Evaluation of Vancomycin Area Under the Concentration-Time Curve Predictive Performance Using Bayesian Modeling Software With and Without Peak Concentration: An Academic Hospital Experience for Adult Patients Without Renal Impairment.

BACKGROUND

The revised U.S. consensus guidelines on vancomycin therapeutic drug monitoring (TDM) recommend obtaining trough and peak samples to estimate the area under the concentration-time curve (AUC) using the Bayesian approach; however, the benefit of such two-point measurements has not been demonstrated in a clinical setting. We evaluated Bayesian predictive performance with and without peak concentration data using clinical TDM data.

METHODS

We retrospectively analyzed 54 adult patients without renal impairment who had two serial peak and trough concentration measurements in a ≤1-week interval. The concentration and AUC values were estimated and predicted using Bayesian software (MwPharm++; Mediware, Prague, Czech Republic). The median prediction error (MDPE) for bias and median absolute prediction error (MDAPE) for imprecision were calculated based on the estimated AUC and measured trough concentration.

RESULTS

AUC predictions using the trough concentration had an MDPE of -1.6% and an MDAPE of 12.4%, whereas those using both peak and trough concentrations had an MDPE of -6.2% and an MDAPE of 16.9%. Trough concentration predictions using the trough concentration had an MDPE of -8.7% and an MDAPE of 18.0%, whereas those using peak and trough concentrations had an MDPE of -13.2% and an MDAPE of 21.0%.

CONCLUSIONS

The usefulness of the peak concentration for predicting the AUC on the next occasion by Bayesian modeling was not demonstrated; therefore, the practical value of peak sampling for AUC-guided dosing can be questioned. As this study was conducted in a specific setting and generalization is limited, results should be interpreted cautiously.