Observations on the transition from intermittent to permanent atrial fibrillation

A Review on Atrial Fibrillation Detection From Ambulatory ECG

Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential. Internet of Things (IoT) and artificial intelligence (AI) technologies have driven the development of wearable electrocardiograph (ECG) devices used for health monitoring, which are an effective means of AF detection. The main challenges of AF analysis using ambulatory ECG include ECG signal quality assessment to select available ECG, the robust and accurate detection of QRS complex waves to monitor heart rate, and AF identification under the interference of abnormal ECG rhythm. Through ambulatory ECG measurement and intelligent detection technology, the probability of postoperative recurrence of AF can be reduced, and personalized treatment and management of patients with AF can be realized. This work describes the status of AF monitoring technology in terms of devices, algorithms, clinical applications, and future directions.

Is Progression From Paroxysmal to Sustained Atrial Fibrillation Bad News?

Atrial fibrillation (AF) is the most common sustained arrhythmia and associated with increased morbidity and mortality resulting from thromboembolism and heart failure. AF often presents initially as paroxysmal and may progress to a sustained form over time. Sustained forms of AF may be associated with increased symptoms and cardiovascular morbidity, and AF progression may be associated with increased risk of clinically adverse events and outcomes. The present review discusses the clinical factors of arrhythmia progression and risk stratification available to assess the probability of AF progression. Furthermore, currently available treatment options for preventing AF progression are explored and evaluated.

Screening and management of atrial fibrillation in primary care

Atrial fibrillation is a common chronic disease seen in primary care offices, emergency departments, inpatient hospital services, and many subspecialty practices. Atrial fibrillation care is complicated and multifaceted, and, at various points, clinicians may see it as a consequence and cause of multi-morbidity, as a silent driver of stroke risk, as a bellwether of an acute medical illness, or as a primary rhythm disturbance that requires targeted treatment. Primary care physicians in particular must navigate these priorities, perspectives, and resources to meet the needs of individual patients. This includes judicious use of diagnostic testing, thoughtful use of novel therapeutic agents and procedures, and providing access to subspecialty expertise. This review explores the epidemiology, screening, and risk assessment of atrial fibrillation, as well as management of its symptoms (rate and various rhythm control options) and stroke risk (anticoagulation and other treatments), and offers a model for the integration of the components of atrial fibrillation care.

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.

Transmitral inflow wave and progression from paroxysmal to permanent atrial fibrillation in Asian people

Objective

Paroxysmal atrial fibrillation could progress to permanent atrial fibrillation. Whether the transmitral inflow waves could be used to predict progression from paroxysmal atrial fibrillation to permanent atrial fibrillation is unknown. Therefore, we investigated the association between the transmitral inflow waves and progression of paroxysmal atrial fibrillation.

Method

We performed a retrospective study by analysing clinical and echocardiographic data from 88 patients with paroxysmal atrial fibrillation. We excluded patients who had structural heart disease, significant valvular disease, cardiomyopathy, cardiac device implantation or a left ventricular ejection fraction <50%.

Result

The patients with progression to permanent atrial fibrillation were more likely to be male and had lower peak A velocity than those without progression. After adjusting for covariates, lower peak A velocity remained the independent predictor of progression to permanent atrial fibrillation (p=0.025).

Conclusion

The A velocity could be useful for predicting progression to permanent atrial fibrillation in Asian people.

Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal

BACKGROUND AND OBJECTIVE

Paroxysmal Atrial Fibrillation (PAF) is one of the most common major cardiac arrhythmia. Unless treated timely, PAF might transform into permanent Atrial Fibrillation leading to a high rate of morbidity and mortality. Therefore, increasing attention has been directed towards prediction of PAF, to enable early detection and prevent further progression of the disease. Notwithstanding the pharmacological and electrical treatments, a validated method to predict the onset of PAF is yet to be developed. We aim to address this issue through integrating classical and modern methods.

METHODS

To increase the predictivity, we have made use of a combination of features extracted through linear, time-frequency, and nonlinear analyses performed on heart rate variability. We then apply a novel approach to local feature selection using meticulous methodologies, developed in our previous works, to reduce the dimensionality of the feature space. Subsequently, the Mixture of Experts classification is employed to ensure a precise decision-making on the output of different processes. In the current study, we analyzed 106 signals from 53 pairs of ECG recordings obtained from the standard database called Atrial Fibrillation Prediction Database (AFPDB). Each pair of data contains one 30-min ECG segment that ends just before the onset of PAF event and another 30-min ECG segment at least 45 min distant from the onset.

