Out With the Old, in With the New: What Rising Pharmacists Need to Know About Vancomycin Therapeutic Drug Monitoring in Adults

The goal of this commentary is to provide recent pharmacy school graduates and student pharmacists completing APPEs the essential background for correct vancomycin therapeutic drug monitoring (TDM) in the inpatient setting.

Risk of Acute Kidney Injury Based on Vancomycin Target Trough Attainment Strategy: Area-Under-the-Curve-Guided Bayesian Software, Nomogram, or Trough-Guided Dosing

BACKGROUND

Guidelines support area-under-the-curve (AUC) monitoring for vancomycin dosing which may lower overall doses and reduce acute kidney injury (AKI).

OBJECTIVE

The aim of this study was to compare incidence of AKI across 3 vancomycin dosing modalities: AUC-targeted Bayesian pharmacokinetic software, AUC-targeted empiric dosing nomogram, and trough-guided dosing using clinical pharmacists' judgment.

METHODS

This retrospective study included adult patients with a pharmacy dosing consult who received ≥1 dose of vancomycin and ≥1 serum vancomycin level documented between January 1, 2018, and December 31, 2019. Patients with baseline serum creatinine ≥2 mg/dL, weight ≥100 kg, receiving renal replacement therapy, AKI prior to vancomycin therapy, or vancomycin ordered only for surgical prophylaxis were excluded. The primary analysis was incidence of AKI adjusted for baseline serum creatinine, age, and intensive care unit admission. A secondary outcome was adjusted incidence of an abnormal trough value (<10 or >20 μg/mL).

RESULTS

The study included 3459 encounters. Incidence of AKI was 21% for Bayesian software (n = 659), 22% for the nomogram (n = 303), and 32% for trough-guided dosing (n = 2497). Compared with trough-guided dosing, incidence of AKI was lower in the Bayesian (adjusted odds ratio [OR] = 0.72, 95% confidence interval [CI]: 0.58-0.89) and the nomogram (adjusted OR = 0.71, 95% CI: 0.53-0.95) groups. Compared with trough-guided dosing, abnormal trough values were less common in the Bayesian group (adjusted OR = 0.83, 95% CI: 0.69-0.98).

CONCLUSION AND RELEVANCE

Study results suggest that use of AUC-guided Bayesian software reduces the incidence of AKI and abnormal trough values compared with trough-guided dosing.

'How to' Guide for Pharmacist-led Implementation of Beta-Lactam Therapeutic Drug Monitoring in the Critically Ill

Beta-lactam therapeutic drug monitoring (TDM) can improve precision dosing and clinical outcomes in critically ill patients, but has not been implemented widely in the United States. Mayo Clinic recently implemented a beta-lactam TDM program. This single-center experience forms the basis of the manuscript which outlines practical considerations involved with implementation, including the pharmacist's role as a leader. Our implementation effort focused on three primary domains. First, we aimed to ensure a supportive organizational infrastructure. Early leadership engagement by the pharmacist-led core team facilitated advocacy for the clinical need, allocation of resources, and assay development. Second, core clinical workflows were developed that addressed the preferred patient population for use, desirable pharmacokinetic and pharmacodynamic targets, and the preferred sampling strategy. Clinical tools to guide pharmacists in interpreting the results (e.g., pharmacokinetics calculator) and documenting decisions were developed. Third, stakeholders were offered repeated exposure to evidence and expertise to facilitate understanding and application of the new practice. This act of 'individual internalization' seems to be uniquely important to beta-lactam TDM implementation compared with implementation of other antimicrobial TDM programs. Educational strategies and supportive materials that were developed were focused on providing substantive and varied information tailored to the stakeholders' role in the process. For pharmacists, this included both clinical and operational considerations. A continuous improvement plan to support management of the process was instituted to address necessary updates and changes that inevitably emerged. In summary, the described approach to implementation of a pharmacist led beta-lactam TDM program could be used as a roadmap to aid other institutions that aim to develop such a program.

Why is the Implementation of Beta-Lactam Therapeutic Drug Monitoring for the Critically Ill Falling Short? A Multicenter Mixed-Methods Study

BACKGROUND

Beta-lactam therapeutic drug monitoring (BL TDM; drug level testing) can facilitate improved outcomes in critically ill patients. However, only 10%-20% of hospitals have implemented BL TDM. This study aimed to characterize provider perceptions and key considerations for successfully implementing BL TDM.

METHODS

This was a sequential mixed-methods study from 2020 to 2021 of diverse stakeholders at 3 academic medical centers with varying degrees of BL TDM implementation (not implemented, partially implemented, and fully implemented). Stakeholders were surveyed, and a proportion of participants completed semistructured interviews. Themes were identified, and findings were contextualized with implementation science frameworks.

RESULTS

Most of the 138 survey respondents perceived that BL TDM was relevant to their practice and improved medication effectiveness and safety. Integrated with interview data from 30 individuals, 2 implementation themes were identified: individual internalization and organizational features. Individuals needed to internalize, make sense of, and agree to BL TDM implementation, which was positively influenced by repeated exposure to evidence and expertise. The process of internalization appeared more complex with BL TDM than with other antibiotics (ie, vancomycin). Organizational considerations relevant to BL TDM implementation (eg, infrastructure, personnel) were similar to those identified in other TDM settings.

CONCLUSIONS

Broad enthusiasm for BL TDM among participants was found. Prior literature suggested that assay availability was the primary barrier to implementation; however, the data revealed many more individual and organizational attributes, which impacted the BL TDM implementation. Internalization should particularly be focused on to improve the adoption of this evidence-based practice.

Awareness, perception, and barriers of healthcare providers toward the revised consensus guideline for therapeutic monitoring of vancomycin

Background

A revised consensus guideline published in 2020 recommended transitioning vancomycin monitoring to the area under the concentration-time curve over 24 h to minimum inhibitory concentration (AUC₂₄/MIC). The decision to transition to AUC₂₄/MIC monitoring or to continue trough-based monitoring is made at the institutional level and is influenced by several factors, including healthcare providers and system-related factors. Changing current practices is expected to be difficult, and it is important to understand healthcare providers' perceptions and potential barriers before the transition. This study assessed the awareness and perception of physicians and pharmacists toward the revised guideline and identified barriers to their implementation in Kuwait.

Methods

A cross-sectional survey that employed a self-administered questionnaire was used. A random sample of physicians (n = 390), clinical microbiologists (n = 37), and clinical pharmacists (n = 48) across six Kuwaiti public hospitals were surveyed. Descriptive and comparative statistical analyses were performed. Factors associated with awareness and perceptions among the participants were identified.

