Yang Chu1, Yifan Luo2, Shuangmin Ji3, Mingyan Jiang4, Baosen Zhou5. 1. Department of Clinical Epidemiology and Center of Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, Shenyang 110001, People's Republic of China; Department of Pharmacy, The First Affiliated Hospital of China Medical University, Shenyang 110001, People's Republic of China. Electronic address: 15002422786@163.com. 2. Department of Pharmacy, The First Affiliated Hospital of China Medical University, Shenyang 110001, People's Republic of China. Electronic address: 13998102689@163.com. 3. School of Pharmaceutical Sciences, Peking University, Beijing 100000, People's Republic of China. Electronic address: 1249965731@qq.com. 4. Department of Pharmacy, The First Affiliated Hospital of China Medical University, Shenyang 110001, People's Republic of China. Electronic address: ydyyyxb@163.com. 5. Department of Clinical Epidemiology and Center of Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, Shenyang 110001, People's Republic of China. Electronic address: bszhou@cmu.edu.cn.
Abstract
BACKGROUND: Augmented renal clearance (ARC) refers to enhanced renal elimination of circulating solute has attracted attention widely and in recent years increasing attention has been paid to patients with ARC. A population pharmacokinetic (PPK) analysis was performed to provide a reference for clinical individual therapy of vancomycin in in ARC patients. METHODS: Patients hospitalized in the First Affiliated Hospital of China Medical University from July 2013 to December 2015 and suspected or confirmed infection caused by gram-positive bacteria were enrolled in this study. The serum concentrations were determined by enzyme multiplied immunoassay technique. A nonlinear mixed effects model (NONMEM) was used to evaluate the influence of covariates on vancomycin pharmacokinetics and obtain the PPK model. Bootstrap, visual predictive checks and normalized prediction distribution errors were used to evaluate the estabolishe model. RESULTS: A total of 186 vancomycin serum samples from 95 patients, including 24 females and 71 males were studied. The final model was as follows: [Formula: see text] and [Formula: see text] . The final PPK model in ARC patients was proved to be robust and reliable. Age was identified as the most significant covariate in the final model. CONCLUSIONS: In this study, a simple population pharmacokinetic (PPK) model of vancomycin in Chinese patients with ARC was established using a nonlinear mixed-effects model (NONMEM). The final PPK model could achieve a good predictive effect, which provides a reference for clinical individual therapy.
BACKGROUND: Augmented renal clearance (ARC) refers to enhanced renal elimination of circulating solute has attracted attention widely and in recent years increasing attention has been paid to patients with ARC. A population pharmacokinetic (PPK) analysis was performed to provide a reference for clinical individual therapy of vancomycin in in ARCpatients. METHODS:Patients hospitalized in the First Affiliated Hospital of China Medical University from July 2013 to December 2015 and suspected or confirmed infection caused by gram-positive bacteria were enrolled in this study. The serum concentrations were determined by enzyme multiplied immunoassay technique. A nonlinear mixed effects model (NONMEM) was used to evaluate the influence of covariates on vancomycin pharmacokinetics and obtain the PPK model. Bootstrap, visual predictive checks and normalized prediction distribution errors were used to evaluate the estabolishe model. RESULTS: A total of 186 vancomycin serum samples from 95 patients, including 24 females and 71 males were studied. The final model was as follows: [Formula: see text] and [Formula: see text] . The final PPK model in ARCpatients was proved to be robust and reliable. Age was identified as the most significant covariate in the final model. CONCLUSIONS: In this study, a simple population pharmacokinetic (PPK) model of vancomycin in Chinese patients with ARC was established using a nonlinear mixed-effects model (NONMEM). The final PPK model could achieve a good predictive effect, which provides a reference for clinical individual therapy.
Authors: Cheuk Hin Twinny Chow; Yuen Shun Janice Li; Pok Him Tom Leung; Long Yin Brian Chan; Ka Ho Matthew Hui; Hugh Simon Lam; Chui Ping Lee; Celeste Lom Ying Ewig; Yin Ting Cheung; Tai Ning Teddy Lam Journal: JMIR Med Inform Date: 2022-01-31
Authors: Cui-Yao He; Pan-Pan Ye; Bin Liu; Lin Song; John van den Anker; Wei Zhao Journal: Antimicrob Agents Chemother Date: 2021-08-02 Impact factor: 5.191