P-Wave Indices and Risk of Ischemic Stroke
Background and Purpose—
Atrial cardiomyopathy is associated with an increased risk of ischemic stroke. P-wave terminal force in lead V1, P-wave duration, and maximum P-wave area are electrocardiographic parameters that have been used to assess left atrial abnormalities related to developing atrial fibrillation. The aim of this systematic review and meta-analysis was to examine their values for predicting ischemic stroke risk.
PubMed and EMBASE databases were searched until December 2016 for studies that evaluated the association between P-wave indices and stroke risk. Both fixed- and random-effects models were used to calculate the overall effect estimates.
Ten studies examining P-wave terminal force in lead V1, P-wave duration, and maximum P-wave area were included. P-wave terminal force in lead V1 was found to be an independent predictor of stroke as both a continuous variable (odds ratio [OR] per 1 SD change, 1.18; 95% confidence interval [CI], 1.12–1.25; P<0.0001) and categorical variable (OR, 1.59; 95% CI, 1.10–2.28; P=0.01). P-wave duration was a significant predictor of incident ischemic stroke when analyzed as a categorical variable (OR, 1.86; 95% CI, 1.37–2.52; P<0.0001) but not when analyzed as a continuous variable (OR, 1.05; 95% CI, 0.98–1.13; P=0.15). Maximum P-wave area also predicted the risk of incident ischemic stroke (OR per 1 SD change, 1.10; 95% CI, 1.04–1.17).
P-wave terminal force in lead V1, P-wave duration, and maximum P-wave area are useful electrocardiographic markers that can be used to stratify the risk of incident ischemic stroke.
The association between atrial fibrillation (AF) and ischemic stroke is well established. Left atrial abnormalities are associated with increased risk of ischemic stroke even in the absence of evident AF.1,2 These abnormalities can be assessed noninvasively using ECG and may indicate increased P-terminal force in the precordial lead V1 (PTFV1) >40 ms·mm,3 prolonged P-wave duration (PWD) >120 ms reflecting interatrial block,4–7 or greater maximum P-wave area (PWA).8–10 Interestingly, some clinical studies have demonstrated the value of some of these ECG markers for stroke risk stratification.9,11,12 Of note, patients with atrial abnormalities without evident AF are not placed on oral anticoagulation.13 Therefore, the identification of such abnormalities may have significant prognostic and therapeutic implications in this setting.
The aim of this study is to conduct a comprehensive systematic review and meta-analysis of these P-wave indices in predicting stroke risk. It will include prospective or retrospective cohort studies or case-reporting odds ratios (ORs), hazard ratios, or relative risk for patients experiencing all types of strokes given their P-wave abnormalities.
The study was performed in accordance with the Guidelines for Meta-Analyses and Systematic Reviews of Observational Studies14 for statement and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses15 for screening studies. Two investigators (J.H. and G.T.) systematically and independently searched PubMed and EMBASE for publications published before December 2016 using separate search items P-wave terminal force and stroke, P-wave duration or interatrial conduction block and stroke, and P-wave area and stroke without language limitations. The abstracts of these publications were screened to identify eligible studies. The full texts of these studies were then retrieved and assessed for compliance with the inclusion criteria. Reference lists of full-text studies were also screened to identify additional relevant articles.
Inclusion and Exclusion Criteria
The following criteria were used to identify eligible studies: (1) the study design was a prospective cohort study, retrospective cohort study, or case–control study; (2) patients with ischemic stroke of various types were enrolled; (3) appropriate measurements of P-wave indices (PTFV1, PWD, and maximum PWA) were reported; and (4) the effect estimates, OR, hazard ratio, or relative risk along with their corresponding 95% confidence interval (CI) for the association between these P-wave indices and stroke were provided. The following exclusion criteria were applied: (1) articles that were letters to the editor16, editorials, or reviews; (2) studies that did not provide multivariate adjusted effect estimates5; and (3) the study end points did not involve the occurrence of ischemic stroke.17–19
Two investigators (J.H. and G.T.) independently performed quality assessment of the included studies using the Newcastle-Ottawa quality assessment scale according to the following 3 aspects: (1) selection of the patients; (2) comparability of groups or cohorts; (3) the exposure evaluation for case–control studies and the outcome evaluation for cohort studies.
