BACKGROUND: Optimal monitoring of vancomycin in children needs evaluation using the exposure target with area under the curve (AUC) of the serum concentrations versus time over 24 hours. Our study objectives were to: (1) compare the accuracy and precision of vancomycin AUC estimations using 2 sampling strategies-1 serum concentration sample (1S, near trough) versus 2 samples (2S, near peak and trough) against the rich sample (RS) method; and (2) determine the performance of these strategies in predicting future AUC against an internal validation sample (VS). METHODS: This was a retrospective cohort study using population-based pharmacokinetic modeling with Bayesian post hoc individual estimations in nonlinear mixed effects modeling (version 7.2). Pediatric subjects 3 months-21 years of age who received vancomycin ≥48 hours and had more than 3 drug samples within the first ≤96 hours of therapy were enrolled. Outcome measures were the accuracy, precision, and internal predictive performance of AUC estimations using 2 monitoring strategies (ie, 1S versus 2S) against the RS (which was derived from modeling all serum vancomycin concentrations obtained anytime during therapy) and VS (from serum concentrations obtained after 96 hours of therapy). RESULTS: Analysis included 138 subjects with 712 vancomycin serum concentrations. Median age was 6.1 (interquartile range, 2.2-12.2) years, weight 22 (13-38) kg, and baseline serum creatinine 0.37 (0.30-0.50) mg/dL. Both accuracy and precision were improved with the 2S, compared with 1S, for AUC estimations (-2.0% versus -7.6% and 10.3% versus 12.8%, respectively) against the RS. Improved accuracy and precision were also observed for 2S when evaluated against VS in predicting future AUC. CONCLUSIONS: Compared with 1S, the 2S sampling strategy for vancomycin monitoring improved accuracy and precision in estimating and predicting future AUC. Evaluating 2 drug concentrations in children may be prudent to ensure adequate drug exposure.
BACKGROUND: Optimal monitoring of vancomycin in children needs evaluation using the exposure target with area under the curve (AUC) of the serum concentrations versus time over 24 hours. Our study objectives were to: (1) compare the accuracy and precision of vancomycin AUC estimations using 2 sampling strategies-1 serum concentration sample (1S, near trough) versus 2 samples (2S, near peak and trough) against the rich sample (RS) method; and (2) determine the performance of these strategies in predicting future AUC against an internal validation sample (VS). METHODS: This was a retrospective cohort study using population-based pharmacokinetic modeling with Bayesian post hoc individual estimations in nonlinear mixed effects modeling (version 7.2). Pediatric subjects 3 months-21 years of age who received vancomycin ≥48 hours and had more than 3 drug samples within the first ≤96 hours of therapy were enrolled. Outcome measures were the accuracy, precision, and internal predictive performance of AUC estimations using 2 monitoring strategies (ie, 1S versus 2S) against the RS (which was derived from modeling all serum vancomycin concentrations obtained anytime during therapy) and VS (from serum concentrations obtained after 96 hours of therapy). RESULTS: Analysis included 138 subjects with 712 vancomycin serum concentrations. Median age was 6.1 (interquartile range, 2.2-12.2) years, weight 22 (13-38) kg, and baseline serum creatinine 0.37 (0.30-0.50) mg/dL. Both accuracy and precision were improved with the 2S, compared with 1S, for AUC estimations (-2.0% versus -7.6% and 10.3% versus 12.8%, respectively) against the RS. Improved accuracy and precision were also observed for 2S when evaluated against VS in predicting future AUC. CONCLUSIONS: Compared with 1S, the 2S sampling strategy for vancomycin monitoring improved accuracy and precision in estimating and predicting future AUC. Evaluating 2 drug concentrations in children may be prudent to ensure adequate drug exposure.
Authors: Catherine Liu; Arnold Bayer; Sara E Cosgrove; Robert S Daum; Scott K Fridkin; Rachel J Gorwitz; Sheldon L Kaplan; Adolf W Karchmer; Donald P Levine; Barbara E Murray; Michael J Rybak; David A Talan; Henry F Chambers Journal: Clin Infect Dis Date: 2011-02-01 Impact factor: 9.079
Authors: Nimish Patel; Manjunath P Pai; Keith A Rodvold; Ben Lomaestro; George L Drusano; Thomas P Lodise Journal: Clin Infect Dis Date: 2011-04-15 Impact factor: 9.079
Authors: Jennifer Le; John S Bradley; William Murray; Gale L Romanowski; Tu T Tran; Natalie Nguyen; Susan Cho; Stephanie Natale; Ivilynn Bui; Tri M Tran; Edmund V Capparelli Journal: Pediatr Infect Dis J Date: 2013-04 Impact factor: 2.129
Authors: Flora T Musuamba; Annick Rousseau; Jean-Louis Bosmans; Jean-Jacques Senessael; Jean Cumps; Pierre Marquet; Pierre Wallemacq; Roger K Verbeeck Journal: Clin Pharmacokinet Date: 2009 Impact factor: 6.447
Authors: Livio Azzoni; Russell Barbour; Emmanouil Papasavvas; Deborah K Glencross; Wendy S Stevens; Mark F Cotton; Avy Violari; Luis J Montaner Journal: PLoS One Date: 2015-12-15 Impact factor: 3.240