Prediction Of Paroxysmal Atrial Fibrillation Research Articles

Prediction of paroxysmal atrial fibrillation using a convolutional neural network and electrocardiogram signals

Atrial fibrillation (AF) is the most clinically diagnosed arrhythmia in cardiac pathology. The incidence of AF begins at a very early age and its initial state is paroxysmal atrial fibrillation (PAF). This type of heart disease can be detected and predicted by analyzing the spectrogram of a surface electrocardiogram (ECG) signal. In many studies, different ECG signal formats and convolutional neural network (CNN) architectures have been used. However, the lack of good signal preprocessing or signal adequacy may have affected the accuracy, especially on short-term ECG signals. In this study, we analyzed a preprocessed ECG signal, determined the optimal set to predict PAF, and evaluated the accuracy using ECG signals of different durations. The PAF Prediction Challenge–PhysioNet database was used to extract spectrograms in 30-sec and 5-sec windows for two classes (Normal, PAF) and 3 classes (Normal, Close-AF, Distant-AF). Then, the AlexNet architecture was used. The proposed method achieved a two-class accuracy of 99.92% with a 30-sec window and 99.42% with a 5-sec window, improving the PAF prediction performance compared with similar works. In addition, the three-class accuracies were 96.92% and 97.43% with windows of 30-sec, and 5-sec, respectively. These results prove the efficacy of the method for the early diagnosis of PAF, even based on short-term ECG signals.

Predictors of paroxysmal atrial fibrillation: Analysis of 24-hour ECG Holter monitoring

main predictors of the development of this arrhythmia. Material and methods.A single-center, cross-control study was conducted. Of all 6630 protocols analyzed, according to 24-hour ECG monitoring, AF paroxysm was detected in 97 people as an accidental finding. These patients were included in the main study group. The control group consisted of 99 patients from the same cohort without paroxysmal AF, having the anthropometric and comorbidity parameters similar to the patients of the main group. Results.In the absolute majority (97.9%) of patients in the main group in whom paroxysmal AF was detected, a special variant of extrasystole was revealed – early atrial “P on T” type (versus 4.0% in patients in the control group) [OR 8461.648 (382.1983;187336)]. The number of supraventricular single, paired and group extrasystoles was significantly higher in the main group, but the number of ventricular extrasystoles did not differ significantly. Conclusion.One of the main ECG predictors for the development of paroxysmal AF in asymptomatic patients is the appearance of supraventricular extrasystole of the “P on T” type. In the mechanism of formation of AF paroxysm during supraventricular extrasystole of the “P on T” type, not only electrophysiological mechanisms play a role, but also the heart biomechanics.

Acute kidney injury as a risk factor for atrial fibrillation after coronary artery bypass grafting — effects of sodium-glucose cotransporter-2 inhibitors

Aim. To identify predictors of paroxysmal atrial fibrillation (pAF) in patients with stable angina after coronary artery bypass grafting (CABG), as well as to evaluate the effect of sodium-glucose cotransporter 2 (SGLT-2) inhibitors.Material and methods. We examined 92 patients with stable angina who received CABG, aged 64±7 years (men, 78,3%). Among this cohort, 81,5% of patients had multivessel coronary artery disease, carbohydrate metabolism disorders — 35,6%, hypertension — 96,7%, chronic kidney disease (CKD) — 23,9%, preprocedural pAF — 10,9%, previous myocardial infarction (MI) — 54,3%. Off-pump CABG was performed in 31,5%, including bilateral inthernal thoracic artery grafting — in 50%. Cardiopulmonary bypass time was 68 (55-83) minutes. The number of grafts was 2,7±0,7. In addition, 18,5% of individuals received SGLT-2 inhibitors. Serum creatinine content was determined by the Jaffe method, and CKD was diagnosed with glomerular filtration rate <60 ml/min. Acute kidney injury (AKI) was assessed according to the KDIGO criteria (2012).Results. The number of patients who had pAF after CABG was 29,3%, AKI — 16,3%. There were following parameters associated with pAF after CABG: creatinine (100;82-142 in patients with pAF and 83;69-105 μmol/l — without pAF, p=0,032) and urea (7,8;5,8-9,7 in patients with pAF and 5,9;4,7-9,1 mmol/l — without pAF, p=0,025) one day after the intervention; postoperative AKI was revealed in 33,3% and 9,2% (p=0,004), while the number of patients taking SGLT-2 inhibitors was 3,7% and 24,6% (p=0,019) in those with and without pAF, respectively. The probability of pAF after CABG increased by an average of 5,5 times with AKI. Patients with pAF after CABG compared to patients without pAF have a higher rate of AKI, intraoperative MI, and cardiac death.Conclusion. The number of patients with pAF after CABG was 29,3%, AKI — 16,3%. Post-CABG pAF predictor was postoperative AKI. The use of SGLT-2 inhibitors was associated with a lower incidence of pAF after intervention. Patients with pAF after CABG have a poor inhospital prognosis.

