 14.8 mg/L (17.2 +/- 14.1 mg/L); for gentamicin were 4.49 +/- 3.58 mg/L (4.26 +/- 3.33 mg/L) and for tobramycin were 4.52 +/- 3.70 mg/L (4.05 +/- 3.49 mg/L) [p > 0.05]. Results demonstrated minimal bias with a mean error in gentamicin concentrations of -0.236 (95% CI -0.533: 0.0613). Significant (p < 0.05) under prediction occurred in concentrations of tobramycin [-0.474 mg/L (95% CI -0.842: -0.0107)] and amikacin [-1.77 mg/L (95% CI -3.42: -0.114)]. Good precision is indicated by a mean squared error for gentamicin of 3.35 mg/L (95% CI 1.70: 4.99) and for tobramycin of 3.64 mg/L (95% CI 1.83: 5.44). Fair precision is demonstrated by amikacin [46.1 mg/L (95% CI 27.3: 65.0)]. Similar results were shown in a separate peak/trough analysis. These data indicate that aminoglycoside pharmacokinetics in patients with cancer can be predicted with minimal bias and good precision using a Bayesian forecasting program for gentamicin and tobramycin.

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“…Aminoglycoside pharmacokinetic parameters may be greatly changed in patients who are seriously ill with septicaemia and/or have large fluctuations in their extra-cellular fluid volume (Pennington et al, 1975;Zaske et al, 1976;Denaro & Ravenscroft, 1987;Lacarelle et al, 1987). The importance of the correlation between peak serum aminoglycoside concentrations and clinical outcome is now widely accepted (Jackson & Riff, 1971;Noone et al, 1974;Moore et al, 1984a,b).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Sawchuk‐Zaske and Bayesian forecasting for aminoglycosides in seriously ill patients.

Denaro

Ravenscroft

1989

Brit J Clinical Pharma

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1. Individual pharmacokinetic parameters and predicted steady‐state serum concentrations of aminoglycosides were calculated by Sawchuck‐ Zaske (SZ) and Bayesian methods. 2. Predicted concentrations were compared with observed steady‐state concentrations for 36 seriously ill patients with systemic infections. Four aminoglycoside concentrations were used for the SZ method. Differing numbers of serum aminoglycoside samples were used in the Bayesian parameter estimation: one sample Bayesian used one post‐infusion concentration, two sample Bayesian used a trough plus one post‐infusion concentrations and four sample Bayesian used a trough plus three post‐infusion concentrations. 3. 79% of the SZ predictions were with +/‐ 2 mg l‐1 of the observed peak concentrations, and 72% of the two sample Bayesian predictions were within the same range. 82% of SZ and the two sample Bayesian predictions were within +/‐ 1 mg l‐1 of the observed trough concentrations. 4. A confidence interval comparison of estimated pharmacokinetic parameters and precision for the predicted concentrations showed no important differences between the SZ and the two sample Bayesian. The four sample Bayesian was the most precise method. 5. We conclude that the Bayesian forecasting method utilizing a trough plus one post‐infusion concentrations is as useful as the SZ method which requires three to four serum concentrations in individualizing aminoglycoside therapy for seriously ill patients.

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Evaluation of a Bayesian Method of Amikacin Dosing in Intensive Care Unit Patients with Normal or Impaired Renal Function

Cited by 20 publications

References 0 publications

A population approach to the forecasting of amikacin plasma and urinary levels using a prescribed dosage regimen

A population approach to the forecasting of amikacin plasma and urinary levels using a prescribed dosage regimen

Aminoglycoside Forecasting in Neutropenic Patients with Cancer

Comparison of Sawchuk‐Zaske and Bayesian forecasting for aminoglycosides in seriously ill patients.

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