Results

The response rate was 85.3% (n = 431). Participants had a high (median = 75%) awareness score for the updated vancomycin guideline, as well as a positive perception (median = 5). The main factor identified to affect the awareness and perception of participants following the group analysis was the years of experience. The main barriers identified were a lack of training to perform vancomycin AUC₂₄ calculations, a lack of accurate documentation sample time, and a long turnaround time for serum levels, which might hinder the implementation of the updated guideline.

Conclusion

Physicians, clinical microbiologists, and pharmacists working in Kuwait public hospitals were aware of the 2020 vancomycin monitoring guidelines with positive perceptions. Participants agreed on the several barriers to transitioning to the AUC₂₄/MIC approach, which should be considered by stakeholders before implementation.

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.

Recommended doses of endovenous vancomycin are insufficient to achieve therapeutic concentrations in paediatric patients

OBJECTIVES

Vancomycin therapeutic drug monitoring is challenging, especially in the paediatric population where evidence is scarce. The main objective of this study was to analyse the achievement of therapeutic concentrations of vancomycin in paediatric patients and to evaluate the current monitoring method (trough levels), doses used, and the time required to achieve target concentrations.

METHODS

Paediatric patients on treatment and monitored with vancomycin from November 2019 to December 2021 were included. Those with only one determination of serum vancomycin concentration were excluded. Demographic variables, analytical and microbiological parameters and toxicity data were collected. Pharmacokinetic parameters were assessed at baseline and during treatment.

RESULTS

225 patients (40.9% female; 108 neonates, 49 infants and 68 children or adolescents) were included in the study. The main indications for vancomycin treatment were sepsis (33.9%) and fever of unknown origin (29.3%). Microbiological cultures were positive in 71.1%, mostly with Gram-positive bacteria (60.4%). Therapeutic levels of vancomycin were reached in only 20.1% of the participants in the first determination. After pharmacokinetic monitoring, 81.7% of patients reached therapeutic concentrations, requiring a 23% increase in the initial dose, a 2-day lag time and 1-2 dosage adjustments until the therapeutic concentration was reached. Of the total patients, 13 developed nephrotoxicity, nine neutropenia and one patient developed red man syndrome.

CONCLUSIONS

In our sample of paediatric patients, the recommended doses of vancomycin were insufficient to achieve therapeutic concentrations. Revision of the recommendations and/or a change in the method of pharmacokinetic monitoring is crucial to optimise treatment in this population.

Accuracy of 4 Free Online Dosing Calculators in Predicting the Vancomycin Area Under the Concentration-Time Curve Calculated Using a 2-Point Pharmacokinetic Approach

BACKGROUND

Free online adaptive vancomycin dosing calculators are available to estimate area under the concentration-time curve (AUC), but the accuracy of predicting vancomycin AUC using these calculators compared with using a 2-point pharmacokinetic approach has not been described.

OBJECTIVE

To evaluate the accuracy of calculator-predicted AUC (cpAUC) using 4 free online calculators compared with reference AUC (rAUC), and to assess pharmacists' impressions of the ease of use.

METHODS

Vancomycin AUC was estimated using (1) the reference method via the Sawchuk-Zaske method and linear-logarithmic trapezoidal rule using 2 steady-state postdistributional vancomycin serum concentrations and (2) 4 free online vancomycin dosing calculators including ClinCalc, VancoPK, TDMx, and DMC. Accuracy was calculated by dividing cpAUC by rAUC. Ease of cpAUC estimation was determined by using a 10-point Likert scale.

RESULTS

All 4 calculators had a median cpAUC accuracy ranging from 89% to 110%. Concordance between cpAUC and rAUC determinations of AUC <400 and > 600 mg·h/L occurred 63.3% to 71.4% and 74.5% to 78.6% of the time, respectively. Pharmacist investigators agreed that ClinCalc and VancoPK calculators were easiest to use.

CONCLUSION AND RELEVANCE

cpAUC accuracy varied among the 4 calculators, but all consistently identified patients with an rAUC <400 mg·h/L and an rAUC > 600 mg·h/L at comparable frequencies. All 4 calculators demonstrated some imprecision based on their wide 95% CIs and potential inaccuracies in predicting an rAUC <400 mg·h/L or an rAUC > 600 mg·h/L. Clin Calc and VancoPK were most user friendly based on our pharmacists' impressions.

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.

Doing More With Less: Pragmatic Implementation of Vancomycin Area-Under-the-Curve (AUC) Monitoring

Universal area-under-the-curve (AUC) guided vancomycin therapeutic drug monitoring (TDM) is resource-intensive, cost-prohibitive, and presents a paradigm shift that leaves institutions with the quandary of defining the preferred and most practical method for TDM. We report a step-by-step quality improvement process using 4 plan-do-study-act (PDSA) cycles to provide a framework for development of a hybrid model of trough and AUC-based vancomycin monitoring. We found trough-based monitoring a pragmatic strategy as a first-tier approach when anticipated use is short-term. AUC-guided monitoring was most impactful and cost-effective when reserved for patients with high-risk for nephrotoxicity. We encourage others to consider quality improvement tools to locally adopt AUC-based monitoring.

Area-Under-Curve-Guided Versus Trough-Guided Monitoring of Vancomycin and Its Impact on Nephrotoxicity: A Systematic Review and Meta-Analysis

BACKGROUND

Conventionally, vancomycin trough levels have been used for therapeutic drug monitoring (TDM). Owing to the increasing evidence of trough levels being poor surrogates of area under the curve (AUC) and the advent of advanced pharmacokinetics software, a paradigm shift has been made toward AUC-guided dosing. This study aims to evaluate the impact of AUC-guided versus trough-guided TDM on vancomycin-associated nephrotoxicity.

METHODS

A systematic review was conducted using PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google scholar, and Cochrane library databases; articles published from January 01, 2009, to January 01, 2021, were retrieved and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Studies that evaluated trough-guided or AUC-guided vancomycin TDM and vancomycin-associated nephrotoxicity were included. Random-effects models were used to compare the differences in nephrotoxicity.

