Comparative evaluation of the predictive performances of three different structural population pharmacokinetic models to predict future voriconazole concentrations

Farkas, Andras, Daroczi, Gergely, Villasurda, Phillip, Dolton, Michael, Nakagaki, Midori and Roberts, Jason A. (2016) Comparative evaluation of the predictive performances of three different structural population pharmacokinetic models to predict future voriconazole concentrations. Antimicrobial Agents and Chemotherapy, 60 11: 6806-6812. doi:10.1128/AAC.00970-16

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Author Farkas, Andras
Daroczi, Gergely
Villasurda, Phillip
Dolton, Michael
Nakagaki, Midori
Roberts, Jason A.
Title Comparative evaluation of the predictive performances of three different structural population pharmacokinetic models to predict future voriconazole concentrations
Journal name Antimicrobial Agents and Chemotherapy   Check publisher's open access policy
ISSN 1098-6596
0066-4804
Publication date 2016-11-01
Sub-type Article (original research)
DOI 10.1128/AAC.00970-16
Open Access Status File (Publisher version)
Volume 60
Issue 11
Start page 6806
End page 6812
Total pages 7
Place of publication Washington, DC, United States
Publisher American Society for Microbiology
Collection year 2017
Language eng
Formatted abstract
Bayesian methods for voriconazole therapeutic drug monitoring (TDM) have been reported previously, but there are only sparse reports comparing the accuracy and precision of predictions of published models. Furthermore, the comparative accuracy of linear, mixed linear and nonlinear, or entirely nonlinear models may be of high clinical relevance. In this study, models were coded into individually designed optimum dosing strategies (ID-ODS) with voriconazole concentration data analyzed using inverse Bayesian modeling. The data used were from two independent data sets, patients with proven or suspected invasive fungal infections (n = 57) and hematopoietic stem cell transplant recipients (n = 10). Observed voriconazole concentrations were predicted whereby for each concentration value, the data available to that point were used to predict that value. The mean prediction error (ME) and mean squared prediction error (MSE) and their 95% confidence intervals (95% CI) were calculated to measure absolute bias and precision, while-ΔME and-ΔMSE and their 95% CI were used to measure relative bias and precision, respectively. A total of 519 voriconazole concentrations were analyzed using three models. MEs (95% CI) were 0.09 (-0.02, 0.22), 0.23 (0.04, 0.42), and 0.35 (0.16 to 0.54) while the MSEs (95% CI) were 2.1 (1.03, 3.17), 4.98 (0.90, 9.06), and 4.97 (-0.54 to 10.48) for the linear, mixed, and nonlinear models, respectively. In conclusion, while simulations with the linear model were found to be slightly more accurate and similarly precise, the small difference in accuracy is likely negligible from the clinical point of view, making all three approaches appropriate for use in a voriconazole TDM program.
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