Rashmi V Shingde1, Stephanie E Reuter2, Garry G Graham1,3, Jane E Carland1,4, Kenneth M Williams1,3, Richard O Day1,3,4, Sophie L Stocker1,4. 1. Department of Clinical Pharmacology & Toxicology, St Vincent's Hospital, Darlinghurst, NSW, Australia. 2. School of Pharmacy & Medical Sciences, University of South Australia, Adelaide, SA, Australia. 3. School of Medical Science, University of New South Wales, Kensington, NSW, Australia. 4. St Vincent's Clinical School, University of New South Wales, Kensington, NSW, Australia.
Abstract
BACKGROUND: Current guidelines for intravenous vancomycin identify drug exposure (as indicated by the AUC) as the best pharmacokinetic (PK) indicator of therapeutic outcome. OBJECTIVES: To assess the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0-∞ in adults with limited blood concentration sampling. METHODS: The application of seven vancomycin population PK models in two Bayesian forecasting programs was examined in non-obese adults (n = 22) with stable renal function. Patients were intensively sampled following a single (1000 mg or 15 mg/kg) dose. For each patient, AUC was calculated by fitting all vancomycin concentrations to a two-compartment model (defined as AUCTRUE). AUCTRUE was then compared with the Bayesian-estimated AUC0-∞ values using a single vancomycin concentration sampled at various times post-infusion. RESULTS: Optimal sampling times varied across different models. AUCTRUE was generally overestimated at earlier sampling times and underestimated at sampling times after 4 h post-infusion. The models by Goti et al. (Ther Drug Monit 2018. 40: 212-21) and Thomson et al. (J Antimicrob Chemother 2009. 63: 1050-7) had precise and unbiased sampling times (defined as mean imprecision <25% and <38 mg·h/L, with 95% CI for mean bias containing zero) between 1.5 and 6 h and between 0.75 and 2 h post-infusion, respectively. Precise but biased sampling times for Thomson et al. were between 4 and 6 h post-infusion. CONCLUSIONS: When using a single vancomycin concentration for Bayesian estimation of vancomycin drug exposure (AUC), the predictive performance was generally most accurate with sample collection between 1.5 and 6 h after infusion, though optimal sampling times varied across different population PK models.
BACKGROUND: Current guidelines for intravenous vancomycin identify drug exposure (as indicated by the AUC) as the best pharmacokinetic (PK) indicator of therapeutic outcome. OBJECTIVES: To assess the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0-∞ in adults with limited blood concentration sampling. METHODS: The application of seven vancomycin population PK models in two Bayesian forecasting programs was examined in non-obese adults (n = 22) with stable renal function. Patients were intensively sampled following a single (1000 mg or 15 mg/kg) dose. For each patient, AUC was calculated by fitting all vancomycin concentrations to a two-compartment model (defined as AUCTRUE). AUCTRUE was then compared with the Bayesian-estimated AUC0-∞ values using a single vancomycin concentration sampled at various times post-infusion. RESULTS: Optimal sampling times varied across different models. AUCTRUE was generally overestimated at earlier sampling times and underestimated at sampling times after 4 h post-infusion. The models by Goti et al. (Ther Drug Monit 2018. 40: 212-21) and Thomson et al. (J Antimicrob Chemother 2009. 63: 1050-7) had precise and unbiased sampling times (defined as mean imprecision <25% and <38 mg·h/L, with 95% CI for mean bias containing zero) between 1.5 and 6 h and between 0.75 and 2 h post-infusion, respectively. Precise but biased sampling times for Thomson et al. were between 4 and 6 h post-infusion. CONCLUSIONS: When using a single vancomycin concentration for Bayesian estimation of vancomycin drug exposure (AUC), the predictive performance was generally most accurate with sample collection between 1.5 and 6 h after infusion, though optimal sampling times varied across different population PK models.
Authors: Alex M Downs; Julian Gerson; Kaylyn K Leung; Kevin M Honeywell; Tod Kippin; Kevin W Plaxco Journal: Sci Rep Date: 2022-04-01 Impact factor: 4.379
Authors: Rachel Constance Yager; Natalie Taylor; Sophie Lena Stocker; Richard Osborne Day; Melissa Therese Baysari; Jane Ellen Carland Journal: BMC Health Serv Res Date: 2022-04-18 Impact factor: 2.908