Evaluation of the predictive performance of Bayesian dosing for warfarin in Chinese patients

Abstract

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.