Predicted Vancomycin Dosage Requirement in Patients With Hematological Malignancies and Dosage Dynamic Adjustment

Song, Xiangqing; Wu, Yi

doi:10.3389/fphar.2022.890748

Cited by 4 publications

(7 citation statements)

References 41 publications

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“…Consequently, there may be an increase in resistance and a failure to respond to treatment, which can result in a higher rate of infection-related morbidity and mortality. Despite the fact that systemic inflammatory response syndrome and malignancy may both be risk factors for the development of vancomycin-associated AKI, this phenomenon and its causal consequences have rarely been considered in most studies ( Song and Wu, 2022 ). These results suggest that early monitoring of vancomycin concentration in these patients may be critical for maintaining desired effects without the occurrence of side effects.…”

Section: Discussionmentioning

confidence: 99%

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

Tang

Guo

et al. 2022

Front. Pharmacol.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Tang

Guo

et al. 2022

Front. Pharmacol.

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“…PK/PD and TDM (or C T-SS ) were used to build the CDM based on the concentration models derived from our earlier research ( Song and Wu, 2022 ); real-world data derived from 21 retrospective cases were applied to assess the superiority and performance of CDM in clinical efficacy prediction by comparison to the current frequently-used dose model (FDM), and based on the availability of TDM, the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for VAN therapy was formulated. Based on CDM and using Monte Carlo simulation, VAN dosages in empirical therapy were predicted for six Staphylococci populations [i.e., Staphylococcus aureus ( S. aureus ), Staphylococcus epidermidis ( S .…”

Section: Methodsmentioning

confidence: 99%

“…This may cause a significant deviation between the predicted and desired dose due to the fact that VAN is often delivered via multiple intermittent infusion (MII) rather than via i.v. bolus, and the simplified AUC model is not well-suited for MII ( Song and Wu, 2022 ). Therefore, the dose derived from these calculators is not necessarily a true personalized dose.…”

Section: Introductionmentioning

confidence: 99%

Individualized, dynamic, and full-course vancomycin dosing prediction: a study on the customized dose model

Song,

Zeng,

Yang

et al. 2024

Front. Pharmacol.

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PurposeThe single-point trough-based therapeutic drug monitoring (TDM) and Bayesian forecasting approaches are still limited in individualized and dynamic vancomycin delivery. Until recently, there has not yet been enough focus on the direct integration of pharmacokinetic/pharmacodynamic (PK/PD) and TDM to construct a customized dose model (CDM) for vancomycin to achieve individualized, dynamic, and full-course dose prediction from empirical to follow-up treatment. This study sought to establish CDM for vancomycin, test its performance and superiority in clinical efficacy prediction, formulate a CDM-driven full-course dosage prediction strategy to overcome the above challenge, and predict the empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various creatinine clearance rates (CLcr).MethodsThe PK/PD and concentration models derived from our earlier research were used to establish CDM. The receiver operating characteristic (ROC) curve, with the area under ROC curve (AUCR) as the primary endpoint, for 21 retrospective cases was applied to test the performance and superiority of CDM in clinical efficacy prediction by comparison to the current frequently-used dose model (FDM). A model with an AUCR of at least 0.8 was considered acceptable. Based on the availability of TDM, the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy was formulated. Based on the CDM, Monte Carlo simulation was used to predict the empirical vancomycin dosages for the target populations and bacteria.ResultsFour CDMs and the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy from empirical to follow-up treatment were constructed. Compared with FDM, CDM showed a greater AUCR value (0.807 vs. 0.688) in clinical efficacy prediction. The empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various CLcr were predicted.ConclusionCDM is a competitive individualized dose model. It compensates for the drawbacks of the existing TDM technology and Bayesian forecasting and offers a straightforward and useful supplemental approach for individualized and dynamic vancomycin delivery. Through mathematical modeling of the vancomycin dosage, this study achieved the goal of predicting doses individually, dynamically, and throughout, thus promoting “mathematical knowledge transfer and application” and also providing reference for quantitative and personalized research on similar drugs.

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“…Note: (1) These equations were built based on steady state and one-compartment intravenous infusion model, (2) Equation 1 and 2 are derived and modified from our previous studies ( Song et al, 2021 ; Song and Wu, 2022 ) and Equation 3 from the published literatures ( Jeurissen et al., 2011 ; Rybak et al., 2020b ).…”

Section: Methodsmentioning

confidence: 99%

Pharmacokinetic/Pharmacodynamic Target Attainment of Vancomycin, at Three Reported Infusion Modes, for Methicillin-Resistant Staphylococcus aureus (MRSA) Bloodstream Infections in Critically Ill Patients: Focus on Novel Infusion Mode

Song

Han

2022

Front. Cell. Infect. Microbiol.

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ObjectiveThe study aimed to evaluate and compare the pharmacokinetic/pharmacodynamic (PK/PD) exposure to vancomycin in the novel optimal two-step infusion (OTSI) vs. intermittent infusion (II) vs. continuous infusion (CI) mode, for MRSA bloodstream infections occurring in critical patients.MethodsWith PK/PD modeling and Monte Carlo simulations, the PK/PD exposure of 15 OTSI, 13 II, and 6 CI regimens for vancomycin, at 1, 2, 3, 4, 5, and 6 g daily dose, was evaluated. Using the Monte Carlo simulations, the vancomycin population PK parameters derived from critical patients, the PD parameter for MRSA isolates [i.e., minimum inhibitory concentration (MIC)], and the dosing parameters of these regimens were integrated into a robust mdel of vancomycin PK/PD index, defined as a ratio of the daily area under the curve (AUC0–24) to MIC (i.e., AUC0–24/MIC), to estimate the probability of target attainment (PTA) of these regimens against MRSA isolates with an MIC of 0.5, 1, 2, 4, and 8 mg/L in patients with varying renal function. The PTA at an AUC0–24/MIC ratio of >400, 400–600, and >600 was estimated. A regimen with a PTA of ≥90% at an AUC0–24/MIC ratio of 400–600, which is supposed to maximize both efficacy and safety, was considered optimal.ResultsAt the same daily dose, almost only the OTSI regimens showed a PTA of ≥90% at an AUC0–24/MIC ratio of 400–600, and this profile seems evident especially in patients with creatinine clearance (CLcr) of ≥60 ml/min and for isolates with an MIC of ≤2 mg/L. However, for patients with CLcr of <60 ml/min and for isolates with an MIC of ≥4 mg/L, the II regimens often displayed a higher or even ≥90% PTA at an AUC0–24/MIC ratio of >400 and of >600. The CI regimens frequently afforded a reduced PTA at an AUC0–24/MIC ratio of >400 and of >600, regardless of CLcr and MIC.ConclusionsThe data indicated that the OTSI regimens allowed preferred PK/PD exposure in terms of both efficacy and safety, and thus should be focused more on, especially in patients with CLcr of ≥60 ml/min and for isolates with an MIC of ≤2 mg/L.

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Predicted Vancomycin Dosage Requirement in Patients With Hematological Malignancies and Dosage Dynamic Adjustment

Cited by 4 publications

References 41 publications

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

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

Individualized, dynamic, and full-course vancomycin dosing prediction: a study on the customized dose model

Pharmacokinetic/Pharmacodynamic Target Attainment of Vancomycin, at Three Reported Infusion Modes, for Methicillin-Resistant Staphylococcus aureus (MRSA) Bloodstream Infections in Critically Ill Patients: Focus on Novel Infusion Mode

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