Evaluation for optimal dosing of vancomycin in patients with different physical types

Impact of physical build on serum vancomycin concentrations of patients undergoing hemodialysis: A retrospective cohort study at an acute care hospital

INTRODUCTION

Estimating the serum vancomycin (VCM) concentrations of lean and patients with obesity is challenging. Additionally, VCM dosing for Japanese patients undergoing hemodialysis remains particularly unclear. This study aimed to determine the impact of the physical build on serum VCM concentrations of Japanese patients undergoing hemodialysis.

METHODS

This retrospective cohort study included hospitalized patients undergoing hemodialysis, who received treatment with VCM between May 1, 2019, and December 31, 2021, at Kansai Rosai Hospital, which is a 642-bed acute care hospital in Japan. Patients were divided into the following three groups based on their body mass index (BMI): lean group (≤18.5 kg/m2), normal group (18.5-25 kg/m2), and obese group (≥25 kg/m2). The VCM dose and predialysis serum VCM concentration (PVC) were compared.

RESULTS

This study included 191 patients. There were 50 patients in the lean group, 85 in the normal group, and 56 in the obese group. The median loading doses per body weight were 24.0, 22.1, and 21.2 mg/kg for the lean, normal, and obese groups, respectively. The VCM dose per body weight decreased significantly with increasing BMI. The median PVCs of the lean and normal groups were approximately 15 mg/L (not significantly different). However, the median PVC of the obese group was 18.3 mg/L, which was significantly higher (P < 0.001).

CONCLUSIONS

The VCM dose per body weight for Japanese patients undergoing hemodialysis should be adjusted based on the BMI.

Retrospective Study of Factors Affecting the Accuracy of Predicting Vancomycin Concentrations in Patients Aged 75 Years and Above

Background and Objectives: The predicted serum concentrations of vancomycin are determined using population pharmacokinetic parameters. However, the accuracy of predicting vancomycin serum concentrations in the older population remains unclear. Therefore, this study aimed to investigate the accuracy of predicting vancomycin serum concentrations and identifying elements that diminish the prediction accuracy in older people. Materials and Methods: A total of 144 patients aged 75 years or older were included. The serum vancomycin concentrations in the patients were predicted based on population pharmacokinetic parameters common in Japan. We examined the accuracy of serum vancomycin concentration prediction in elderly individuals by comparing the predicted and measured serum vancomycin concentrations in each patient. The prediction accuracy was evaluated using the mean prediction error (ME) and mean absolute error of prediction (MAE) calculated from the measured and predicted serum vancomycin concentrations in each patient. Results: The ME for all patients was 0.27, and the 95% CI included 0, indicating that the predicted values were not significantly biased compared to the measured values. However, the predicted serum concentrations in the <50 kg body weight and serum creatinine (Scr) < 0.6 mg/dL groups were significantly biased compared to the measured values. The group with a history of intensive care unit (ICU) admission showed the largest values for the ME and MAE. Conclusions: Our prediction accuracy was satisfactory but tended to be lower in underweight patients, those with low creatinine levels, and patients admitted to the ICU. Patients with multiple of these factors may experience a greater degree of decreased predictive accuracy.

Comparison of the predictive accuracy of the physiologically based pharmacokinetic (PBPK) model and population pharmacokinetic (PPK) model of vancomycin in Japanese patients with MRSA infection

INTRODUCTION

The latest therapeutic drug monitoring guidelines for vancomycin (VCM) recommend that area under the concentration-time curve is estimated based on model-informed precision dosing and used to evaluate efficacy and safety. Therefore, we predicted VCM concentrations in individual methicillin-resistant Staphylococcus aureus-infected patients using existing a physiologically based pharmacokinetic (PBPK) model and 1- and 2-compartment population pharmacokinetic (PPK) models and confirmed and verified the accuracy of the PBPK model in estimating VCM concentrations with the PPK model.

METHODS

The subjects of the study are 20 patients, and the predicted concentrations were evaluated by comparing the observed and predicted trough and peak values of VCM concentrations for individual patients.

RESULTS

The results showed good correlation between the observed and predicted trough and peak concentrations of VCM was observed generally in the PBPK model, R2 values of 0.72, 0.62, and 0.40 with trough values of 0.49, 0.40, and 0.34 with peak values for PBPK model, 1-compartment, and 2-compartment model, respectively.

CONCLUSIONS

Although the performance of the PBPK model is not as predictive as the PPK model, generally similar predictive trends were obtained, suggesting that it may be a valuable tool for rapid and accurate prediction of AUC for VCM.

Clinical Practice Guidelines for Therapeutic Drug Monitoring of Vancomycin in the Framework of Model-Informed Precision Dosing: A Consensus Review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring

Background: To promote model-informed precision dosing (MIPD) for vancomycin (VCM), we developed statements for therapeutic drug monitoring (TDM). Methods: Ten clinical questions were selected. The committee conducted a systematic review and meta-analysis as well as clinical studies to establish recommendations for area under the concentration-time curve (AUC)-guided dosing. Results: AUC-guided dosing tended to more strongly decrease the risk of acute kidney injury (AKI) than trough-guided dosing, and a lower risk of treatment failure was demonstrated for higher AUC/minimum inhibitory concentration (MIC) ratios (cut-off of 400). Higher AUCs (cut-off of 600 μg·h/mL) significantly increased the risk of AKI. Although Bayesian estimation with two-point measurement was recommended, the trough concentration alone may be used in patients with mild infections in whom VCM was administered with q12h. To increase the concentration on days 1–2, the routine use of a loading dose is required. TDM on day 2 before steady state is reached should be considered to optimize the dose in patients with serious infections and a high risk of AKI. Conclusions: These VCM TDM guidelines provide recommendations based on MIPD to increase treatment response while preventing adverse effects.

A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning

This study aimed to construct an optimal algorithm for initial dose settings of vancomycin (VCM) using machine learning (ML) with decision tree (DT) analysis. Patients who were administered intravenous VCM and underwent therapeutic drug monitoring (TDM) at the Hokkaido University Hospital were enrolled. The study period was November 2011 to March 2019. In total, 654 patients were included in the study. Patients were divided into two groups, training (patients who received VCM from November 2011 to December 2017; n = 496) and testing (patients who received VCM from January 2018 to March 2019; n = 158) groups. For the training group, DT analysis of the classification and regression tree algorithm was performed to construct an algorithm (called DT algorithm) for the initial dose settings of VCM. For the testing group, the rates of attaining the VCM therapeutic range (trough value = 10-15 and 10-20 mg/L) with the DT algorithm and three conventional dose-setting methods were compared for model evaluation. The DT algorithm was constructed to be used for patients with estimated glomerular filtration rate ≥50 mL/min and body weight ≥40 kg. As a result, the recommended daily doses ranged from 20.0 to 58.1 mg/kg. In model evaluation, the DT algorithm obtained the highest rates of attaining the VCM therapeutic range compared to conventional dose-setting methods. Therefore, our DT algorithm can be applied to clinical practice. In addition, ML is useful for setting drug doses.