BACKGROUND: The aim of this study was to compare the predictive performance of different @entity1229 dosing methods. METHODS: Data from 46 @entity1 who were initiating @entity1229 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 @entity1 characteristics (2 clinical and 2 genotype-driven algorithms), 4 algorithms based on international normalized ratio (INR) response feedback and @entity1 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 < 0.05) than the genotype-driven algorithms by Gage et al and Klein et al and the Bayesian method (RMSE, 1.60, 1.62, and 1.81 mg/d respectively). The dose predictions from clinical and genotype-driven algorithms by Gage et al, Klein et al, and Horne et al were all negatively biased. Only the INR feedback algorithms (clinical and genotype) by Lenzini et al produced unbiased dose predictions. The Bayesian method produced unbiased dose predictions overall (mean prediction error, +0.37 mg/d; 95% confidence interval, 0.89 to -0.15) but overpredicted doses in @entity1 requiring >8 mg/d. CONCLUSIONS: Overall, @entity1229 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 @entity1 who required doses >8 mg/d. Further research to assess differences in clinical endpoints when @entity1229 doses are predicted using Bayesian or INR-driven algorithms is warranted.

Methods for Predicting XXXX Dose Requirements.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity1229 :: ('MESH:D014859', 'Chemical') :: ['warfarin']
@entity1229 :: (MESH:D014859,Chemical) :: ['Warfarin']