The exploratory clinical trial of AI-enabled Holter electrocardiogram for diagnosis of paroxysmal atrial fibrillation from sinus rhythm waveforms

Abstract Backgrounds Machine learning (ML) models using convolutional neural networks (CNNs) have been successfully applied to arrhythmia detection and prediction. The current study aimed to develop a CNN-based AI algorithm for the prediction of paroxysmal atrial fibrillation (PAF) patients during sinus rhythm waveforms from Holter electrocardiograms. Methods A ML model was developed by using 2-leads from Holter ECG waveforms divided into slots of 30 seconds each. As training data, we prepared > 300000 slots with sinus rhythm and they were subsequently converted to image data for application to the CNN-based AF detection algorithm. The performance metrics obtained for the ML model for PAF detection were as follows: sensitivity, 85.2 %; specificity, 64.0 % and AUC for PAF prediction was 0.84. To validate the performance of the ML model, multicenter clinical trial was conducted. The clinical trial was conducted at three independent centers and included 24 patients with PAF and 20 patients in true sinus rhythm. Five factors were matched between the PAF and sinus groups: age, gender, hypertension under treatment, diabetes under treatment, and smoking. Waveform data from validation datasets to Grad-CAM were applied to obtain explainable AI (XAI) images, which allowed for a heat map over the waveform to be produced. Results For the primary endpoint, the estimated sensitivity and specificity [95% CI] were 91.7% [73.0-99.0%] and 65.0% [40.8-84.6%], respectively (Table 1). The sensitivity and specificity of the secondary endpoints using 10 time-independent slots were the same as for the primary endpoint. Regarding safety endpoints, no adverse events attributable to the test device itself were observed. Based on the regions of interest highlighted via Grad-CAM, the most contributing featured regions to predict PAF were indicated by areas on P waves and QT area (Figure 1). Featured regions to rule out the existence of PAF indicated areas just on QRS waves were also present. It was therefore determined that the proposed CNN-based ML model focuses on the P-wave area and atrial repolarization area in the case of PAF. Conclusion The study suggested that 2-lead Holter ECG with 30 second slot could detect PAF patients from their sinus rhythm waveforms and the performance was more effectively than using 12-lead ECGs. The results of this clinical trial are expected to contribute to the screening of patients with PAF that could not be detected by a single time Holter ECG.Table 1Figure 1

Comprehensive analysis of heart rate variability features for accurate prediction of paroxysmal atrial fibrillation

ObjectiveParoxysmal atrial fibrillation (PAF) is the first stage of atrial fibrillation and can escalate to a chronic stage without early intervention. This study aims to explore the predictive potential of heart rate variability (HRV) features for PAF and investigate their time variabilities and correlations to enhance prediction accuracy. MethodWe analyzed HRV data extracted from 30-min ECG recordings in the Paroxysmal Atrial Fibrillation Prediction Database (AFPDB). A total of 57 features were extracted from each 5-min segment, including time-domain, frequency-domain, and nonlinear features. Statistical tests, recursive feature elimination, feature inclusion and Bayesian networks were employed for feature optimization, and supervised learning classifiers and 10-fold cross validation were utilized for PAF prediction. ResultsThe random forest classifier demonstrated the best predictive performance, with accuracy, sensitivity, specificity, and F1 score reaching 98.0%, 96.7%, 100.0% and 0.980, respectively. Time-domain features consistently showed excellent classification abilities. Bispectral features and Poincaré plot features performed well in specific segments. The analysis of feature interactions provided valuable insights into the relationships among different feature groups. ConclusionOur comprehensive analysis of HRV features reveals their significance in predicting PAF and emphasizes the importance of considering feature time variabilities and correlations. The proposed prediction method with novel feature optimization strategies shows promising results in enhancing prediction accuracy. SignificanceThe accurate prediction of PAF using HRV features has important clinical implications for early detection and intervention. The findings contribute to the advancement of AF prediction and personalized healthcare strategies.

