DosOpt: A Tool for Personalized Bayesian Dose Adjustment of Vancomycin in Neonates

Subgroup identification-based model selection to improve the predictive performance of individualized dosing

Currently, model-informed precision dosing uses one population pharmacokinetic model that best fits the target population. We aimed to develop a subgroup identification-based model selection approach to improve the predictive performance of individualized dosing, using vancomycin in neonates/infants as a test case. Data from neonates/infants with at least one vancomycin concentration was randomly divided into training and test dataset. Population predictions from published vancomycin population pharmacokinetic models were calculated. The single best-performing model based on various performance metrics, including median absolute percentage error (APE) and percentage of predictions within 20% (P20) or 60% (P60) of measurement, were determined. Clustering based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm was used to group neonates/infants according to their best-performing model. Subsequently, classification trees to predict the best-performing model using clinical and demographic characteristics were developed. A total of 208 vancomycin treatment episodes in training and 88 in test dataset was included. Of 30 identified models from the literature, the single best-performing model for training dataset had P20 26.2-42.6% in test dataset. The best-performing clustering approach based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm had P20 44.1-45.5% in test dataset, whereas P60 was comparable. Our proof-of-concept study shows that the prediction of the best-performing model for each patient according to the proposed model selection approaches has the potential to improve the predictive performance of model-informed precision dosing compared with the single best-performing model approach.

Prospective validation of a model-informed precision dosing tool for vancomycin treatment in neonates

We recruited 48 neonates (50 vancomycin treatment episodes) in a prospective study to validate a model-informed precision dosing (MIPD) software. The initial vancomycin dose was based on a population pharmacokinetic model and adjusted every 36-48 h. Compared with a historical control group of 53 neonates (65 episodes), the achievement of a target trough concentration of 10-15 mg/L improved from 37% in the study to 62% in the MIPD group (P = 0.01), with no difference in side effects.

Clinical decision support in the neonatal ICU

Clinical Decision Support (CDS) tools help the healthcare team diagnose, monitor, and treat patients more efficiently and consistently by executing clinical practice guidelines and recommendations. As a result, CDS has a direct impact on the delivery and healthcare outcomes. This review covers the fundamental concepts, as well as the infrastructure needed to create a CDS tool and examples of its use in the neonatal setting. This article also serves as a primer on what to think about when proposing the development of a new CDS tool, or when upgrading an existing one. We also highlight important elements that influence CDS development, such as informatics methodologies, data and device interoperability, and regulation.

An Audit to Evaluate Vancomycin Therapeutic Drug Monitoring in a Neonatal Intensive Care Unit

BACKGROUND

Therapeutic drug monitoring (TDM) is routinely used for optimization of vancomycin therapy, because of exposure-related efficacy and toxicity, in addition to significant variability in pharmacokinetics, which leads to unpredictable drug exposure.

OBJECTIVE

The aim of this study was to evaluate target attainment and TDM of vancomycin in neonates.

METHODS

The authors conducted a retrospective study and collected data from medical records of all neonates who received vancomycin therapy in the neonatal intensive care unit between January 2019 and December 2019. The primary outcome was the proportion of vancomycin courses that reached target trough concentrations of 10-20 mg/L based on appropriate TDM samples collection. Secondary outcomes included proportion of courses with appropriate dose and dose frequency, and proportion of patients who achieved target concentrations after the first dose adjustment.

RESULTS

In total, 69 patients were included, with 129 vancomycin courses. The median initial vancomycin trough concentration was 12 (range: 4-36) mg/L. The target trough concentration was achieved in 75% of courses after the initial dose with appropriate TDM, and 84% of courses after TDM-guided dose adjustments. Patients were dosed appropriately in 121/129 courses and TDM was performed correctly according to protocol in 51/93 courses. A dose adjustment was performed in 18/29 courses, to increase target attainment.

CONCLUSIONS

This study showed that there is a need for an increase in dose to improve target attainment. There is also a need to explore more effective TDM strategies to increase the proportion of neonatal patients attaining vancomycin target trough concentrations.

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.

