A regression model to predict warfarin dose from clinical variables and polymorphisms in CYP2C9, CYP4F2, and VKORC1: Derivation in a sample with predominantly a history of venous thromboembolism

Abstract Background Pharmacogenomic warfarin dosing has been suggested to produce more accurate dosing and an improved patient safety profile; however, very few models have been derived in patients with venous thromboembolism. We sought to develop a new algorithm to predict maintenance dose in a cohort of patients, using clinical variables and genetic polymorphism in CYP2C9, VKORC1, and CYP4F2. Methods Patients on a stable maintenance dose of warfarin, with observed dose ranging from 0.6 to 12 mg were recruited from a specialized anticoagulation clinic (Ottawa Hospital Thrombosis Clinic) with genotyping and standardized patient interviews being conducted to collect clinical and genomic variables known to impact warfarin dose. Multivariate linear regression was used to develop the model using a stepwise backwards elimination approach. Results From 249 enrolled patients with a mean clinical maintenance dose of 5.58 mg/day, a model with an R2 of 58% was developed as: Dose = 1.85-0.048(Age) + 0.041(BMI) + 0.05(Height in cm) - 0.73(Less Exercise) - 1.13(2C9*2 Hetero) - 2.09(2C9*2 Homo) - 1.51(2C9*3 Hetero) -1.43(VKORC1 GA) - 2.86(VKORC1 AA) - 1.33(4F2 CC) -1.24(4F2 CT) - 1.46(Angiotensin II Receptor Antagonist) - 0.84(β-Blockers). Analysis of residual plots revealed that prediction errors were a function of observed maintenance dose with the model tending to predict higher doses than observed in those with low dose requirements and lower doses than observed in those with higher dose requirement. Conclusion Our study confirms the importance of the CYP4F2 polymorphism. Our model may prove useful in clinical practice but further validation studies are required before implementation into clinical practice.