Two investigators (J.H. and G.T.) extracted data (information of the studies, baseline characteristics, pooled effect estimates) and entered them into standardized spreadsheets. Any data discrepancy was resolved by consulting a third reviewer (T.L.).
Measurement of P-Wave Indices
The components of PTFV1 have been reported previously.8 PWD was measured in milliseconds as the first-onset and last-offset deflection from the baseline. Maximum PWA was measured in microvolt×milliseconds2 as the area under the P-wave in the 12 leads of the ECG.1 It comprised the sum of the absolute areas of upward and downward P-wave deflections and was multiplied by 19.52 to harmonize the 12-SL program’s calculations with other machines).8
Data analysis was performed using Review Manager 5.3 software. The generic inverse variance method was used to calculate the pooled estimates. The multivariate adjusted hazard ratio and relative risk values in multivariate Cox proportional hazards model were directly regarded as OR values to pool together according to the previous literature.20 As 2 forms of P-wave indices (adjusted effect estimates for a 1 SD change as a continuous variable and adjusted effect estimates as a categorical variable) were provided in the relevant studies, separate meta-analyses were conducted to evaluate their associations with ischemic stroke.
The I2 statistic, which represents the overall heterogeneity that includes clinical, method, and statistical components, was used to select the appropriate effect model for pooling estimates. I2 value between 0% and 25% indicates insignificant heterogeneity, 26% and 50% indicates low heterogeneity, 51% and 75% indicates moderate heterogeneity, and 76% and 100% indicate high heterogeneity.21 The fixed-effects model was used when I2 was <50%. When this was >50%, the random-effects model was used. Subgroup analyses were not performed because only a small number of studies was identified. Publication bias was evaluated using funnel plots. Statistical significance was defined as a 2-tailed P value of 0.05 for pooled estimates and a 2-tailed P value of 0.1 for heterogeneity test.
A flow diagram of the data search and study selection process is detailed in Figure 1. A total of 388 records were identified from the 2 databases and additional reference searching. Three hundred sixty-nine duplicate and irrelevant studies were discarded. Nine publications were excluded, because they were letters to the editor, editorials, or reviews; provided insufficient data; or did not examine the defined end points. Ten studies, including 8 data sets on PTFV1,1–3,8–10,22,23 6 data sets on maximum PWD,1,2,8,9,24,25 and 3 data sets on maximum PWA,1,8,9 were included in this meta-analysis.1–3,8–10,22–25 The ECG appearances of these indices are depicted in Figure 2.
Study characteristics, patients characteristics of individual study, and pooled effect estimates, are presented in Tables I–III in the online-only Data Supplement, respectively. These include first author, year of publication, location, study design, race, follow-up duration, age, stroke risk factors, the proportion of male subjects, the 3 P-wave indices (maximum rather than average PWD and PWA were used), and adjusted pooled effect estimates with 95% CIs (Figures 3 through 5). Possible publication bias was assessed using Funnel plots (Figure 6). The quality scores of the included studies are shown in Table IV in the online-only Data Supplement.
