Prediction of optimal warfarin maintenance dose using advanced artificial neural networks

Background: In recent years, pharmacogenetic algorithms were developed for estimating the appropriate dose of vitamin K antagonists. Aim: To evaluate the performance of new generation artificial neural networks (ANNs) to predict the warfarin maintenance dose. Methods: Demographic, clinical and genetic data (CYP2C9 and VKORC1 polymorphisms) from 377 patients treated with warfarin were used. The final prediction model was based on 23 variables selected by TWIST (R) system within a bipartite division of the data set (training and testing) protocol. Results: The ANN algorithm reached high accuracy, with an average absolute error of 5.7 mg of the warfarin maintenance dose. In the subset of patients requiring 21 mg and 21-49 mg (45 and 51% of the cohort, respectively) the absolute error was 3.86 mg and 5.45 with a high percentage of subjects being correctly identified (71 and 73%, respectively). Conclusion: ANN appears to be a promising tool for vitamin K antagonist maintenance dose prediction.