RESULTS

Of the 1191 retrieved studies, 57 were included. Most studies included adults and older adults (n = 47, 82.45%). The pooled prevalence of nephrotoxicity was lower in AUC-guided TDM [6.2%; 95% confidence interval (CI): 2.9%-9.5%] than in trough-guided TDM (17.0%; 95% CI: 14.7%-19.2%). Compared with the trough-guided approach, the AUC-guided approach had a lower risk of nephrotoxicity (odds ratio: 0.53; 95% CI: 0.32-0.89). The risk of nephrotoxicity was unaffected by the AUC derivation method. AUC thresholds correlated with nephrotoxicity only within the first 96 hours of therapy.

CONCLUSIONS

The AUC-guided approach had a lower risk of nephrotoxicity, supporting the updated American Society of Health-System Pharmacists guidelines. Further studies are needed to evaluate the optimal AUC-derivation methods and clinical utility of repeated measurements of the AUC and trough levels of vancomycin.

Bayesian method application: Integrating mathematical modeling into clinical pharmacy through vancomycin therapeutic monitoring

The most recent consensus guidelines for dosing and monitoring vancomycin recommended the use of area-under-the-curve with Bayesian estimation for therapeutic monitoring. As this is a modern concept in the practice of clinical pharmacy, the main objective of this review is to introduce the fundamentals of Bayesian estimation and its mathematical application as it relates to vancomycin therapeutic drug monitoring. In addition, we aim to identify pharmacokinetic (PK) software programs that incorporate Bayesian estimation for vancomycin dosing and to describe the PK models utilized in those software programs for the adult population. Twelve software programs that utilize Bayesian estimation were identified, which included: Adult and Pediatric Kinetics, Best Dose, ClinCalc, DoseMeRx, ID-ODS, InsightRx, MwPharm++, NextDose, PrecisePK, TDMx, Tucuxi, and VancoCalc. The software programs varied in the population PK models used as the Bayesian a priori. With the presence of various vancomycin Bayesian software programs, it is important to choose those that utilize PK models reflective of the specific patient population.

Trough-Guided Versus AUC/MIC-Guided Vancomycin Monitoring: A Cost Analysis

PURPOSE

Recent vancomycin dosing and monitoring guidelines recommend monitoring vancomycin area under the 24-hour time-concentration curve instead of traditional trough-only monitoring. This study aimed to compare the total costs of vancomycin dosing and monitoring between trough-guided and AUC-guided approaches in a quaternary hospital from Brazil.

METHODS

In this retrospective cohort study, patients were divided into 2 groups according to the monitoring method. Patients with previous renal impairment were excluded. Vancomycin AUC was estimated by using 2 steady-state serum concentrations and first-order kinetics equations. The primary outcome was total cost of vancomycin therapy and monitoring from the hospital perspective, which included costs of cumulative doses, laboratory fees, materials used in blood collection, nursing time for collection, and pharmacist time for result interpretation.

FINDINGS

A total of 68 patients were included in the AUC/MIC-guided monitoring group, and 76 patients were included in the trough-guided monitoring group. There were no significant differences between groups regarding baseline serum creatinine level, duration of vancomycin therapy, and cumulative vancomycin dose. The median (interquartile range) total vancomycin drug and monitoring cost was $298.32 ($153.81-$429.85) for the AUC/MIC-guided group compared with $285.59 ($198.81-$435.57) for the trough-guided group (P = 0.9658).

IMPLICATIONS

Vancomycin AUC estimation using 2 steady-state serum concentrations and first-order kinetics equations is a feasible alternative for limited-resource institutions that intend to transition from a trough approach to AUC/MIC-guided monitoring. (Clin Ther. 2022;44:XXX-XXX) © 2022 Elsevier HS Journals, Inc.

Optimization of the vancomycin administration regimen by clinical pharmacists based on a population pharmacokinetics model: a prospective interventional study

In vancomycin treatment, the rates of correct blood sampling and initial trough concentrations within the target range are very low. Studies of interventions by clinical pharmacists based on population pharmacokinetics (PPK) models are limited. This study aimed to evaluate the intervention effect of clinical pharmacist-mediated optimization of the vancomycin administration regimen based on a PPK model. Retrospectively enrolled patients constituted the control group, and prospectively enrolled patients constituted the intervention group. The vancomycin administration regimen, trough concentration, pharmacokinetic parameters, and clinical outcomes of the two groups were compared. The control and intervention groups comprised 236 and 138 patients, respectively. Compared with those in the control group, the therapeutic drug monitoring (TDM) and correct TDM sampling time rates in the intervention group were significantly higher (76.92% vs. 43.59%; 63.9% vs. 39.0%, both p < 0.001). The rates of an initial trough concentration within 10-20 mg/L and an adjusted regimen were also significantly higher in the intervention group (55.80% vs. 30.51%, 71.95% vs. 39.18%, both p < 0.001). The rate of an area under the curve (AUC) within 400-650 mg·h/L was higher in the intervention group than in the control group (52.7% vs. 36.6%, p < 0.001). The eradication rates of Gram-positive bacteria were 91.4% in the intervention group and 81.3% in the control group (p = 0.049). Eight patients developed acute kidney injury (AKI) in the control group; however, no AKI occurred in the intervention group (p = 0.029). Intervention by clinical pharmacists can increase the rate of correct sampling time. Using the PPK model combined with Bayesian estimation, clinical pharmacists can greatly increase the trough concentration and AUCs within the target range, especially for adjusted regimens. Higher PK/PD target rates resulted in better Gram-positive bacterial eradication and reduced renal toxicity of vancomycin.

Optimal exposure targets for vancomycin in the treatment of neonatal coagulase-negative Staphylococcus infection: A retrospective study based on electronic medical records

BACKGROUND

The currently advocated ratio of area under the curve (AUC) over 24 h to minimum inhibitory concentration (AUC/MIC) > 400 and AUC < 600 mg h/L as the therapeutic drug monitoring (TDM) target of vancomycin is based on data from multiple observational studies in adult patients with methicillin-resistant Staphylococcus aureus (MRSA) infection. It may not be applicable to newborns with coagulase-negative Staphylococcus (CoNS) infection. We conducted a retrospective study to identify the optimal exposure targets for vancomycin in the treatment of neonatal CoNS infection.

METHODS

Based on the inclusion and exclusion criteria, serum vancomycin concentration, demographics, clinical data, and related laboratory data of newborns who received vancomycin intravenous infusion from June 1, 2016 to February 1, 2021 were collected retrospectively. The AUC was calculated using the maximum a posteriori Bayesian (MAPB) method. The vancomycin exposure threshold of AUC/MIC for efficacy and AUC for toxicity (acute kidney injury, AKI) were determined based on receiver operating characteristic (ROC) curve analysis. The correlation between vancomycin exposure and both clinical effect and nephrotoxicity was analyzed using logistic multivariate regression.