Relationship Between P-Wave Terminal Force, PWD, or Maximum PWA and Ischemic Stroke
When PTFV1 was analyzed as a continuous variable,1,8,9,22 it was significantly associated with incident ischemic stroke (OR per 1 SD change, 1.18; 95% CI, 1.12–1.25; P<0.0001) with mild heterogeneity (I2=35%; P=0.20; Figure 2A). When analyzed as a categorical variable,2,3,10,23 PTFV1 >40 ms·mm was also associated with incident ischemic stroke (OR, 1.59; 95% CI, 1.10–2.28; P=0.01) with significant heterogeneity (I2=93%; P<0.00001; Figure 2B). Sensitivity analysis was conducted to explore the potential origin of heterogeneity among the studies that examined PTFV1 as a continuous variable. The CHS (Cardiovascular Health Study) by Kamel et al8 is different from the remaining studies an outcome of magnetic resonance imaging (MRI)–defined infarct rather than clinical stroke was used, in contrast to the other studies. Excluding the Kamel (CHS) study did not have an impact on the main results (OR per 1 SD change, 1.22; 95% CI, 1.15–1.29; P<0.0001) while reducing heterogeneity to zero (I2=0%; P=0.98). Hence, the method of stroke detection seems to be an important origin of the heterogeneity among the studies. Interestingly, this was the only study out of the 4 studies that did not demonstrate a significant increase in stroke risk, suggesting that diagnosis based on clinical criteria may be better for risk stratification purposes.
When maximum PWD was analyzed as a continuous variable,1,8,9 no significant association with ischemic stroke was demonstrated (OR per 1 SD change, 1.05; 95% CI, 0.98–1.13; P=0.15). No heterogeneity was observed among the studies (I2=0%; P=0.62; Figure 3A). A significant relationship was observed when PWD was analyzed as a categorical variable (>110–120 ms; odds ratio, 1.86; 95% CI, 1.37–2.25; P<0.0001) without significant heterogeneity (I2=0%; P=0.68; Figure 3B).2,24,25
PWA has also been proposed as a marker for left atrial abnormality, but limited data on the relationship of maximum PWA and ischemic stroke are available. The pooled analysis of 3 studies1,8,9 showed that maximum PWA, which was studied as a continuous variable, predicted incident ischemic stroke (OR per 1 SD change, 1.10; 95% CI, 1.04–1.17; P=0.0009) without significant heterogeneity (I2=35%; P=0.22; Figure 4).
The present systematic review and meta-analysis demonstrated that common ECG markers including PTFV1, PWD, and PWA are predictive of ischemic stroke. Although cardioembolic strokes have been traditionally associated with AF, recent evidence suggests that AF is not a prerequisite for strokes to occur.26 There is a clear lack of temporal relationship between episodes of paroxysmal AF and incident stroke.27 Indeed, atrial electric dysfunction or mechanical dysfunction from fibrosis can increase the risk of stroke in the absence of AF. Moreover, increased atrial endothelial dysfunction and inflammation also contribute to thrombosis.28 Abnormal atrial activation can lead to increased atrial pressure, with subsequent electrophysiological and structural remodeling, such as atrial dilatation and fibrosis. Furthermore, endothelial damage and dysfunction, along with impaired atrial mechanical activity, is thrombogenic.29 This atrial remodeling can promote dyssynchronous electric activation of the atria predisposing to supraventricular tachycardia development if left untreated.30 This scenario was demonstrated in the original Framingham Heart Study cohort.31 The ECG abnormalities may also be a marker for developing AF, which may occur asymptomatically but lead to strokes in the presence of associated risk factors.
