Risk Prediction of Atrial Fibrillation Based on Electrocardiographic Interatrial Block. Issue 11 (30th May 2018)

• Record Type: Journal Article
• Authors: Skov, Morten W.; Ghouse, Jonas; Kühl, Jørgen T.; Platonov, Pyotr G.; Graff, Claus; Fuchs, Andreas; Rasmussen, Peter V.; Pietersen, Adrian; Nordestgaard, Børge G.; Torp‐Pedersen, Christian; Hansen, Steen M.; Olesen, Morten S.; Haunsø, Stig; Køber, Lars; Gerds, Thomas A.; Kofoed, Klaus F.; Svendsen, Jesper H.; Holst, Anders G.; Nielsen, Jonas B.
• Is Part Of: Journal of the American Heart Association. Volume 7:Issue 11(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-05-30
• Subjects: atrial fibrillation -- ECG -- epidemiology -- interatrial -- interatrial block -- ischemic stroke -- risk prediction; Heart -- Diseases -- Periodicals; Cardiovascular system -- Diseases -- Periodicals; Cerebrovascular disease -- Periodicals; Cardiology -- Periodicals; 616.1
• DOI: 10.1161/JAHA.117.008247
• Languages: English
• ISSNs: 2047-9980

Abstract

Abstract : Background: The electrocardiographic interatrial block (IAB) has been associated with atrial fibrillation (AF). We aimed to test whether IAB can improve risk prediction of AF for the individual person. Methods and Results: Digital ECGs of 152 759 primary care patients aged 50 to 90 years were collected from 2001 to 2011. We identified individuals with P‐wave ≥120 ms and the presence of none, 1, 2, or 3 biphasic P‐waves in inferior leads. Data on comorbidity, medication, and outcomes were obtained from nationwide registries. We observed a dose‐response relationship between the number of biphasic P‐waves in inferior leads and the hazard of AF during follow‐up. Discrimination of the 10‐year outcome of AF, measured by time‐dependent area under the curve, was increased by 1.09% (95% confidence interval 0.43–1.74%) for individuals with cardiovascular disease at baseline (CVD) and 1.01% (95% confidence interval 0.40–1.62%) for individuals without CVD, when IAB was added to a conventional risk model for AF. The highest effect of IAB on the absolute risk of AF was observed in individuals aged 60 to 70 years with CVD. In this subgroup, the 10‐year risk of AF was 50% in those with advanced IAB compared with 10% in those with a normal P‐wave. In general, individuals with advanced IAB and no CVD had a higher risk of AF than patients with CVD and no IAB. Conclusions: IAB improves risk prediction of AF when added to a conventional risk model. Clinicians may consider monitoring patients with IAB more closely for the occurrence of AF, especially for high‐risk subgroups.