Supraventricular premature beats and risk of new‐onset atrial fibrillation in coronary artery disease. (16th August 2017)

• Record Type: Journal Article
• Authors: Nortamo, Santeri; Kenttä, Tuomas V.; Ukkola, Olavi; Huikuri, Heikki V.; Perkiömäki, Juha S.
• Is Part Of: Journal of cardiovascular electrophysiology. Volume 28:Number 11(2017)
• Page Start: 1269
• Page End: 1274
• Publication Date: 2017-08-16
• Subjects: atrial fibrillation -- premature atrial contraction -- supraventricular premature beats -- supraventricular runs; Blood vessels -- Physiology -- Periodicals; Electrophysiology -- Periodicals; Heart -- Physiology -- Periodicals; 612.1
• DOI: 10.1111/jce.13304
• Languages: English
• ISSNs: 1045-3873

Abstract

Abstract: Introduction: The significance of premature atrial contraction (PAC) count and supraventricular runs (SVR) for the risk of development of new‐onset atrial fibrillation (AF) in patients with coronary artery disease (CAD) is not well established. Methods: The Innovation to Reduce Cardiovascular Complications of Diabetes at the Intersection (ARTEMIS) study cohort consisted of 1, 946 patients with CAD who underwent clinical and echocardiographic examinations, 24‐hour ambulatory ECG monitoring, and laboratory tests. After excluding patients who were not in sinus rhythm at baseline or were lost from the follow‐up, the present study included 1, 710 patients. SVR was defined as at least four PACs in a row with a duration <30 seconds. Results: During a follow‐up for an average 5.6 ± 1.5 years, new‐onset AF was identified in 143 (8.4%) patients. In the univariate analysis, both SVR and PAC count were associated with the development of new‐onset AF. When SVR and PAC count were adjusted with the established AF risk markers of the modified CHARGE‐AF model in the Cox multivariate regression analysis, both parameters remained significant predictors of the occurrence of new‐onset AF (HR = 2.529, 95 % CI = 1.763–3.628, P ˂ 0.001 and HR = 8.139 for ≥1, 409 PACs [the fourth quartile] vs. ≤507 PACs [the first quartile], 95 % CI = 3.967–16.696, P ˂ 0.001, respectively). Together these parameters improved the C‐index of the established AF risk model from 0.649 to 0.718, P < 0.001. Conclusion: Including SVR and PAC count to the established AF risk model improves the discrimination accuracy in predicting AF in patients with CAD.