Predictive performance and analysis of a vancomycin population pharmacokinetic model in Chinese pediatric patients
10.16438/j.0513-4870.2018-0853
- VernacularTitle:中国儿童万古霉素群体药代动力学模型的外部验证与分析
- Author:
Qing GUO
1
;
Tao-tao LIU
1
;
Li JING
1
;
Hui-mei PANG
1
;
Guang-min NONG
2
;
Shuang-yi TANG
1
;
Xun CHEN
2
Author Information
1. Department of Pharmacy
2. Department of Pediatrics, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
- Publication Type:Research Article
- Keywords:
vancomycin;
Chinese pediatric patients;
population pharmacokinetic model;
predictive performance
- From:
Acta Pharmaceutica Sinica
2019;54(3):528-532
- CountryChina
- Language:Chinese
-
Abstract:
This study aimed to evaluate the predictive performance of a vancomycin population pharmacokinetic model in 0-10 year Chinese pediatric patients. This study was approved by the Ethics Research Committee of the First Affiliated Hospital of Guangxi Medical University, data from hospitalized children ≤ 10 years of age who receiving vancomycin were collected retrospectively. Individual predictive values (IPRED) were estimated by Bayesian Analysis based on a previous published population pharmacokinetic model, and compared with the observed steady state trough concentration. As results, a total of 371 vancomycin serum concentrations from 191 patients were taken for the external validation. The mean error (ME), the mean relative prediction error (ME%), the mean absolute error (MAE) and the root mean square error (RMSE) in individual prediction method for the total patients were -0.50 mg·L-1, 6.03%, 1.84 mg·L-1, 2.86 mg·L-1 respectively. The correlation coefficient between individual predictions and detection values was 0.95. The stability and the predictive performance of model were accepted by goodness-of-fit, visual predictive check (VPC) and Bland-Altman. The percentage of individual prediction error within ± 30% was 82.75%. The above results suggest that, this Chinese pediatric population pharmacokinetic model in 0-10 years old has a good prediction performance. It can be applied to the design of initial treatment plan and predicting the extent of drug exposure.