With advances in computing technology, it has become easier to analyze different components of the ECG.32 In terms of atrial electrophysiology, several P-wave characteristics have been studied for prediction of stroke with well-demonstrated associations in epidemiological studies.9,12 The initial and terminal portions of the P wave are generally accepted as a reflection of right and left atrial activation, respectively. First, PTFV1 is the product of the depth (μV) of the terminal portion of the P-wave in ECG lead V1 multiplied by its duration (ms). Increased PTFV1 has been associated with left ventricular interstitial fibrosis,33 increased stroke risk,3,10,34 long-term major adverse cardiovascular events,35 and hospitalization and cardiac death.36 Second, prolonged PWD occurs when there is abnormal conduction from the sinoatrial node to the left atria, and its presence has been associated with higher stroke risk37–39 and with cardiovascular and all-cause mortality.24,40 Finally, increases in the maximal PWA, which is the product of the duration and amplitude of the P-wave, are also linked to higher risk of stroke.9
The findings of this meta-analysis are as follows. First, out of the 4 studies that examined stroke outcomes with higher PTFV1 as a continuous variable,1,8,9,22 3 studies used the traditional clinical criteria for diagnosis,1,9,22 whereas 1 study based diagnosis on MRI findings.8 Exclusion of the latter study did not significantly affect the pooled odds ratio, suggesting that the increased stroke risk can be detected independent of the method of diagnosis. However, heterogeneity was reduced from 35% to 0%, suggesting that this study was the origin of the observed heterogeneity. Moreover, 4 studies studied PTFV1 as a categorical variable, and the pooled meta-analysis showed that the higher PTFV1 conferred an ≈59% increase in stroke risk.2,3,10,23 Of the 4 studies, 1 failed to demonstrate a statistically significant effect in the overall population.3 The authors of this study showed that PTFV1 interacted with age, and for subjects aged >60 years, abnormal PTFV1 conferred a >2-fold increase in the risk of incident stroke in multivariate analysis adjusted for sex, history of stroke or transient ischemic attack, ischemic heart disease or diabetes mellitus, baseline creatinine, and in-treatment systolic blood pressure.
Second, maximum PWD was analyzed as a continuous variable in 3 studies.1,8,9 Our pooled meta-analysis showed a nonstatistically significant, 5% increase in the risk of incident ischemic stroke. None of these 3 studies alone demonstrated predictive value of this parameter for stroke outcomes. Thus, in the CHS that used MRI diagnostic criteria for stroke, prolonged PWD did not predict outcomes for incident or prevalent infarcts of any type, incident or prevalent nonlacunar infarcts, and baseline or worsening white matter grade in patients aged >65 years. In the MESA study (Multi-Ethnic Study of Atherosclerosis), which included patients aged between 45 and 84 years,9 prolonged PWD conferred a 11% increase in the risk of incident ischemic stroke without reaching statistical significance. In this study, patients with stroke were older than those without stroke (68 versus 62 years). In the ARIC study (Atherosclerosis Risk in Communities),1 prolongations in PWD in lead II, mean PWD across all ECG leads, or maximum PWD predicted stroke outcomes in multivariate analysis adjusted for age, sex, and ethnicity, but this statistical significance was lost when further adjusted for hypertension, systolic blood pressure, diabetes mellitus, lipid levels, smoking status, and body mass index. Moreover, AF was not adjusted for in these analyses. By contrast, the pooled meta-analysis of the 3 studies that examined maximum PWD as a categorical variable,2,24,25 increased maximum PWD conferred a 86% significant increase in stroke risk. Of these studies, Kohsaka et al analyzed outcomes of a cohort of subjects in the Northern Manhattan Stroke Study, demonstrating that prolongations in maximum PWD did not predict ischemic stroke risk either in univariate analysis or multivariate analysis adjusted for stroke risk factors and left atrial anteroposterior diameter on echocardiography. The remaining 2 studies showed significant predictive values for prolongations in maximum PWD. Thus, Ariyarajah et al17 reported that prolonged PWDs was associated with a 2.32-fold increase in ischemic stroke risk. Moreover, O’Neal et al25 studied patients with advanced interatrial block, defined as PWD >120 ms with biphasic P waves in inferior leads, conferred a 1.7-fold increase in the risk of ischemic stroke after adjustment for age, sex, ethnicity, and stroke risk factors including smoking status, systolic blood pressure, diabetes mellitus, body mass index, low-density lipoprotein cholesterol, antihypertensive medication use, coronary heart disease, and heart failure as well as incident AF. The reason for the discrepancy between PWD as a continuous variable (with 1 SD increase) and as a categorical variable is unclear. Perhaps, there is a threshold effect not captured in linear analyses, although exploratory analyses in CHS did not reveal any obvious nonlinearities in the relationship between PWD and MRI-defined outcomes.8 Together, these findings suggest that PWD, which can be readily measured in surface ECGs, may offer incremental information valuable for risk stratification, but further research is needed to identify the correct form and threshold of this predictor. Monitoring of PWD remains useful because it is also associated with higher PTFV1.41
Finally, three studies examined maximum PWA as a continuous variable (1 SD increase)1,8,9 with our pooled meta-analysis demonstrating a significant 10% increase the in risk of incident stroke. Of these studies, 2 did not demonstrate a significant increase in stroke risk with increases in maximum PWA. In the CHS study by Kamel et al8 that used MRI diagnostic criteria, maximum PWA did not predict outcomes for incident or prevalent infarcts of any type, incident or prevalent nonlacunar infarcts, and baseline or worsening white matter grade in patients aged >65 years. In the MESA study that included patients aged between 45 and 84 years,9 prolonged PWD conferred a 11% increase in the risk of incident ischemic stroke without reaching statistical significance. In this study, patients with stroke were older than those without stroke (68 versus 62). By contrast, the ARIC study showed that increased maximum PWA predicted incident ischemic stroke in multivariate analysis when adjusted for age, sex, ethnicity, hypertension, systolic blood pressure, diabetes mellitus, blood lipid levels, smoking status, and body mass index.
