Bruce Green (1,2) and Robert McLeay (1)
(1) DoseMe Pty Ltd, Brisbane, Australia, (2) Model Answers Pty Ltd, Brisbane, Australia
Objectives: To compare the probability of successful INR attainment using individualised dosing for warfarin via Bayesian forecasting (DoseMe) or nomogram-based dosing methods.
Methods: A pre-existing K-PD model for warfarin [1] was used to simulate INR over 6 weeks using 2 nomograms and a Bayesian forecasting program (DoseMe) [2]. The first nomogram adjusted dose based upon genotype and INR [3], whereas the second nomogram adjusted dose based upon INR alone [4,5]. DoseMe was also used to adjust dose with and without genotype information.
Results: At day 20 and 60, 40% [24 – 46%] and 78% [67 – 86%] (median, 95%CI) of subjects were expected to have an INR in range using the genotype nomogram-based dosing. At day 20 and 60, 26% [16 – 36%] and 26% [16 – 38%] of subjects were expected to have an INR in range using the non-genotype nomogram-based dosing. Comparative genotype Bayesian-based dosing was expected to have 64% [49.0 – 76%] and 72% [59 – 82%] of subjects in the range at day 20 and 60, respectively, whilst non-genotype Bayesian-dosing was expected to have 63% [48 – 72%] and 74% [60 – 84%] of subjects in the range. The observed clinical trial result for the genotype nomogram-based dosing was 66.7% [3], which was captured by the simulation model.
Conclusions: Non-genotype Bayesian dosing resulted in quicker and more accurate attainment of therapeutic INR when compared to non-genotype nomogram-based dosing. Genotype-based Bayesian dosing also resulted in quicker attainment of therapeutic INR compared to genotype nomogram-based dosing. As genotype is rarely available in clinical practice, Bayesian methods such as DoseMe provide an easy to use practical solution that should be used in clinical practice to optimise patient outcomes.
References:
[1] Hamburg A-K, Wadelius M, Lindh JD, et al. A Pharmacometric Model Describing the Relationship Between Warfarin Dose and INR Response With Respect to Variations in CYP2C9, VKORC1, and Age. Clin Pharm Ther (2010) 87, 727-734.
[2] www.doseme.com.au
[3] Perlstein TS, Goldhaber SZ, Nelson K, et al. The Creating an Optimal Warfarin Nomogram (CROWN) Study. Thromb Haemost (2012)107, 59-68.
[4] http://www.health.qld.gov.au/qhcss/mapsu/documents/warfarin-guidelines.pdf
[5] Van Spall HG, Wallentin L, Yusuf S, et al. Variation in warfarin dose adjustment practice is responsible for differences in the quality of anticoagulation control between centers and countries: an analysis of patients receiving warfarin in the randomized evaluation of long-term anticoagulation (RE-LY) trial. Circulation (2012) 126, 2309-2316.
Reference: PAGE 22 () Abstr 2823 [www.page-meeting.org/?abstract=2823]
Poster: Other Modelling Applications