RESULTS

Combining the features that are extracted using both classical and modern analyses was found to be significantly more effective in predicting the onset of PAF, compared to using either analyses independently. Also, the Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which led to sensitivity, specificity, and accuracy of 100%, 95.55%, and 98.21% respectively.

CONCLUSION

Prediction of PAF has been a matter of clinical and theoretical importance. We demonstrated that utilising an optimized combination of - as opposed to being restricted to - linear, time-frequency, and nonlinear features, along with applying the Mixture of Experts, contribute greatly to an early detection of PAF, thus, the proposed method is shown to be superior to those mentioned in similar studies in the literature.

Assessment of anticoagulation treatments in non-valvular atrial fibrillation patients diagnosed in a basic health area

INTRODUCTION

Atrial fibrillation (AF) is the most common cardiac arrhythmia. To assess the need for anticoagulation is essential for its management. Our objective was to investigate whether the indication of anticoagulation was adequate in patients diagnosed with non-valvular AF, given the CHA2-DS2-VASc scale, measuring the International Normalizad Ratio range (INR) in patients treated with anti-vitamin K drugs.

METHODS

This is an observational and cross sectional study. 232 patients with atrial fibrillation were included. We analyzed demographic, the CHA2-DS2-VASc and HAS-BLED variables, the treatment and INR values for 6 consequentive months. The confrontation of variables was performed using chi-square and Mantel-Haenzel test.

RESULTS

The prevalence of AF was 1.05%. The 88.4% had CHA2-DS2-VASc ≥ 2. The 71.1% were taking anticoagulants, of which 58.2% were under antivitamin k. The 46.7% of patients taking antivitamin K, presented inadequate range of INR. There was a greater prescription of antivitamin k in patients with persistent or permanent AF compared to the paroxysmal form (62.8 vs. 37.2% p<.001). The use of drugs that increase bleeding was associated with a worse control of INR after adjustment for the main variables of clinical relevance (odds ratio 2.17 [1.02-4.59], p=.043).

CONCLUSIONS

The level of anticoagulation with antivitamin K was inadequate in our sample, despite a proper follow up and adherence to treatment. Patients with paroxysmal AF received less antivitamin K than those with persistent/permanent AF.

Screening strategies for atrial fibrillation: a systematic review and cost-effectiveness analysis

Background

Atrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk of thromboembolic events. Anticoagulation therapy to prevent AF-related stroke has been shown to be cost-effective. A national screening programme for AF may prevent AF-related events, but would involve a substantial investment of NHS resources.

Objectives

To conduct a systematic review of the diagnostic test accuracy (DTA) of screening tests for AF, update a systematic review of comparative studies evaluating screening strategies for AF, develop an economic model to compare the cost-effectiveness of different screening strategies and review observational studies of AF screening to provide inputs to the model.

Design

Systematic review, meta-analysis and cost-effectiveness analysis.

Setting

Primary care.

Participants

Adults.

Intervention

Screening strategies, defined by screening test, age at initial and final screens, screening interval and format of screening {systematic opportunistic screening [individuals offered screening if they consult with their general practitioner (GP)] or systematic population screening (when all eligible individuals are invited to screening)}.

Main outcome measures

Sensitivity, specificity and diagnostic odds ratios; the odds ratio of detecting new AF cases compared with no screening; and the mean incremental net benefit compared with no screening.

Review methods

Two reviewers screened the search results, extracted data and assessed the risk of bias. A DTA meta-analysis was perfomed, and a decision tree and Markov model was used to evaluate the cost-effectiveness of the screening strategies.

Results

Diagnostic test accuracy depended on the screening test and how it was interpreted. In general, the screening tests identified in our review had high sensitivity (> 0.9). Systematic population and systematic opportunistic screening strategies were found to be similarly effective, with an estimated 170 individuals needed to be screened to detect one additional AF case compared with no screening. Systematic opportunistic screening was more likely to be cost-effective than systematic population screening, as long as the uptake of opportunistic screening observed in randomised controlled trials translates to practice. Modified blood pressure monitors, photoplethysmography or nurse pulse palpation were more likely to be cost-effective than other screening tests. A screening strategy with an initial screening age of 65 years and repeated screens every 5 years until age 80 years was likely to be cost-effective, provided that compliance with treatment does not decline with increasing age.