Until further data are available to support a benefit to patients, these ECG measures should not be used in clinical practice. In the meantime, these measures may be useful markers for risk stratification and pathogenic classification for use in observational studies and clinical trials. Recently, a prospective, multicenter, observational registry has been initiated to examine the outcomes of patients with prolonged PWDs during a 3-year follow-up period.42 Moreover, the ARCADIA trial (AtRial Cardiopathy and Antithrombotic Drugs in prevention After cryptogenic stroke) will begin in the fall of 2017 and use one of these measures, PTFV1, as a key eligibility criteria for identifying cryptogenic stroke patients with potential underlying atrial cardiomyopathy. Eligible patients will be randomized to apixaban or aspirin and followed up for the primary outcome of recurrent stroke. Such a trial may lead the way to the use of PTFV1 as an ECG biomarker for personalizing secondary stroke prevention therapies rather than treating all cryptogenic stroke patients uniformly.
This systematic review and meta-analysis has some potential limitations. First, because the PTFV1 and PWD were analyzed as a continuous or categorical variable in the individual studies, it was not possible to pool all the studies together. Thus, separate analyses were performed. Second, given that the number of identified studies was relatively small and the available data limited, subgroup analyses could not be performed. Third, 4 studies included in our pooled analysis were retrospective, which may be more prone to recall bias. Finally, our systematic review included only articles published in PubMed and Embase. Thus, some articles that were not indexed in these search engines may have been omitted.
PTFV1, PWD, and PWA are simple electrocardiographic markers that can be used to predict the occurrence of ischemic stroke. If their predictive values are demonstrated in prospective trials, they can be eventually incorporated into routine clinical practice for patient monitoring, prognostication, and risk stratification. These findings support the notion of an atrial cardiomyopathic process in which thromboembolism risk is elevated even in the absence of AF.
Sources of Funding
This work was supported by grants (81570298 and 81270245 to Dr Liu) from the National Natural Science Foundation of China, Tianjin Natural Science Foundation (16JCZDJC34900 to Dr Liu), and The Croucher Foundation of Hong Kong (Clinical Assistant Professorship to Dr Tse).
