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Prediction of optimal warfarin maintenance dose using advanced artificial neural networks.
Pharmacogenomics 2014 January
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® 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.
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® 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.
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