Prediction Of Paroxysmal Atrial Fibrillation Research Articles

Left Atrial Volume Is the Strongest Predictor of Paroxysmal Atrial Fibrillation in Patients with Hypertension

Hypertension is an independent risk factor for atrial fibrillation (AF); left atrial enlargement is also associated with AF. The aim of this study was to investigate the use of left atrial volume (LAV), which was measured by multidetector computed tomography (MDCT) (LAV-CT), for predicting AF in patients with hypertension. Thirty patients (age, 66.6±9.3 years) with hypertension were evaluated. The patients were divided into 2 groups: AF group consisted of 14 patients with paroxysmal AF who underwent catheter ablation and non-AF group included 16 control patients without a history of AF. In the AF group, left atrial dimension (LAD) and LAV by using transthoracic echocardiography (TTE) (LAV-TTE) were greater than those in the non-AF group (40.5±7.6 mm vs. 34.7±4.6 mm; p=0.016 and 66.9±18.8 ml vs. 49.2±17.7 ml; p=0.031, respectively). LAV-CT was significantly greater in the AF group (134.8±29.9 cc vs. 96.0±16.4 cc, p<0.001). Receiver operating characteristics curve (ROC) analysis showed that LAV-CT was highly predictive, with an optimal cut-off value of 111 cc (sensitivity, 79%; specificity, 94%) for AF occurrence (area under the curve [AUC]. 0.902) and superior compared to LAD (AUC, 0.779) and LAV-TTE (AUC, 0.769). In conclusion, these results show that LAV-CT is useful for predicting the occurrence of AF in patients with hypertension.

Usefulness for Time-Frequency Analysis of P Wave to Predict Paroxysmal Atrial Fibrillation

Purposes: Paroxysmal atrial fibrillation (PAF) is known to be a potential risk of systemic thromboembolism, but the prediction of PAF is difficult. In this study, the frequency distribution within P wave in patients with and without PAF. Methods: ECG (leads V1 and II) were recorded from patients with and without PAF-patient (n=20), and saved as MFER file. After QRS-T waves were subtracted from ECG, time-frequency analysis using wavelet transform was performed as following conditions: frequency range; 1–200 Hz, virtual sampling rate; 1 KHz, analyzing wavelet; Gabor. The wavelet-transformed ECG signals were integrated during 120 msec from the onset of P wave (ITFP: integrated time frequency power). The ITFPs of sequential 10 P waves was summed (S-ITFP). S-ITFPs in 80 frequency bands plotted against frequency axis indicate the spectral profile of P wave. Results: The spectral profile of P waves In lead II was different between with and without PAF-patients. The frequency power was greater in lead V1 than lead II, ranged from 20–30 Hz. Conclusions: It was suggested that the frequency profile of P wave is beneficial for the prediction of PAF-patient.

Prevalence and Predictors of Paroxysmal Atrial Fibrillation on Holter Monitor in Patients With Stroke or Transient Ischemic Attack

Our aims were to quantify the yield of Holter monitor for detection of paroxysmal atrial fibrillation (PAF) in patients with stroke and TIA, and to determine potential predictors of PAF to allow more focused testing. We reviewed records of 1128 consecutive patients attending a university stroke clinic from September 2005 to September 2006 and identified 426 patients with definite TIA or stroke. We abstracted clinical, cardiac imaging, and neuroimaging data. Logistic regression analysis was performed to determine independent predictors of PAF on Holter monitor. Overall, 413 of 426 patients (65 ± 15 years; male, 49.8%) with a definite TIA (53%) or stroke (47%) underwent Holter monitoring for a mean of 22.6 hours. PAF occurred in 39 patients (9.2%) all older than age 55 years. PAF lasting > 30 seconds was evident in 11 patients (2.5%). The other 28 patients had PAF < 30 seconds (6.5%). In multivariate analyses, number of acute (odds ratio [OR], 1.7 for each 1 lesion increase; 95% confidence interval [CI], 1.2-2.6; P = 0.0047) and chronic (OR, 1.6 for each 1 lesion increase; 95% CI, 1.2-2.3; P = 0.0001) infarcts on brain CT, number of chronic infarcts on MRI (OR, 3.0 for each 1 lesion increase; 95% CI, 1.7-5.1; P < 0.0001), and any acute cortical infarct on imaging (OR, 5.8; 95% CI, 1.9-17.8; P = 0.0023) were associated with PAF. PAF is present in 9.2% of patients with definite stroke or TIA. Age older than 55 years and presence of acute or chronic brain infarcts on neuroimaging are strongly associated with PAF.

Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal

In this paper, an effective paroxysmal atrial fibrillation (PAF) prediction algorithm is presented, which is based on analysis of the heart rate variability (HRV) signal. The proposed method consists of a preprocessing step for QRS detection and HRV signal extraction. In the next step, several features which can be used as markers for the prediction of PAF are extracted from the HRV signal. These features consist of spectrum features, bispectrum features, and non-linear features including sample entropy and Poincaré plot-extracted features. The spectrum features are able to discriminate the sympathetic and parasympathetic contents of the HRV signal, which are affected before PAF attacks. The bispectrum features are used in order to reveal information not presented on the spectral domain, and to detect quadratic phase coupled harmonics arising from non-linearities of the HRV signal. Moreover, the non-linear analysis can map the heart rate irregularities in the feature space and it leads to better understanding of the system dynamics before PAF attacks. In the final step, a support vector machine (SVM)-based classifier has been used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB). The obtained sensitivity, specificity, and positive predictivity were 96.30%, 93.10%, and 92.86%, respectively. The proposed methodology presents better results than the other existing approaches. The other important advantage of the proposed method when compared to the other approaches is that we do not need the both records of a subject to specify which episode preceding PAF events.

Intra‐Left Atrial Mechanical Delay Detected by Tissue Doppler Echocardiography Can Be a Useful Marker for Paroxysmal Atrial Fibrillation

There are some clinical and echocardiographic parameters to predict paroxysmal atrial fibrillation (PAF), but more sensitive predictors are needed. Tissue Doppler imaging may be a sensitive method for this purpose. Thirty-four patients with PAF and 31 control subjects were studied. Time intervals from the beginning of P-wave to beginning of A-wave from lateral and septal mitral and right ventricular tricuspid annuli in tissue Doppler imaging were recorded. The differences between these intervals gave the mechanical delays between/within the corresponding atria. There were no differences between groups with regard to age. PAF patients were found to have increased left atrial dimension and intra-left atrial mechanical delay. Twenty-five milliseconds was calculated as cutoff value to predict PAF. P-wave dispersion was found to be increased in PAF. This study shows an increase in intra-left atrial mechanical delay in PAF patients. This method can be used as an early marker to detect PAF.

Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods

Paroxysmal atrial fibrillation (PAF) is a serious arrhythmia associated with morbidity and mortality. We explore the possibility of distant prediction of PAF by analyzing changes in heart rate variability (HRV) dynamics of non-PAF rhythms immediately before PAF event. We use that model for distant prognosis of PAF onset with artificial intelligence methods. We analyzed 30-min non-PAF HRV records from 51 subjects immediately before PAF onset and at least 45min distant from any PAF event. We used spectral and complexity analysis with sample (SmEn) and approximate (ApEn) entropies and their multiscale versions on extracted HRV data. We used that features to train the artificial neural networks (ANNs) and support vector machine (SVM) classifiers to differentiate the subjects. The trained classifiers were further tested for distant PAF event prognosis on 16 subjects from independent database on non-PAF rhythm lasting from 60 to 320 min before PAF onset classifying the 30-min segments as distant or leading to PAF. We found statistically significant increase in 30-min non-PAF HRV recordings from 51 subjects in the VLF, LF, HF bands and total power (p<0.0001) before PAF event compared to PAF distant ones. The SmEn and ApEn analysis provided significant decrease in complexity (p<0.0001 and p<0.001) before PAF onset. For training ANN and SVM classifiers the data from 51 subjects were randomly split to training, validation and testing. ANN provided better results in terms of sensitivity (Se), specificity (Sp) and positive predictivity (Pp) compared to SVM which became biased towards positive case. The validation results of the ANN classifier we achieved: Se 76%, Sp 93%, Pp 94%. Testing ANN and SVM classifiers on 16 subjects with non-PAF HRV data preceding PAF events we obtained distant prediction of PAF onset with SVM classifier in 10 subjects (58+/-18 min in advance). ANN classifier provided distant prediction of PAF event in 13 subjects (62+/-21 min in advance). From the results of distant PAF prediction we conclude that ANN and SVM classifiers learned the changes in the HRV dynamics immediately before PAF event and successfully identified them during distant PAF prognosis on independent database. This confirms the reported in the literature results that corresponding changes in the HRV data occur about 60 min before PAF onset and proves the possibility of distant PAF prediction with ANN and SVM methods.

