Development of a Bayesian Forecasting Method for Warfarin Dose Individualisation

Purpose The aim of this study was to develop a Bayesian dose individualisation tool for warfarin. This was incorporated into the freely available software TCIWorks (www.tciworks.info) for use in the clinic. Methods All pharmacokinetic and pharmacodynamic (PKPD) models for warfarin in the medical literature were identified and evaluated against two warfarin datasets. The model with the best external validity was used to develop an optimal design for Bayesian parameter control. The performance of this design was evaluated using simulation-estimation techniques. Finally, the model was implemented in TCIWorks. Results A recently published warfarin KPD model was found to provide the best fit for the two external datasets. Optimal sampling days within the first 14 days of therapy were found to be days 3, 4, 5, 11, 12, 13 and 14. Simulations and parameter estimations suggested that the design will provide stable estimates of warfarin clearance and EC50. A single patient example showed the potential clinical utility of the method in TCIWorks. Conclusions A Bayesian dose individualisation tool for warfarin was developed. Future research to assess the predictive performance of the tool in warfarin patients is required.