RESULTS

In total, 153 patients and 245 vancomycin concentrations (160 trough and 85 peak concentrations) were included. The ROC curve analysis showed that the exposure thresholds of AUC/MIC for clinical efficacy and AUC for nephrotoxicity were 281 and 602 mg h/L, respectively. The multivariate regression analysis showed that AUC/MIC > 280 was a predictor of efficacy (OR: 13.960, 95% CI: 1.891-103.078, P < 0.05) and AUC > 600 mg h/L was associated with AKI (OR: 9.008, 95% CI: 2.706-29.983, P < 0.05). The vancomycin AUC/MIC threshold for treating neonatal CoNS infection with vancomycin is lower than the currently advocated AUC/MIC >400.

CONCLUSION

The optimal exposure targets for vancomycin in neonatal CoNS infection were AUC/MIC > 280 and AUC < 600 mg h/L.

Pharmacokinetics and pharmacodynamics of peptide antibiotics

Antimicrobial resistance is a major global health challenge. As few new efficacious antibiotics will become available in the near future, peptide antibiotics continue to be major therapeutic options for treating infections caused by multidrug-resistant pathogens. Rational use of antibiotics requires optimisation of the pharmacokinetics and pharmacodynamics for the treatment of different types of infections. Toxicodynamics must also be considered to improve the safety of antibiotic use and, where appropriate, to guide therapeutic drug monitoring. This review focuses on the pharmacokinetics/pharmacodynamics/toxicodynamics of peptide antibiotics against multidrug-resistant Gram-negative and Gram-positive pathogens. Optimising antibiotic exposure at the infection site is essential for improving their efficacy and minimising emergence of resistance.

A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study

Background

It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin.

Objective

The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc.

Methods

The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters.

Results

A total of 52 unique patients’ initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was –0.600 ug/mL (IQR –3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34.

Conclusions

We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.

Therapeutic drug monitoring of vancomycin in neurosurgery patients, from trough concentration to area under the curve: a retrospective single center cohort study in a tertiary hospital

OBJECTIVE

To evaluate the effect of therapeutic drug monitoring (TDM) of vancomycin in neurosurgery patients.

METHODS

In this retrospective, single-center cohort study, data were collected from patients administered vancomycin after neurosurgery during 2020. Intervention by a pharmacist using an area under the curve (AUC)-based strategy for TDM of vancomycin was started on 1 July 2020. The trough concentration was monitored previously. Data regarding basic demographics, vancomycin application, and TDM were collected and analyzed.

RESULTS

Ninety and 155 samples were included in the non-intervention and intervention groups, respectively. No difference in baseline characteristics was detected. After intervention, the attainment rate of vancomycin TDM was significantly increased from 36.7% to 52.3%. The intervention resulted in an increased daily vancomycin dose (28.9 vs. 26.7 mg/kg/day), a more reasonable sample extraction time (sixth vs. ninth dose), reductions in dose adjustments (37.4% vs. 54.4%) and preventative applications (66.7% vs. 52.3%), and no difference in kidney function impact. The intervention group had a shorter hospital stay.

CONCLUSIONS

Intervention by a clinical pharmacist using an AUC-based strategy for vancomycin TDM can provide benefits other than pharmacokinetic attainment in neurosurgery patients. Further prospective multi-center studies are needed to establish standardized intervention measures and evaluation indicators.

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.

Pharmacokinetic equations versus Bayesian guided vancomycin monitoring: Pharmacokinetic model and model-informed precision dosing trial simulations

The recently released revised vancomycin consensus guideline endorsed area under the concentration‐time curve (AUC) guided monitoring. Means to AUC‐guided monitoring include pharmacokinetic (PK) equations and Bayesian software programs, with the latter approach being preferable. We aimed to evaluate the predictive performance of these two methods when monitoring using troughs or peaks and troughs at varying single or mixed dosing intervals (DIs), and evaluate the significance of satisfying underlying assumptions of steady‐state and model transferability. Methods included developing a vancomycin population PK model and conducting model‐informed precision dosing clinical trial simulations. A one‐compartment PK model with linear elimination, exponential between‐subject variability, and mixed (additive and proportional) residual error model resulted in the best model fit. Conducted simulations demonstrated that Bayesian‐guided AUC can, potentially, outperform that of equation‐based AUC predictions depending on the quality of model diagnostics and met assumptions. Ideally, Bayesian‐guided AUC predictive performance using a trough from the first DI was equivalent to that of PK equations using two measurements (peak and trough) from the fifth DI. Model transferability diagnostics can guide the selection of Bayesian priors but are not strong indicators of predictive performance. Mixed versus single fourth and/or fifth DI sampling seems indifferent. This study illustrated cases associated with the most reliable AUC predictions and showed that only proper Bayesian‐guided monitoring is always faster and more reliable than equations‐guided monitoring in pre‐steady‐state DIs in the absence of a loading dose. This supports rapid Bayesian monitoring using data as sparse and early as a trough at the first DI.

Vancomycin therapeutic monitoring by measured trough concentration versus Bayesian-derived area under the curve in critically ill patients with cancer

The updated vancomycin guideline and recent studies suggested that trough concentrations may result in underestimation of the actual area under the curve (AUC), leading to excessive dosing and nephrotoxicity. With limited data available on critically ill cancer patients, this study aimed to compare the two methods in this patient population. This was a 5‐year retrospective study on patients treated with vancomycin in the intensive care unit (ICU) of a comprehensive cancer center. The measured trough concentration was compared to Bayesian‐derived AUC/minimum‐inhibitory‐concentration (MIC), considering MIC as 1. Trough concentrations of 15–20 mg/L and AUC of 400–600 mg h/L were considered the targeted goal. Multivariate analysis was performed to identify factors associated with an AUC below the targeted goal. During the study period, 316 patients were included. The mean age was 54 years ±16 (SD); most patients had solid tumors (75%), and 11% had neutropenia. A targeted goal AUC and trough were recorded in 128 (41%) patients and in 64 (20%) patients, respectively. Of the 128 patients with targeted goal AUC, 31 (24%) had targeted goal trough concentrations and 91 (71%) had trough concentrations below 15 mg/L. Furthermore, among the patients with targeted goal trough concentration (n = 64), 33 (52%) had higher than targeted goal AUC. Augmented renal clearance (ARC), defined as a calculated creatinine‐clearance ≥130 ml/min, was associated with an AUC below the targeted goal. In a cohort of critically ill patients with cancer, over two‐thirds of the patients with a targeted goal Bayesian AUC/MIC had trough concentrations below the targeted goal. ARC was associated with AUC below the targeted goal.