Predictors of paroxysmal atrial fibrillation in patients undergoing aortic valve replacement

Atrial fibrillation is one of the most common complications after cardiac surgery. This study evaluates the risk factors of paroxysmal atrial fibrillation in patients who underwent aortic valve replacement. The study comprised 300 patients with aortic valve defects of either aortic stenosis (n = 150) or regurgitation (n = 150) who underwent aortic valve replacement. For each patient, 2-mode and Doppler echocardiographic examinations were performed in the preoperative period, early postoperative period, and long-term observation, and selected hemodynamic parameters were analyzed. Factors significantly associated with atrial fibrillation in patients with aortic stenosis were heart failure (odds ratio = 5.5), age 70 years or more (4.5), low (3.9) and high body mass index (1.7), maximal transvalvular gradient (3.7), low left ventricular ejection fraction (5.1), end-systolic (2.9) and end-diastolic intraventricular septum thickness (1.5), and insignificant mitral regurgitation (1.9) in the preoperative period; and left ventricular ejection fraction (4.4) and end-systolic intraventricular septum thickness (1.8) in the early postoperative period. In the aortic regurgitation group, factors significantly associated with atrial fibrillation were age (1.8), left ventricular ejection fraction (3.7), left ventricular end-systolic diameter (1.7), end-diastolic intraventricular septum thickness (1.7), left atrium dimension (4.1) and insignificant mitral regurgitation (2.5) in the postoperative period; essential arterial hypertension (3.3), diabetes mellitus (2.6), and heart failure in the history (4.5) in the preoperative period; and left ventricular ejection fraction (1.9) and left atrium dimension (2.9) in the early postoperative period. On the basis of the separated risk factors, all patients should be preoperatively classified to applicable groups of risk of postoperative atrial fibrillation appearance, and the prophylactic treatment should be administered in the group of patients with the highest risk. It may essentially decrease the rate of complications and deaths, and, consequently, the costs of postoperative medical care.

693 Doppler-derived indexes and B-type natriuretic peptide in prediction of paroxysmal atrial fibrillation in essential hypertension. A prospective study

693 Doppler-derived indexes and B-type natriuretic peptide in prediction of paroxysmal atrial fibrillation in essential hypertension. A prospective study

Doppler‐Derived Indexes and B‐Type Natriuretic Peptide in Prediction of Paroxysmal Atrial Fibrillation in Essential Hypertension: A Prospective Study

