Methods for Predicting Warfarin Dose Requirements

BACKGROUND: The aim of this study was to compare the predictive performance of different warfarin dosing methods. METHODS: Data from 46 patients who were initiating warfarin therapy were available for analysis. Nine recently published dosing tools including 8 dose prediction algorithms and a Bayesian forecasting method were compared with each other in terms of their ability to predict the actual maintenance dose. The dosing tools included 4 algorithms that were based on patient characteristics (2 clinical and 2 genotype-driven algorithms), 4 algorithms based on international normalized ratio (INR) response feedback and patient characteristics (2 clinical and 2 genotype-driven algorithms), and a Bayesian forecasting method. Comparisons were conducted using measures of bias (mean prediction error) and imprecision root mean square error (RMSE). RESULTS: The 2 genotype-driven INR feedback algorithms by Horne et al and Lenzini et al produced more precise maintenance dose predictions (RMSE, 1.16 and 1.19 mg/d, respectively; P 8 mg/d. CONCLUSIONS: Overall, warfarin dosing methods that included some measure of INR response (INR feedback algorithms and Bayesian methods) produced unbiased and more precise dose predictions. The Bayesian forecasting method produced positively biased dose predictions in patients who required doses >8 mg/d. Further research to assess differences in clinical endpoints when warfarin doses are predicted using Bayesian or INR-driven algorithms is warranted.