Increased Vancomycin Clearance in Patients with Solid Malignancies

Izumisawa, Tomohiro; Wakui, Nobuyuki; Kaneko, Tomoyoshi; Soma, Masakazu; Imai, Masahiko; Saito, Daisuke; Hasegawa, Hideo; Horino, Tetsuya; Takahashi, Noriko

doi:10.1248/bpb.b20-00083

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…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. ( Nakayama et al, 2019 ; Izumisawa et al, 2020 ). Other studies have shown that diabetes mellitus and heptic insufficiency are independent risk factors for vancomycin-associated AKI ( Chambers et al, 2020 ; Wang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…When the vancomycin dose was changed in the middle of administration, the trough value measured before the dose change was used if it was after 3 days from the start of administration, and if not, the trough value measured after 3 days from the dose change was used ( Izumisawa et al, 2020 ). Daily dosage (mg/day) was calculated by Single dosage (mg)*Dosing frequency (/day); Dosage (mg/day/kg) was calculated by Daily dosage (mg/day)/Weight (/kg).…”

Section: Methodsmentioning

confidence: 99%

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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%

Section: Methodsmentioning

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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“…Data were collected at the Izumi Regional Medical Center between October 2009 and November 2018 and included the following parameters: sex, age, height, body weight, SCr, blood urea nitrogen, body mass index, infusion volume (mL/kg/d), combined administration of tazobactam/piperacillin, steady-state VCM serum concentration before infusion (VCM trough), steady-state VCM serum concentration 1 h after infusion (VCM peak). The following patients with factors that affect VCM clearance or kidney function were excluded from this study: cancer patients, 16,17) heart failure patients with ejection fraction less than 40%, 18) patients taking drugs such as trimethoprim-sulfamethoxazole combination, 19) cimetidine, 20) and cobicistat, 21) patients with eGFR <30 mL/min/1.73 m 2 , patients undergoing dialysis, patients with VCM dosing intervals >24 h, and patients lacking data. The SCr level measured on the same day as the VCM was used.…”

Section: Methodsmentioning

confidence: 99%

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

Sonoda

Iwashita

Takada

et al. 2022

Biological & Pharmaceutical Bulletin

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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.

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“…Also, patients with solid malignancies had higher Cl V values that resulted in lower vancomycin plasma concentration. Therefore, precise and early plasma concentration monitoring could be helpful to achieve effective target concentrations with minimal unwanted adverse reactions [69]. Results of a retrospective study in advanced cancer patients revealed that cachexia associated with cancer can give rise to changes in pharmacokinetic parameters during vancomycin administration.…”

Section: Patients With Cancermentioning

confidence: 99%

Therapeutic Drug Monitoring of Vancomycin in Patients with Altered Pharmacokinetics: A Narrative Review on Pharmacokinetic Assessments

Ghasemiyeh

Vazin

Mohammadi-Samani

2021

Preprint

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Introduction: Vancomycin is a glycopeptide antibiotic that is considered as the drug of choice against many Gram-positive bacterial infections, especially Methicillin-resistant Staphylococcus aureus (MRSA). Also, it is a hydrophilic drug with predominantly renal elimination. Given the vancomycin narrow therapeutic index, therapeutic drug monitoring (TDM) is essential to achieve an optimum clinical response and avoid vancomycin-induced adverse drug reactions including nephrotoxicity and ototoxicity. Although different studies are available on vancomycin pharmacokinetic assessment and vancomycin TDM, still there are controversies regarding the selection among different pharmacokinetic parameters including trough concentration (Cmin), the daily area under the curve to minimum inhibitory concentration (AUC24h/MIC) ratio, AUC of intervals (AUCτ), elimination constant (k), vancomycin clearance (ClV) and methods of their calculations for TDM purposes. Methods: In this review, different pharmacokinetic parameters for vancomycin TDM have been discussed in detail along with corresponding advantages and disadvantages, based on the literature review. Determination of vancomycin concentration at steady state (Css) during 24h continuous injection are mentioned. Also, vancomycin pharmacokinetic assessments are discussed in detail in patients with altered pharmacokinetic parameters including those with renal and/or hepatic failure, critically ill patients, patients with burn injuries, intravenous (IV) drug users, obese and morbidly obese patients, those with cancer, patients undergoing organ transplantation, and vancomycin administration during pregnancy and lactation. Results and Discussion: An individualized dosing regimen is required to guarantee the optimum therapeutic results and minimize severe adverse reactions such as acute kidney injury (AKI) in these special groups of patients with altered pharmacokinetic parameters. Also, according to the pharmacoeconomic data on vancomycin TDM, pharmacokinetic assessments would be cost-effective in the mentioned groups of patients with altered pharmacokinetics and associated with shorter hospitalization period, faster clinical stability status, and shorter courses of inpatient vancomycin administration.

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Increased Vancomycin Clearance in Patients with Solid Malignancies

Cited by 6 publications

References 25 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

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

Therapeutic Drug Monitoring of Vancomycin in Patients with Altered Pharmacokinetics: A Narrative Review on Pharmacokinetic Assessments

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