Individualization of vancomycin dosing based on therapeutic drug monitoring (TDM) data is known to improve patient outcomes compared with fixed or empirical dosing strategies. There is increasing evidence to support area-under-the-curve (AUC24)-guided TDM to inform vancomycin dosing decisions for patients receiving therapy for more than 48 hours. It is acknowledged that there may be institutional barriers to the implementation of AUC24-guided dosing, and additional effort is required to enable the transition from trough-based to AUC24-based strategies. Adequate documentation of sampling, correct storage and transport, accurate laboratory analysis, and pertinent data reporting are required to ensure appropriate interpretation of TDM data to guide vancomycin dosing recommendations. Ultimately, TDM data in the clinical context of the patient and their response to treatment should guide vancomycin therapy. Endorsed by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology, the IATDMCT Anti-Infectives Committee, provides recommendations with respect to best clinical practice for vancomycin TDM. 

Optimal Practice for Vancomycin Therapeutic Drug Monitoring: Position Statement From the Anti-infectives Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

Augmented renal clearance (ARC) is a phenomenon of increased renal function in patients with risk factors. Sub-therapeutic drug concentrations and antibacterial exposure in ARC patients are the main reasons for clinical treatment failure. Decades of increased research have focused on these phenomena, but there are still some existing disputes and unresolved issues. This article reviews information on some important aspects of what we have known and provides suggestion on what we will do regarding ARC. In this article, we review the current research progress and its limitations, including clinical identification, special patients, risk factors, metabolism, animal models and clinical treatments, and provide some promising directions for further research in this area.

Augmented Renal Clearance: What Have We Known and What Will We Do?

Objectives: Augmented renal clearance (ARC) is a state of enhanced renal function commonly observed in 30%-65% of critically ill patients despite normal serum creatinine levels. Using unadjusted standard dosing regimens of renally eliminated drugs in ARC patients often leads to subtherapeutic concentrations, poor clinical outcomes, and the emergence of multidrug-resistant bacteria. We summarized pharmaceutical, pharmacokinetic, and pharmacodynamic research on the definition, underlying mechanisms, and risk factors of ARC to guide individualized dosing of antibiotics and various strategies for optimizing outcomes. Methods: We searched for articles between 2010 and 2022 in the MEDLINE database about ARC patients and antibiotics and further provided individualized antibiotic dosage regimens for patients with ARC. Results: 25 antibiotic dosage regimens for patients with ARC and various strategies for optimization of outcomes, such as extended infusion time, continuous infusion, increased dosage, and combination regimens, were summarized according to previous research. Conclusion: ARC patients, especially critically ill patients, need to make individualized adjustments to antibiotics, including dose, frequency, and method of administration. Further comprehensive research is required to determine ARC staging, expand the range of recommended antibiotics, and establish individualized dosing guidelines for ARC patients. 

Individualized antibiotic dosage regimens for patients with augmented renal clearance

Infections are important factors contributing to the morbidity and mortality among elderly patients. High rates of consumption of antimicrobial agents by the elderly may result in increased risk of toxic reactions, deteriorating functions of various organs and systems and leading to the prolongation of hospital stay, admission to the intensive care unit, disability, and lethal outcome. Both safety and efficacy of antibiotics are determined by the values of their plasma concentrations, widely affected by physiologic and pathologic age-related changes specific for the elderly population. Drug absorption, distribution, metabolism, and excretion are altered in different extents depending on functional and morphological changes in the cardiovascular system, gastrointestinal tract, liver, and kidneys. Water and fat content, skeletal muscle mass, nutritional status, use of concomitant drugs are other determinants of pharmacokinetics changes observed in the elderly. The choice of a proper dosing regimen is essential to provide effective and safe antibiotic therapy in terms of attainment of certain pharmacodynamic targets. The objective of this review is to perform a structure of evidence on the age-related changes contributing to the alteration of pharmacokinetic parameters in the elderly.

/pdf/pharmacokinetics-of-antibacterial-agents-in-the-elderly-the-chg4bg5z.pdf

Pharmacokinetics of Antibacterial Agents in the Elderly: The Body of Evidence

In critically ill patients, measured creatinine clearance (CrCl) is the most reliable method to evaluate glomerular filtration rate in routine clinical practice and may vary subsequently on a day-to-day basis. We developed and externally validated models to predict CrCl one day ahead and compared them with a reference reflecting current clinical practice.A gradient boosting method (GBM) machine-learning algorithm was used to develop the models on data from 2825 patients from the EPaNIC multicenter randomized controlled trial database. We externally validated the models on 9576 patients from the University Hospitals Leuven, included in the M@tric database. Three models were developed: a "Core" model based on demographic, admission diagnosis, and daily laboratory results; a "Core + BGA" model adding blood gas analysis results; and a "Core + BGA + Monitoring" model also including high-resolution monitoring data. Model performance was evaluated against the actual CrCl by mean absolute error (MAE) and root-mean-square error (RMSE).All three developed models showed smaller prediction errors than the reference. Assuming the same CrCl of the day of prediction showed 20.6 (95% CI 20.3-20.9) ml/min MAE and 40.1 (95% CI 37.9-42.3) ml/min RMSE in the external validation cohort, while the developed model having the smallest RMSE (the Core + BGA + Monitoring model) had 18.1 (95% CI 17.9-18.3) ml/min MAE and 28.9 (95% CI 28-29.7) ml/min RMSE.Prediction models based on routinely collected clinical data in the ICU were able to accurately predict next-day CrCl. These models could be useful for hydrophilic drug dosage adjustment or stratification of patients at risk.Not applicable. 

