Zhihong Li1, Weiqi Gao1, Guifen Liu2, Weihong Chen1. 1. Department of Pharmacy, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences); and. 2. Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Abstract
BACKGROUND: In patients with hypoalbuminemia after craniotomy, total serum concentrations of valproic acid (VPA) may provide poor clinical insights, owing to saturated protein binding and increased unbound fractions. However, very few clinical laboratories routinely analyze free concentrations of the drug. The aim of this study was to develop a model to predict serum-free and cerebrospinal fluid (CSF) levels of VPA based on its total concentration and to investigate the model's applicability. METHODS: Total serum and CSF concentrations of VPA in 79 patients were measured using a validated immunoassay between January 2015 and December 2015. The demographic, clinical, and laboratory information of patients were retrieved from medical records. A multiple linear regression analysis was adopted to determine the potential variations and establish the functional relationship between CSF concentration and significant clinical factors. RESULTS: Based on the stepwise multiple linear regression analysis performed using the natural logarithm of the concentration of VPA in the CSF as the dependent variable, serum concentrations of VPA (X1, β' = 0.844), serum albumin concentration (X2, β' = -0.393), and CSF protein concentration (X3, β' = 0.098) were identified as the 3 variables that significantly predicted the dependent variable: (Equation is included in full-text article.), with a coefficient of determination (R) of 0.874. As the CSF protein level is often unavailable, the model was redefined to include 2 variables-serum concentrations of VPA (X1, β' = 0.840) and serum albumin concentration (X2, β' = -0.359): (Equation is included in full-text article.), with R = 0.813. CONCLUSIONS: Based on total VPA and serum albumin concentrations, we developed a model to predict serum-free and CSF levels of VPA. This model is useful for correcting dose adjustment in patients with hypoalbuminemia after craniotomy.
BACKGROUND: In patients with hypoalbuminemia after craniotomy, total serum concentrations of valproic acid (VPA) may provide poor clinical insights, owing to saturated protein binding and increased unbound fractions. However, very few clinical laboratories routinely analyze free concentrations of the drug. The aim of this study was to develop a model to predict serum-free and cerebrospinal fluid (CSF) levels of VPA based on its total concentration and to investigate the model's applicability. METHODS: Total serum and CSF concentrations of VPA in 79 patients were measured using a validated immunoassay between January 2015 and December 2015. The demographic, clinical, and laboratory information of patients were retrieved from medical records. A multiple linear regression analysis was adopted to determine the potential variations and establish the functional relationship between CSF concentration and significant clinical factors. RESULTS: Based on the stepwise multiple linear regression analysis performed using the natural logarithm of the concentration of VPA in the CSF as the dependent variable, serum concentrations of VPA (X1, β' = 0.844), serum albumin concentration (X2, β' = -0.393), and CSF protein concentration (X3, β' = 0.098) were identified as the 3 variables that significantly predicted the dependent variable: (Equation is included in full-text article.), with a coefficient of determination (R) of 0.874. As the CSF protein level is often unavailable, the model was redefined to include 2 variables-serum concentrations of VPA (X1, β' = 0.840) and serum albumin concentration (X2, β' = -0.359): (Equation is included in full-text article.), with R = 0.813. CONCLUSIONS: Based on total VPA and serum albumin concentrations, we developed a model to predict serum-free and CSF levels of VPA. This model is useful for correcting dose adjustment in patients with hypoalbuminemia after craniotomy.
Authors: Glenn K Wakam; Ben E Biesterveld; Manjunath P Pai; Michael T Kemp; Rachel L O'Connell; Krishani K Rajanayake; Kiril Chtraklin; Claire A Vercruysse; Hasan B Alam Journal: J Trauma Acute Care Surg Date: 2021-11-01 Impact factor: 3.313