Conclusions

A national screening programme for AF is likely to represent a cost-effective use of resources. Systematic opportunistic screening is more likely to be cost-effective than systematic population screening. Nurse pulse palpation or modified blood pressure monitors would be appropriate screening tests, with confirmation by diagnostic 12-lead electrocardiography interpreted by a trained GP, with referral to a specialist in the case of an unclear diagnosis. Implementation strategies to operationalise uptake of systematic opportunistic screening in primary care should accompany any screening recommendations.

Limitations

Many inputs for the economic model relied on a single trial [the Screening for Atrial Fibrillation in the Elderly (SAFE) study] and DTA results were based on a few studies at high risk of bias/of low applicability.

Future work

Comparative studies measuring long-term outcomes of screening strategies and DTA studies for new, emerging technologies and to replicate the results for photoplethysmography and GP interpretation of 12-lead electrocardiography in a screening population.

Study registration

This study is registered as PROSPERO CRD42014013739.

Funding

The National Institute for Health Research Health Technology Assessment programme.

Paroxysmal atrial fibrillation prediction method with shorter HRV sequences

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes.

Clinical, Echocardiographic, and Electrocardiographic Predictors of Persistent Atrial Fibrillation after Dual-Chamber Pacemaker Implantation: An Integrated Scoring Model Approach

Persistent atrial fibrillation (PeAF) predictors after dual-chamber pacemaker (PM) implantation remain unclear. We sought to determine these predictors and establish an integrated scoring model. Data were retrospectively reviewed for 649 patients (63.8 ± 12.3 years, 48.6% male, mean CHA₂DS₂–VASC score 2.7 ± 2.0) undergoing dual-chamber PM implantation. PeAF was defined as documented AF on two consecutive electrocardiograms acquired ≥7 days apart. During a 7.1-year median follow-up (interquartile range 4.5–10.1 years), 67 (10.3%) patients had PeAF. Multivariable analysis showed the following independent predictors of future PeAF: ischemic stroke or transient ischemic accident history (hazard ratio [HR] 2.03, 95% confidence interval [CI] 1.03–3.50, p = 0.040), atrial fibrillation/flutter history (HR 1.80, 95% CI 1.01–3.20, p = 0.046), sinus node disease (HR 2.24, 95% CI 1.16–4.35, p = 0.016), left atrial enlargement (>45 mm, HR 2.14, 95% CI 1.26–3.63, p = 0.005), and time in automatic mode switching >1% at first follow-up interrogation (HR 2.58, 95% CI 1.51–4.42, p < 0.001). An integrated scoring model combining these predictors showed good discrimination performance at the seven-year follow-up. (C-statistic 0.716, 95% CI 0.629–0.802, p < 0.001). Significantly greater seven-year PeAF incidences were seen in patients with higher scores (2–5) than in those with lower scores (0–1) (22.8% ± 3.8% vs. 5.3% ± 1.7%, p < 0.001). In conclusion, an integrated scoring model combining clinical, echocardiographic, and electrocardiographic characteristics is useful for predicting future PeAF in patients with a dual-chamber PM.

A NOVEL APPLICATION OF HIGHER ORDER STATISTICS OF R-R INTERVAL SIGNAL IN EMD DOMAIN FOR PREDICTING TERMINATION OF ATRIAL FIBRILLATION

Predicting termination of atrial fibrillation (AF), based on noninvasive techniques, can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. Currently, no reliable method exists to predict the termination of AF. We propose an algorithm for predicting termination of AF using higher order statistical moments of R-R interval signal calculated in both time and empirical mode decomposition (EMD) domains. In the proposed method, R-R interval signal is decomposed into a set of intrinsic mode functions (IMF) and higher order moments including skewness, and kurtosis, as well as mean and variance, are calculated from the first four IMFs. The appropriateness of these features in predicting the termination of AF is studied using atrial fibrillation termination database (AFTDB) which consists of three types of AF episodes: N-type (non-terminated AF episode), S-type (terminated 1'min after the end of the record), and T-type (terminated immediately after the end of the record). By using a support vector machine (SVM) classifier for classification of AF episodes, we obtained sensitivity, specificity, and positive predictivity 92.47%, 95.29%, and 92.80%, respectively. The important advantage of the proposed method compared to the other existing approaches is that our algorithm can simultaneously discriminate the three types of AF episodes with high accuracy. The results demonstrate that the EMD domain is a particularly well-suited domain for analyzing nonstationary and nonlinear R-R interval signal in AF termination prediction application.

