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Aim: To analyze the impact of nongenetic factors and gene polymorphisms on warfarin dose requirements in elderly Shanghai Han Chinese patients. Materials & methods: Genotypes of CYP2C9 (rs1799853 and rs1057910), FPGS (rs7856096), ApoE (rs7412 and rs429358), GGCX (rs699664 and rs12714145), EPHX1 (rs4653436, rs1877724, rs1051740 and rs1131873), NQO1 (rs1800566 and rs10517), ABCB1 (rs1045642), VKORC1 (rs9923231) and CYP4F2 (rs2108622) in 214 patients with stable warfarin dose were determined and their demographic characteristics were recorded. Results: Multiple linear regression analysis revealed that VKORC1 rs9923231, CYP2C9*3 rs1057910, ApoE rs7412, age, BMI and concomitant amiodarone could explain 37.0% of the individual variations of daily stable warfarin dose. Conclusion: VKORC1 rs9923231, CYP2C9*3 rs1057910, ApoE rs7412, age, BMI and concomitant amiodarone play an important role in stable dose variation of warfarin in elderly Shanghai Han Chinese patients, whereas ABCB1 rs1045642 is not a significant genetic factor.

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.