Vancomycin area under the curve/minimum inhibitory concentration and trough level concordance–evaluation on an urban health unit

Background:

A vancomycin AUC/MIC (area under the curve/minimum inhibitory concentration) of 400–600 mg•h/L is associated with improved clinical outcomes for the treatment of methicillin resistant Staphylococcus aureus (MRSA) infections. Currently, there are still limited studies evaluating the relationship between vancomycin trough and AUC.

Objectives:

To evaluate the correlation between vancomycin trough and AUC/MIC and determine if trough-guided monitoring is an adequate predictor of AUC/MIC in the Urban Health population at St Paul’s Hospital.

Methods:

This was a retrospective chart review of 29 patients from November 2019 to February 2021. Patient demographics and laboratory data were collected from electronic medical records. The two-level equation-based approach was used to calculate AUC/MIC. The proportion of AUC/MIC values within target (400–600 mg•h/L) despite subtherapeutic troughs, and the proportion of AUC/MIC values supratherapeutic when trough is within target (15–20 mg/L) were the primary endpoints.

Main Results:

Fifty-seven sets of levels were collected and 75% of vancomycin troughs and AUC24 were found to be discordant. When trough was 10–14.9 mg/L, AUC24 was > 400 mg•h/L in 94% of cases and when trough was 15–20 mg/L, AUC24 was > 600 mg•h/L in 69% of cases. There was a moderate correlation between vancomycin trough and AUC24h ( R2 = 0.57; p < 0.001).

Conclusion:

A vancomycin trough between 15 and 20 mg/L may result in an AUC/MIC greater than necessary for clinical efficacy. Considering these findings and the practical concerns of AUC-guided monitoring, a modest reduction in target troughs to prevent vancomycin toxicity warrants clinical consideration and further evaluation.

Vancomycin Advanced Therapeutic Drug Monitoring: Exercise in Futility or Virtuous Endeavor to Improve Drug Efficacy and Safety?

Vancomycin is commonly prescribed to hospitalized patients. Decades of pharmacokinetic/pharmacodynamic research culminated in recommendations to monitor the ratio of the area under the concentration-time curve (AUC) to the minimum inhibitory concentration in order to optimize vancomycin exposure and minimize toxicity in the revised 2020 guidelines. These guideline recommendations are based on limited data without high-quality evidence and limitations in strength. Despite considerable effort placed on vancomycin therapeutic drug monitoring (TDM), clinicians should recognize that the majority of vancomycin use is empiric. Most patients prescribed empiric vancomycin do not require it beyond a few days. For these patients, AUC determinations during the initial days of vancomycin exposure are futile. This added workload may detract from high-level patient care activities. Loading doses likely achieve AUC targets, so AUC monitoring after a loading dose is largely unnecessary for broad application. The excessive vancomycin TDM for decades has been propagated with limitations in evidence, and it should raise caution on contemporary vancomycin TDM recommendations.

Assessment of the Implementation of AUC Dosing and Monitoring Practices With Vancomycin at Hospitals Across the United States

INTRODUCTION

The latest vancomycin therapeutic drug monitoring guidelines for serious MRSA infections have made a pivotal change in dosing, switching from targeting trough levels to AUC dosing. Because of these new recommendations, antimicrobial stewardship programs across the country are tasked with implementing AUC based dosing.

OBJECTIVES

To assess plans for institutional adoption of vancomycin AUC dosing programs and perceptions of currently used programs in hospitals across the US.

METHODS

An electronic survey was distributed to members of the American College of Clinical Pharmacy IDprn Listserv and American Society of Health-System Pharmacists between May and June 2020 to assess current institutional vancomycin dosing. Institutional program use and multiple software user parameters were analyzed using descriptive statistics.

RESULTS

Two hundred two pharmacists responded to the survey with the majority practicing in institutions with 251-500 beds. Most respondents have yet to implement AUC dosing (142/202, 70.3%) with many of them planning to do so in the next year (81/142, 57.0%). Of those that already implemented AUC dosing programs, purchased Bayesian software (23/60, 38.3%) and homemade software (21/60, 35.0%) were the 2 methods most frequently utilized. Purchased Bayesian software users were more likely to recommend their software to other institutions and ranked user friendliness higher compared to non-purchased software.

CONCLUSION

Most respondents have not made the switch to vancomycin AUC dosing, but there is a growing interest with many institutions looking to adopt a program within the next year.

Transitioning From Consult-Based to Automatic Pharmacy-Driven Vancomycin Management: Results From a Single, Primary Care Center

OBJECTIVE

The transition to area under the curve (AUC) vancomycin monitoring requires substantial updates in pharmacy policies and procedures. The study facility was tasked with transitioning from a consult-based collaborative agreement to an automatic pharmacist management policy on all intravenous (IV) vancomycin orders. The purpose of this quality assessment (QA) study was to evaluate the effectiveness of this transition.

METHODS

The primary outcome was the proportion of patients with pharmacist assessment of pharmacokinetics and dosing with documentation in IV vancomycin treated patients from January-June 2020. Secondary outcomes included the proportion of AUC₂₄ levels within therapeutic range, the incidence of acute kidney injury (AKI) and treatment failures in patients treated ≥72 hours compared to a historical, trough-based cohort.

RESULTS

There were 88 patients in the QA analysis with 100% having a pharmacist assessment with documentation. There were 34 patients treated ≥72 hours in the AUC group, 36 in the trough-based group. AUC₂₄ fell within desired range in 45% of monitored patients. Rates of AKI (9% vs 11%, p = 0.75) and treatment failures were similar (3% vs 0%, p = 0.3).

CONCLUSION

The transition from consult-based to an automatic pharmacy management agreement was successful with similar safety and efficacy compared to a historical trough-based cohort.

Performance of Area under the Concentration-Time Curve Estimations of Vancomycin with Limited Sampling by a Newly Developed Web Application

PURPOSE

Therapeutic drug monitoring guided by the area under the concentration-time curve (AUC-guided TDM) is recommended for vancomycin. However, validated efficient software remains elusive to popularize AUC-guided TDM in Japan. The aim of this study was to validate a newly developed web application, PAT, for AUC estimation.