Soliman EZ, Prineas RJ, Case LD, Zhang ZM, Goff DC. Ethnic distribution of ECG predictors of atrial fibrillation and its impact on understanding the ethnic distribution of ischemic stroke in the Atherosclerosis Risk in Communities (ARIC) study.Stroke. 2009; 40:1204–1211. doi: 10.1161/STROKEAHA.108.534735.LinkGoogle Scholar
Kohsaka S, Sciacca RR, Sugioka K, Sacco RL, Homma S, Di Tullio MR. Electrocardiographic left atrial abnormalities and risk of ischemic stroke.Stroke. 2005; 36:2481–2483. doi: 10.1161/01.STR.0000185682.09981.26.LinkGoogle Scholar
Okin PM, Kamel H, Kjeldsen SE, Devereux RB. Electrocardiographic left atrial abnormalities and risk of incident stroke in hypertensive patients with electrocardiographic left ventricular hypertrophy.J Hypertens. 2016; 34:1831–1837. doi: 10.1097/HJH.0000000000000989.CrossrefMedlineGoogle Scholar
Tse G, Lai ET, Yeo JM, Yan BP. Electrophysiological mechanisms of Bayés syndrome: insights from clinical and mouse studies.Front Physiol. 2016; 7:188. doi: 10.3389/fphys.2016.00188.MedlineGoogle Scholar
Lorbar M, Levrault R, Phadke JG, Spodick DH. Interatrial block as a predictor of embolic stroke.Am J Cardiol. 2005; 95:667–668. doi: 10.1016/j.amjcard.2004.10.059.CrossrefMedlineGoogle Scholar
Acar G, Sayarlioğlu M, Akçay A, Sökmen A, Sökmen G, Yalçintaş S,. Evaluation of atrial electromechanical delay and left atrial mechanical functions in patients with rheumatoid arthritis.Turk Kardiyol Dern Ars. 2009; 37:447–453.MedlineGoogle Scholar
Conde D, Seoane L, Gysel M, Mitrione S, Bayés de Luna A, Baranchuk A. Bayés’ syndrome: the association between interatrial block and supraventricular arrhythmias.Expert Rev Cardiovasc Ther. 2015; 13:541–550. doi: 10.1586/14779072.2015.1037283.CrossrefMedlineGoogle Scholar
Kamel H, Bartz TM, Longstreth WT, Okin PM, Thacker EL, Patton KK,. Association between left atrial abnormality on ECG and vascular brain injury on MRI in the Cardiovascular Health Study.Stroke. 2015; 46:711–716. doi: 10.1161/STROKEAHA.114.007762.LinkGoogle Scholar
Kamel H, Soliman EZ, Heckbert SR, Kronmal RA, Longstreth WT, Nazarian S,. P-wave morphology and the risk of incident ischemic stroke in the Multi-Ethnic Study of Atherosclerosis.Stroke. 2014; 45:2786–2788. doi: 10.1161/STROKEAHA.114.006364.LinkGoogle Scholar
Kamel H, O’Neal WT, Okin PM, Loehr LR, Alonso A, Soliman EZ. Electrocardiographic left atrial abnormality and stroke subtype in the atherosclerosis risk in communities study.Ann Neurol. 2015; 78:670–678. doi: 10.1002/ana.24482.CrossrefMedlineGoogle Scholar
Conde D, Baranchuk A. [Interatrial block as anatomical-electrical substrate for supraventricular arrhythmias: Bayés syndrome].Arch Cardiol Mex. 2014; 84:32–40. doi: 10.1016/j.acmx.2013.10.004.MedlineGoogle Scholar
Chhabra L, Devadoss R, Chaubey VK, Spodick DH. Interatrial block in the modern era.Curr Cardiol Rev. 2014; 10:181–189.CrossrefMedlineGoogle Scholar
Bayés de Luna A, Baranchuk A, Martínez-Sellés M, Platonov PG. Anticoagulation in patients at high risk of stroke without documented atrial fibrillation. Time for a paradigm shift?Ann Noninvasive Electrocardiol. 2017; 22:e12417.CrossrefGoogle Scholar
Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D,. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.JAMA. 2000; 283:2008–2012.CrossrefMedlineGoogle Scholar
Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.Ann Intern Med. 2009; 151:264–269, W64.CrossrefMedlineGoogle Scholar
Chhabra L. Importance of P-wave indices in stroke.Int J Cardiol. 2016; 203:962–963. doi: 10.1016/j.ijcard.2015.11.102.CrossrefMedlineGoogle Scholar
Ariyarajah V, Apiyasawat S, Najjar H, Mercado K, Puri P, Spodick DH. Frequency of interatrial block in patients with sinus rhythm hospitalized for stroke and comparison to those without interatrial block.Am J Cardiol. 2007; 99:49–52. doi: 10.1016/j.amjcard.2006.07.060.CrossrefMedlineGoogle Scholar
Pirinen J, Putaala J, Aro AL, Surakka I, Haapaniemi A, Kaste M,. Resting 12-lead electrocardiogram reveals high-risk sources of cardioembolism in young adult ischemic stroke.Int J Cardiol. 2015; 198:196–200. doi: 10.1016/j.ijcard.2015.06.095.CrossrefMedlineGoogle Scholar
Pirinen J, Putaala J, Aarnio K, Aro AL, Sinisalo J, Kaste M,. Are 12-lead ECG findings associated with the risk of cardiovascular events after ischemic stroke in young adults?Ann Med. 2016; 48:532–540. doi: 10.1080/07853890.2016.1202443.CrossrefMedlineGoogle Scholar
Greenland S. Quantitative methods in the review of epidemiologic literature.Epidemiol Rev. 1987; 9:1–30.CrossrefMedlineGoogle Scholar
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses.BMJ. 2003; 327:557–560. doi: 10.1136/bmj.327.7414.557.CrossrefMedlineGoogle Scholar
Kamel H, Hunter M, Moon YP, Yaghi S, Cheung K, Di Tullio MR,. Electrocardiographic left atrial abnormality and risk of stroke: Northern Manhattan Study.Stroke. 2015; 46:3208–3212. doi: 10.1161/STROKEAHA.115.009989.LinkGoogle Scholar
Soliman EZ, Juma H, Nkosi N. A simple electrocardiogram marker for risk stratification of ischemic stroke in low-resources settings.J Stroke Cerebrovasc Dis. 2010; 19:388–392. doi: 10.1016/j.jstrokecerebrovasdis.2009.07.007.CrossrefMedlineGoogle Scholar
Ariyarajah V, Puri P, Apiyasawat S, Spodick DH. Interatrial block: a novel risk factor for embolic stroke?Ann Noninvasive Electrocardiol. 2007; 12:15–20. doi: 10.1111/j.1542-474X.2007.00133.x.CrossrefMedlineGoogle Scholar
O’Neal WT, Kamel H, Zhang ZM, Chen LY, Alonso A, Soliman EZ. Advanced interatrial block and ischemic stroke: the Atherosclerosis Risk in Communities Study.Neurology. 2016; 87:352–356. doi: 10.1212/WNL.0000000000002888.CrossrefMedlineGoogle Scholar
Kamel H, Okin PM, Longstreth WT, Elkind MS, Soliman EZ. Atrial cardiopathy: a broadened concept of left atrial thromboembolism beyond atrial fibrillation.Future Cardiol. 2015; 11:323–331. doi: 10.2217/fca.15.22.CrossrefMedlineGoogle Scholar
Brambatti M, Connolly SJ, Gold MR, Morillo CA, Capucci A, Muto C,; ASSERT Investigators. Temporal relationship between subclinical atrial fibrillation and embolic events.Circulation. 2014; 129:2094–2099. doi: 10.1161/CIRCULATIONAHA.113.007825.LinkGoogle Scholar
Katoh S, Honda S, Watanabe T, Suzuki S, Ishino M, Kitahara T,. Atrial endothelial impairment through Toll-like receptor 4 signaling causes atrial thrombogenesis.Heart Vessels. 2014; 29:263–272. doi: 10.1007/s00380-013-0369-3.CrossrefMedlineGoogle Scholar
Martinez-Selles M, Fernandez Lozano I, Baranchuk A, Bayes-Genis A, Bayes de Luna A. Should we anticoagulate patients at high risk of atrial fibrillation?Rev Esp Cardiol (Engl Ed). 2016; 69:374–376.MedlineGoogle Scholar
