Risk Prediction of Atrial Fibrillation Based on Electrocardiographic Interatrial Block

Morten W Skov, Jonas Ghouse, Jørgen T Kühl, Pyotr G Platonov, Claus Graff, Andreas Fuchs, Peter V Rasmussen, Adrian Pietersen, Børge G Nordestgaard, Christian Torp-Pedersen, Steen M Hansen, Morten S Olesen, Stig Haunsø, Lars Køber, Thomas A Gerds, Klaus F Kofoed, Jesper H Svendsen, Anders G Holst, Jonas B Nielsen, Morten W Skov, Jonas Ghouse, Jørgen T Kühl, Pyotr G Platonov, Claus Graff, Andreas Fuchs, Peter V Rasmussen, Adrian Pietersen, Børge G Nordestgaard, Christian Torp-Pedersen, Steen M Hansen, Morten S Olesen, Stig Haunsø, Lars Køber, Thomas A Gerds, Klaus F Kofoed, Jesper H Svendsen, Anders G Holst, Jonas B Nielsen

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.

Keywords: ECG; atrial fibrillation; epidemiology; interatrial; interatrial block; ischemic stroke; risk prediction.

© 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Figures

Figure 1
Figure 1
Examples of interatrial block as well as a normal ECG. IAB indicates interatrial block.
Figure 2
Figure 2
Multivariable‐adjusted hazard ratios for atrial fibrillation, ischemic stroke, conduction disorder, and all‐cause mortality by interatrial block. CI 95 indicates 95% confidence interval; IAB, interatrial block.
Figure 3
Figure 3
Differences in area under the curve for the 10‐year outcomes of atrial fibrillation and ischemic stroke obtained by adding interatrial block to conventional risk models for atrial fibrillation and ischemic stroke, respectively, stratified by the presence or absence of cardiovascular disease at baseline. AF indicates atrial fibrillation; AUC, area under the curve; CI, confidence interval; CVD, cardiovascular disease at baseline; IAB, interatrial block; noCVD, no cardiovascular disease at baseline.
Figure 4
Figure 4
Cumulative incidence curves of interatrial block for the outcome of atrial fibrillation in patients with and without cardiovascular disease at baseline and stratified into 10‐year age‐groups. Predictions were based on multivariable‐adjusted Cox models fitted within the respective age‐group and cardiovascular disease group (yes/no). AF indicates atrial fibrillation; CVD, cardiovascular disease; ECG, electrocardiogram; IAB, interatrial block.
Figure 5
Figure 5
Cumulative incidence curves of interatrial block for the outcome of ischemic stroke in patients with and without cardiovascular disease at baseline and stratified into 10‐year age‐groups. Predictions were based on multivariable‐adjusted Cox models fitted within the respective age‐group and cardiovascular disease group (yes/no). CVD indicates cardiovascular disease; ECG, electrocardiogram; IAB, interatrial block.
Figure 6
Figure 6
A, Violin plot displaying median, interquartile range, range, and probability density of left atrial end‐diastolic volume for normal P‐wave and interatrial block. IAB indicates inter‐atrial blockIAB‐1, interatrial block with one biphasic P‐wave in inferior leads; IAB‐2, interatrial block with 2 biphasic P‐waves in inferior leads. B, Receiving operator curve for the 2 models. In both models, interatrial block (yes/no) is outcome. AUC indicates area under the curve; LAEDV, left atrial end‐diastolic volume.