The role of the TAPSE/PASP ratio in the prediction of paroxysmal atrial fibrillation in patients with acute ischemic stroke

Aims: Atrial fibrillation (AF) remains the most prevalent cause of cardioembolic stroke. Paroxysmal AF (PAF) is often difficult to be diagnosed and is sometimes first detected during embolic stroke. Yet, TAPSE and PASP can be easily revealed in routine transthoracic echocardiography (TTE). Then, the TAPSE/PASP ratio is often shown to have prognostic significance in many cardiac disorders. Nevertheless, the insufficient scholarly knowledge of the relationship between this ratio and the development of PAF became the primary motive of the present study.
 Methods: We carried out this study with 114 patients experiencing acute ischemic stroke without a previous diagnosis of AF. We noted down the patients’ blood parameters and prescribed drugs and calculated TAPSE/PASP ratio relying on their TTE findings. We also recorded the 24-hour heart rhythm findings of each patient through Holter monitoring. After categorizing PAF attacks (i.e.,PAF attack was (not) observed), we explored any statistical relationship between the TAPSE/PASP ratio and the presence of PAF.
 Results: The findings revealed a significant difference between the TAPSE/PASP ratios between the groups with PAF (0.62±0.07) and without PAF (0.77±0.08). Moreover, the receiver operating characteristic (ROC) curve analysis yielded the TAPSE/PASP ratio to demonstrate a diagnostic value in predicting PAF (area under the ROC curve [AUC]=0.89;82.7%; p

Inside the “brain” of an artificial neural network: an interpretable deep learning approach to paroxysmal atrial fibrillation diagnosis from electrocardiogram signals during sinus rhythm

Abstract Background With the ongoing, rapid advances in Deep Learning (DL), such solutions can now detect medical conditions even invisible to the human eye. In this direction, efforts have been made to develop DL algorithms that diagnose paroxysmal atrial fibrillation (PAF) from electrocardiogram (ECG) signals in sinus rhythm (SR). However, many of the available approaches function as “black boxes”, with physicians unable to understand and trust their predictions. Purpose To train a DL model to detect PAF patients while in SR and apply an algorithm that interprets and visualises its decisions. Methods We obtained ECG samples from PAF and non-PAF patients during SR, from the PAF Prediction Challenge Database. After discarding unannotated samples and augmenting the sample size (by dividing each signal into 30-second segments), we split the whole dataset into a train (68%), a validation (16%) and a test (16%) set. No pair of samples belonging to different sets originated from the same patient. We trained the InceptionTime neural network on the train/validation sets and tested on the “unseen” test set after “hiding” the correct answers. Its performance was evaluated with the following metrics: Accuracy, f1-score, precision and recall (sensitivity). After repeating this process 20 times, we obtained a distribution for each score. Finally, we adjusted the Grad-CAM interpretation algorithm to our data and used it to visualise the areas perceived as important by the model. Results After pre-processing, 4,080, 30-second, two-lead ECG signals were allocated to the train set, 960 to the validation and 960 to the test set. Each subset contained an equal number of PAF and non-PAF samples. After repeated training and testing, we obtained a median accuracy of 0.84 (interquartile range, IQR: 0.66–0.88), an f1-score of 0.82 (IQR: 0.68–0.88) and a median precision and recall equal to 0.93 (IQR: 0.67–0.99) and 0.77 (IQR: 0.68–0.93), respectively. The Grad-CAM technique highlighted the ECG areas of interest that led to each decision. We selected and present both PAF-positive and -negative samples, perceived either correctly or falsely. Interestingly, correct model decisions tend to focus on the P-wave, while false ones fixate on other regions. Conclusions Although a pilot study with considerable limitations (small sample size, disregard of possible confounding due to comorbidities or other factors), this work shows how DL can be employed to distinguish between PAF and non-PAF patients from SR ECG samples, and confirms the potential of DL-enabled approaches to offer novel diagnostic capabilities. Most importantly, our effort provides a comprehensible, visual interpretation of the model's decisions. Demystifying DL behaviour can, not only improve such efforts by explaining false decisions, but also cultivate trust among clinicians and, possibly, point out directions for future research, since we can now see through the magnifying lens of a neural network. Funding Acknowledgement Type of funding sources: None.

