Bayesian Approach to Control of Amikacin Serum Concentrations in Critically Ill Patients with Sepsis

Lugo-Goytia, Gustavo; Castañeda‐Hernández, Gilberto

doi:10.1345/1542-6270(2000)034<1389:batcoa>2.0.co;2

Cited by 5 publications

(6 citation statements)

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“…Population PK estimates of the drug confirmed the sepsis-induced PK modifications (ie, increased Vd and decreased CL) 4-9 and were consistent with most reported values for critically ill septic patients. [11][12][13][14][15]18 As expected, patients were characterized by a wide interindividual variability in the AMK PK disposition, particularly in the elimination of the drug (71.9%). Although (patho)physiological changes associated to sepsis determine a wide PK variability, 10,11 only CL CR (estimated by the Cockcroft-Gault) was found to significantly influence the AMK elimination.…”

Section: Discussionmentioning

confidence: 93%

“…However, few models have been described for AMK in critically ill septic patients. [11][12][13][14]18 Moreover, to our knowledge, no clinical study has focused on the critical period in the management of these patients (ie, first hours of treatment). In the present study, a population PK model has been developed based on AMK concentrations of 88 critically ill septic patients during the first 24 hours of antibiotic treatment (25 mg/kg AMK once daily combined with a broad-spectrum b-lactam).…”

Section: Discussionmentioning

confidence: 99%

“…To date, several (patho)physiological factors are reported to account for PK variability of AMK in ICU patients with sepsis: body weight, oxygen extraction ratio, serum albumin, and sepsis severity (estimated by the Acute Physiology And Chronic Health Evaluation [APACHE] II score) for the Vd variability, creatinine clearance, positive end-expiratory pressure, and catecholamine administration for the CL variability. [11][12][13][14][15] Among PK-based dosing methods dedicated to the aminoglycoside TDM, the Bayesian maximum a posteriori approach appears attractive for ICU patients. Unlike nonBayesian methods (eg, nomogram, non-Bayesian least-squares method), the maximum a posteriori estimation displays better predictive performance for individualized drug dosage, because it incorporates some prior information (ie, covariates, PK model, residual error model, prior distributions).…”

Section: Introductionmentioning

confidence: 99%

“…18 In addition, the relative lack of suitable population data may limit the success of the Bayesian approach. To date, some population models have been described for AMK PK in critically ill septic patients at steady state, [11][12][13][14]18 but none has been developed in the early phase of the septic process.…”

Section: Introductionmentioning

confidence: 99%

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Population Pharmacokinetic Modeling and Optimal Sampling Strategy for Bayesian Estimation of Amikacin Exposure in Critically Ill Septic Patients

Delattre¹,

Musuamba²,

Nyberg³

et al. 2010

Therapeutic Drug Monitoring

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Because the sepsis-induced pharmacokinetic (PK) modifications need to be considered in aminoglycoside dosing, the present study aimed to develop a population PK model for amikacin (AMK) in severe sepsis and to subsequently propose an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters. Concentration-time profiles for AMK were obtained from 88 critically ill septic patients during the first 24 hours of antibiotic treatment. The population PK model was developed using a nonlinear mixed effects modeling approach. Covariate analysis included demographic data, pathophysiological characteristics, and comedication. Optimal sampling times were selected based on a robust Bayesian design criterion. Taking into account clinical constraints, a two-point sampling approach was investigated. A two-compartment model with first-order elimination best fitted the AMK concentrations. Population PK estimates were 19.2 and 9.34 L for the central and peripheral volume of distribution and 4.31 and 2.21 L/h for the intercompartmental and total body clearance. Creatinine clearance estimated using the Cockcroft-Gault equation was retained in the final model. The two optimal sampling times were 1 hour and 6 hours after onset of the drug infusion. Predictive performance of individual Bayes estimates computed using the proposed optimal sampling strategy was reported: mean prediction errors were less than 5% and root mean square errors were less than 30%. The present study confirmed the significant influence of the creatinine clearance on the PK disposition of AMK during the first hours of treatment in critically ill septic patients. Based on the population estimates, an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters was developed, meeting the need of clinical practice.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Population Pharmacokinetic Modeling and Optimal Sampling Strategy for Bayesian Estimation of Amikacin Exposure in Critically Ill Septic Patients

Delattre¹,

Musuamba²,

Nyberg³

et al. 2010

Therapeutic Drug Monitoring

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show abstract

“…C max concentration is determined by the administered dose and by the Vd [24•]. The Vd of aminoglycosides is largely increased in critically ill patients when compared to healthy volunteers and patients with mild infections and an association between sepsis severity, estimated by the APACHE II score, serum albumin or adrenergic support with aminoglycoside Vd has been described [79,80]. Giving recommended aminoglycoside regimens, the peaks obtained were largely below the desired concentrations to treat Pseudomonas aeruginosa and resistant GNB, suggesting that higher doses of these drugs should be administered to achieve optimal C max [79,81].…”

Section: Aminoglycosidesmentioning

confidence: 99%

Appropriate Antibiotic Dosage Levels in the Treatment of Severe Sepsis and Septic Shock

Taccone

Hites

Beumier

et al. 2011

Curr Infect Dis Rep

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Antibiotic treatment of critically ill patients remains a significant challenge. Optimal antibacterial strategy should achieve therapeutic drug concentration in the blood as well as the infected site. Achieving therapeutic drug concentrations is particularly difficult when infections are caused by some pathogens, such as Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus (MRSA) and Gram-negative rods, because of their low susceptibility to antimicrobials. In sepsis, pharmacokinetics (PKs) of antibiotics are profoundly altered and may result in inadequate drug concentrations, even when recommended regimens are used, which potentially contribute to increased mortality and spread of resistance. The wide inter-individual PK variability observed in septic patients strongly limits the a priori prediction of the optimal dose that should be administered. Higher than standard dosages are necessary for the drugs, such as β-lactams, aminoglycosides, and glycopeptides, that are commonly used as first-line therapy in these patients to maximize their antibacterial activity. However, the benefit of reaching adequate drug concentrations on clinical outcome needs to be further determined.

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Can changes in renal function predict variations in β-lactam concentrations in septic patients?

Casu

Hites

Jacobs

et al. 2013

International Journal of Antimicrobial Agents

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Bayesian Approach to Control of Amikacin Serum Concentrations in Critically Ill Patients with Sepsis

Cited by 5 publications

References 0 publications

Population Pharmacokinetic Modeling and Optimal Sampling Strategy for Bayesian Estimation of Amikacin Exposure in Critically Ill Septic Patients

Population Pharmacokinetic Modeling and Optimal Sampling Strategy for Bayesian Estimation of Amikacin Exposure in Critically Ill Septic Patients

Appropriate Antibiotic Dosage Levels in the Treatment of Severe Sepsis and Septic Shock

Can changes in renal function predict variations in β-lactam concentrations in septic patients?

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