Does calculation method matter for targeting vancomycin area under the curve?

Author:

Chang Jack123,Patel Dhara1,Vega Ana4,Claeys Kimberly C5,Heil Emily L5,Scheetz Marc H1236ORCID

Affiliation:

1. Midwestern University College of Pharmacy, Department of Pharmacy Practice , Downers Grove, IL , USA

2. Midwestern University College of Pharmacy, Pharmacometrics Center of Excellence , Downers Grove, IL , USA

3. Northwestern Memorial Hospital, Department of Pharmacy , Chicago, IL , USA

4. Jackson Memorial Hospital, Department of Pharmacy , Miami, FL , USA

5. University of Maryland School of Pharmacy, Department of Pharmacy Practice and Science , Baltimore, MD , USA

6. Midwestern University College of Graduate Studies, Department of Pharmacology , Downers Grove, IL , USA

Abstract

Abstract Objectives To assess differences in vancomycin AUC estimates from two common, clinically applied first-order pharmacokinetic equation methods compared with Bayesian estimates. Methods A cohort of patients who received vancomycin and therapeutic drug monitoring was studied. First-order population pharmacokinetic equations were used to guide initial empirical dosing. After receipt of the first dose, patients had peak and trough serum levels drawn and steady-state AUC was estimated using first-order pharmacokinetic equations as standard care. We subsequently created a Bayesian model and used individual Empirical Bayes Estimates to precisely calculate vancomycin AUC24–48, AUC48–72 and AUC72–96 in this cohort. AUC at steady state (AUCSS) differences from the first-order methods were compared numerically and categorically (i.e. below, within or above 400–600 mg·h/L) to Bayesian AUCs, which served as the gold standard. Results A total of 65 adult inpatients with 409 plasma samples were included in this analysis. A two-compartment intravenous infusion model with first-order elimination fit the data well. The mean of Bayesian AUC24–48 was not significantly different from AUC estimates from the two first-order pharmacokinetic equation methods (P = 0.68); however, Bayesian AUC48–72 and Bayesian AUC72–96 were both significantly different when compared with both first-order pharmacokinetic equation methods (P < 0.01 for each). At the patient level, categorical classifications of AUC estimates from the two first-order pharmacokinetic equation methods differed from categorizations derived from the Bayesian calculations. Categorical agreement was ∼50% between first-order and Bayesian calculations, with declining categorical agreement observed with longer treatment courses. Differences in categorical agreement between calculation methods could potentially result in different dose recommendations for the patient. Conclusions Bayesian-calculated AUCs between 48–72 and 72–96 h intervals were significantly different from first-order pharmacokinetic method-estimated AUCs at steady state. The various calculation methods resulted in different categorical classification, which could potentially lead to erroneous dosing adjustments in approximately half of the patients.

Funder

National Institute of Allergy and Infectious Diseases

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Pharmacology (medical),Pharmacology,Microbiology (medical)

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