/pdf/development-and-validation-of-the-creatinine-clearance-17h7lnj1.pdf

Development and validation of the creatinine clearance predictor machine learning models in critically ill adults

Background There is a significant correlation between augmented renal clearance (ARC) and lower serum trough concentrations of vancomycin (VCM) during therapy. There is a need to evaluate the predictive performance of the population pharmacokinetic (PPK) model used for individual calculation of dosage regimens in ARC patients. Objective Our study aimed to estimate the predictive performance differences of the reported VCM PPK software JPKD-vancomycin and SmartDose in patients with varying renal function status, especially those with ARC. Methods Patients receiving VCM treatment from May 2014 to December 2019 were enrolled, and divided into the ARC group, the normal renal function (NRF) group, and the impaired renal function (IRF) group. VCM dosage, trough concentration, area under the curve (AUC) and pharmacokinetic parameters were compared among the three groups. The predictive performance of PPK software was expressed using absolute prediction error (APE), sensitivity, specificity, and regression coefficient (r2) of linear regression analysis between the measured VCM trough concentration and the predicted trough concentration. Results A total of 388 patients were included: 86 patients in the ARC group, 241 patients in the NRF group, and 61 patients in the IRF group. The daily dose of the adjusted regimen in the ARC group was higher than in the NRF group, but the trough concentration was significantly lower than in the NRF group (2.8±0.6 g vs 1.9±0.6 g, p 0.6 for the initial regimen and adjusted regimen of VCM in the ARC and NRF groups, and the r2 of the adjusted regimen of JPKD was >0.8 in the ARC and NRF groups. In the IRF group, 31.1% of patients had a change in serum creatinine (Scr) level of >50%. The r2 increased from 0.527 to 0.7347 in SmartDose and from 0.55 to 0.7802 in JPKD when using Scr at the sampling time. The ARC group showed a significant decrease in AUC (p Conclusion ARC was significantly associated with subtherapeutic serum VCM concentration. The pharmacokinetic parameters of VCM were diverse in patients with different renal function status. The PPK model JPKD and SmartDose had a good predictive performance for predicting VCM trough concentrations of the ARC and NRF patients, especially using JPKD for prediction of the adjusted regimen. The change of Scr is a main factor affecting the accuracy of software prediction.

Predictive performance of reported vancomycin population pharmacokinetic model in patients with different renal function status, especially those with augmented renal clearance.

Background: Augmented renal clearance (ARC) risk factors and effects on vancomycin (VCM) of obstetric patients were possibly different from other populations based on pathophysiological characteristics. Our study was to establish a regression model for prediction of ARC and analyze the effects of ARC on VCM treatment in critically ill obstetric patients. Methods: We retrospectively included 427 patients, grouped into ARC and non-ARC patients. Logistic regression analysis was used to analyze the factors related to ARC. Receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of the model for ARC. Patients who received VCM therapy were collected. The published VCM population pharmacokinetic (PPK) model was used to calculate pharmacokinetic parameters. A linear regression analysis was made between the predicted and measured concentrations. Results: Of the 427 patients, ARC was present in 201 patients (47.1%). The independent risk factors of ARC were heavier, greater gestational age, higher albumin level, fewer caesarean section, severe preeclampsia and vasoactive drug; more infection, hypertriglyceridemia and acute pancreatitis. We established the above nine-variable prediction regression model and calculated the predicted probability. ROC curve showed that the predicted probability of combined weight, albumin and gestational age had better sensitivity (70.0%) and specificity (89.8%) as well as the maximal area under the curve (AUC, AUC = 0.863). 41 cases received VCM; 21 cases (51.2%) had ARC. The initial trough concentration in ARC patients was lower than in non-ARC patients (7.9 ± 3.2 mg/L vs 9.5 ± 3.3 mg/L; p = 0.033). Comparing the predicted trough concentration of two published VCM PPK models with the measured trough concentration, correlation coefficients (r) were all more than 0.8 in the ARC group and non-ARC group. AUC was significantly decreased in the ARC group (p = 0.003; p = 0.013), and clearance (CL) increased in the ARC group (p < 0.001; p = 0.008) when compared with the non-ARC group. Conclusion: ARC is a common state in critically ill obstetric patients. The regression model of nine variables had high predictive value for predicting ARC. The published VCM PPK models had good predictive performance for predicting trough concentrations of obstetric patients. Pharmacokinetic parameters of VCM are different in ARC obstetric patients, which results in enhanced VCM clearance and decreased trough concentration.

/pdf/a-regression-model-to-predict-augmented-renal-clearance-in-209fh4jleq.pdf

A Regression Model to Predict Augmented Renal Clearance in Critically Ill Obstetric Patients and Effects on Vancomycin Treatment

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Predictive performance of reported vancomycin population pharmacokinetic model in patients with different renal function status, especially those with augmented renal clearance.

A Regression Model to Predict Augmented Renal Clearance in Critically Ill Obstetric Patients and Effects on Vancomycin Treatment