The use of a change in gentamicin clearance as an early predictor of gentamicin-induced nephrotoxicity

A retrospective review was undertaken in 744 patients who were dose-individualized with gentamicin once daily to evaluate a change in gentamicin clearance as a potential predictor of nephrotoxicity. The definition of nephrotoxicity was chosen to be a change in creatinine clearance greater than 20%. Similarly, a change in gentamicin clearance of greater than 20% was also considered a possible index of nephrotoxicity. Four criteria were developed to assess the usefulness of gentamicin clearance as a predictor of nephrotoxicity. Following the application of the inclusion/exclusion criteria, 132 patients were available for the analysis. The sensitivity, specificity, positive predictive value, and negative predictive value were assessed for each of the criteria. Receiver operating characteristic (ROC) curves were produced to determine if an optimum value in the change of gentamicin clearance could be found to maximize sensitivity and specificity. The overall incidence of nephrotoxicity based on a decrease in creatinine clearance by 20% or more was 3.8%. Women were overrepresented in the nephrotoxic group [71.4% versus 40.1% (P = 0.0025)]. Patients with nephrotoxicity had statistically longer treatment periods, increased cumulative dose, and more dosing predictions (P < 0.05 in each case). The sensitivity of the criteria ranged from 43 to 46%, and specificity ranged from 93 to 99%. The positive and negative predictive values ranged from 63 to 94% and 86 to 89%, respectively. In those patients in whom nephrotoxicity was predicted from a change in gentamicin clearance, this change occurred on average 3 days before the change in creatinine clearance (P < 0.05). A change in gentamicin clearance to predict nephrotoxicity may be a useful addition to current monitoring methods, although it is not the complete answer.

Development of a semimechanistic model to describe the pharmacokinetics of gentamicin in patients receiving Hemodialysis

The aim of this study was to ascertain the most suitable dosing schedule for gentamicin in patients receiving hemodialysis. We developed a model to describe the concentrationtime course of gentamicin in patients receiving hemodialysis. Using the model, an optimal dosing schedule was evaluated. Various dosing regimens were compared in their ability to achieve maximum concentration (C-max, >= 8 mg/L) and area under the concentration time-curve (AUC >= 70 mg(.)h/L and <= 120 mg(.)h/L per 24 hours). The model was evaluated by comparing model predictions against real data collected retrospectively. Simulations from the model confirmed the benefits of predialysis dosing. The mean optimal dose was 230 mg administered immediately before dialysis. The model was found to have good predictive performance when simulated data were compared to data observed in real patients. In summary, a model was developed that describes gentamicin pharmacokinetics in patients receiving hemodialysis. Predialysis dosing provided a superior pharmacokinetic profile than did postdialysis dosing.

Dosing of gentamicin in patients with end-stage renal disease receiving hemodialysis

The aim of this study was to evaluate dosing schedules of gentamicin in patients with end-stage renal disease and receiving hemodialysis. Forty-six patients were recruited who received gentamicin while on hemodialysis. Each patient provided approximately 4 blood samples at various times before and after dialysis for analysis of plasma gentamicin concentrations. A population pharmacokinetic model was constructed using NONMEM (version 5). The clearance of gentamicin during dialysis was 4.69 L/h and between dialysis was 0.453 L/h. The clearance between dialysis was best described by residual creatinine clearance (as calculated using the Cockcroft and Gault equation), which probably reflects both lean mass and residual clearance mechanisms. Simulation from the final population model showed that predialysis dosing has a higher probability of achieving target maximum concentration (C-max) concentrations (> 8 mg/L) within acceptable exposure limits (area under the concentration-time curve [AUC] values > 70 and < 120 mg.h/L per 24 hours) than postdialysis dosing.

