Increased Vancomycin Clearance in Patients with Solid Malignancies

Altered Pharmacokinetics Parameters of Vancomycin in Patients with Hematological Malignancy with Febrile Neutropenia, a Bayesian Software Estimation

The pharmacokinetics of vancomycin vary significantly between specific groups of patients, such as critically ill patients and patients with hematological malignancy (HM) with febrile neutropenia (FN). Recent evidence suggests that the use of the usual standard dose of antibiotics in patients with FN may not offer adequate exposure due to pharmacokinetic variability (PK). Therefore, the purpose of this study is to assess the effect of FN on AUC_0-24 as a key parameter for vancomycin monitoring, as well as to determine which vancomycin PK parameters are affected by the presence of FN using Bayesian software PrecisePK in HM with FN. This study was carried out in King Abdulaziz Medical City. All adult patients who were admitted to the Princess Norah Oncology Center PNOC between 1 January and 2017 and 31 December 2020, hospitalized and received vancomycin with a steady-state trough concentration measured before the fourth dose, were included. During the trial period, 297 patients received vancomycin during their stay at the oncology center, 217 of them meeting the inclusion criteria. Pharmacokinetic parameters were estimated for the neutropenic and non-FN patients using the precise PK Bayesian platform. The result showed that there was a significant difference (p < 0.05) in vancomycin clearance Cl_van, the volume of distribution at a steady-state V_dss, the volume of distribution for peripheral compartment V_dp, half-life for the elimination phase t½_β, and the first-order rate constant for the elimination process β in FN compared to non-FN patients. Furthermore, AUC_0-24 was lower for FN patients compared to non-FN patients, p < 0.05. FN has a significant effect on the PK parameters of vancomycin and AUC_0-24, which may require specific consideration during the treatment initiation.

A Brief Review of Pharmacokinetic Assessments of Vancomycin in Special Groups of Patients with Altered Pharmacokinetic Parameters

Vancomycin is considered the drug of choice against many Gram-positive bacterial infections. Therapeutic drug monitoring (TDM) is essential to achieve an optimum clinical response and avoid vancomycin-induced adverse reactions including nephrotoxicity. Although different studies are available on vancomycin TDM, still there are controversies regarding the selection among different pharmacokinetic parameters including trough concentration, the area under the curve to minimum inhibitory concentration ratio (AUC24h/MIC), AUC of intervals, elimination constant, and vancomycin clearance. In this review, different pharmacokinetic parameters for vancomycin TDM have been discussed along with corresponding advantages and disadvantages. Also, vancomycin pharmacokinetic assessments are discussed in patients with altered pharmacokinetic parameters including those with renal and/or hepatic failure, critically ill patients, patients with burn injuries, intravenous drug users, obese and morbidly obese patients, those with cancer, patients undergoing organ transplantation, and vancomycin administration during pregnancy and lactation. An individualized dosing regimen is required to guarantee the optimum therapeutic responses and minimize adverse reactions including acute kidney injury in these special groups of patients. According to the pharmacoeconomic data on vancomycin TDM, pharmacokinetic assessments would be cost-effective in patients with altered pharmacokinetics and are associated with shorter hospitalization period, faster clinical stability status, and shorter courses of inpatient vancomycin administration.

Analysis of a machine learning-based risk stratification scheme for acute kidney injury in vancomycin

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

Prediction Accuracy of Area under the Concentration-Time Curve of Vancomycin by Bayesian Approach Using Creatinine-Based Equations of Estimated Kidney Function in Bedridden Elderly Japanese Patients

An administration plan for vancomycin (VCM) in bedridden elderly patients has not been established. This retrospective study aimed to evaluate the prediction accuracy of the area under the concentration-time curve (AUC) of VCM by the Bayesian approach using creatinine-based equations of estimated kidney function in such patients. Kidney function was estimated using the Japanese equation of estimated glomerular filtration rate (eGFR) and the Cockcroft-Gault equation of estimated creatinine clearance (eCCr). eCCr (serum creatinine (SCr) + 0.2) was calculated by substituting the SCr level +0.2 mg/dL into the Cockcroft-Gault equation. For eGFR/0.789, eGFR, eCCr, and eCCr (SCr + 0.2), the AUC values were calculated by the Bayesian approach using the therapeutic drug monitoring (TDM) software, BMs-Pod (ver 8.06) and denoted as AUC_eGFR/0.789, AUC_eGFR, AUC_eCCr, and AUC_{eCCr (SCr + 0.2)} respectively. The reference AUC (AUC_REF) was calculated by applying VCM's peak and trough steady-state concentrations to first-order pharmacokinetic equations. The medians (range) of AUC_eGFR/0.789/AUC_REF, AUC_eGFR/AUC_REF, AUC_eCCr/AUC_REF, and AUC_{eCCr (SCr + 0.2)}/AUC_REF were 0.88 (0.74-0.93), 0.90 (0.79-1.04), 0.92 (0.81-1.07), and 1.00 (0.88-1.11), respectively. Moreover, the percentage of patients within 10% of the AUC_REF, defined as |Bayesian-estimated AUC - AUC_REF| < AUC_REF × 0.1, was the highest (86%) in AUC_{eCCr (SCr + 0.2)}. These results suggest that the Bayesian approach using eCCr (SCr + 0.2) has the highest prediction accuracy for the AUC_REF in bedridden elderly patients. Although further studies are required with more accurate determination methods of the CCr and AUC, our findings highlight the potential of eCCr (SCr + 0.2) for estimating VCM's AUC by the Bayesian approach in such patients.