Analysis of the P Wave with Signal Averaging to Assess the Risk of Atrial Fibrillation after Coronary Artery Bypass Surgery

Dimmer, C.; Jordaens, Luc; Gorgov, N; Peene, I.; François, K.; Nooten, Guido Van; Clément, Denis

doi:10.1159/000006738

Cited by 24 publications

(19 citation statements)

References 14 publications

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“…Age is the variable most consistently associated with post-CABG AFIB (1,4,9,11,13,16,(23)(24)(25)(26)(27). In this study, age provided the greatest contribution to the correct prediction of AFIB in the logistic regression model (14% of a total of 25% cumulative percent correct prediction).…”

Section: Discussion Preoperative Predictive Characteristicsmentioning

confidence: 50%

“…Neither study related a cumulative percentage of prediction for the model. Age was also found to be the best predictor of AFIB in studies where signal-averaged ECG data were utilized in the model (16,23). When Frost et al (16) performed regression analysis on a variety of variables in a study utilizing P-wave duration and morphology on signal-averaged ECG, they noted that only increased age (>60 yrs) and increased body weight (> 80 kg) were independent predictors of AFIB, for a cumulative positive prediction of 37%.…”

Section: Regression Analysismentioning

confidence: 99%

“…However, other researchers have examined preoperative ECG data. Dimmer et al (23) noted that a longer P-wave duration on the surface ECG was associated with a higher incidence of AFIB, although only age and the signal-averaged ECG P-wave entered their regression model. Other teams (32, 37, 38) noted trends toward longer P-wave durations on surface ECG and AFIB, but no statistical significance.…”

Section: Postoperative Predictive Characteristicsmentioning

confidence: 99%

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Predictors and impact of atrial fibrillation after isolated coronary artery bypass grafting

Hravnak

Hoffman

Saul

et al. 2002

Critical Care Medicine

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Objective-Although an extensive number of studies have attempted to identify predictors of new-onset atrial fibrillation (AFIB) after coronary artery bypass grafting (CABG), a strong predictive model does not exist. Prior studies have included patients recruited from multiple centers with variant AFIB prevalence rates and those who underwent CABG in combination with other surgical procedures. Also, most studies have focused on pre-and perioperative characteristics, with less attention given to the initial postoperative period. The purpose of this study was to comprehensively examine pre-, peri-, and postoperative characteristics that might predict new-onset AFIB in a large sample of patients undergoing isolated CABG in a single medical center, utilizing data readily available to clinicians in electronic data repositories. In addition, length of stay and selected postoperative complications and disposition were compared in patients with AFIB and no AFIB. Design-Retrospective, comparative survey.Setting-University-affiliated tertiary care hospital.Patients-Patients with new-onset AFIB who underwent isolated standard CABG or minimally invasive direct vision coronary artery bypass were identified from an electronic clinical data repository. Interventions-None.Measurements and Main Results-The prevalence of AFIB in the total sample (n = 814) was 31.9%. Predictors of AFIB included age (p = .0004), number of vessels bypassed (p = .013), vessel location (diagonal [p < .003] or posterior descending artery [p < .001]), and net fluid balance on the operative day (p = .015). Forward stepwise regression analysis produced a model that correctly predicted AFIB in only 24% of cases, with age (14%) and body surface area (9%) providing the most prediction. The incidence of embolic stroke was higher in AFIB (n = 8) vs. no AFIB (n = 4) patients, but stroke preceded AFIB onset in seven of eight cases. Subjects with AFIB had a longer stay (p = .0004), more intensive care unit readmissions (p = .0004), and required more assistance at hospital discharge (p = .017).Conclusions-Despite attempts to examine comprehensively predictors of new-onset AFIB, we were unable to identify a robust predictive model. Our findings, in combination with prior work, imply that it may not be feasible to predict the development of new-onset AFIB after CABG using data readily available to the bedside clinician. In this sample, stroke was uncommon and, when it occurred, preceded AFIB in all but one case. As anticipated, AFIB increased length of stay, and patients with this complication required more assistance at discharge. Atrial fibrillation (AFIB) is the most common complication after coronary artery bypass grafting (CABG), with an incidence ranging from 5% to 40% (1-5). Although post-CABG AFIB is not associated with a significant increase in mortality (6), it has been noted to result in increased morbidity related to postoperative stroke (7,8), hemodynamic compromise (9), ventricular dysrhythmias (10), and iatrogenic complications associated with...

