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
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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.
Abstract
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.
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