贝叶斯法对华法林在中国患者中的给药预测性能评估。
Evaluation of the predictive performance of Bayesian dosing for warfarin in Chinese patients.
作者信息
Dong Jing, Shi Guo-Hua, Lu Man, Huang Shu, Liu Yan-Hui, Yao Jia-Chen, Li Wen-Yan, Li Long-Xuan
机构信息
Department of Pharmacy, Gongli Hospital, The Second Military Medical University, 219 Miaopu Road, Shanghai 200135, PR China.
Department of Neurology, Gongli Hospital, The Second Military Medical University, 219 Miaopu Road, Shanghai 200135, PR China.
出版信息
Pharmacogenomics. 2019 Feb;20(3):167-177. doi: 10.2217/pgs-2018-0127. Epub 2019 Feb 19.
AIM
To evaluate the accuracy and predictive performance of Bayesian dosing for warfarin in Chinese patients.
MATERIALS & METHODS: Six multiple linear regression algorithms (Wei, Lou, Miao, Huang, Gage and IWPC) and a Bayesian method implemented in Warfarin Dose Calculator were compared with each other.
RESULTS
Six multiple linear regression warfarin dosing algorithms had similar predictive ability, except Miao and Lou. The mean prediction error of Bayesian priori and posteriori method were 0.01 mg/day (95% CI: -0.18 to 0.19) and 0.17 mg/day (95% CI: -0.05 to 0.29), respectively, and Bayesian posteriori method demonstrated better performance in all dose ranges.
CONCLUSION
The Bayesian method showed a good potential for warfarin maintenance dose prediction in Chinese patients requiring less than 6 mg/day.
目的
评估对华法林进行贝叶斯给药在中国患者中的准确性和预测性能。
材料与方法
将六种多元线性回归算法（魏氏、罗氏、苗氏、黄氏、盖奇氏和国际华法林药效学协作组算法）与华法林剂量计算器中实施的贝叶斯方法进行相互比较。
结果
六种多元线性回归华法林给药算法具有相似的预测能力，苗氏和罗氏算法除外。贝叶斯先验和后验方法的平均预测误差分别为0.01毫克/天（95%可信区间：-0.18至0.19）和0.17毫克/天（95%可信区间：-0.05至0.29），并且贝叶斯后验方法在所有剂量范围内均表现出更好的性能。
结论
贝叶斯方法在对华法林维持剂量预测方面显示出良好潜力，适用于对华法林每日需求量小于6毫克的中国患者。
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