Community-acquired methicillin-resistant Staphylococcus aureus carrying Panton-Valentine leukocidin genes: Worldwide emergence

Infections caused by community-acquired (CA)-methicillin-resistant Staphylococcus aureus (MRSA) have been reported worldwide. We assessed whether any common genetic markers existed among 117 CA-MRSA isolates from the United States, France, Switzerland, Australia, New Zealand, and Western Samoa by performing polymerase chain reaction for 24 virulence factors and the methicillin-resistance determinant. The genetic background of the strain was analyzed by pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing (MLST). The CA-MRSA strains shared a type IV SCCmec cassette and the Panton-Valentine leukocidin locus, whereas the distribution of the other toxin genes was quite specific to the strains from each continent. PFGE and MLST analysis indicated distinct genetic backgrounds associated with each geographic origin, although predominantly restricted to the agr3 background. Within each continent, the genetic background of CA-MRSA strains did not correspond to that of the hospital-acquired MRSA.

Resistance development in community-acquired strains of methicillin-resistant Staphylococcus aureus: an in vitro study

This study compares in vitro antimicrobial resistance development between strains of Staphylococcus aureus including newly described community-acquired methicillin-resistant strains (CA-MRSA). High-level resistance developed in all strains of S. aureus after exposure to rifampicin and gentamicin and in some strains after fusidic acid exposure, independent of methicillin resistance phenotype. Resistance did not develop after exposure to clindamycin, cotrimoxazole, ciprofloxacin, linezolid, or vancomycin. These results have important implications for therapy of CA-MRSA infections. (C) 2004 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

A standard weight descriptor for dose adjustment in the obese patient

Objective: To develop a standard weight descriptor that can be used for estimation of patient size for obese patients. Patients and methods: Data were available from 3849 patients: 2839 from oncology patients (index data set) and 1010 from general medical patients (validation data set). The patients had a wide range of age (16-100 years), weight (25-165kg) and body mass index (BMI) [12-52 kg/m(2)] in both data sets. From the normal-weight patients in the oncology data set, an equation for male and female patients was developed to predict their normal weight as the sum of the lean body mass and normal fat body mass. The equations were evaluated by predicting the weight of patients in the general medical data set who had a normal BMI (30 kg/m(2)).

Quantitative justification for target concentration intervention - parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides

Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

Genetic diversity among community methicillin-resistant Staphylococcus aureus strains causing outpatient infections in Australia

Increasing reports of the appearance of novel nonmultiresistant methicillin-resistant Staphylococcus aureus MRSA (MRSA) strains in the community and of the spread of hospital MRSA strains into the community are cause for public health concern. We conducted two national surveys of unique isolates of S. aureus from clinical specimens collected from nonhospitalized patients commencing in 2000 and 2002, respectively. A total of 11.7% of 2,498 isolates from 2000 and 15.4% of 2,486 isolates from 2002 were MRSA. Approximately 54% of the MRSA isolates were nonmultiresistant (resistant to less than three of nine antibiotics) in both surveys. The majority of multiresistant MRSA isolates in both surveys belonged to two strains (strains AUS-2 and AUS-3), as determined by pulsed-field gel electrophoresis (PFGE) and resistogram typing. The 3 AUS-2 isolates and 10 of the 11 AUS-3 isolates selected for multilocus sequence typing (MLST) and staphylococcal chromosomal cassette mec (SCCmec) analysis were ST239-MRSA-III (where ST is the sequence type) and thus belonged to the same clone as the eastern Australian MRSA strain of the 1980s, which spread internationally. Four predominant clones of novel nonmultiresistant MRSA were identified by PFGE, MLST, and SCCmec analysis: ST22-MRSA-IV (strain EMRSA-15), ST1-MRSA-IV (strain WA-1), ST30-MRSA-IV (strain SWP), and ST93-MRSA-IV (strain Queensland). The last three clones are associated with community acquisition. A total of 14 STs were identified in the surveys, including six unique clones of novel nonmultiresistant MRSA, namely, STs 73, 93, 129, 75, and 80sIv and a new ST. SCCmec types IV and V were present in diverse genetic backgrounds. These findings provide support for the acquisition of SCCmec by multiple lineages of S. aureus. They also confirm that both hospital and community strains of MRSA are now common in nonhospitalized patients throughout Australia.

Analysis of population pharmacokinetic data using NONMEM and WinBUGS

The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.