Michiel Haeseker1, Sander Croes, Cees Neef, Cathrien Bruggeman, Leo Stolk, Annelies Verbon. 1. *Medical Microbiology, Maastricht University Medical Centre; †Care and Public Health Research Institute (CAPHRI); ‡Clinical Pharmacy and Toxicology, Maastricht University Medical Centre; and §Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, the Netherlands.
Abstract
BACKGROUND: The aim of this study was to investigate whether vancomycin clearance (CLva) can be adequately predicted with CLva prediction methods. Additionally, other covariates influencing the CLva were investigated and predictivity of monitoring of only trough levels to 24-hour area under the curve (AUC24) was evaluated. METHODS: Routine vancomycin plasma levels were measured with a fluorescence polarization immunoassay. Pharmacokinetic (PK) parameters of individual patients, that is, CLva and volume of distribution, were determined with maximum a posteriori Bayesian estimation. CLva was calculated with the 3 prediction methods, which are solely based on creatinine clearance (CLcr) estimated with Cockcroft and Gault formula and was compared with the calculated CLva with maximum a posteriori Bayesian estimation. Prediction errors were calculated. Correlations between CLva and CLcr, creatinine, age, weight, sex, and neutropenia were made. Furthermore, correlations between trough levels and AUC24 were evaluated. RESULTS: A total of 171 patients were included. Prediction errors and absolute prediction errors of the 3 methods ranged from 28% to 80% and 39% to 83%, respectively. In the multivariate analysis, CLva was significantly associated with CLcr, creatinine, age, weight, sex, and neutropenia. Linear correlation between AUC24 and trough levels was R(2) 0.38. CONCLUSIONS: Large prediction errors make the CLva algorithms based on estimated plasma CLcr unsuitable for use in patient care. Additionally, other factors, which are not accounted for in the current algorithms, influence the CLva individually. Owing to low association of AUC24 and trough levels, the AUC24 cannot be predicted with through levels. For a reliable AUC24 guided vancomycin dosing, therapeutic drug monitoring is necessary.
BACKGROUND: The aim of this study was to investigate whether vancomycin clearance (CLva) can be adequately predicted with CLva prediction methods. Additionally, other covariates influencing the CLva were investigated and predictivity of monitoring of only trough levels to 24-hour area under the curve (AUC24) was evaluated. METHODS: Routine vancomycin plasma levels were measured with a fluorescence polarization immunoassay. Pharmacokinetic (PK) parameters of individual patients, that is, CLva and volume of distribution, were determined with maximum a posteriori Bayesian estimation. CLva was calculated with the 3 prediction methods, which are solely based on creatinine clearance (CLcr) estimated with Cockcroft and Gault formula and was compared with the calculated CLva with maximum a posteriori Bayesian estimation. Prediction errors were calculated. Correlations between CLva and CLcr, creatinine, age, weight, sex, and neutropenia were made. Furthermore, correlations between trough levels and AUC24 were evaluated. RESULTS: A total of 171 patients were included. Prediction errors and absolute prediction errors of the 3 methods ranged from 28% to 80% and 39% to 83%, respectively. In the multivariate analysis, CLva was significantly associated with CLcr, creatinine, age, weight, sex, and neutropenia. Linear correlation between AUC24 and trough levels was R(2) 0.38. CONCLUSIONS: Large prediction errors make the CLva algorithms based on estimated plasma CLcr unsuitable for use in patient care. Additionally, other factors, which are not accounted for in the current algorithms, influence the CLva individually. Owing to low association of AUC24 and trough levels, the AUC24 cannot be predicted with through levels. For a reliable AUC24 guided vancomycin dosing, therapeutic drug monitoring is necessary.