Your browser doesn't support javascript.
loading
Predictive performance and analysis of a vancomycin population pharmacokinetic model in Chinese pediatric patients / 药学学报
Acta Pharmaceutica Sinica ; (12): 528-532, 2019.
Article in Chinese | WPRIM | ID: wpr-780126
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.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Acta Pharmaceutica Sinica Year: 2019 Type: Article

Similar

MEDLINE

...
LILACS

LIS

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Acta Pharmaceutica Sinica Year: 2019 Type: Article