Key result
Pharmacogenetic algorithm explains ~64% of variance in stable acenocoumarol dosing.
Why the study?
Genetic polymorphisms strongly influence acenocoumarol dosage requirements, and incorporating genotyping before therapy could improve dosing accuracy and healthcare efficiency.
Does a pharmacogenetic algorithm improve the prediction of stable acenocoumarol dosage compared to clinical variables alone in patients requiring anticoagulation?
Observational (n=685)
Yes
Does a pharmacogenetic algorithm improve the prediction of stable acenocoumarol dosage compared to clinical variables alone in patients requiring anticoagulation?
A pharmacogenetic algorithm incorporating clinical and genetic variables explains up to 64% of the variability in stable acenocoumarol dosing, particularly improving prediction in extreme-dosage patients.
May support genotype-guided acenocoumarol dosing algorithms; leaves open clinical utility and resource benefits pending prospective validation.
There is a strong association between genetic polymorphisms and the acenocoumarol dosage requirements. Genotyping the polymorphisms involved in the pharmacokinetics and pharmacodynamics of acenocoumarol before starting anticoagulant therapy would result in a better quality of life and a more efficient use of healthcare resources. The objective of this study is to develop a new algorithm that includes clinical and genetic variables to predict the most appropriate acenocoumarol dosage for stable anticoagulation in a wide range of patients. We recruited 685 patients from 2 Spanish hospitals and 1 primary healthcare center. We randomly chose 80% of the patients (n = 556), considering an equitable distribution of genotypes to form the generation cohort. The remaining 20% (n = 129) formed the validation cohort. Multiple linear regression was used to generate the algorithm using the acenocoumarol stable dosage as the dependent variable and the clinical and genotypic variables as the independent variables. The variables included in the algorithm were age, weight, amiodarone use, enzyme inducer status, international normalized ratio target range and the presence of CYP2C9*2 (rs1799853), CYP2C9*3 (rs1057910), VKORC1 (rs9923231) and CYP4F2 (rs2108622). The coefficient of determination (R2) explained by the algorithm was 52.8% in the generation cohort and 64% in the validation cohort. The following R2 values were evaluated by pathology: atrial fibrillation, 57.4%; valve replacement, 56.3%; and venous thromboembolic disease, 51.5%. When the patients were classified into 3 dosage groups according to the stable dosage (<11 mg/week, 11-21 mg/week, >21 mg/week), the percentage of correctly classified patients was higher in the intermediate group, whereas differences between pharmacogenetic and clinical algorithms increased in the extreme dosage groups. Our algorithm could improve acenocoumarol dosage selection for patients who will begin treatment with this drug, especially in extreme-dosage patients. The predictability of the pharmacogenetic algorithm did not vary significantly between diseases.
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Tong et al. (2016) conducted an observational in Stable anticoagulation (n=685). Pharmacogenetic algorithm (clinical and genetic variables) was evaluated on Coefficient of determination (R2) for predicting stable acenocoumarol dosage. A new pharmacogenetic algorithm incorporating clinical and genetic variables explained 64% of the variance in stable acenocoumarol dosage in a validation cohort.
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