Heart rate during sinus rhythm predicts paroxysmal atrial fibrillation on 30-day mobile cardiac telemetry monitoring

Abstract Background Low mean heart rate at ambulatory ECG monitoring and low resting heart rate are both predictors of later incident atrial fibrillation (AF). The aim of this study is to determine whether mean heart rate at ambulatory ECG is a predictor of paroxysmal AF that can be detected with extended ambulatory ECG registration. Methods We included all patients who recorded 2–30 full days of ambulatory ECG using a full disclosure mobile cardiac telemetry device in USA 2019–2020, and without AF on the first full day of registration (n=18,220, mean age 64.4 years, 57.2% women). The association between mean heart rate on the first day and the occurrence of paroxysmal AF (≥30 seconds duration) during subsequent up to 30 day ECG recording was analysed using logistic regression models adjusted for age, sex and duration of ECG recording, according to tertiles of mean heart rate (39–66, 67–76 and >77 beats per minute) as well as modelled using restricted cubic splines. Results During a median registration duration of 16 days there were 1,290 cases of paroxysmal AF (7.1%). More paroxysmal AF occurred in the lower tertiles of mean heart rate (11.2% in tertile 1, 6.2% in tertile 2, and 4.4% in tertile 3). After adjustment, low mean heart rate was associated with a higher risk of AF (OR 1.69, 95% CI 1.47–1.93 p<0.001 for tertile 1 vs tertile 2, and OR 1.84, 95% CI 1.58–2.15, p<0.001 for tertile 1 vs tertile 3). Cubic spline curves indicated that lowest risks of AF were seen among subjects with a mean heart rate ≈80 beats per minute. Conclusion Low mean heart rate at 24hECG predicts detection of paroxysmal AF during extended ≤30 day ambulatory ECG monitoring. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Swedish Heart Lung foundationSwedish Society for Medical Research

Inside the “brain” of an artificial neural network: an interpretable deep learning approach to paroxysmal atrial fibrillation diagnosis from electrocardiogram signals during sinus rhythm

Abstract Background With the ongoing, rapid advances in Deep Learning (DL), such solutions can now detect medical conditions even invisible to the human eye. In this direction, efforts have been made to develop DL algorithms that diagnose paroxysmal atrial fibrillation (PAF) from electrocardiogram (ECG) signals in sinus rhythm (SR). However, many of the available approaches function as “black boxes”, with physicians unable to understand and trust their predictions. Purpose To train a DL model to detect PAF patients while in SR and apply an algorithm that interprets and visualises its decisions. Methods We obtained ECG samples from PAF and non-PAF patients during SR, from the PAF Prediction Challenge Database. After discarding unannotated samples and augmenting the sample size (by dividing each signal into 30-second segments), we split the whole dataset into a train (68%), a validation (16%) and a test (16%) set. No pair of samples belonging to different sets originated from the same patient. We trained the InceptionTime neural network on the train/validation sets and tested on the “unseen” test set after “hiding” the correct answers. Its performance was evaluated with the following metrics: Accuracy, f1-score, precision and recall (sensitivity). After repeating this process 20 times, we obtained a distribution for each score. Finally, we adjusted the Grad-CAM interpretation algorithm to our data and used it to visualise the areas perceived as important by the model. Results After pre-processing, 4,080, 30-second, two-lead ECG signals were allocated to the train set, 960 to the validation and 960 to the test set. Each subset contained an equal number of PAF and non-PAF samples. After repeated training and testing, we obtained a median accuracy of 0.84 (interquartile range, IQR: 0.66–0.88), an f1-score of 0.82 (IQR: 0.68–0.88) and a median precision and recall equal to 0.93 (IQR: 0.67–0.99) and 0.77 (IQR: 0.68–0.93), respectively. The Grad-CAM technique highlighted the ECG areas of interest that led to each decision. We selected and present both PAF-positive and -negative samples, perceived either correctly or falsely. Interestingly, correct model decisions tend to focus on the P-wave, while false ones fixate on other regions. Conclusions Although a pilot study with considerable limitations (small sample size, disregard of possible confounding due to comorbidities or other factors), this work shows how DL can be employed to distinguish between PAF and non-PAF patients from SR ECG samples, and confirms the potential of DL-enabled approaches to offer novel diagnostic capabilities. Most importantly, our effort provides a comprehensible, visual interpretation of the model's decisions. Demystifying DL behaviour can, not only improve such efforts by explaining false decisions, but also cultivate trust among clinicians and, possibly, point out directions for future research, since we can now see through the magnifying lens of a neural network. Funding Acknowledgement Type of funding sources: None.