Atrial fibrillation patterns and risks of subsequent stroke, heart failure, or death in the community

Background

Atrial fibrillation (AF) patterns and their relations with long‐term prognosis are uncertain, partly because pattern definitions are challenging to implement in longitudinal data sets. We developed a novel AF classification algorithm and examined AF patterns and outcomes in the community.

Methods and Results

We characterized AF patterns between 1980 and 2005 among Framingham Heart Study participants who survived ≥1 year after diagnosis. We classified participants based on their pattern within the first 2 years after detection as having AF without recurrence, recurrent AF, or sustained AF. We examined associations between AF patterns and 10‐year survival using proportional hazards regression. Among 612 individuals with AF, mean age was 72.5±10.8 years, and 53% were men. Of these, 478 participants had ≥2 electrocardiograms (median, 3; limits 2 to 23) within 2 years after initial AF and were classified as having AF without 2‐year recurrence (n=63, 10%), recurrent AF (n=162, 26%) or sustained AF (n=207, 34%), although some (n=46, 8%) were indeterminate. Of 432 classified participants, 363 died, 75 had strokes, and 110 were diagnosed with heart failure during the next 10 years. Relative to individuals without AF recurrence, the multivariable‐adjusted mortality was higher among people with recurrent AF (hazard ratio [HR], 2.04; 95% confidence interval [CI], 1.26 to 3.29) and sustained AF (HR, 2.36; 95% CI, 1.49 to 3.75).

Conclusions

In our community‐based AF sample, only 10% had AF without early‐term (2‐year) recurrence. Compared with individuals without 2‐year AF recurrences, the 10‐year prognosis was worse for individuals with either sustained or recurrent AF. Our proposed AF classification algorithm may be applicable in longitudinal datas ets.

Application of Wavelet Entropy to predict atrial fibrillation progression from the surface ECG

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory (f) waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.

Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings

Background

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in the clinical practice, being the subject of intensive research.

Methods

The present work introduces two different Wavelet Transform (WT) applications to electrocardiogram (ECG) recordings of patients in AF. The first one predicts spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the prediction of electrical cardioversion (ECV) outcome in persistent AF patients. In both cases, the central tendency measure (CTM) from the first differences scatter plot was applied to the AF wavelet decomposition. In this way, the wavelet coefficients vector CTM associated to the AF frequency scale was used to assess how atrial fibrillatory (f) waves variability can be related to AF events.

Results

Structural changes into the f waves can be assessed by combining WT and CTM to reflect atrial activity organization variation. This fact can be used to predict organization-related events in AF. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity and accuracy were 100%, 91.67% and 96%, respectively. On the other hand, for ECV outcome prediction, 82.93% sensitivity, 90.91% specificity and 85.71% accuracy were obtained. Hence, CTM has reached the highest diagnostic ability as a single predictor published to date.

Conclusions

Results suggest that CTM can be considered as a promising tool to characterize non-invasive AF signals. In this sense, therapeutic interventions for the treatment of paroxysmal and persistent AF patients could be improved, thus, avoiding useless procedures and minimizing risks.

The potential utility of (123)I-mIBG in atrial fibrillation and in the electrophysiology laboratory

A novel indication for (123)I-mIBG has recently been proposed to risk stratify patients with atrial fibrillation. This review puts into perspective the utility and importance of such a risk stratification modality, in the setting of the epidemic of atrial fibrillation, which also relates to the prevalence of heart failure, obesity, hypertension, and stroke. The authors argue that the epic cost of care for patients with paroxysmal and more advanced forms of atrial fibrillation-including catheter ablation, heart failure, and stroke management-coupled with the poor efficacy of all treatment modalities in advanced atrial fibrillation, make necessary a paradigm shift where only paroxysmal atrial fibrillation that demonstrably will turn into permanent fibrillation should be targeted aggressively. If this premise is accepted, then (123)I-mIBG nuclear imaging for risk stratification becomes a vital tool for the care of the individual patient, as well as for disease control and cost containment in the population, since (123)I-mIBG scanning alone can predict (with hazard ratios of the order of 3.0-5.0) the future occurrence of permanent atrial fibrillation or heart failure.

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.

Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space

Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space.

The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets.

The results showed that 86.67% of dataset A and 80% of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination.

Paroxysmal atrial fibrillation in cryptogenic stroke

INTRODUCTION

Paroxysmal atrial fibrillation (PAF) is perhaps the most underdiagnosed mechanism of apparent cryptogenic stroke (CS). Various studies have shown that increasing the duration of monitoring can increase the diagnosis of PAF in CS.