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Section: Discussion Preoperative Predictive Characteristicsmentioning

confidence: 50%

Section: Regression Analysismentioning

confidence: 99%

Section: Postoperative Predictive Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

Predictors and impact of atrial fibrillation after isolated coronary artery bypass grafting

Hravnak

Hoffman

Saul

et al. 2002

Critical Care Medicine

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show abstract

“…23 Changes in P-wave morphology indicative of atrial dilatation or hypertrophy are useful, 23,24 but their accuracy as predictors of imminent or even eventual fibrillation is variable. Patient populations with postoperative AF have been used to study the utility of signal-averaged P waves, [25][26][27][28][29] and here an increase in P-wave duration has been a reasonable indicator of risk. 25 However, other studies have demonstrated that nonspecific variables, such as gender and age, are equally or more accurate markers.…”

Section: Noninvasive Predictors Of Afmentioning

confidence: 99%

Atrial gradient as a potential predictor of atrial fibrillation

et al. 2005

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“…In this context, P-wave signalaveraged electrocardiography proved to be a noninvasive, low-cost, easily performed and reproducible method; therefore, it has been widely used in the evaluation and stratification of the risk of atrial fibrillation [16][17][18][19][20][21][22][23][24][25][26][27] .…”

Section: Discussionmentioning

confidence: 99%

Atrial activation during sinus rhythm in patients with rheumatic and non-rheumatic paroxysmal atrial fibrillation. Frequency-domain analysis using signal-averaged electrocardiography

Dantas¹,

Barbosa²,

Benchimol-Barbosa³

et al. 2001

Arq. Bras. Cardiol.

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Objective -Using P-wave signal-averaged electrocardiography, we assessed the patterns of atrial electrical activation in patients with idiopathic atrial fibrillation as compared with patterns in patients with atrial fibrillation associated with structural heart disease. Atrial fibrillation is the most common sustained arrhythmia diagnosed in clinical practice [1][2][3][4] . In the general population, its prevalence increases with age: it is lower than 1% in the age bracket from 40 to 65 years, ranges from 2% to 5% in the age bracket from 65 to 74 years, and is higher than 5% above the age of 75 years. No difference in incidence has been observed between the sexes 1 . The increase in prevalence and incidence of atrial fibrillation with age is mainly associated with coexisting cardiac diseases. However, an independent effect of direct ageing on the atrial tissue cannot be excluded 5 . Methods -Eighty patients with recurrent paroxysmalA history of congestive heart failure, rheumatic valvar disease, left ventricular hypertrophy, and left atrial enlargement are independent risk factors associated with the genesis of atrial fibrillation. The presence of this arrhythmia in structurally normal hearts, although infrequent, may be detected in clinical practice. The triggering and maintenance factors of idiopathic atrial fibrillation are not very well defined. However, a significant number of patients with the paroxysmal or persistent form of atrial fibrillation, even though without evidence of anatomical cardiac alterations, evolve towards chronic atrial fibrillation. Such patients include those with structural heart disease despite the use of antiarrhythmic drugs. The objective of our study was to identify the electrophysiological behavior of atrial activation in patients with paroxysmal or persistent atrial fibrillation, through the use of P-wave signal-averaged electrocardiography for frequency-domain analysis. MethodsIn a cross-sectional study, we analyzed 80 patients who were being followed up in the outpatient cardiac arrhythmia care unit of the Hospital Universitário Pedro Ernesto. These patients had a history of at least 2 episodes of paroxysmal or persistent atrial fibrillation confirmed on electrocardiography in the last year, and they had not used any antiarrhythmic drug for at least 5 half-lives of each drug in

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Analysis of the P Wave with Signal Averaging to Assess the Risk of Atrial Fibrillation after Coronary Artery Bypass Surgery

Cited by 24 publications

References 14 publications

Predictors and impact of atrial fibrillation after isolated coronary artery bypass grafting

Predictors and impact of atrial fibrillation after isolated coronary artery bypass grafting

Atrial gradient as a potential predictor of atrial fibrillation

Atrial activation during sinus rhythm in patients with rheumatic and non-rheumatic paroxysmal atrial fibrillation. Frequency-domain analysis using signal-averaged electrocardiography

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