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

Pharmacogenomics. 2019 Feb;20(3):167-177. doi: 10.2217/pgs-2018-0127. Epub 2019 Feb 19.

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.

Keywords: Bayesian method; multiple linear regression algorithm; warfarin maintenance dose.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Anticoagulants / administration & dosage*
  • Anticoagulants / adverse effects
  • Atrial Fibrillation / drug therapy
  • Bayes Theorem
  • Cytochrome P-450 CYP2C9 / genetics
  • Dose-Response Relationship, Drug*
  • Ethnicity
  • Female
  • Genotype
  • Humans
  • International Normalized Ratio
  • Linear Models
  • Male
  • Middle Aged
  • Pharmacogenetics*
  • Pulmonary Embolism / drug therapy
  • Venous Thrombosis / drug therapy
  • Warfarin / administration & dosage*
  • Warfarin / adverse effects

Substances

  • Anticoagulants
  • Warfarin
  • CYP2C9 protein, human
  • Cytochrome P-450 CYP2C9