Onset of atrial fibrillation in hypertensive patients is usually associated with a high occurrence of cardiovascular complications. Despite its leading importance as a highly prevalent and modifiable risk factor, only a few data are available regarding the predictors of paroxysmal atrial fibrillation (PAF) in hypertensive patients. This study was undertaken to determine if PAF could be predicted in hypertensive patients while in sinus rhythm using Doppler-derived indexes and the plasma B-type natriuretic peptide (BNP) concentration. We prospectively evaluated 165 consecutive patients with hypertension and no known history of PAF or cardiovascular events who attended the cardiology outpatient clinic. Their mean age was 62 +/- 12, 94 male, 71 female. The conventional echocardiographic parameters were measured including: left atrial (LA) volume, mitral regurgitation (MR), left ventricular (LV) function, LV mass. The ratio of transmitral peak E-wave velocity to flow propagation velocity (E/V(p)), ratio of E-wave to mitral annular early diastolic velocity (E/E(a)) obtained by Doppler tissue at the lateral and septal corners of the mitral annulus were calculated. The plasma BNP was measured at the study entry. After a mean follow-up of 15 +/- 3 months, PAF (symptomatic attacks or documented on the ECG) occurred in 36 (21.8%) of 165 patients. The patients with PAF had significant higher BNP levels than those with sinus rhythm (160 +/- 109.8 vs. 87.9 +/- 57.7 pg/ml, P < 0.001) Also, E/E(a) and E/V(p) ratios were significantly higher in hypertensives with PAF (15.1 +/- 2.8 vs. 8.39 +/- 1.33, P < 0.001), and (1.65 +/- 1.29 vs. 1.19 +/- 1.06, P < 0.001) respectively. In univariate analysis, E/V(p), E/E(a), and BNP and LV hypertrophy were significant predictors of PAF. Barely E/V(p) and E/E(a) remained independently significant after adjustment of clinical and other echocardiographic variables by multivariate logistic regression analysis (odd ratio: 3.36, P < 0.001 and 4.93, P < 0.001 respectively). A cutoff value of > or =1.7 for E/V(p) predicted PAF with 91% sensitivity and 88% specificity; E/E(a) >12 has sensitivity 98%, specificity 89%, while BNP>170 pg/ml has 83% and 72% specificity, respectively, for prediction of PAF in hypertensive patients. Paroxysmal atrial fibrillation could be predicted in hypertensive patients while in sinus rhythm using Doppler-derived indexes. Increased E/V(p), E/E(a) ratios and elevated BNP appear to be useful parameters to identify patients at heightened risk. They may reflect early left ventricular dysfunction and atrial hypertension in this population.

829 Doppler-derived indexes and B-type natruretic peptide in prediction of paroxysmal atrial fibrillation in essential hypertension. A prospective study

829 Doppler-derived indexes and B-type natruretic peptide in prediction of paroxysmal atrial fibrillation in essential hypertension. A prospective study

Prediction of Paroxysmal Atrial Fibrillation Using Nonlinear Analysis of the R-R Interval Dynamics Before the Spontaneous Onset of Atrial Fibrillation

New methods based on nonlinear theory have been developed to give more insight into complex heart rate (HR) dynamics. This study was designed to test the hypothesis that altered HR dynamics, as analyzed with complexity and fractal measures, may precede the spontaneous onset of paroxysmal atrial fibrillation (PAF). Secondly, the difference in the temporal change of these measurements between the different types of atrial fibrillation (AF) was assessed. From 105 Holter tapes in which PAF was recorded, 44 PAF (>or=5 min) episodes in 33 patients (22 men, 58 +/- 12 years), preceded by sinus rhythm for more than 1 h, were selected and submitted to time-and frequency-domain HR variability analyses, along with detrended fluctuation analysis, approximate entropy (ApEn) and sample entropy (SampEn). The 60 min before the onset of AF were divided into 6 10-min periods and studied using repeated measures ANOVA. PAF episodes were divided into 3 subgroups: an increased HF component and decreased L/H ratio (vagal type, n=20); increased L/H ratio and decreased HF component (sympathetic type, n=14); and non-related type (n=10). None of the time- or frequency-domain parameters showed any significant change before AF in any type of AF. The alpha(1) showed a tendency to decrease before the onset of AF and the change in alpha(1) was divergent according to the AF type. The ApEn decreased before the onset of AF (1.005+/-0.046, 60-50 min before AF to 0.894+/-0.052, 10-0 min before AF; p=0.032). The SampEn also decreased progressively before the start of AF (1.165 +/- 0.085, 60-50 min before AF to 0.887 +/- 0.077, 10-0 min before AF, p=0.003). The decrease in both the ApEn and SampEn was irrespective of the AF type. A reduction in the ApEn and SampEn, which reflects the nonlinear complexity of HR variability, is a hallmark of altered HR dynamics preceding the spontaneous onset of AF.

Prediction of paroxysmal atrial fibrillation after coronary aretery bypass surgery by P wave signalaveraged electrocardiography.

Prediction of paroxysmal atrial fibrillation after coronary aretery bypass surgery by P wave signalaveraged electrocardiography.

