Data di Pubblicazione:
2014
Citazione:
Prediction of optimal warfarin maintenance dose using advanced artificial neural networks / E. Grossi, G.M. Podda, M. Pugliano, S. Gabba, A. Verri, G. Carpani, M. Buscema, G. Casazza, M. Cattaneo. - In: PHARMACOGENOMICS. - ISSN 1462-2416. - 15:1(2014), pp. 29-37. [10.2217/pgs.13.212]
Abstract:
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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Artificial neural network; CYP2C9; Pharmacogenetic algorithm; Vitamin K antagonist; VKORC1; Pharmacology; Genetics; Molecular Medicine
Elenco autori:
E. Grossi, G.M. Podda, M. Pugliano, S. Gabba, A. Verri, G. Carpani, M. Buscema, G. Casazza, M. Cattaneo
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