A New Approach towards Minimizing the Risk of Misdosing Warfarin Initiation Doses

It is a challenge to be able to prescribe the optimal initial dose of warfarin. There have been many studies focused on an efficient strategy to determine the optimal initial dose. Numerous clinical, genetic, and environmental factors affect the warfarin dose response. In practice, it is common that the initial warfarin dose is substantially different from the stable maintenance dose, which may increase the risk of bleeding or thrombosis prior to achieving the stable maintenance dose. In order to minimize the risk of misdosing, despite popular warfarin dose prediction models in the literature which create dose predictions solely based on patients' attributes, we have taken physicians' opinions towards the initial dose into consideration. The initial doses selected by clinicians, along with other standard clinical factors, are used to determine an estimate of the difference between the initial dose and estimated maintenance dose using shrinkage methods. The selected shrinkage method was LASSO (Least Absolute Shrinkage and Selection Operator). The estimated maintenance dose was more accurate than the original initial dose, the dose predicted by a linear model without involving the clinicians initial dose, and the values predicted by the most commonly used model in the literature, the Gage clinical model.

[1]  Stephen E Kimmel,et al.  Ethnic differences in warfarin maintenance dose requirement and its relationship with genetics. , 2008, Pharmacogenomics.

[2]  Christine W. Duarte,et al.  High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans , 2011, Bioinform..

[3]  C. Thorn,et al.  Dosing Algorithms to Predict Warfarin Maintenance Dose in Caucasians and African Americans , 2008, Clinical pharmacology and therapeutics.

[4]  Nianjun Liu,et al.  VKORC1 polymorphisms, haplotypes and haplotype groups on warfarin dose among African-Americans and European-Americans. , 2008, Pharmacogenomics.

[5]  Valentin Fuster,et al.  American Heart Association/American College of Cardiology Foundation guide to warfarin therapy. , 2003, Circulation.

[6]  Ashkan Sharabiani Medical Decision Making for Warfarin Dosing Using Machine Learning Methods , 2016 .

[7]  Houshang Darabi,et al.  Machine learning based prediction of warfarin optimal dosing for African American patients , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[8]  Deborah A Nickerson,et al.  Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose. , 2005, The New England journal of medicine.

[9]  Panos Deloukas,et al.  The largest prospective warfarin-treated cohort supports genetic forecasting. , 2009, Blood.

[10]  Houshang Darabi,et al.  Revisiting Warfarin Dosing Using Machine Learning Techniques , 2015, Comput. Math. Methods Medicine.

[11]  J N Douglas,et al.  Ethnicity-specific pharmacogenetics: the case of warfarin in African Americans , 2013, The Pharmacogenomics Journal.

[12]  Julie A. Johnson,et al.  Warfarin pharmacogenetics. , 2015, Trends in cardiovascular medicine.

[13]  M. Whirl‐Carrillo,et al.  Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 Genotypes and Warfarin Dosing , 2011, Clinical pharmacology and therapeutics.

[14]  Paolo Massimo Buscema,et al.  An optimized experimental protocol based on neuro-evolutionary algorithms: Application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment , 2005, Artif. Intell. Medicine.

[15]  Valentin Fuster,et al.  AHA/ACC Scientific StatementAmerican Heart Association/American College of Cardiology Foundation guide to warfarin therapy1 , 2003 .

[16]  M. Rieder,et al.  Use of Pharmacogenetic and Clinical Factors to Predict the Therapeutic Dose of Warfarin , 2008, Clinical pharmacology and therapeutics.

[17]  Massimo Buscema,et al.  Prediction of optimal warfarin maintenance dose using advanced artificial neural networks. , 2014, Pharmacogenomics.

[18]  Mario Plebani,et al.  VKORC1, CYP2C9 and CYP4F2 genetic-based algorithm for warfarin dosing: an Italian retrospective study. , 2011, Pharmacogenomics.

[19]  R. Altman,et al.  Estimation of the warfarin dose with clinical and pharmacogenetic data. , 2009, The New England journal of medicine.