External validation of vancomycin population pharmacokinetic models in ten cohorts of infected Chinese patients. (September 2022)

• Record Type: Journal Article
• Authors: Zhou, Ying; Long, Enwu; Shi, Tianlu; Wang, Zhuo; Zhao, Jun; Liu, Hua; Lin, Yang; Jiang, Mingyan; Lao, Haiyan; Ge, Weihong; Cui, Yimin
• Is Part Of: Journal of global antimicrobial resistance. Volume 30(2022)
• Page Start: 163
• Page End: 172
• Publication Date: 2022-09
• Subjects: External validation -- Vancomycin -- Predictive performance; Drug resistance -- Periodicals; Drug resistance -- Periodicals; Drug resistance; Periodicals; 616.9041
• DOI: 10.1016/j.jgar.2022.05.016
• Languages: English
• ISSNs: 2213-7165

Abstract

Highlights: The external validation of vancomycin population pharmacokinetic models was evaluated. It is important to carry out sufficient external validation before clinical application. ABSTRACT: Objectives: Appropriate dosage of vancomycin is critical for safety and efficacy in treating infectious diseases. Various population pharmacokinetic (PPK) models have been established. To lay a foundation for the clinical application, it is important to perform external validation of the published model. The aim of this study was to find the most suitable vancomycin PPK model for treatment of infection in China amongst all studied models developed for adults. Methods: A systematic literature search of published population vancomycin pharmacokinetic analyses was performed using the PubMed database. The identified models were evaluated in ten independent cohorts from China. Nonlinear mixed-effects modeling (NONMEM) was used for data analysis. The median prediction error (MPE), the distribution of prediction error (PE), and normalized prediction distribution errors (NPDE) were calculated and described. Predictive performance was evaluated by bias and accuracy. Results: A total of 449 vancomycin concentrations from 397 infected participants were used for external validation. The MPE of the three models from the Chinese population was less than 10%. Half of the models had an acceptable MPE. For patients with different site infections and different renal functions, each model differed in its predictive ability to some extent. The NPDE results showed that all models had an obvious bias. Conclusions: None of the models had good performance in both prediction- and simulation-based diagnostics. Carrying out sufficient external validation of the PPK model before clinical application is of utmost importance. In clinical practice, the model's population closest to the application population should be used.