Summary
Acenocoumarol is a commonly prescribed anticoagulant drug for the prophylaxis and treatment of venous and arterial thromboembolic disorders in several countries. In counterpart of warfarin, there is scarce information about pharmacogenetic algorithms for steady acenocoumarol dose estimation. The aim of this study was to develop an algorithm of prediction for acenocoumarol.The algorithm was created using the data from 973 retrospectively selected anticoagulated patients and was validated in a second independent cohort adding up to 2,683 patients. The best regression model to predict stable dosage in the Primary Cohort included clinical factors (age and body mass index, BSA) and genetic variants (VKORC1, CYP2C9* and CYP4F2 polymorphisms) and explained up to 50% of stable dose. In the validation study the clinical algorithm yielded an adjusted R2=0.15 (estimation´s standard error=4.5) and the genetic approach improved the dose forecast up to 30% (estimation´s standard error=4.6). Again, the best model combined clinical and genetic factors (R2= 0.48; estimation´s standard error=4) which provided the best results of doses estimates within 20% of the real dose in patients taking lower (≤7mg/week) or higher (≥25mg/week) acenocoumarol doses. In conclusion, we developed a prediction algorithm using clinical data and three polymorphisms in VKORC1, CYP2C9* and CYP4F2 genes that provided a steady acenocoumarol dose for about 50% of patients in the Validation Cohort. Such algorithm was especially useful to patients who need higher or lower acenocoumarol doses, those patients with higher time required until their stabilisation and are more prone to suffer a treatment derived complication.
Keywords
Thrombosis - stroke prevention - vitamin K-dependent factors