Abstract TP217: Potential Utility Of Magnetocardiogram For Patients With Embolic Stroke Of Undetermined Source

Background: The magnetocardiogram (MCG) is a promising medical tool for detecting and visualizing abnormal cardiac electrical activation in cardiovascular disease patients. MCG has higher spatial resolution than the electrocardiogram (ECG) because the magnetic field is not distorted by flow through tissues. Taking advantage of these features, MCG has been applied for studying arrhythmia, such as atrial fibrillation (AF) and long-QT syndrome. Paroxysmal AF is the most common sources of thromboembolism for ESUS. Since factors that reflect left atrial overload (LAO) are independent predictors of paroxysmal AF, evaluation of these factors in ESUS patients is essential to improve their prognosis. We aimed to investigate the potential utility of MCG in ESUS patients for evaluation of the factors that reflect LAO. Methods: ESUS patients who underwent MCG (MC-6400 MCG system with 64 magnetic sensors) in our hospital were enrolled between September 2018 and July 2021. The peak P-wave magnitude of the right atrium segment and the left atrium segment on the MCG (MCG-RA, MCG-LA) were measured. Subsequently, the ratio of peak magnitude of the LA segment to that of the RA segment (MCG-LA/RA) was used to evaluate LAO. Other factors that reflect LAO, such as PAC frequency on Holter monitoring, LAD in transthoracic echocardiography (TTE), serum level of brain natriuretic peptide, left ventricular ejection fraction (LVEF) in TTE, and P-wave terminal force in lead V1 (PTFV1) on ECG were also evaluated. Results: Fifty-two ESUS patients with a mean age of 67.9±14.3 years (females, 42%) were included. The mean MCV-RA and MCG-LA were 7.70±12.82 and 4.50±7.74 pico-Tesla, respectively. The mean MCG-LA/RA was 0.66±0.31. The MCG-LA/RA was significantly correlated with the PAC frequency (r=0.293, p=0.039), although either MCG-RA or MCG-LA was not correlated. Also, the MCG-LA/RA ratio was significantly correlated with LAD (r=0.301, p=0.03) but not with brain natriuretic peptide (r= 0.25, p= 0.09), LVEF (r= -0.06, p= 0.654), or PTFV1 (r= 0.02, p=0.91). Conclusion: This is the first study that evaluated the relationship of the MCG-LA/RA ratio with factors that reflect LAO in ESUS patients. These results suggest that the MCG-LA/RA ratio is a novel biomarker for LAO in ESUS patients.

Methodology for the prediction of paroxysmal atrial fibrillation based on heart rate variability feature analysis

Atrial fibrillation (AF) is the most clinically diagnosed arrhythmia, as its prevalence increases with age, and its initial stage is paroxysmal atrial fibrillation (PAF). This pathology usually triggers hemodynamic disorders that can generate cerebrovascular accidents (CVA), causing morbidity and even death. The aim of this study is to predict the occurrence of PAF episodes in order to take precautions to prevent PAF episodes. The PhysioNet AFPDB prediction database was used to extract 77 heart rate variability (HRV) features using time domain, geometrical analysis, Poincaré plot, nonlinear analysis, detrended fluctuation analysis, autoregressive modeling, fast Fourier transform (FFT), Lomb-Scargle periodogram, wavelet packet transform (WPT) and bispectrum measurements. The number of features was reduced using the near-zero value, correlation, and recursive feature elimination (RFE) methods for time windows of 1, 2, 5, 10, and 30 min. Feature selection was performed using backwards selection, genetic algorithm, analysis of variance (ANOVA), and non-dominated sorting genetic algorithm (NSGA-III) methods, and then random forest, conditional random forest, k-nearest neighbor (KNN), and support vector machine (SVM) classification algorithms were applied and evaluated using 10-fold cross-validation. The proposed method achieved a precision of 93.24% with a 5-minute window and 89.21% with a 2-minute window, improving performance in predicting PAF when compared with similar studies in the literature.

Paroxysmal atrial fibrillation prediction based on morphological variant P-wave analysis with wideband ECG and deep learning

Background and objectiveAtrial fibrillation (AF) is one of the most frequent asymptomatic arrhythmias associated with significant morbidity and mortality. Identifying the susceptibility to AF based on routine or continuous ECG recording is of considerable interest. Despite several P-wave characteristics and skin sympathetic nerve activity (SKNA) linked to AF onset, neither factor has offered accurate predictability. We propose a deep learning enabled method for AF risk prediction. MethodsWe develop a novel MVPNet to predict the upcoming onset of paroxysmal AF. MVPNet combines wavelet-based feature extraction and a deep learning classifier. MVPNet detect the approaching of AF onset by analyzing the template and frequency in P-wave segments. The morphological variant P-wave (MVP) analysis includes P-wave and SKNA features cross temporal-spectral domain. Subsequently, we designed an optimized lightweight convolutional neural network model to detect the MVP features of pre-AF episodes during sinus rhythm segments. Wideband ECG data obtained through the neuECG protocol from eight PAF patients with 177 times AF occurrence in this study. We compared the accuracy of AF prediction between ordinary ECG and neuECG. ResultsThe MVPNet effectively predicted the onset of AF episodes. 89% of ECG recorded at 5 min before the AF onset can be identified using neuECG. The proposed deep learning model, MVPNet, obtained a better precision and inference speed with less computing resources than existing models. The gradient activation map showed that neuECG recording may be a superior AF risk predictor. ConclusionsMVP analysis combined SKNA and P-wave parameters to improve predictive accuracy. The proposed MVPNet based on neuECG is superior to existing AF risk assessment with improved reliability and effectiveness. The method can be potentially applied in clinical scenarios for real-time, continuous AF prediction.

