A Canadian perspective on the revised 2020 ASHP-IDSA-PIDS-SIDP guidelines for vancomycin AUC-based therapeutic drug monitoring for serious MRSA infections

Development of a model to predict vancomycin serum concentration during continuous infusion of vancomycin in critically ill pediatric patients

Vancomycin is a frequently used antibiotic in intensive care units, and the patient's renal clearance affects the pharmacokinetic characteristics of vancomycin. Several advantages have been reported for vancomycin continuous intravenous infusion, but studies on continuous dosing regimens based on patients' renal clearance are insufficient. The aim of this study was to develop a vancomycin serum concentration prediction model by factoring in a patient's renal clearance. Children admitted to our institution between July 1, 2021, and July 31, 2022 with records of continuous infusion of vancomycin were included in the study. Sex, age, height, weight, vancomycin dose by weight, interval from the start of vancomycin administration to the time of therapeutic drug monitoring sampling, and vancomycin serum concentrations were analyzed with the linear regression analysis of the mixed effect model. Univariable regression analysis was performed using the vancomycin serum concentration as a dependent variable. It showed that vancomycin dose (p < 0.001) and serum creatinine (p = 0.007) were factors that had the most impact on vancomycin serum concentration. Vancomycin serum concentration was affected by vancomycin dose (p < 0.001) and serum creatinine (p = 0.001) with statistical significance, and a multivariable regression model was obtained as follows: Vancomycin serum concentration (mg/l) = -1.296 + 0.281 × vancomycin dose (mg/kg) + 20.458 × serum creatinine (mg/dl) (adjusted coefficient of determination, R2 = 0.66). This prediction model is expected to contribute to establishing an optimal continuous infusion regimen for vancomycin.

Toward Model-Based Informed Precision Dosing of Vancomycin in Hematologic Cancer Patients: A First Step

BACKGROUND AND OBJECTIVE

There is no consensus on the optimal vancomycin dose to achieve pharmacokinetic/pharmacodynamic (PK/PD) target in patients with hematologic cancer or in hematopoietic stem cell transplant (HSCT) recipients. A 24-h area under the concentration-time curve (AUC) >400 mg*h/L must be achieved early for successful treatment of severe methicillin-resistant Staphylococcus aureus (MRSA) infections. Current nomograms derived from general population data are not sufficiently accurate to allow AUC-based model-informed precision dosing. The objective of this study was to characterize vancomycin PK in patients with hematologic cancer or in HSCT recipients and to develop a model-informed dosing tool based on PK/PD target requirements.

METHODS

Pooled retrospective and prospective vancomycin serum concentrations were analyzed using NONMEM® to evaluate the performance of previously published population PK (popPK) models built from hematologic cancer datasets and to develop a novel Bayesian PK model. Patients' characteristics and clinical data were tested as potential covariates. The popPK model was validated internally and externally. Predictions of vancomycin concentrations for different dosing regimens were made using Monte-Carlo simulations, and a nomogram strategy was proposed according to selected probability of target attainment (PTA).

RESULTS

The predictive performance of the published popPK models was found to be suboptimal for our population. A novel popPK model was developed using 240 vancomycin concentrations (60 patients). A two-compartment structural model with an additive error model best described the data. Ideal body weight and estimated glomerular filtration rate (eGFR) [Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)] were selected as covariates for volume of distribution (V) and clearance (CL). Bootstrapping confirmed the stability and precision of the popPK parameters. The volume of distribution was V1 = 46.8 L and V2 = 56.1 L, while CL = 5.63 L/h. External validation using 107 vancomycin concentrations (24 patients) demonstrated the predictivity of the model. A nomogram was developed to reach minimally PTA >50% for 400 < AUC < 600 mg*h/L.

CONCLUSION

To our knowledge, this study provides the first model-informed AUC-based strategy in North American hematologic cancer patients with or without HSCT. The resulting nomogram generated provides a simplified approach to improving the accuracy of initial vancomycin dosing in this population.

Pharmacokinetic assessment of vancomycin in critically ill patients and nephrotoxicity prediction using individualized pharmacokinetic parameters

Introduction: Therapeutic drug monitoring (TDM) and pharmacokinetic assessments of vancomycin would be essential to avoid vancomycin-associated nephrotoxicity and obtain optimal therapeutic and clinical responses. Different pharmacokinetic parameters, including trough concentration and area under the curve (AUC), have been proposed to assess the safety and efficacy of vancomycin administration.

Methods: Critically ill patients receiving vancomycin at Nemazee Hospital were included in this prospective study. Four blood samples at various time intervals were taken from each participated patient. Vancomycin was extracted from plasma samples and analyzed using a validated HPLC method.

Results: Fifty-three critically ill patients with a total of 212 blood samples from June 2019 to June 2021 were included in this study. There was a significant correlation between baseline GFR, baseline serum creatinine, trough and peak concentrations, AUCτ, AUC_24h, Cl, and V_d values with vancomycin-induced AKI. Based on trough concentration values, 66% of patients were under-dosed (trough concentration <15 μg/ml) and 18.9% were over-dosed (trough concentration ≥20 μg/ml). Also, based on AUC_24h values, about 52.2% were under-dosed (AUC_24h < 400 μg h/ml), and 21.7% were over-dosed (AUC_24h > 600 μg h/ml) that emphasizes on the superiority of AUC-based monitoring approach for TDM purposes to avoid nephrotoxicity occurrence.

Conclusion: The AUC-based monitoring approach would be superior in terms of nephrotoxicity prediction. Also, to avoid vancomycin-induced AKI, trough concentration and AUCτ values should be maintained below the cut-off points.

Systematic Comparison of Hospital-Wide Standard and Model-Based Therapeutic Drug Monitoring of Vancomycin in Adults

We aimed to evaluate the predictive performance and predicted doses of a single-model approach or several multi-model approaches compared with the standard therapeutic drug monitoring (TDM)-based vancomycin dosing. We performed a hospital-wide monocentric retrospective study in adult patients treated with either intermittent or continuous vancomycin infusions. Each patient provided two randomly selected pairs of two consecutive vancomycin concentrations. A web-based precision dosing software, TDMx, was used to evaluate the model-based approaches. In total, 154 patients contributed 308 pairs. With standard TDM-based dosing, only 48.1% (148/308) of all of the second concentrations were within the therapeutic range. Across the model-based approaches we investigated, the mean relative bias and relative root mean square error varied from −5.36% to 3.18% and from 24.8% to 28.1%, respectively. The model averaging approach according to the squared prediction errors showed an acceptable bias and was the most precise. According to this approach, the median (interquartile range) differences between the model-predicted and prescribed doses, expressed as mg every 12 h, were 113 [−69; 427] mg, −70 [−208; 120], mg and 40 [−84; 197] mg in the case of subtherapeutic, supratherapeutic, and therapeutic exposure at the second concentration, respectively. These dose differences, along with poor target attainment, suggest a large window of opportunity for the model-based TDM compared with the standard TDM-based vancomycin dosing. Implementation studies of model-based TDM in routine care are warranted.