BNP plasma levels or left atrial dimension for the prediction of paroxysmal atrial fibrillation in subjects with essential hypertension?

P-533 Key Words: Left Atrial Dimension, Brain Natriuretic Peptide, Paroxysmal Atrial Fibrillation

P-wave dispersion evaluated on ecg or 24h-ECG is a predictor of paroxysmal atrial fibrillation in essential hypertension

P-316 Key Words: P-Wave Dispersion, Paroxysmal Atrial Fibrillation, Hypertension

Simple electrocardiographic markers for the prediction of paroxysmal atrial fibrillation in hyperthyroidism

Background: Hyperthyroidism is a major cause of paroxysmal atrial fibrillation (AF). The purpose of this study was to evaluate the predictors of AF in the patients with clinical and subclinical hyperthyroidism. Methods and Results: The study population consisted of four groups: group I (57 euthyroid healthy persons), group II (33 patients with subclinical hyperthyroidism), group III (69 patients with overt hyperthyroidism) and group IV (31 patients with overt hyperthyroidism and documented paroxysmal AF). The maximum P wave duration ( P maximum) in group IV (114±8 ms) was significantly higher than group I (102±7 ms, p<0.001), group II (106±7 ms, p<0,001) and group III (108±9 ms, p0.005). The P wave dispersion (PWD) was measured as 46±9 ms in group IV and this was significantly higher than group I (29±8 ms, p< 0.001), group II (36±9 ms, p<0.001) and grup III (38±8 ms, p=0.001). The P maximum and PWD were higher in the patients with subclinical hyperthyroidism compared to healthy individuals. Univariate regression analysis revealed that age, P maximum and PWD, multivariate analysis showed that P maximum and PWD were significant predictors of paroxysmal AF. A PWD value of 37.5 ms separated group IV from others with a sensitivity of 90%, specificity of 85%, and positive predictive accuracy of 77%. Conclusion: Simply measuring P maximum and PWD values, we could identify the patients with high risk for the development of AF and these simple ECG parameters may help in clinical judgement to determine the requirement for treatment in the patients with subclinical hyperthyroidism.

Comparison of p-wave duration and dispersion in patients aged ≥65 years with those aged ≤45 years

P-wave dispersion (PWD) is a new electrocardiographic marker that reflects discontinuous and inhomogeneous propagation of sinus impulses, which has been studied in some cardiac conditions as a useful predictor of paroxysmal atrial fibrillation (AF). The aim of the peresent study was to compare P-wave duration and PWD in patients ≤45 versus ≥65 years of age. The study consisted of 2 groups. Group I included 118 patients aged ≥65 years (86 men, 32 women, mean age= 69 ± 4 years). Group II included 72 patients aged ≤45 years (53 men, 19 women, mean age= 41 ± 4 years). All patients were selected from those who were undertaken coronary angiography in our hospital with a suspicion of coronary artery disease and detected as having angiographically normal coronary arteries. All patients were undertaken transthoracic echocardiography to evaluate the presence of any structural and functional cardiac abnormality. Maximum and minimum P-wave durations and PWD were calculated from 12-lead surface electrocardiogram. Maximum P-wave duration and PWD were significantly higher in group I patients than in group II patients ( P < .001). However, there was no statistically significant difference between group I patients and group II patients regarding minimum P-wave duration (p=0.9). Left atrial diameter, left ventricular wall thicknesses, mitral A velocity, deceleration time and isovolumic relaxation time were significantly higher in group I patients than in group II patients. However, mitral E velocity were significantly lower in group I patients than in group II patients. A significant positive correlation was detected between PWD and age, left atrial diameter, mitral A velocity, deceleration time and isovolumic relaxation time. In addition, we found a significant negative correlation between PWD and mitral E velocity. PWD, indicating increased risk for paroxysmal AF, was found to be significantly higher in patients ≥65 years of age than in those ≤45 years of age. Further prospective studies that include larger series and long term follow-up are needed to clarify the clinical utility of PWD as a predictor of increased risk for paroxysmal AF in old patients.

