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Comparison of the predictive abilities of pharmacogenetics-based warfarin dosing algorithms using seven mathematical models in Chinese patients

    Xi Li

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    Authors contributed equally

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    ,
    Rong Liu

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    Authors contributed equally

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    ,
    Zhi-Ying Luo

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    ,
    Han Yan

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    ,
    Wei-Hua Huang

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    ,
    Ji-Ye Yin

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    ,
    Xiao-Yuan Mao

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    ,
    Xiao-Ping Chen

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    ,
    Zhao-Qian Liu

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    ,
    Hong-Hao Zhou§

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    Authors contributed equally

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    &
    Wei Zhang

    *Author for correspondence:

    E-mail Address: yjsd2003@163.com

    Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China

    Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, PR China

    Authors contributed equally

    Search for more papers by this author

    Published Online:https://doi.org/10.2217/pgs.15.26

    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

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