title: Methods for Predicting Warfarin Dose Requirements.
abstract: 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 < 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 patients requiring >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.
authors.affiliation: *School of Pharmacy, University of Otago, Dunedin, New Zealand; †Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur; and ‡Department of Surgical Sciences, University of Otago, Dunedin, New Zealand.
authors.forename: Shamin M
authors.initials: SM
authors.lastname: Saffian
authors.forename: Daniel F B
authors.initials: DF
authors.lastname: Wright
authors.forename: Rebecca L
authors.initials: RL
authors.lastname: Roberts
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Duffull
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000925:Anticoagulants / Q000008:administration & dosage; D001499:Bayes Theorem; D065729:Cytochrome P-450 CYP2C9 / Q000235:genetics; D054796:Drug Dosage Calculations; D005260:Female; D005838:Genotype; D006801:Humans; D019934:International Normalized Ratio; D008297:Male; D008875:Middle Aged; D064417:Vitamin K Epoxide Reductases / Q000235:genetics; D014859:Warfarin / Q000008:administration & dosage*
chemical_list: D000925:Anticoagulants; D014859:Warfarin; C450260:CYP2C9 protein, human; D065729:Cytochrome P-450 CYP2C9; C578523:VKORC1 protein, human; D064417:Vitamin K Epoxide Reductases