P3222 Prediction of paroxysmal atrial fibrillation by P-wave wavelet analysis

P3222 Prediction of paroxysmal atrial fibrillation by P-wave wavelet analysis

Increased p-wave duration and p-wave dispersion in patients with aortic stenosis.

P-wave dispersion (PWD), defined as the difference between the maximum and minimum P-wave duration, has been proposed as being useful for the prediction of paroxysmal atrial fibrillation (AF). AF is the most common arrhythmia and an important prognostic indicator for clinical deterioration in patients with aortic stenosis (AS). The aim of the present study was to evaluate PWD in patients with AS. The study population consisted of two groups: Group I consisted of 98 patients with AS (76 men, 22 women; aged 63 +/- 8 years) and group II consisted of 98 healthy subjects (same age and sex) without any cardiovascular disease. A 12-lead electrocardiogram was recorded for each subject. The P-wave duration was calculated in all leads of the surface electrocardiogram. The difference between the maximum and minimum P-wave duration was calculated and was defined as the PWD. All patients and control subjects were also evaluated by echocardiography to measure the left atrial diameter, left ventricular ejection fraction, left ventricular wall thicknesses, and the maximum and mean aortic gradients. Patients were also evaluated for the presence of paroxysmal AF. Maximum P-wave duration and PWD of group I were found to be significantly higher than those of group II. In addition, patients with paroxysmal AF had significantly higher PWD than those without paroxysmal AF. There was no significant difference between the two groups regarding minimum P-wave duration. In addition, there was no significant correlation between echocardiographic variables and PWD. PWD, indicating increased risk for paroxysmal AF, was found to be significantly higher in patients with AS than in those without it. Further assessment of the clinical utility of PWD for the prediction of paroxysmal AF in patients with severe AS will require longer prospective studies.

P-wave dispersion: a novel predictor of paroxysmal atrial fibrillation.

The prolongation of intraatrial and interatrial conduction time and the inhomogeneous propagation of sinus impulses are well known electrophysiologic characteristics in patients with paroxysmal atrial fibrillation (AF). Previous studies have demonstrated that individuals with a clinical history of paroxysmal AF show a significantly increased P-wave duration in 12-lead surface electrocardiograms (ECG) and signal-averaged ECG recordings. The inhomogeneous and discontinuous atrial conduction in patients with paroxysmal AF has recently been studied with a new ECG index, P-wave dispersion. P-wave dispersion is defined as the difference between the longest and the shortest P-wave duration recorded from multiple different surface ECG leads. Up to now the most extensive clinical evaluation of P-wave dispersion has been performed in the assessment of the risk for AF in patients without apparent heart disease, in hypertensives, in patients with coronary artery disease and in patients undergoing coronary artery bypass surgery. P-wave dispersion has proven to be a sensitive and specific ECG predictor of AF in the various clinical settings. However, no electrophysiologic study has proven up to now the suspected relationship between the dispersion in the atrial conduction times and P-wave dispersion. The methodology used for the calculation of P-wave dispersion is not standardized and more efforts to improve the reliability and reproducibility of P-wave dispersion measurements are needed. P-wave dispersion constitutes a recent contribution to the field of noninvasive electrocardiology and seems to be quite promising in the field of AF prediction.

P wave dispersion as a predictor of idiopathic paroxysmal atrial fibrillation

Background: P wave dispersion(PWD) is defined as the difference between the maximum and minimal P wave duration in any of the 12 leads of the surface ECG. The prolongation of atrial conduction time and the inhomogeneous propagation of sinus impulse are known electrophysiologic features in patients with paroxysmal atrial fibrillation(PAF). The purpose of this study was to determine the role of P wave dispersion for the prediction of PAF and to evaluate the effectiveness of prophylactic antiarrhythmic therapy. Materials and Methods: The study population included 20 patients with a history of idiopathic PAF and 20 age and sex matched healthy control subjects. We measured the maximum P wave duration(P maximum) and P wave dispersion from 12 lead ECG. Results: P maximum and P dispersion in idiopathic PAF were significantly higher than normal control group(, msec vs, , msec, respectively p, msec vs. , msec, respectively p