Zhao Yichang, Xiao Chenlin, Hou Jingjing, Wu Jiamin, Xiao Yiwen, Zhang Bikui, Sandaradura Indy, Yan Miao
Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
School of Medicine, University of New South Wales, Sydney, NSW 2052, Australia.
Pharmaceuticals (Basel). 2021 Nov 29;14(12):1239. doi: 10.3390/ph14121239.
Voriconazole (VRZ) is widely used to prevent and treat invasive fungal infections; however, there are a few studies examining the variability and influencing the factors of VRZ plasma concentrations across different clinical departments. This study aimed to evaluate distinction of VRZ concentrations in different clinical departments and provide a reference for its reasonable use. From 1 May 2014 to 31 December 2020, VRZ standard rates and factors affecting the VRZ trough concentration were analyzed, and a multiple linear regression model was constructed. The standard rates of VRZ in most departments were above 60%. A total of 676 patients with 1212 VRZ trough concentrations using a dosing regimen of 200 mg q12h from seven departments were enrolled in the correlation analysis. The concentration distribution varied significantly among different departments ( < 0.001). Fifteen factors, including department, CYP2C19 phenotype, and gender, correlated with VRZ concentration. A multiple linear regression model was established as follows: VRZ trough concentration = 5.195 + 0.049 × age + 0.007 × alanine aminotransferase + 0.010 × total bilirubin - 0.100 × albumin - 0.004 × gamma-glutamyl transferase. According to these indexes, we can predict possible changes in VRZ trough concentration and adjust its dosage precisely and individually.
伏立康唑（VRZ）被广泛用于预防和治疗侵袭性真菌感染；然而，很少有研究探讨不同临床科室中VRZ血药浓度的变异性及其影响因素。本研究旨在评估不同临床科室中VRZ浓度的差异，并为其合理使用提供参考。2014年5月1日至2020年12月31日，分析了VRZ的达标率及影响VRZ谷浓度的因素，并构建了多元线性回归模型。大多数科室的VRZ达标率高于60%。对来自七个科室的676例患者的1212次VRZ谷浓度进行相关性分析，这些患者采用200mg每12小时一次的给药方案。不同科室之间的浓度分布差异显著（<0.001）。包括科室、CYP2C19表型和性别在内的15个因素与VRZ浓度相关。建立了如下多元线性回归模型：VRZ谷浓度 = 5.195 + 0.049×年龄 + 0.007×谷丙转氨酶 + 0.010×总胆红素 - 0.100×白蛋白 - 0.004×γ-谷氨酰转移酶。根据这些指标，我们可以预测VRZ谷浓度的可能变化，并精确地个体化调整其剂量。
