Table 3.
Warfarin-dosing equation in derivation cohort (N = 1,015)
| Entry into model | Variable | Effect on warfarin dose | R2 after entry | P value |
|---|---|---|---|---|
| 1 | VKOR 3673G>A | −28% (−30 to −25%) | 25% | <0.0001 |
| 2 | BSA, per 0.25 m2 | 11% (9−14%) | 34% | <0.0001 |
| 3 | CYP2C9*3 | −33% (−37 to −29%) | 40% | <0.0001 |
| 4 | Age, per decade | −7% (−9 to −6%) | 45% | <0.0001 |
| 5 | CYP2C9*2 | −19% (−22 to −15%) | 50% | <0.0001 |
| 6 | Target INR, per 0.5 increase | 11% (7−14%) | 51% | <0.0001 |
| 7 | Amiodarone | −22% (−30% to −14%) | 52% | <0.0001 |
| 8 | Current smoker | 10% (3−16%) | 52.4% | 0.002 |
| 9 | African-American race | −9% (−14 to −3%) | 52.8% | 0.002 |
| 10 | Venous thromboembolism | 7% (1−13%) | 53.1% | 0.013 |
BSA, body surface area in meters2; DVT, deep venous thrombosis; INR, international normalized ratio; PE, pulmonary embolism; SNP, single-nucleotide polymorphism. The optimal pharmacogenetics algorithm that estimated the daily warfarin dose (mg/day) was: