Comparative Evaluation of the Predictive Performances of Three Different Structural Population Pharmacokinetic Models To Predict Future Voriconazole Concentrations.
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.
Department of Pharmacy, Mount Sinai West Hospital, New York, New York, USA afarkas@chpnet.org.|Optimum Dosing Strategies, Bloomingdale, New Jersey, USA.
Andras
A
Farkas
EasyStats Ltd., London, United Kingdom.
Gergely
G
Daroczi
Department of Pharmacy, Vassar Brothers Medical Center, Poughkeepsie, New York, USA.
Phillip
P
Villasurda
School of Biomedical Engineering, University of Southern California, Los Angeles, California, USA.
Michael
M
Dolton
Pharmacy Department, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.
Midori
M
Nakagaki
Pharmacy Department, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.|Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.|Burns, Trauma and Critical Care Research Centre, The University of Queensland, Brisbane, Queensland, Australia.|School of Pharmacy, The University of Queensland, Brisbane, Queensland, Australia.
Jason A
JA
Roberts
D000328:Adult; D019072:Antibiotic Prophylaxis; D000935:Antifungal Agents / Q000008:administration & dosage / Q000493:pharmacokinetics; D001499:Bayes Theorem; D016903:Drug Monitoring / Q000379:methods*; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D065819:Voriconazole / Q000008:administration & dosage / Q000493:pharmacokinetics*
D000935:Antifungal Agents; D065819:Voriconazole