Development and evaluation of a vancomycin dosing nomogram to achieve the target area under the concentration-time curve. A retrospective study

Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age

BACKGROUND

Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation.

AIM

This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms.

METHOD

A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked.

RESULTS

The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration.

CONCLUSION

An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.

A cohort study of the risk factors and the target AUC to avoid vancomycin-associated acute kidney injury in pediatric patients

OBJECTIVES

In recent years, Vancomycin (VCM) dosing design using area under the concentration-time curve (AUC) has been recommended as a measure of efficacy and safety, but there are fewer reports on pediatric patients than on adults. In this study, we evaluated the threshold of AUC for AKI occurrence in pediatric patients and investigated the factors that contribute to the occurrence of AKI.

METHODS

Pediatric patients aged 1-15 years on VCM treatment who underwent TDM at Kagoshima University Hospital from April 2016 to March 2022 were included in the computation of AUC using pediatric population pharmacokinetic parameters.

RESULTS

The ROC curve showed that the AUC threshold for the risk of developing AKI was 583.0 μg・h/mL, and the AUC-ROC curve was 0.873 (sensitivity 0.930, specificity 0.750). Univariate analysis showed that factors associated with AKI incidence were the duration of VCM administration, ICU admission, and AUCSS. Concomitant medications identified as risk factors for AKI incidence were tazobactam/piperacillin, liposomal amphotericin B, calcineurin inhibitors, contrast agents, and H2-receptor blockers. The multivariate analysis showed that AUC ≧ 583.0 μg・h/mL (odds ratio 20.14, 95% CI 3.52-115.22, p < 0.001) and H2-receptor blockers (odds ratio 8.70, 95% confidence interval = 1.38-54.87, p = 0.02) were independent factors for AKI incidence.

CONCLUSIONS

We showed that in pediatric patients receiving VCM, the risk of AKI increases as AUC increases. The findings imply that concurrent use of VCM and H2-receptor blockers may increase the risk of AKI.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

Two Innovative Approaches to Optimize Vancomycin Dosing Using Estimated AUC after First Dose: Validation Using Data Generated from Population PK Model Coupled with Monte-Carlo Simulation and Comparison with the First-Order PK Equation Approach

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice.

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.

Development of a mobile application for vancomycin dosing calculation: A useful tool for the rational use of antimicrobials

Background

Mobile applications (app) provide many benefits for healthcare professionals, making them a useful support clinical decision system.

Objectives

To describe the development of a mobile app, CalcVAN, to calculate vancomycin dosage regimens for adult and pediatric patients.

Methods

This study is a technological production research to develop a mobile app through the rapid prototyping type for the Android system in the Brazilian context. The mobile app structure was developed in four steps: 1) conception, including the needs assessment, the target audience, the literature search, and the definition of contents; 2) prototype planning, including the definition of topics and writing of modules, the selection of media, and the layout; 3) production of the mobile app, including the selection of multimedia tools, the navigation structure, and planning of environment configuration; and 4) make the mobile app available.

Results

The CalcVAN has six screens, containing the vancomycin dosing calculator for adult and pediatric patients based on weight and estimated creatinine clearance parameters. Moreover, the mobile app is free and can be used without internet connection.

Conclusions

A free mobile app was developed to calculate vancomycin dosage regimens for inpatients. This tool assists to optimize the vancomycin dosing, contributing to the antimicrobial stewardship.

Development of a decision flowchart to identify the patients need high-dose vancomycin in early phase of treatment

Background

The standard dose of vancomycin (VCM, 2 g/day) sometimes fails to achieve therapeutic concentration in patients with normal renal function. In this study, we aimed to identify factors to predict patients who require high-dose vancomycin (> 2 g/day) to achieve a therapeutic concentration and to develop a decision flowchart to select these patients prior to VCM administration.

Methods

Patients who had an estimated creatinine clearance using the Cockcroft–Gault equation (eCCr) of ≥50 mL/min and received intravenous VCM were divided into 2 cohorts: an estimation set (n = 146, from April to September 2016) and a validation set (n = 126, from October 2016 to March 2017). In each set, patients requiring ≤2 g/day of VCM to maintain the therapeutic trough concentration (10–20 μg/mL) were defined as standard-dose patients, while those who needed > 2 g/day were defined as high-dose patients. Univariate and multivariate logistic regression analysis was performed to identify the predictive factors for high-dose patients and decision tree analysis was performed to develop decision flowchart to identify high-dose patients.

Results

Among the covariates analyzed, age and eCCr were identified as independent predictors for high-dose patients. Further, the decision tree analysis revealed that eCCr (cut off value = 81.3 mL/min) is the top predictive factor and is followed by age (cut off value = 58 years). Based on these findings, a decision flowchart was constructed, in which patients with eCCr ≥81.3 mL/min and age < 58 years were designated as high-dose patients and other patients were designated as standard-dose patients. Subsequently, we applied this decision flowchart to the validation set and obtained good predictive performance (positive and negative predictive values are 77.6 and 84.4%, respectively).

Conclusion

These results suggest that the decision flowchart constructed in this study provides an important contribution for avoiding underdosing of VCM in patients with eCCr of ≥50 mL/min.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40780-021-00231-w.

Reduced nephrotoxicity with vancomycin therapeutic drug monitoring guided by area under the concentration-time curve against a trough 15-20 μg/mL concentration

Vancomycin is often employed as an antibacterial agent against Gram-positive bacteria, although dose-dependent nephrotoxicity is a concern. Although the risk may be reduced by therapeutic drug monitoring (TDM) guided by area under the concentration-time curve (an attempt to target an AUC > 400 μg•h/mL by Bayesian prediction: AUC₄₀₀-guided TDM), the clinical efficacy of AUC₄₀₀-guided TDM compared with trough concentration-guided TDM within 15-20 μg/mL (Trough_15-20-guided TDM) has yet to be determined. We aimed to retrospectively evaluate the difference in the incidence rate of acute kidney injury (AKI), classified according to the Acute Kidney Injury Network, between these TDM groups. Individual AUC in the AUC₄₀₀-guided TDM group was calculated by Bayesian prediction using trough and peak concentrations (within 3 h after the end of infusion). The AKI incidence in the Trough_15-20-guided TDM group was 28.8% (15/52 patients) compared with an AKI incidence in the AUC₄₀₀-guided TDM group of 9.1% (2/22 patients). Application of AUC₄₀₀-guided TDM was identified as an independent factor for avoiding the incidence of AKI by Cox hazard regression analysis [hazard ratio = 0.168, 95% confidence interval (CI) 0.034-0.839] and logistic regression analysis (odds ratio = 0.037, 95% CI 0.003-0.285). As the estimated glomerular filtration rate (eGFR) improved, the surrogate target trough concentration for an AUC > 400 μg•h/mL was lowered (intercept 15.0074, slope -0.0598). In conclusion, AUC₄₀₀-guided TDM may be superior to Trough_15-20-guided TDM for the reduction of nephrotoxicity during vancomycin therapy.