Comparison of the Predictive Abilities of Pharmacogenetics-Based Warfarin Dosing Algorithms Using Seven Mathematical Models in Chinese Patients

Abstract

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

Keywords::

• machine learning
• pharmacogenetics dosing algorithms
• predictive
• warfarin

Financial & competing interests disclosure

This study was partially supported by the Major project of 863 Plan (No. 2012AA02A518), National Scientific Foundation of China (Nos. 81273595 and 81403017), the Fundamental Research Funds for the Central Universities (2012QNZT086), China Postdoctoral Science Foundation funded project (2013M531818) and Specialized Research Fund for the Doctoral Program of Higher Education (20120162120078). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

Additional information

Funding

This study was partially supported by the Major project of 863 Plan (No. 2012AA02A518), National Scientific Foundation of China (Nos. 81273595 and 81403017), the Fundamental Research Funds for the Central Universities (2012QNZT086), China Postdoctoral Science Foundation funded project (2013M531818) and Specialized Research Fund for the Doctoral Program of Higher Education (20120162120078). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.