Why the study?
The accuracy and predictive performance of Bayesian dosing for warfarin had not been evaluated in Chinese patients.
Does a Bayesian dosing method improve the accuracy of warfarin maintenance dose prediction compared to multiple linear regression algorithms in Chinese patients?
Does a Bayesian dosing method improve the accuracy of warfarin maintenance dose prediction compared to multiple linear regression algorithms in Chinese patients?
The Bayesian method shows good potential for predicting warfarin maintenance doses in Chinese patients requiring less than 6 mg/day.
May support Bayesian warfarin dosing in select Chinese patients; leaves open prospective validation before practice change.
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.
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Dong et al. (2019) studied this question.
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