Establishment and application of population pharmacokinetics model of vancomycin in infants with meningitis

BACKGROUND

To establish a population pharmacokinetics (PPK) model of vancomycin (VCM) for dose individualization in Chinese infants with meningitis.

METHODS

We collected the data of 82 children with meningitis in hospital from July 2014 to June 2016. The initial vancomycin dosage regimen for children was 10 or 15 mg/kg for q12 h, q8 h or q6 h. Serum concentrations were determined by Viva-E Analyzer before and after the fifth administration. The PPK model was developed by nonlinear mixed-effect model software, assessed by the bootstrap method and then tested in 20 infant patients.

RESULTS

The VCM clearance (CL) was increased by body weight (WT) and decreased by blood urea nitrogen (BUN). Pharmacokinetic parameters of VCM were not influenced by co-administered drugs. The trough concentrations of VCM were accurately predicted by the PPK model, with the prediction errors less than 32%.

CONCLUSION

A new individual strategy for VCM regimens was proposed and validated by the PPK model.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

Population pharmacokinetics and dose optimization of vancomycin in neonates

The pharmacokinetics of vancomycin vary among neonates, and we aimed to conduct population pharmacokinetic analysis to determine the optimal dosage of vancomycin in Korean neonates. From a retrospective chart review, neonates treated with vancomycin from 2008 to 2017 in a neonatal intensive care unit (NICU) were included. Vancomycin concentrations were collected based on therapeutic drug monitoring, and other patient characteristics were gathered through electronic medical records. We applied nonlinear mixed-effect modeling to build the population pharmacokinetic model. One- and two-compartment models with first-order elimination were evaluated as potential structural pharmacokinetic models. Allometric and isometric scaling was applied to standardize pharmacokinetic parameters for clearance and volume of distribution, respectively, using fixed powers (0.75 and 1, respectively, for clearance and volume). The predictive performance of the final model was developed, and dosing strategies were explored using Monte Carlo simulations with AUC_0–24 targets 400–600. The patient cohort included 207 neonates, and 900 vancomycin concentrations were analyzed. Only 37.4% of the analyzed concentrations were within trough concentrations 5–15 µg/mL. A one-compartment model with first-order elimination best described the vancomycin pharmacokinetics in neonates. Postmenstrual age (PMA) and creatinine clearance (CLcr) affected the clearance of vancomycin, and model evaluation confirmed the robustness of the final model. Population pharmacokinetic modeling and dose optimization of vancomycin in Korean neonates showed that vancomycin clearance was related to PMA and CLcr, as well as body weight. A higher dosage regimen than the typical recommendation is suggested.

The antibiotic vancomycin induces complexation and aggregation of gastrointestinal and submaxillary mucins

Vancomycin, a branched tricyclic glycosylated peptide antibiotic, is a last-line defence against serious infections caused by staphylococci, enterococci and other Gram-positive bacteria. Orally-administered vancomycin is the drug of choice to treat pseudomembranous enterocolitis in the gastrointestinal tract. However, the risk of vancomycin-resistant enterococcal infection or colonization is significantly associated with oral vancomycin. Using the powerful matrix-free assay of co-sedimentation analytical ultracentrifugation, reinforced by dynamic light scattering and environmental scanning electron microscopy, and with porcine mucin as the model mucin system, this is the first study to demonstrate strong interactions between vancomycin and gastric and intestinal mucins, resulting in very large aggregates and depletion of macromolecular mucin and occurring at concentrations relevant to oral dosing. In the case of another mucin which has a much lower degree of glycosylation (~60%) - bovine submaxillary mucin - a weaker but still demonstrable interaction is observed. Our demonstration - for the first time - of complexation/depletion interactions for model mucin systems with vancomycin provides the basis for further study on the implications of complexation on glycopeptide transit in humans, antibiotic bioavailability for target inhibition, in situ generation of resistance and future development strategies for absorption of the antibiotic across the mucus barrier.

