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
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Abstract
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.
Patients on a stable maintenance dose of warfarin, with observed dose ranging from
0.6 to 12mg 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.
From 249 enrolled patients with a mean clinical maintenance dose of 5.58mg/day, a
model with an R(2) 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(beta-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.
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.
Copyright 2009 Elsevier Ltd. All rights reserved.