METHODS

PAT was developed on the R ver. 3.6.2 platform for use with mobile phones and personal computers. AUC estimated by PAT (AUC_PAT) was evaluated against the reference AUC (AUC_REF) calculated with the log-linear trapezoidal rule using eight measured concentrations, or against AUC (AUC_BM-P) calculated using an evaluated available software with clinical data.

RESULTS

Investigating the best sampling points with limited sampling, PAT produced the least bias using two concentrations at 1 h and 11 h after the end of infusion (slope 1.18, intercept -15.57, median AUC_PAT/AUC_REF 0.93 [range 0.81-1.24]), where only one estimation (6%) was out of the predetermined acceptable range of 0.8-1.2. Employment of only a trough concentration was more biased (AUC_PAT/AUC_REF range 0.73-1.30 for 11 h, AUC_PAT/AUC_REF range 0.62-1.40 for 23 h). In comparison with the evaluated software, AUC_PAT was not biased against the AUC_BM-P (slope 1.04, intercept -15.80, median AUC_PAT/AUC_BM-P 1.00 [range 0.86-1.10]).

CONCLUSIONS

The new application using two concentrations was appropriately validated and might be efficient in popularizing the AUC-guided TDM of vancomycin.

Provider perspectives on beta-lactam therapeutic drug monitoring programs in the critically ill: a protocol for a multicenter mixed-methods study

Background

Beta-lactams (i.e., penicillins, cephalosporins, carbapenems, monobactams) are the most widely used class of antibiotics in critically ill patients. There is substantial interpatient variability in beta-lactam pharmacokinetics which renders their effectiveness and safety largely unpredictable. One strategy to ensure achievement of therapeutic concentrations is drug level testing (“therapeutic drug monitoring”; TDM). While studies have suggested promise with beta-lactam TDM, it is not yet widely available or implemented. This protocol presents a mixed-methods study designed to examine healthcare practitioners’ perspectives on the use and implementation of beta-lactam TDM in the critically ill.

Methods

An explanatory sequential mixed-methods design will be used [QUANT → qual]. First, quantitative data will be collected through a web-based questionnaire directed at clinicians at three academic medical centers at different phases of beta-lactam TDM implementation (not yet implemented, partially implemented, fully implemented). The sampling frame will include providers from a variety of disciplines that interact with drug level testing and interpretation in the critical care environment including pharmacists, intensivists, infectious diseases experts, medical/surgical trainees, and advanced practice providers. Second, approximately 30 individuals will be purposively sampled from survey respondents to conduct in-depth qualitative interviews to explain and expand upon the results from the quantitative strand. Normalization Process Theory and the Consolidated Framework for Implementation Science will be used to guide data analysis.

Discussion

These data will be used to answer two specific questions: “What are ICU practitioners’ perspectives on implementing beta-lactam TDM?” and “What factors contribute to the success of beta-lactam TDM program implementation?” Results of this study will be used to design future implementation strategies for beta-lactam TDM programs in the critically ill.

Trial registration

NCT04755777.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43058-021-00134-9.

Efficacy and Safety of Vancomycin Therapy After the Transition to AUC/MIC Monitoring in a Primary Facility

BACKGROUND

New guidance recommends area under the curve/minimum inhibitory concentration (AUC/MIC) instead of trough-based monitoring for vancomycin therapy. While this transition has demonstrated improved safety and efficacy in large, tertiary centers, this has not been assessed in the primary hospital setting.

OBJECTIVE

The primary objectives were to evaluate the efficacy and safety of AUC/MIC monitoring in inpatient veterans treated with intravenous vancomycin for ≥72 hours compared to a historical cohort of trough-based monitoring.

METHODS

This was a retrospective, quasi-experimental study over 2 five-month study periods. Efficacy was evaluated by comparing clinical failure rates as defined by a persistent fever, clinical deterioration, or escalation of gram-positive therapy. Safety was determined by the incidence of acute kidney injury (AKI) defined by an acute increase in serum creatinine ≥0.3 mg/dL over 48 hours.

RESULTS

25 patients met the criteria in the before group and 19 in the after group. Efficacy was equivalent between groups; no patients exhibited clinical failure of vancomycin therapy. In the before group, 2 patients (8%) met defined criteria for AKI, while none in the after group experienced AKI (P = 0.21). Total vancomycin exposure was similar between groups (P = 0.56).

CONCLUSION

AUC-based monitoring was equally efficacious as trough-based monitoring with similarly low rates of AKI.

Individualized Vancomycin Dosing with Therapeutic Drug Monitoring and Pharmacokinetic Consultation Service: A Large-Scale Retrospective Observational Study

Background

To date, outcome data with a large sample size and data regarding the clinical outcomes of pharmacokinetic-guided (PK) dosing of vancomycin are limited.

Aim

We evaluated the pharmacokinetic and clinical outcomes of a PK-guided dosing advisory program, pharmacokinetic consultation service (PKCS), in vancomycin treatment.

Methods

We investigated vancomycin therapeutic drug monitoring (TDM) and PKCS use through a retrospective review of patients who had serum vancomycin trough concentration data from October 2017 to November 2018. Among these patients, we selected non-critically ill adult patients satisfying our selection criteria to evaluate the effect of PKCS. Target trough attainment rate, time to target attainment, vancomycin-induced nephrotoxicity (VIN), vancomycin treatment failure rate, and duration of vancomycin therapy were compared between patients whose dosing was adjusted according to PKCS (PKCS group), and those whose dose was adjusted at the discretion of the attending physician (non-PKCS group).

Results

A total of 280 patients met the selection criteria for the VIN analysis (PKCS, n=134; non-PKCS, n=146). The incidence of VIN was similar between the two groups (PKCS, n=5; non-PKCS, n=5); however, the target attainment rate was higher in the PKCS group (75% vs 60%, P = 0.012). The time to target attainment was similar between the two groups. Further exclusions yielded 112 patients for the clinical outcome evaluation (PKCS, n=51; non-PKCS, n=61). The treatment failure rate was similar, and the duration of vancomycin therapy was longer in the PKCS group (12 vs 8 days, P = 0.008).

Conclusion

In non-critically ill patients, an increase in target trough achieved by PKCS did not lead to decreased vancomycin treatment failures, shorter vancomycin treatment, or decreased nephrotoxicity in vancomycin treatment. Considering the excessive amount of effort currently put into vancomycin dosing and monitoring, more selective criteria for individualized pharmacokinetic-guided dosing needs to be applied.

