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

Towards precision dosing of vancomycin in critically ill patients: an evaluation of the predictive performance of pharmacometric models in ICU patients

OBJECTIVES

Vancomycin dose recommendations depend on population pharmacokinetic models. These models have not been adequately assessed in critically ill patients, who exhibit large pharmacokinetic variability. This study evaluated model predictive performance in intensive care unit (ICU) patients and identified factors influencing model performance.

METHODS

Retrospective data from ICU adult patients administered vancomycin were used to evaluate model performance to predict serum concentrations a priori (no observed concentrations included) or with Bayesian forecasting (using concentration data). Predictive performance was determined using relative bias (rBias, bias) and relative root mean squared error (rRMSE, precision). Models were considered clinically acceptable if rBias was between ±20% and 95% confidence intervals included zero. Models were compared with rRMSE; no threshold was used. The influence of clinical factors on model performance was assessed with multiple linear regression.

RESULTS

Data from 82 patients were used to evaluate 12 vancomycin models. The Goti model was the only clinically acceptable model with both a priori (rBias 3.4%) and Bayesian forecasting (rBias 1.5%) approaches. Bayesian forecasting was superior to a priori prediction, improving with the use of more recent concentrations. Four models were clinically acceptable with Bayesian forecasting. Renal replacement therapy status (p < 0.001) and sex (p = 0.007) significantly influenced the performance of the Goti model.

CONCLUSIONS

The Goti, Llopis and Roberts models are clinically appropriate to inform vancomycin dosing in critically ill patients. Implementing the Goti model in dose prediction software could streamline dosing across both ICU and non-ICU patients, considering it is also the most accurate model in non-ICU patients.

Development and comparison of population pharmacokinetic models of vancomycin in neurosurgical patients based on two different renal function markers

WHAT IS KNOWN AND OBJECTIVES

Some previous studies have indicated that serum cystatin C (Cys C) is a better marker than serum creatinine (SCR) for assessing the glomerular filtering rate (GFR). However, in almost all population pharmacokinetic models of vancomycin, the GFR is usually estimated from SCR. Therefore, the aim of this study was to compare the GFR estimated from SCR (sGFR) with the GFR estimated from Cys C (cGFR) and investigate which one can describe the characteristics of vancomycin population pharmacokinetics better in Chinese neurosurgical adult patients.

METHODS

Patients from the Neurosurgery Department aged ≥18 years were enrolled retrospectively. Among these patients, the data from 222 patients were used to establish two population pharmacokinetic models based on sGFR and cGFR, separately. The data from another 95 patients were used for the external validation of these two models. Non-linear mixed-effect modelling (NONMEM) 7.4.3 was used for the population pharmacokinetic analysis.

RESULTS

We developed two one-compartment models with first-order absorption based on Cys C and SCR, separately. In the Cys C model, age, body weight and cGFR were significant covariates on the clearance rate (CL) of vancomycin (typical value, 6.4 L/hour). In the SCR model, age and sGFR were significant covariates on the CL (typical value, 6.46 L/hour). The external validation results showed that the predictive performance of the two models was similar.

WHAT IS NEW AND CONCLUSION

In this study, the predictive performance of two models was similar in neurosurgical patients. We did not find a significant improvement in the predictive performance of the model when GFR was estimated from Cys C.

Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting

OBJECTIVES

Vancomycin is a vital treatment option for patients suffering from critical infections, and therapeutic drug monitoring is recommended. Bayesian forecasting is reported to improve trough concentration monitoring for dose adjustment. However, the predictive performance of pharmacokinetic models that are utilized for Bayesian forecasting has not been systematically evaluated.

METHOD

Thirty-one published population pharmacokinetic models for vancomycin were encoded in NONMEM®7.4. Data from 292 hospitalized patients were used to evaluate the predictive performance (forecasting bias and precision, visual predictive checks) of the models to forecast vancomycin concentrations and area under the curve (AUC) by (a) a priori prediction, i.e., solely by patient characteristics, and (b) also including measured vancomycin concentrations from previous dosing occasions using Bayesian forecasting.

RESULTS

A priori prediction varied substantially-relative bias (rBias): -122.7-67.96%, relative root mean squared error (rRMSE) 44.3-136.8%, respectively-and was best for models which included body weight and creatinine clearance as covariates. The model by Goti et al. displayed the best predictive performance with an rBias of -4.41% and an rRMSE of 44.3%, as well as the most accurate visual predictive checks and AUC predictions. Models with less accurate predictive performance provided distorted AUC predictions which may lead to inappropriate dosing decisions.

CONCLUSION

There is a diverse landscape of population pharmacokinetic models for vancomycin with varied predictive performance in Bayesian forecasting. Our study revealed the Goti model as suitable for improving precision dosing in hospitalized patients. Therefore, it should be used to drive vancomycin dosing decisions, and studies to link this finding to clinical outcomes are warranted.