Prediction of paroxysmal atrial fibrillation using new heart rate variability features

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any further complications and avoid fatalities. An implantable defibrillator device could be used to both detect and treat the condition though such devices have limited computational capability. With this constraint in mind, this paper presents a novel set of features to accurately predict the presence of PAF. The method is evaluated using ECG signals from the widely used atrial fibrillation prediction database (AFPDB) from PhysioNet. We analysed 106 signals from 53 pairs of ECG recordings. Each pair of signals contains one 5-min ECG segment that ends just before the onset of a PAF event and another 5-min ECG segment at least 45 min distant from the PAF event, to represent a non-PAF event. Seven novel features are extracted through the Poincaré representation of R-R interval signals, and are prioritised through feature ranking schemes. The features are used with four standard classification techniques for PAF prediction and compared to the existing state of the art from the literature. Using only the seven proposed features, classification performance outperforms those of the classical state-of-the-art feature set, registering sensitivity and specificity measurements of over 96%. The results further improve when the features are combined with several of the classical features, with an accuracy increasing to 98% using a linear kernel SVM. The results show that the proposed features provide a useful representation of the PAF condition and achieve good prediction with off-the-shelf classification techniques that would be suitable for ICU deployment.

Can left atrial mechanics predict anticoagulation in cryptogenic stroke?

Abstract Introduction Ischemic stroke is a leading cause of death and disability in the Western world, frequently due to cardioembolism and atherothromboembolism. Cryptogenic strokes occur without a well-defined aetiology after a standard vascular and cardiac evaluation, and secondary prevention may include antiplatelet therapy while awaiting results of long-term cardiac monitoring. In this study, we aimed to identify echocardiographic predictors of paroxysmal atrial fibrillation (AF) latter identified in follow-up of patients with cryptogenic stroke. Methods We retrospectively assessed all patients with cryptogenic stroke admitted in our hospital in the last 2 years. Only patients in normal sinus rhythm with a minimum of 24 hours of cardiac monitoring at admission and 24 hours Holter monitor within 6 months after discharge were included. Echocardiographic measures included left ventricle ejection fraction, left atrium (LA) volume, left and right atrium longitudinal strain, left and right ventricle longitudinal strain, E/A ratio, E/e' ratio, isovolumetric relaxation time (IVRT) and E wave deacceleration time. Echocardiographic data was assessed to determine its accuracy to identify AF. Results The study included 32 patients with a mean age of 72±10 years and a male preponderance (87.5%). AF was identified in 12 (37.5%) patients. This group of patients had a larger indexed LA volume (44.3 vs 29.1 mL/m2, p=0.043), a lower IVRT (87 vs 116 ms, p=0.028), and a lower LA longitudinal strain in contractile (6.7 vs 13.6%, p<0.001) and in reservoir phase (17.1 vs 23.6%, p=0.042). All other variables were not significantly different among groups, including LA longitudinal strain in conduit phase. LA longitudinal strain in contractile phase showed the best predictive power with an area under the ROC curve of 0.925 (95% CI 0.82–1 p=0.001). The cut-off value that best predicted AF was 8.17% with a sensitivity of 1 and specificity of 0.9. Conclusion LA longitudinal strain in contractile phase is a powerful method to identify AF in cryptogenic stroke. When reduced, anticoagulation may be considered in order to prevent recurrence. Further studies are warranted to reproduce these results in larger cohorts. Funding Acknowledgement Type of funding source: None

Value of tissue Doppler imaging and peak acceleration time in prediction of paroxysmal atrial fibrillation in patients with paroxysmal atrial fibrillation