Target-Controlled Infusion of Cefepime in Critically Ill Patients

Attainment of appropriate pharmacokinetic-pharmacodynamic (PK-PD) targets for antimicrobial treatment is challenging in critically ill patients, particularly for cefepime, which exhibits a relative narrow therapeutic-toxic window compared to other beta-lactam antibiotics. Target-controlled infusion (TCI) systems, which deliver drugs to achieve specific target drug concentrations, have successfully been implemented for improved dosing of sedatives and analgesics in anesthesia. We conducted a clinical trial in an intensive care unit (ICU) to investigate the performance of TCI for adequate target attainment of cefepime. Twenty-one patients treated with cefepime according to the standard of care were included. Cefepime was administered through continuous infusion using TCI for a median duration of 4.5 days. TCI was based on a previously developed population PK model incorporating the estimated creatinine clearance based on the Cockcroft-Gault formula as the input variable to calculate cefepime clearance. A cefepime blood concentration of 16 mg/liter was targeted. To evaluate the measured versus predicted plasma concentrations, blood samples were taken (median of 10 samples per patient), and total cefepime concentrations were measured using ultraperformance liquid chromatography-tandem mass spectrometry. The performance of the TCI system was evaluated using Varvel criteria. Half (50.3%) of the measured cefepime concentrations were within ±30% around the target value of 16 mg liter^-1 The wobble was 11.4%, the median performance error (MdPE) was 21.1%, the median absolute performance error (MdAPE) was 32.0%, and the divergence was -3.72% h^-1 Based on these results, we conclude that TCI is useful for dose optimization of cefepime in ICU patients. (This study has been registered at ClinicalTrials.gov under identifier NCT02688582.).

Rational Use of Antibiotics in Neonates: Still in Search of Tailored Tools

Rational medicine use in neonates implies the prescription and administration of age-appropriate drug formulations, selecting the most efficacious and safe dose, all based on accurate information on the drug and its indications in neonates. This review illustrates that important uncertainties still exist concerning the different aspects (when, what, how) of rational antibiotic use in neonates. Decisions when to prescribe antibiotics are still not based on robust decision tools. Choices (what) on empiric antibiotic regimens should depend on the anticipated pathogens, and the available information on the efficacy and safety of these drugs. Major progress has been made on how (beta-lactam antibiotics, aminoglycosides, vancomycin, route and duration) to dose. Progress to improve rational antibiotic use necessitates further understanding of neonatal pharmacology (short- and long-term safety, pharmacokinetics, duration and route) and the use of tailored tools and smarter practices (biomarkers, screening for colonization, and advanced therapeutic drug monitoring techniques). Implementation strategies should not only facilitate access to knowledge and guidelines, but should also consider the most effective strategies (‘skills’) and psychosocial aspects involved in the prescription process: we should be aware that both the decision not to prescribe as well as the decision to prescribe antibiotics is associated with risks and benefits.

Association between increased mortality rate and antibiotic dose adjustment in intensive care unit patients with renal impairment

PURPOSE

Adjusting the antibiotic dose based on an estimation of the glomerular filtration rate (eGFR) may result in subdosing, which may actually be significantly more problematic for intensive care unit (ICU) patients than not adjusting the dose. The aim of this study was to assess the outcomes of antibiotic dose adjustment in ICU patients with renal impairment.

METHODS

A retrospective cohort study was conducted in adult patients admitted to an ICU of a Brazilian hospital from January 2014 to December 2015. The eGFR was determined using Cockcroft-Gault and Modified Diet in Renal Disease equations for each day of hospitalization. Treatment failure was defined based on the clinical, laboratory, and radiological criteria.

RESULTS

A total of 126 patients were assessed to meet the inclusion criteria and subsequently enrolled in the study (19.9% of patients admitted to the ICU during the study period). Of the 168 opportunities for dose adjustment, 99 (58.9%) adjustments were made. The mean eGFR in the group with dose adjustment was lower than that in the group without dose adjustment (38.5 vs. 40.7 mL/min/1.73 m², respectively). The treatment failure rate among patients with dose adjustment and those treated with the usual dose was 59.3 and 38.9%, respectively (p = 0.023), and the mortality rates in the respective groups were 74.1 and 55.5% (p = 0.033). An association between dose adjustment and treatment failure/mortality rates was also observed in the multivariate analysis including the prognostic score.

CONCLUSIONS

In ICU patients with renal impairment, adjustments in antibiotic dose based on eGFR, significantly increased the risk of treatment failure and death.