Population Pharmacokinetics and Model-Based Dosing Optimization of Teicoplanin in Pediatric Patients

Objectives: The pharmacokinetics (PK) of teicoplanin differs in children compared with adults. Our aim was to determine the PK of teicoplanin in an Asian pediatric population and to optimize dosage regimens. Methods: This was a retrospective PK study and all the data were collected from hospitalized children. We developed a population PK model using sparse data, and Monte Carlo simulation was used to assess the ability of standard teicoplanin regimen and other different dosage regimens. The optimal dosing regimens were defined as achieving the target trough concentration (C _min) of 10 mg/L and pharmacokinetic/pharmacodynamic (PK/PD, [AUC₂₄/MIC]) of 125 for moderate infection. For severe infection, the optimal dosing regimens were defined as achieving the target 15 mg/L and AUC₂₄/MIC of 345. Results: 159 children were included and 1.5 samples/children on average were provided. Estimated clearance of teicoplanin was 0.694 L/h (0.784/L/h/70 kg) and volume of distribution was 1.39 L. Teicoplanin standard loading dose was adequate for moderate infection, while 13 mg/kg was needed for severer infection. With standard maintenance doses, both patients with moderate and severe infection failed to achieve the target C _min. 12 and 16 mg/kg/day were required to achieve a C _min ≥ 10 and 15 mg/L, respectively. However, standard maintenance dose was adequate to achieve AUC₂₄/MIC ≥ 125 for moderate infection, and 12 mg/kg/day was needed to achieve AUC₂₄/MIC ≥ 345 for severe infection. Lower weight and serum creatinine were associated with higher dose. Conclusion: Optimal doses based on the target C _min were higher than that based on the PK/PD target. To achieve the C _min and PK/PD targets simultaneously, a standard loading dose was adequate for moderate infection based on simulation, while dosing higher than standard doses were required in other situation. Further clinical studies with rich sampling from children is required to confirm our findings.

Cost-benefit analysis comparing trough, two-level AUC and Bayesian AUC dosing for vancomycin

OBJECTIVES

Area under the time-concentration curve (AUC) -guided dosing provides better estimates of exposure than vancomycin trough concentrations. Though clinical benefits have been reported, the costs of AUC-guided dosing are uncertain. The objective of this study was to quantify the costs of single-sample Bayesian or two-sample AUC strategies versus trough-guided dosing.

METHODS

A cost-benefit analysis from the institutional perspective was conducted using a decision tree to model the probabilities and costs of acute kidney injury (AKI) associated with vancomycin administered over 48 hours up to 21+ days. Costs included vancomycin concentrations, Bayesian software and AKI hospitalization costs, and probabilities were obtained from primary literature. Robustness was assessed via both one-way and probabilistic sensitivity analyses.

RESULTS

In the base-case model, two-sample AUC versus trough dosing saved an average of US$ 846 per patient encounter, and single-sample Bayesian AUC versus trough dosing saved an average of US$ 2065 per patient encounter. This translates into annual cost-savings of US$ 846 810 and US$ 2 065 720 for two-sample and single-sample Bayesian methods versus trough dosing, respectively, assuming 1000 vancomycin-treated patients per year. Assuming a budget of US$ 100 000 per year for Bayesian software, an institution would need to treat ≥41 patients with vancomycin for at least 48 hours to break even.

CONCLUSIONS

There are significant institutional cost benefits using two-sample AUC or single-sample Bayesian methods over trough dosing, even after accounting for the annual costs of Bayesian programs. The potential to decrease rates of AKI, improve clinical outcomes and reduce costs to the institution strongly warrants consideration of improved dosing methods for vancomycin.

Dosing Common Medications in Hospitalized Pediatric Patients with Obesity: A Review

Medication management in children and adolescents with obesity is challenging because both developmental and pathophysiological changes may impact drug disposition and response. Evidence to date indicates an effect of obesity on drug disposition for certain drugs used in this population. This work identified published studies evaluating drug dosing, pharmacokinetics (PK), and effect in pediatric patients with obesity, focusing on 70 common medications used in a pediatric network of 42 US medical centers. A PubMed search revealed 33 studies providing PK and/or effectiveness data for 23% (16 of 70) of medications, 44% of which have just one study and can be considered exploratory. This work appraising 4 decades of literature shows several promising approaches: greater use of PK models applied to prospective clinical studies, dosing recommendations derived from both PK and safety, and multiyear effectiveness data on drugs for chronic conditions (e.g., asthma). Most studies make dose recommendations but are weakened by retrospective study design, small study populations, and no controls or historic controls. Dosing decisions continue to rely on extrapolating knowledge, including targeting systemic drug exposure typically achieved in adults. Optimal weight-based dosing strategies vary by drug and warrant prospective, controlled studies incorporating PK and modeling and simulation to complement clinical assessment.

Gap Analysis for the Conversion to Area Under the Curve Vancomycin Monitoring in a Small Rural Hospital

OBJECTIVE

Consensus guidelines for vancomycin monitoring now recommend area under the curve (AUC) calculations for optimal vancomycin efficacy and safety. This will be a major practice change for many facilities. Implementation guidance is available but has not been reported in smaller, primary care hospitals. The objective of this study was to measure the uptake of AUC monitoring implementation in a rural facility.

METHODS

This is a gap analysis evaluating the appropriateness of vancomycin levels tests after the April 1, 2019 transition. All vancomycin levels between April 2019 and June 2019 after the go-live date were included with no exclusions in a retrospective chart review. The primary outcome was the proportion of levels in the appropriate time frame: peaks 1 to 2 hours after infusion with troughs at least 1 half-life after initial dose and prior to the next dose. Secondary outcomes included reasons identified for inappropriate levels and the proportion of AUC₂₄ calculations within therapeutic range (400-600 mg.h/L). Descriptive statistics were used to measure the scope and outcomes of this transition.

RESULTS

The transition was effective with 97% of cases utilizing AUC-based methods. There were 65 vancomycin levels in the 3-month study period with 86% deemed appropriate. Of the 9 inappropriate levels, 4 had to be repeated for accurate monitoring. There were 28 two-level couplets used for AUC₂₄ calculations, 17 (61%) fell within therapeutic range.

CONCLUSION

Implementation strategies for the AUC transition described in tertiary medical centers can be successfully utilized in primary facilities.

Practice survey on the use of vancomycin in pediatrics in the New Aquitaine region and guidelines of learned societies

INTRODUCTION

Vancomycin is an old antibiotic whose use is still being debated today. The objective of this work was to establish an inventory of the use of vancomycin in the various pediatric and neonatal hospital services in the New Aquitaine region.

