BACKGROUND: Warfarin sodium is commonly prescribed for the prophylaxis and treatment of venous thromboembolism. Dosing algorithms have not been widely adopted because they require a fixed initial warfarin dose (eg, 5 mg) and are not tailored to other factors that may affect the international normalized ratio (INR). OBJECTIVE: To develop an algorithm that could predict a therapeutic warfarin dose based on drug interactions, INR response after the initial warfarin doses, and other clinical factors. METHODS: We used stepwise regression to quantify the relationship between these factors in patients beginning prophylactic warfarin therapy immediately prior to joint replacement. In the derivation cohort (n = 271), we separately modeled the therapeutic dose after 2 and 3 initial doses. We prospectively validated these 2 models in an independent cohort (n = 105). RESULTS: About half of the therapeutic dose variability was predictable after 3 days of therapy: R2 was 53% in the derivation cohort and 42% in the validation cohort. INR response after 3 warfarin doses (INR3) inversely correlated with therapeutic dose (p < 0.001). Intraoperative blood loss transiently, but significantly, elevated the postoperative INR values. Other significant (p < 0.03) predictors were the first and second warfarin doses (+7% and +6%, respectively, per 1 mg), and statin use (-15.0%). The model derived after 2 warfarin doses explained 32% of the variability in therapeutic dose. CONCLUSIONS: We developed and validated algorithms that estimate therapeutic warfarin doses based on clinical factors and INR response available after 2-3 days of warfarin therapy. The algorithms are implemented online at www.WarfarinDosing.org.
BACKGROUND:Warfarin sodium is commonly prescribed for the prophylaxis and treatment of venous thromboembolism. Dosing algorithms have not been widely adopted because they require a fixed initial warfarin dose (eg, 5 mg) and are not tailored to other factors that may affect the international normalized ratio (INR). OBJECTIVE: To develop an algorithm that could predict a therapeutic warfarin dose based on drug interactions, INR response after the initial warfarin doses, and other clinical factors. METHODS: We used stepwise regression to quantify the relationship between these factors in patients beginning prophylactic warfarin therapy immediately prior to joint replacement. In the derivation cohort (n = 271), we separately modeled the therapeutic dose after 2 and 3 initial doses. We prospectively validated these 2 models in an independent cohort (n = 105). RESULTS: About half of the therapeutic dose variability was predictable after 3 days of therapy: R2 was 53% in the derivation cohort and 42% in the validation cohort. INR response after 3 warfarin doses (INR3) inversely correlated with therapeutic dose (p < 0.001). Intraoperative blood loss transiently, but significantly, elevated the postoperative INR values. Other significant (p < 0.03) predictors were the first and second warfarin doses (+7% and +6%, respectively, per 1 mg), and statin use (-15.0%). The model derived after 2 warfarin doses explained 32% of the variability in therapeutic dose. CONCLUSIONS: We developed and validated algorithms that estimate therapeutic warfarin doses based on clinical factors and INR response available after 2-3 days of warfarin therapy. The algorithms are implemented online at www.WarfarinDosing.org.
Authors: Brian F Gage; Anne R Bass; Hannah Lin; Scott C Woller; Scott M Stevens; Noor Al-Hammadi; Jeffrey L Anderson; Juan Li; Tomás Rodriguez; J Philip Miller; Gwendolyn A McMillin; Robert C Pendleton; Amir K Jaffer; Cristi R King; Brandi Whipple; Rhonda Porche-Sorbet; Lynnae Napoli; Kerri Merritt; Anna M Thompson; Gina Hyun; Wesley Hollomon; Robert L Barrack; Ryan M Nunley; Gerard Moskowitz; Victor Dávila-Román; Charles S Eby Journal: JAMA Date: 2019-09-03 Impact factor: 56.272
Authors: P A Lenzini; G R Grice; P E Milligan; M B Dowd; S Subherwal; E Deych; C S Eby; C R King; R M Porche-Sorbet; C V Murphy; R Marchand; E A Millican; R L Barrack; J C Clohisy; K Kronquist; S K Gatchel; B F Gage Journal: J Thromb Haemost Date: 2008-07-24 Impact factor: 5.824
Authors: N S Ferder; C S Eby; E Deych; J K Harris; P M Ridker; P E Milligan; S Z Goldhaber; C R King; T Giri; H L McLeod; R J Glynn; B F Gage Journal: J Thromb Haemost Date: 2009-10-30 Impact factor: 5.824
Authors: B F Gage; C Eby; J A Johnson; E Deych; M J Rieder; P M Ridker; P E Milligan; G Grice; P Lenzini; A E Rettie; C L Aquilante; L Grosso; S Marsh; T Langaee; L E Farnett; D Voora; D L Veenstra; R J Glynn; A Barrett; H L McLeod Journal: Clin Pharmacol Ther Date: 2008-02-27 Impact factor: 6.875
Authors: Brian F Gage; Anne R Bass; Hannah Lin; Scott C Woller; Scott M Stevens; Noor Al-Hammadi; Juan Li; Tomás Rodríguez; J Philip Miller; Gwendolyn A McMillin; Robert C Pendleton; Amir K Jaffer; Cristi R King; Brandi DeVore Whipple; Rhonda Porche-Sorbet; Lynnae Napoli; Kerri Merritt; Anna M Thompson; Gina Hyun; Jeffrey L Anderson; Wesley Hollomon; Robert L Barrack; Ryan M Nunley; Gerard Moskowitz; Victor Dávila-Román; Charles S Eby Journal: JAMA Date: 2017-09-26 Impact factor: 56.272
Authors: E J Do; P Lenzini; C S Eby; A R Bass; G A McMillin; S M Stevens; S C Woller; R C Pendleton; J L Anderson; P Proctor; R M Nunley; V Davila-Roman; B F Gage Journal: Pharmacogenomics J Date: 2011-05-24 Impact factor: 3.550