使用七种数学模型的基于药物遗传学的华法林给药算法在中国患者中的预测能力比较。
Comparison of the predictive abilities of pharmacogenetics-based warfarin dosing algorithms using seven mathematical models in Chinese patients.
作者信息
Li Xi, Liu Rong, Luo Zhi-Ying, Yan Han, Huang Wei-Hua, Yin Ji-Ye, Mao Xiao-Yuan, Chen Xiao-Ping, Liu Zhao-Qian, Zhou Hong-Hao, Zhang Wei
机构信息
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China.
出版信息
Pharmacogenomics. 2015;16(6):583-90. doi: 10.2217/pgs.15.26. Epub 2015 Apr 15.
AIM
This study is aimed to find the best predictive model for warfarin stable dosage.
MATERIALS & METHODS: Seven models, namely multiple linear regression (MLR), artificial neural network, regression tree, boosted regression tree, support vector regression, multivariate adaptive regression spines and random forest regression, as well as the genetic and clinical data of two Chinese samples were employed.
RESULTS
The average predicted achievement ratio and mean absolute error of the algorithms were ranging from 52.31 to 58.08% and 4.25 to 4.84 mg/week in validation samples, respectively. The algorithm based on MLR showed the highest predicted achievement ratio and the lowest mean absolute error.
CONCLUSION
At present, MLR may be still the best model for warfarin stable dosage prediction in Chinese population. Original submitted 10 November 2014; Revision submitted 18 February 2015.
目的
本研究旨在寻找华法林稳定剂量的最佳预测模型。
材料与方法
采用了七种模型，即多元线性回归（MLR）、人工神经网络、回归树、增强回归树、支持向量回归、多元自适应回归样条和随机森林回归，以及两个中国样本的遗传和临床数据。
结果
在验证样本中，算法的平均预测成就率和平均绝对误差分别在52.31%至58.08%和4.25至4.84毫克/周之间。基于MLR的算法显示出最高的预测成就率和最低的平均绝对误差。
结论
目前，MLR可能仍是中国人群华法林稳定剂量预测的最佳模型。原始提交于2014年11月10日；修订提交于2015年2月18日。
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