Aim: Atrial fibrillation is the most common arrhythmia in the population and its prevalance increases with age; and also is the most morbid and mortal arrhythmia. Usually the beginning of the persistant atrial fibrillation is recurrent episodes of the paroxysmal atrial fibrillation (PAF). Prediction of the paroxysmal atrial fibrillation can cause prevention of this arrhythmia and thus prevention of the adverse outcomes. We aimed to investigate tissue Doppler imaging (TDI) and peak acceleration time (pkAcc) parameters that can predict the paroxysmal atrial fibrillation in this study. Material and Methods: 20-73 years old (mean 47,5) 50 individuals that are diagnosed with PAF included the patient group. 50 individuals who have the similar baseline demographic charasteristics with patient group and who have no persistant or PAF included the control group. Transthoracic echocardiogaphic (TTE) evaluation is applied all of the control and study groups. Tissue Doppler parameters and pkAcc is measured in TTE and statistical analyses is performed. Results: In TTE evaluation, left atrium ejection fraction is lower in the patient group than the study group (%50,6 vs. %59,2, p<0,001). In TDI evaluation, the average of E/E’ which was measured from the anterior, inferior, lateral and septal walls of the left ventricle; is found higher in the patient group compared to the control group (8,17 vs. 7,04; p=0,004). When two groups are compared in terms of pkAcc, it was found that patient group is higher, but this difference did not reach the statistical significance (1063 vs. 994, p=0,14). Conclusions: TDI evaluation can play an important role in prediction of paroxysmal atrial fibrillation.

Predictive value of transmitral A‐wave acceleration time for paroxysmal atrial fibrillation

It has been shown that transmitral A-wave (TMAW) is affected in paroxysmal atrial fibrillation (PAF), and decreased left atrial contractions cause a decrease in the TMAW velocity. The relationship between TMAW acceleration time (TMAW-AccT) and PAF is unknown. In this study, the predictive value of TMAW-AccT for PAF was investigated. Seventy PAF patients (mean age: 57.8±11.0years) and 70 control patients (mean age: 58.1±10.5years) were included in the study. Transthoracic echocardiography was performed in sinus rhythm in all patients. For TMAW-AccT, the time between the basal point of the A-wave and the highest point reached was measured. Transmitral A-wave -AccT was significantly longer in the PAF group than the control group (TMAW-AccT; 88.5±18.1 vs 77.2±16.4ms, P=.000, respectively). The left atrial diameter and the left atrial volume index were similar between the groups (P=.07, P=.18, respectively). According to a multivariate logistic regression analysis, the TMAW-AccT (OR: 1.02, 95% confidence interval 1.02-1.07, P<.001) was a predictor of PAF. A receiver operating characteristic curve analysis showed a cutoff value of TMAW-AccT of >82ms (sensitivity: 65.7%, specificity: 64.3%, positive predictive value: 64.8%, negative predictive value: 65.2%). Transmitral A-wave-AccT can be used as an early marker in detecting PAF without dilated left atrium.

Prediction of recurrence of paroxysmal atrial fibrillation based on RR interval

Atrial fibrillation (AF) is one of the most common arrhythmias, which does great harm to patients. Effective methods were urgently required to prevent the recurrence of AF. Four methods were used to analyze RR sequence in this paper, and differences between Pre-AF (preceding an episode of AF) and Normal period (far away from episodes of AF) were analyzed to find discriminative criterion. These methods are: power spectral analysis, approximate entropy (ApEn) and sample entropy (SpEn) analysis, recurrence analysis and time series symbolization. The RR sequence data used in this research were downloaded from the Paroxysmal Atrial Fibrillation Prediction Database. Supporting vector machine (SVM) classification was used to evaluate the methods by calculating sensitivity, specificity and accuracy rate. The results showed that the comprehensive utilization of recurrence analysis parameters reached the highest accuracy rate (95%); power spectrum analysis took second place (90%); while the results of entropy analyses and time sequence symbolization were not satisfactory, whose accuracy were both only 70%. In conclusion, the recurrence analysis and power spectrum could be adopted to evaluate the atrial chaotic state effectively, thus having certain reference value for prediction of AF recurrence.

Abstract 124: How to Preselect Stroke Patients for Enhanced ECG-Monitoring - Evaluation of Predictors of Paroxysmal Atrial Fibrillation in "Find-AFrandomised"

