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Am J Cardiol ; 112(4): 509-12, 2013 Aug 15.
Article in English | MEDLINE | ID: mdl-23800552

ABSTRACT

This study determined the association between co-morbidities, including heart failure (HF) and time in therapeutic range (TTR), in patients with nonvalvular atrial fibrillation. Longitudinal patient-level anticoagulation management records collected from 2006 to 2010 were analyzed. Adult patients with nonvalvular atrial fibrillation who used warfarin for a 12-month period with no gap of >60 days between visits were identified. TTR <55% was defined as "lower" TTR. CHADS2 score of ≥2 was defined as "higher" CHADS2. Logistic regression analyses were conducted to determine the association between co-morbidities and TTR. A total of 23,425 patients met the study criteria. The mean age ± SD was 74.8 ± 9.7 years, with 84.8% aged ≥65 years. The most common co-morbidities were hypertension (41.7%), diabetes (24.1%), HF (11.7%), and previous stroke (11.1%). The mean TTR ± SD was 67.3 ± 14.4%, with 18.6% of patients in the lower TTR range. In multivariate analyses using age, gender, hypertension, diabetes, stroke, and region as covariates, HF (adjusted odds ratio [OR] 1.41, 95% confidence interval [CI] 1.28 to 1.56; p <0.001), diabetes (OR 1.28, 95% CI 1.19 to 1.38; p <0.001), and previous stroke (OR 1.15, 95% CI 1.04 to 1.27; p <0.001) were associated with lower TTR. In a second set of multivariate analyses using gender and region as covariates, a higher CHADS2 score was associated with lower TTR (OR 1.11, 95% CI 1.04 to 1.18; p <0.001). In conclusion, HF was associated with the greatest likelihood of a lower TTR, followed by diabetes, then stroke. Anticoagulation control may be more challenging for patients with these conditions.


Subject(s)
Anticoagulants/therapeutic use , Atrial Fibrillation/drug therapy , Warfarin/therapeutic use , Adult , Aged , Comorbidity , Decision Support Techniques , Diabetes Complications , Female , Heart Failure/complications , Humans , Hypertension/complications , International Normalized Ratio , Logistic Models , Longitudinal Studies , Male , Middle Aged , Risk Factors , Software , Stroke/complications , Time Factors
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