Background: AUC-based dosing with validated Bayesian software is recommended as a good approach to guide bedside vancomycin dosing.
Objectives: To compare treatment and vancomycin-associated acute kidney injury (AKI) costs between Bayesian AUC-based dosing and conventional therapeutic drug monitoring (TDM) using steady-state plasma concentrations of vancomycin administered as continuous infusion in hospitalized non-critically ill patients with severe Gram-positive infection.
Methods: A cost-benefit analysis presented as a return on investment (ROI) analysis from a hospital perspective was conducted using a decision tree model (TDM versus AUC-based dosing) to simulate treatment cost (personnel, serum sampling and drug cost), vancomycin-associated AKI risk and cost up to 14 days. ROI was calculated against AUC-based software cost. One-way and probabilistic sensitivity analyses (respectively OWSA and PSA) were performed to check for robustness.
Results: In base case, an overall cost per patient of €621.0 with TDM and €543.6 with AUC-based dosing resulted in a treatment saving of €77.4 per patient when applying AUC-based dosing. This saving against the software cost (€26.9/patient) generated an ROI per patient of €1.9 per invested € in software [€1.9 (95% CI 1.6-2.2) in PSA]. Enrolling 900 AUC-based dosed patients annually translated to a net saving of €45 469. Software break-even was reached after 313 patients. In OWSA, a higher AKI risk with TDM strongly contributed to a positive ROI.
Conclusions: AUC-based dosing appeared a cost-saving strategy compared with conventional TDM when applying base-case settings of vancomycin-associated AKI risk, treatment and AKI costs.
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http://dx.doi.org/10.1093/jac/dkaf011 | DOI Listing |
J Antimicrob Chemother
January 2025
Pharmacy Department, Ghent University Hospital, Corneel Heymanslaan 10, 9000 Ghent, Belgium.
Background: AUC-based dosing with validated Bayesian software is recommended as a good approach to guide bedside vancomycin dosing.
Objectives: To compare treatment and vancomycin-associated acute kidney injury (AKI) costs between Bayesian AUC-based dosing and conventional therapeutic drug monitoring (TDM) using steady-state plasma concentrations of vancomycin administered as continuous infusion in hospitalized non-critically ill patients with severe Gram-positive infection.
Methods: A cost-benefit analysis presented as a return on investment (ROI) analysis from a hospital perspective was conducted using a decision tree model (TDM versus AUC-based dosing) to simulate treatment cost (personnel, serum sampling and drug cost), vancomycin-associated AKI risk and cost up to 14 days.
Trials
October 2024
Department of Pediatrics, Pediatric Infectious Diseases, Specialized Pediatric Service, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
J Burn Care Res
November 2024
Department of Pharmacy, Regional One Health, Memphis, TN 38103, USA.
Vancomycin is a glycopeptide antibiotic that requires close therapeutic monitoring. Prolonged exposure to elevated concentrations increases risk for serious adverse effects such as nephrotoxicity. However, subtherapeutic concentrations may lead to bacterial resistance and clinical failure or death.
View Article and Find Full Text PDFHosp Pharm
June 2024
St. Joseph's/Candler Health System, Savannah, GA, USA.
Vancomycin is recommended as first-line treatment of methicillin-resistant (MRSA) bacteremia, dosed by area-under-the-curve (AUC) with an assumed minimum inhibitory concentration (MIC) of 1 mcg/mL via broth microdilution. The purpose of this study was to compare effectiveness of AUC-based and trough-based dosing in MRSA bacteremia with an MIC > 1 mcg/mL via Etest. This was a retrospective, observational cohort that compared vancomycin dosed by AUC or trough between January 1, 2017 and September 1, 2022.
View Article and Find Full Text PDFAntimicrob Agents Chemother
May 2024
Service de Pharmacologie, Toxicologie et Pharmacovigilance, CHU Dupuytren, Limoges, France.
Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!