Abstract

Abstract Introduction Atrial Fibrillation (AF) is associated with increased risk of cardiovascular (CV) morbidity and mortality. Heart Failure (HF) represents the most common CV complication, more common than thromboembolic events. Purpose We aimed to determine clinical factors associated with HF hospitalization and mortality in a contemporary cohort of patients with AF without previous history of HF. Methods The Effective Anticoagulation with Factor Xa Next Generation in AF–Thrombolysis in Myocardial Infarction 48 (ENGAGE-AF TIMI 48) study tested the oral factor Xa inhibitor edoxaban in comparison with warfarin for the prevention of stroke or systemic embolism, in 21,105 patients with AF. We assessed the composite endpoint of HF hospitalization, death due to HF or sudden cardiac death in 8981 patients without a history of HF. Cox proportional hazard models were used to evaluate the significant clinical predictors associated with the endpoint of interest. Results Over a median follow-up of 2.8 years, 589 patients (6.5%) experienced the composite endpoint. Older patients, cardiovascular risk factors (hypertension, diabetes, heart valve disease), history of stroke and coronary artery disease, impaired renal function (ClCr ≤50 ml/min), heart rate at baseline and diuretic use were associated with increased risk of the composite endpoint (model c-statistic 0.66) (Figure 1). Outcomes were not affected by randomization to edoxaban or warfarin. In patients with available cardiac-derived biomarkers, elevated levels of both NT-proBNP and Troponin I were significantly associated with the endpoint after adjustment for the clinical predictors (Figure 1). The addition of the biomarkers to clinical predictors enhanced risk estimation (c-statistics 0.69, NRI 0.40, IDI 0.01, all p<0.001 for NT-proBNP and c-statistics 0.70, NRI 0.43, IDI 0.03, all p<0.001 for Troponin I). Figure 1 Conclusions HF hospitalization and mortality are important complications in AF patients without a history of HF. The addition of cardiac biomarkers to clinical characteristics enhances risk estimation. These findings may improve risk stratification.

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