Background: Detecting concealed paroxysmal atrial fibrillation (pAF) is a major focus of the workup of ischemic-stroke-patients, but requires elaborate ECG-monitoring. We aimed to evaluate pre-specified predictors to determine stroke patients with an increased probability of underlying pAF. We applied cut-off-levels recently suggested by the German Stroke Society 1 . Methods: The Find-AF RANDOMISED -trial (NCT01855035) evaluated 3x10-day Holter-ECG vs. standard-care in 398 patients &gt; 60 years with acute ischemic strokes. The primary endpoint was novel pAF after 6 months. 27 patients in the intervention arm (13.5 %) and 9 patients in the control arm (9.0 %) met the primary endpoint (p=0.002). We used Pearson’s Chi 2 -test and the Mann-Whitney-U-test, as applicable. In variables with p&lt;0.05 we calculated the number-needed-to-screen (NNS). We also performed ROC-analyses to determine the areas-under-the-curve (AUC). Results: Table 1 shows known pAF-risk-factors in relation to whether patients had pAF. The absence of a clinical lacunar syndrome (p=0.007) or lacunar lesions (p=0.018), increased NIHSS-Score (p=0.003), frequent atrial premature beats (APB, p=0.003), prolonged supraventricular runs (p=0.01), enlarged left atria (p=0.07) and increased brain-natriuretic-peptide-levels (p=0.001) were associated with underlying pAF. Frequent APB differentiated best between those with and without pAF (area under the curve, AUC: 0.71). Conclusions: Several of the evaluated markers are useful to preselect stroke patients for enhanced ECG-monitoring. APB and comprehensive measures of both left atrial size and function (LAVI/a’) showed the highest AUC and should be used when preselecting stroke patients for prolonged ECG-monitoring.

Can the development of atrial fibrillation in patients with ischemic heart failure with low ejection fraction be predicted?

OBJECTIVE:Our aim is to determine the triggering factors of paroxysmal atrial fibrillation (PAF) in ischemic heart failure (HF) patients with low ejection fraction (EF).METHODS:Sixty patients were included in this study. Echocardiography and 24-hours Holter monitoring were performed after measurement of serum NT-pro BNP concentration. The patients were classified into two groups concerning the occurrence of PAF on Holter recordings. Biochemical and echocardiographic parameters of patients with and without PAF were compared.RESULTS:PAF was detected in 28 (46%) patients. Patients with PAF demonstrated higher NT-pro BNP levels, mitral and aortic regurgitation velocities, E/A, E/E’, pulmonary capillary wedge pressure, pulmonary artery systolic pressure, left atrial volume and volume indices. NT-pro BNP was established as the predictor of PAF (OR=1.23, 95% CI: 1.08–1.42; p=0.001). ROC analysis showed an NT-pro BNP value of 2188 pg/mL as cut-off value with 68% sensitivity and 84% specificity [Area under the ROC curve (AUC)=0.826, CI 95%: 0.724–0.927; p<0.001].CONCLUSION:The triggering factors for AF are increased intracardiac pressures, left atrial dilatation and increased wall tension. As an indicator of increased wall tension, elevated levels of NT-pro BNP predict the development of PAF.

Right atrial function by speckle tracking echocardiography in atrial septal defect: Prediction of atrial fibrillation.

Most antiarrhythmic interventional therapies for atrial fibrillation (AF) have been provided with special focus on the treatment of left-sided valvular disease and enlarged left atrium but few studies have assessed AF associated with congenital heart disease and dilated right atrium. We hypothesized that right atrial (RA) function assessed by two-dimensional (2DSTE) and three-dimensional (3DSTE) speckle-tracking echocardiography in patients with atrial septal defect (ASD) before and after percutaneous trancatheter closure could predict paroxysmal atrial fibrillation (PAF) development. Seventy-three patients with hemodynamically significant secundum ASD were prospectively studied and followed up for 6 months after occluder insertion and compared with a normal age-matched group (n = 73). A subgroup of 17 patients who developed PAF after device implantation was also studied. RA peak global longitudinal strain (PS) was determined using 2DSTE. Standard deviations (SDs) of times to peak strain (TPS) were calculated as indices of dyssynchrony. RA volumes, emptying fraction (EF), and expansion index (EI) were determined using 3DSTE. RA-PS, EF, and EI (pre-closure values) were reduced in patients with atrial devices compared with controls, and further reductions were observed in patients with PAF. Pre-closure 3D-RA-EI (P = 0.009) and RA-TPS (P = 0.023) were independent predictors of PAF by multivariate analysis after adjustment for age and left atrial dysfunction. The areas under the ROC-curve (AUC) for 3D-RA-EI, RA-PS, RA-TPS (pre-closure values) showed high discriminative values(from 0.76 to 0.85) in predicting PAF. By combining 3D-RA-EI and RA-TPS, the AUC increased to 0.90. Two-dimensional and three-dimensional speckle tracking echocardiography was clinically helpful in ASD patients in revealing right atrial dilatation and dysfunction pre-existent to device closure and associated with PAF development. RA parameters had a higher association with PAF compared to both the size of the implanted device and left atrial indices.