Predictors of unstable anticoagulation in African Americans

Abstract

We conclude that poor warfarin adherence and gastrointestinal illness are major contributors to unstable anticoagulation in African Americans. Our data suggest that, similar to Caucasians, improving warfarin adherence rates may be an important mean to improve anticoagulation control in African Americans. In addition, close monitoring during acute illness may be particularly important in this population.

Effect of Chronic Kidney Disease on Warfarin Management in a Pharmacist-Managed Anticoagulation Clinic

“…16 In addition, genotype has not been associated with anticoagulation stability in this population. 17 Thus, because all but 2 of our patients were of African ancestry, it appears unlikely that the lower average dose or decrease in anticoagulation stability we observed in patients with CKD is attributable to genetic differences.…”

Outcomes and predictors of very stable INR control during chronic anticoagulation therapy

“…The time in therapeutic range for all patients managed by CPAS is typically about 64%. 9 Although other investigations have examined predictors of very poor INR control, [16][17][18][19] to our knowledge this study is the first to assemble a large cohort of anticoagulated patients and carefully evaluate them for predictors of INR stability.…”

Combined CYP2C9 , VKORC1 and CYP4F2 Frequencies among Racial and Ethnic Groups

“…Although the African–American population has largely been under-represented in studies devoted to the genetic contribution of warfarin dose variability, they have similar frequencies of unstable anticoagulation compared with Caucasians [25], and the highest incidence rates of thromboembolism [26,27]. In addition, African–Americans typically require higher doses of warfarin than other racial groups [25,28,29].…”

“…In addition, African–Americans typically require higher doses of warfarin than other racial groups [25,28,29]. Interestingly, several studies have found that pharmacogenetic warfarin dosing algorithms perform less well for African–Americans than Caucasians, even when race/ethnicity is included as a variable [5,7,30,31].…”