Bayes de Luna A, Guindo J, Vinolas X, Martinez-Rubio A, Oter R, Bayes-Genis A. Third-degree inter-atrial block and supraventricular tachyarrhythmias.Europace. 1999; 1:43–46.CrossrefMedlineGoogle Scholar
Magnani JW, Johnson VM, Sullivan LM, Gorodeski EZ, Schnabel RB, Lubitz SA,. P wave duration and risk of longitudinal atrial fibrillation in persons ≥ 60 years old (from the Framingham Heart Study).Am J Cardiol. 2011; 107:917–921.e1. doi: 10.1016/j.amjcard.2010.10.075.CrossrefMedlineGoogle Scholar
Darbar D, Jahangir A, Hammill SC, Gersh BJ. P wave signal-averaged electrocardiography to identify risk for atrial fibrillation.Pacing Clin Electrophysiol. 2002; 25:1447–1453.CrossrefMedlineGoogle Scholar
Tiffany Win T, Ambale Venkatesh B, Volpe GJ, Mewton N, Rizzi P, Sharma RK,. Associations of electrocardiographic P-wave characteristics with left atrial function, and diffuse left ventricular fibrosis defined by cardiac magnetic resonance: the PRIMERI Study.Heart Rhythm. 2015; 12:155–162. doi: 10.1016/j.hrthm.2014.09.044.CrossrefMedlineGoogle Scholar
Yaghi S, Boehme AK, Hazan R, Hod EA, Canaan A, Andrews HF,. Atrial cardiopathy and cryptogenic stroke: a cross-sectional pilot study.J Stroke Cerebrovasc Dis. 2016; 25:110–114. doi: 10.1016/j.jstrokecerebrovasdis.2015.09.001.CrossrefMedlineGoogle Scholar
Li Q, Gu LD, Zhang C, Liu W, Peng Y, Chai H,. A predictive study of the dynamic development of the P-wave terminal force in lead V1 in the electrocardiogram in relation to long-term prognosis in non-ST-segment elevation acute coronary syndrome patients during hospitalization.Ann Noninvasive Electrocardiol. 2015; 20:542–553. doi: 10.1111/anec.12254.CrossrefMedlineGoogle Scholar
Liu G, Tamura A, Torigoe K, Kawano Y, Shinozaki K, Kotoku M,. Abnormal P-wave terminal force in lead V1 is associated with cardiac death or hospitalization for heart failure in prior myocardial infarction.Heart Vessels. 2013; 28:690–695. doi: 10.1007/s00380-012-0307-9.CrossrefMedlineGoogle Scholar
Bayes de Luna A, Platonov P, Cosio FG, Cygankiewicz I, Pastore C, Baranowski R,. Interatrial blocks. A separate entity from left atrial enlargement: a consensus report.J Electrocardiol. 2012; 45:445–451.CrossrefMedlineGoogle Scholar
Baranchuk A, Bayés de Luna A. The P-wave morphology: what does it tell us?Herzschrittmacherther Elektrophysiol. 2015; 26:192–199. doi: 10.1007/s00399-015-0385-3.CrossrefMedlineGoogle Scholar
Baranchuk A, de Luna AB, Breithardt G. To the editor— the role of advanced interatrial block pattern as a predictor of atrial fibrillation.Heart Rhythm. 2016; 13:e87.CrossrefMedlineGoogle Scholar
Magnani JW, Gorodeski EZ, Johnson VM, Sullivan LM, Hamburg NM, Benjamin EJ,. P wave duration is associated with cardiovascular and all-cause mortality outcomes: the National Health and Nutrition Examination Survey.Heart Rhythm. 2011; 8:93–100. doi: 10.1016/j.hrthm.2010.09.020.CrossrefMedlineGoogle Scholar
Spodick DH, Ariyarajah V, Goldberg R. Interatrial block: correlation with P-terminal force.Clin Cardiol. 2009; 32:181–182. doi: 10.1002/clc.20329.CrossrefMedlineGoogle Scholar
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. doi: 10.1002/clc.22647.CrossrefMedlineGoogle Scholar