MATERIALS AND METHODS

A declaratory practice survey was conducted in 49 pediatric and neonatal hospital units. These practices were compared with the guidelines of several learned societies.

RESULTS

A total of 36 responses could be analyzed: 12 units (33%) used vancomycin in discontinuous administration, 18 (50%) had opted for continuous infusion, and six used it in both modalities (17%). The reported dosages were highly variable. Blood tests were performed by 26 units (72%), but the target values of the trough serum concentration were also highly variable. After dosing, all units reported adjusting the dosage and re-dosing after modification (26/26). Finally, 21 units (58%) reported taking into account the MIC of the possibly isolated bacterium.

CONCLUSION

Our study shows that vancomycin is used in very different ways from one unit to another, within the same region, including in ways not recommended by the main learned societies. Much work remains to be done to determine the optimal dosages of vancomycin in pediatrics, to set the serum trough concentration of vancomycin values, and to determine whether continuous infusion use is comparable to discontinuous administration in terms of efficacy.

Vancomycin area under the curve to minimum inhibitory concentration ratio predicting clinical outcome: a systematic review and meta-analysis with pooled sensitivity and specificity

BACKGROUND

Vancomycin is a first-line antibiotic for methicillin-resistant Staphylococcus aureus infections or other Gram-positive infections. The area under the curve (AUC) to minimum inhibitory concentration (MIC) ratio is proposed as a therapeutic drug-monitoring parameter. How well clinical efficacy is predicted by this measure has not been established.

OBJECTIVE

Determine the test performance characteristics (TPC) of AUC:MIC of vancomycin for prediction of positive outcome.

DATA SOURCES

PubMed and Ovid Medline (1946 to 2018) and EMBASE (1974 to 2018). Study Eligibility Criteria and Participants: Studies of patients treated with vancomycin for any type of infection in peer reviewed publications. All patient populations were included.

INTERVENTIONS

Vancomycin AUC:MIC or AUC was related to patient clinical outcome.

METHODS

Searches of medical databases using relevant terms were performed. Screening, study reviewing, data extracting and assessing data quality was performed independently by two reviewers. Studies were stratified by type of primary outcome for calculation of pooled sensitivity, specificity and construction of hierarchical summary receiver operating characteristic (HSROC) curves.

RESULTS

Nineteen studies including 1699 patients were meta-analysed. Pooled sensitivity and specificity were 0.77 (95% CI 0.67-0.84) and 0.62 (95% CI 0.53-0.71) respectively for the seven studies with primary outcome of mortality and 0.65 (95% CI 0.53-0.75), 0.58 (95% CI 0.48-0.67) for studies with composite or clinical cure outcome (n = 12). HSROC curves suggested considerable heterogeneity. An additional 11 studies were described but could not be included for meta-analysis because data were not available. The majority of these studies (9/11) failed to demonstrate a relationship between AUC:MIC and positive clinical outcome.

CONCLUSIONS

Vancomycin AUC:MIC performance was modest and inconsistent. Analysis was limited by studies without sufficient data; therefore, meta-analytic results may overestimate TPC values. Given this, as well as the lack of standardization of methods, widespread adoption of AUC:MIC as the preferred vancomycin monitoring parameter may be premature.

The Benefit of Individualized Vancomycin Dosing Via Pharmacokinetic Tools: A Systematic Review and Meta-analysis

Background: Various pharmacokinetic (PK) equations and software have been developed to individualize vancomycin dosing. However, the benefit of using any PK information to guide vancomycin dosing has not been fully elucidated. Objective: To appraise available evidence on the effectiveness and safety of individualized vancomycin dosing via PK tools. Methods: PubMed, EMBASE, the Cochrane Library, and 2 Chinese literature databases were searched through August 1, 2019. Randomized controlled trials (RCTs) and cohort studies that reported the PK and clinical outcomes of individualized vancomycin dosing versus empirical dosing were included. Pooled risk ratios (RRs) and mean differences were calculated for dichotomous and continuous outcomes, respectively. Results: A total of 21 studies involving 4346 patients were finally included, of which 3 were RCTs and 18 were cohort studies. Meta-analysis revealed that PK-guided vancomycin dosing significantly increased the attainment of target trough concentration (RR = 1.59; 95% CI = 1.49-1.70) and decreased the incidence of nephrotoxicity (RR = 0.57; 95% CI = 0.46-0.71). Additionally, the available evidence showed that target area under the curve/minimum inhibitory concentration attainment rate and time to target concentration could improve. However, the evidence on clinical outcomes was scarce, and no significant differences were detected in clinical response rate, microbiological eradication rate, mortality, and length of hospital stay between PK-guided vancomycin dosing and empirical dosing strategies. Conclusion and Relevance: Individualized vancomycin dosing via PK tools significantly increases the attainment of target trough concentration and decreases the incidence of nephrotoxicity. Evidence on clinical effectiveness was limited and showed no significant benefit. Further well-designed studies are warranted to assess its clinical effectiveness and inform routine care.

Pharmacokinetic modelling and Bayesian estimation-assisted decision tools to optimize vancomycin dosage in neonates: only one piece of the puzzle

Introduction: Vancomycin is commonly administered to neonates, while observational data on therapeutic drug monitoring (TDM, trough levels) suggest that vancomycin exposure and dosage remain substandard. Area covered: Data on vancomycin pharmacokinetics (PK) and its covariates are abundant. Consequently, modeling is an obvious tool to improve targeted exposure, with a shift from TDM trough levels to area under the curve (AUC_24h) targets, as in adults. Continuous administration appeared as a practice to facilitate AUC_24h target attainment, while Bayesian model-supported targeting emerged as a novel tool. However, the AUC_24h/MIC (minimal inhibitory concentration) target itself should consider neonate-specific aspects (bloodstream infections, coagulase-negative staphylococci, protein binding, underexplored causes of variability, like assays, preparation and administration inaccuracies, or missing covariates). Expert opinion: To improve targeted exposure in neonates, initial vancomycin prescription should be based on 'a priori model-based individual dosing' using validated dosing regimens, followed by further tailoring by dosing optimization applying Bayesian estimation-assisted TDM. Future research should focus on the feasibility to integrate these tools (individualized dosing, Bayesian models) in clinical practice, and to perform PK/PD studies in the relevant animal models and human neonatal setting (coagulase-negative staphylococci, bloodstream infections).