Hospitalized Patients With and Without Hemodialysis Have Markedly Different Vancomycin Pharmacokinetics: A Population Pharmacokinetic Model-Based Analysis

Goti, Vineet; Chaturvedula, Ayyappa; Fossler, Michael J.; Mok, Steve; Jacob, Jesse T

doi:10.1097/ftd.0000000000000490

Cited by 61 publications

(152 citation statements)

References 43 publications

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“…The predictive performance of the MSA and MAA were compared with the single-model approach using the six PopPK models as well as to the "reference" PopPK model. 22 Predictive performance was assessed via the differences between the predicted and observed plasma vancomycin concentrations (clinical data) or the simulated and predicted AUC. Three different scenarios (outlined below) were used to predict plasma concentrations and the AUC in the third observed dosing occasion (ODO), where observed plasma vancomycin concentrations were "hidden" from the models or algorithms.…”

Section: Forecasting Performance Metricsmentioning

confidence: 99%

A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study

Uster

Stocker

Carland

et al. 2020

Clin Pharma and Therapeutics

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Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the 'correct' model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software 'TDMx'. The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ±8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%).In the clinical dataset the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy:-16-25%), while the MSA or MAA utilizing these models simultaneously, resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy:-5% and 0%, respectively). MSA and MAA approaches implemented in 'TDMx' might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.

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Section: Forecasting Performance Metricsmentioning

confidence: 99%

A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study

Uster

Stocker

Carland

et al. 2020

Clin Pharma and Therapeutics

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“…Variation in AUC estimates by Bayesian programs may be due to differences in the pharmacokinetic model or the inclusion of true peak levels in the Bayesian prior. These models nonspecifically included intensive care unit patients similar to those included in the study used for model validation . However, including intensive care patients in the Bayesian prior (by utilizing the model based on Goti and colleagues) did not produce better estimates than using a methodology that excluded these patients (such as the model based on Buelga and colleagues).…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, the model utilized by DoseMe (based on Buelga and colleagues) only included peaks collected 3 hours after the end of the infusion. Several models used by InsightRx did not appear to include many peak levels. Because the standard practice for many years has been to collect trough levels only, many of the published contemporary models may not accurately estimate peak concentrations and consequently may not accurately estimate AUC.…”

Section: Discussionmentioning

confidence: 99%

“…These models nonspecifically included intensive care unit patients similar to those included in the study used for model validation . However, including intensive care patients in the Bayesian prior (by utilizing the model based on Goti and colleagues) did not produce better estimates than using a methodology that excluded these patients (such as the model based on Buelga and colleagues). For those programs that underestimated the AUC, visual examination of estimated time‐concentration curves identified apparent underestimation of the peak.…”

Section: Discussionmentioning

confidence: 99%

“…In InsightRx, the Thomson 16 and Goti 17 models were similar (p=0.73) and fit the AUC REF better than the Buelga model 15 (p<0.001 for both comparisons). Because the two models 16,17 did not differ significantly and the Thomson model is considered the default, only data from that model are shown.…”

Section: Bayesian Dose-optimizing Software With Trough Onlymentioning

confidence: 99%

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Review and Validation of Bayesian Dose‐Optimizing Software and Equations for Calculation of the Vancomycin Area Under the Curve in Critically Ill Patients

et al. 2018

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Background Vancomycin area under the concentration‐time curve (AUC) has been linked to efficacy and safety. An accurate method of calculating the AUC is needed. Methods Bayesian dose‐optimizing software programs available for clinician use and first‐order pharmacokinetic equations were evaluated for their ability to estimate vancomycin AUC. A previously published rich pharmacokinetic data set of 19 critically ill patients was used for validation of the AUC estimation. The AUC estimated using subsets of the full data set by Bayesian software and equations was compared with the reference AUC. Accuracy (ratio of estimated AUC to the reference AUC) and bias (percentage difference of estimated AUC to reference AUC) were calculated. Results Five Bayesian dose‐optimizing software programs (Adult and Pediatric Kinetics [APK], BestDose, DoseMe, InsightRx, and Precise PK) and two first‐order pharmacokinetic equations were included. Of the Bayesian programs, InsightRx was the most adaptable, visually appealing, easiest to use, and had the most company support. Utilizing only the trough, accuracy (range 0.79–1.03) and bias (range 5.1–21.2%) of the Bayesian dose‐optimizing software were variable. Precise PK and BestDose had the most accurate estimates with the accuracy values of BestDose exhibiting the most variability of all the programs; however, both programs were more difficult to use. Precise PK was the least biased (median 5.1%). Using a single nontrough value produced similar results to that of the trough for most programs. The addition of a second level to the trough improved the accuracy and bias for DoseMe and InsightRx but not Precise PK and BestDose. APK did not reliably estimate the AUC with input of two levels. Using two levels, the pharmacokinetic equations produced similar or better accuracy and bias as compared with Bayesian software. Conclusion Bayesian dose‐optimizing software using one or more vancomycin levels and pharmacokinetic equations utilizing two vancomycin levels produce similar estimates of the AUC.

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Norvancomycin plasma concentration monitoring in hemodialysis patients with end stage kidney disease: A retrospective cohort study

Huo

Hou

et al. 2023

Biopharm & Drug Disp

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Blood concentration monitoring plays an important role in the rational use of norvancomycin. However, the reference interval for the norvancomycin plasma concentration in the treatment of infections in hemodialysis patients with end stage kidney disease is undefined. To determine the safe and effective interval for the norvancomycin plasma trough concentration, 39 patients treated with hemodialysis and norvancomycin were analyzed retrospectively. The norvancomycin plasma concentration before hemodialysis was tested as the trough concentration. The associations of the norvancomycin trough concentration with efficacy and adverse reactions were evaluated. No norvancomycin concentration above 20 μg/mL was detected. The trough concentration, but not the dose, had a significant effect on the anti‐infectious efficacy. Compared with the low norvancomycin trough concentration group (<9.30 μg/mL), the high concentration group (9.30–20.0 μg/mL) had improved efficacy (OR = 15.45, p < 0.01) with similar side effects (OR = 0.5417, p = 0.4069). It is beneficial to maintain the norvancomycin trough concentration at 9.30–20.0 μg/mL to achieve a good anti‐infectious effect in hemodialysis patients with end stage kidney disease. Plasma concentration monitoring provides a data basis for the individual treatment of infections with norvancomycin in hemodialysis patients.

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Hospitalized Patients With and Without Hemodialysis Have Markedly Different Vancomycin Pharmacokinetics: A Population Pharmacokinetic Model-Based Analysis

Abstract: CL and Vc differ markedly between patients undergoing hemodialysis and those not undergoing hemodialysis. Dosing nomogram based on these covariate relationships may potentially help in accurate dosing of vancomycin.

Cited by 61 publications

References 43 publications

A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study

A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study

Review and Validation of Bayesian Dose‐Optimizing Software and Equations for Calculation of the Vancomycin Area Under the Curve in Critically Ill Patients

Norvancomycin plasma concentration monitoring in hemodialysis patients with end stage kidney disease: A retrospective cohort study

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