References

    1. Bayés de Luna A, Platonov P, Cosio FG, Cygankiewicz I, Pastore C, Baranowski R, Bayés‐Genis A, Guindo J, Viñolas X, Garcia‐Niebla J, Barbosa R, Stern S, Spodick D. Interatrial blocks. A separate entity from left atrial enlargement: a consensus report. J Electrocardiol. 2012;45:445–451.
    1. Martínez‐Sellés M, Baranchuk A, Elosua R, de Luna AB. Rationale and design of the BAYES (Interatrial Block and Yearly Events) registry. Clin Cardiol. 2017;40:196–199.
    1. O'Neal WT, Zhang Z‐M, Loehr LR, Chen LY, Alonso A, Soliman EZ. Electrocardiographic advanced interatrial block and atrial fibrillation risk in the general population. Am J Cardiol. 2016;117:1755–1759.
    1. O'Neal WT, Kamel H, Zhang Z‐M, Chen LY, Alonso A, Soliman EZ. Advanced interatrial block and ischemic stroke: the Atherosclerosis Risk in Communities Study. Neurology. 2016;87:352–356.
    1. Martínez‐Sellés M, García‐Izquierdo Jaén E, Fernández Lozano I. Anticoagulation in elderly patients at high risk of atrial fibrillation without documented arrhythmias. J Geriatr Cardiol. 2017;14:166–168.
    1. Nielsen JB, Graff C, Pietersen A, Lind B, Struijk JJ, Olesen MS, Haunsø S, Gerds TA, Svendsen JH, Køber L, Holst AG. J‐shaped association between QTc interval duration and the risk of atrial fibrillation: results from the Copenhagen ECG study. J Am Coll Cardiol. 2013;61:2557–2564.
    1. GE Healthcare . Marquette™ 12SL™ ECG Analysis Program. Physician's Guide 2036070‐006 Revision A, 2010. Available at: . Accessed April 2, 2012.
    1. Nielsen JB, Kühl JT, Pietersen A, Graff C, Lind B, Struijk JJ, Olesen MS, Sinner MF, Bachmann TN, Haunsø S, Nordestgaard BG, Ellinor PT, Svendsen JH, Kofoed KF, Køber L, Holst AG. P‐wave duration and the risk of atrial fibrillation: results from the Copenhagen ECG Study. Heart Rhythm. 2015;12:1887–1895.
    1. Frank L. Epidemiology. When an entire country is a cohort. Science. 2000;287:2398–2399.
    1. Olesen JB, Lip GYH, Hansen ML, Hansen PR, Tolstrup JS, Lindhardsen J, Selmer C, Ahlehoff O, Olsen A‐MS, Gislason GH, Torp‐Pedersen C. Validation of risk stratification schemes for predicting stroke and thromboembolism in patients with atrial fibrillation: nationwide cohort study. BMJ. 2011;342:d124.
    1. Christiansen MN, Køber L, Weeke P, Vasan RS, Jeppesen JL, Smith JG, Gislason GH, Torp‐Pedersen C, Andersson C. Age‐specific trends in incidence, mortality, and comorbidities of heart failure in Denmark, 1995 to 2012. Circulation. 2017;135:1214–1223.
    1. Nordestgaard BG, Palmer TM, Benn M, Zacho J, Tybjærg‐Hansen A, Smith GD, Timpson NJ. The effect of elevated body mass index on ischemic heart disease risk: causal estimates from a mendelian randomisation approach. PLoS Med. 2012;9:e1001212.
    1. Kühl JT, Lønborg J, Fuchs A, Andersen MJ, Vejlstrup N, Kelbæk H, Engstrøm T, Møller JE, Kofoed KF. Assessment of left atrial volume and function: a comparative study between echocardiography, magnetic resonance imaging and multi slice computed tomography. Int J Cardiovasc Imaging. 2012;28:1061–1071.
    1. Schemper M, Smith TL. A note on quantifying follow‐up in studies of failure time. Control Clin Trials. 1996;17:343–346.
    1. Benichou J, Gail MH. Estimates of absolute cause‐specific risk in cohort studies. Biometrics. 1990;46:813–826.
    1. Blanche P, Dartigues J‐F, Jacqmin‐Gadda H. Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32:5381–5397.
    1. Gerds TA, Scheike TH, Andersen PK. Absolute risk regression for competing risks: interpretation, link functions, and prediction. Stat Med. 2012;31:3921–3930.
    1. Wolbers M, Blanche P, Koller MT, Witteman JCM, Gerds TA. Concordance for prognostic models with competing risks. Biostatistics. 2014;15:526–539.
    1. Magnani JW, Zhu L, Lopez F, Pencina MJ, Agarwal SK, Soliman EZ, Benjamin EJ, Alonso A. P‐wave indices and atrial fibrillation: cross‐cohort assessments from the Framingham Heart Study (FHS) and Atherosclerosis Risk in Communities (ARIC) study. Am Heart J. 2015;169:53–61.e1.
    1. Olgun Kucuk H, Kucuk U, Yalcin M, Isilak Z. Time to use mobile health devices to diagnose paroxysmal atrial fibrillation. Int J Cardiol. 2016;222:1061.
    1. Goyal SB, Spodick DH. Electromechanical dysfunction of the left atrium associated with interatrial block. Am Heart J. 2001;142:823–827.
    1. Rix TA, Riahi S, Overvad K, Lundbye‐Christensen S, Schmidt EB, Joensen AM. Validity of the diagnoses atrial fibrillation and atrial flutter in a Danish patient registry. Scand Cardiovasc J. 2012;46:149–153.

Source: PubMed

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