METHODS

We compared demographic and risk factors for ischemic stroke across different TOAST (Trial of ORG 10172 in Acute Stroke Treatment) stroke subtypes to look for clinical differences between other subtypes and CS and subsequently performed periodic Holter monitoring and imaging studies in apparent CS patients.

RESULTS

Of the 298 patients with ischemic stroke, 17% had CS. Periodic holter monitoring enabled diagnosis of PAF in 29% of patients. Five of 51 patients with CS had recurrent ischemic stroke and all 5 were demonstrated as PAF on repeated Holter monitoring.

CONCLUSIONS

Long-term periodic rhythm monitoring in patients with apparent CS showed PAF in a significant percentage of CS patients, which altered subsequent treatment.

Challenges in the classification of atrial fibrillation

The incidence and prevalence of atrial fibrillation (AF) are increasing worldwide. AF is of public health importance because it accounts for substantial morbidity, mortality, and health-care costs. AF may be transient initially, but many patients have progressive disease marked by increasing frequency and duration of episodes. Various classification schemes for AF have been proposed, although current guidelines are based on temporal rhythm-based patterns. We discuss existing schemes for the classification of AF, focusing on the advantages and limitations of the pattern-based scheme, in the context of new knowledge about AF pathophysiology, AF patterns, and clinical outcomes. Furthermore, we address gaps in knowledge that present opportunities to re-examine the current pattern-based classification of AF. A future classification scheme should ideally combine elements such as the risk of stroke, an assessment of symptoms, and the degree of impairment of the atrial substrate.

Optimizing the management of atrial fibrillation: focus on current guidelines and the impact of new agents on future recommendations

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in everyday clinical practice. It affects ~2.3 million individuals in the United States, and the prevalence is expected to increase ~2.5-fold over the next 40 years. Atrial fibrillation accounts for more than 2 million hospitalizations each year and contributes to nearly 67 000 deaths. Our understanding of the pathophysiology of AF has increased dramatically over the past few decades. Recent treatment guidelines have heightened our awareness of the challenges involved in the treatment of AF and provided useful recommendations for its diagnosis and management. Because AF is usually associated with multiple comorbid conditions, greater emphasis must be placed on individualizing treatment. This review focuses on current treatment guidelines for patients with AF, assessing the benefits and shortcomings of current pharmacologic options and discussing new agents and trials that may provide better opportunities to improve and individualize patient management.

Noninvasive assessment of the complexity and stationarity of the atrial wavefront patterns during atrial fibrillation

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording. A significant inverse correlation between complexity and stationarity is highlighted by this analysis. The discriminatory power of the parameters in identifying different groups in the set of patients under study is also analyzed. The obtained results present analogies with earlier invasive studies in terms of number of significant components necessary to describe 95% of the variance in the AA (four for more organized AF, and eight for more disorganized AF). These findings suggest that automated analysis of AF organization exploiting spatial diversity in surface recordings is indeed possible, potentially leading to an improvement in clinical decision making and AF treatment.

Predicting spontaneous termination of atrial fibrillation based on the RR interval

It is important to characterize conditions under which atrial fibrillation (AF) is likely to terminate spontaneously. A novel method is proposed here. Eleven features are first extracted to characterize RR interval and Poincaré plot from a statistical viewpoint and a geometric viewpoint respectively. Then sequential forward search (SFS) algorithm is utilized for feature selection. Finally, a fuzzy support vector machine (FSVM) with a new fuzzy membership is applied for AF termination prediction. The method is studied with an AF database of electrocardiogram (ECG) recordings provided by PhysioNet for the Cardiology Challenge 2004. It is divided into a training set and two testing sets (A and B). Experiment results show that 100 per cent of testing set A and 100 per cent of testing set B are correctly classified, together with 92.3 per cent of non-terminating and soon-terminating AF correctly classified. It demonstrates that the proposed method can predict spontaneous termination of AF effectively.

Contemporary management of atrial fibrillation: update on anticoagulation and invasive management strategies

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Its increasing prevalence, particularly among the elderly, renders it one of the most serious current medical epidemics. Several management questions confront the clinician treating a patient with AF: Should the condition be treated? Is the patient at risk of death or serious morbidity as a result of this diagnosis? If treatment is necessary, is rate control or rhythm control superior? Which patients need anticoagulation therapy, and for how long? This review of articles obtained by a search of the PubMed and MEDLINE databases presents the available evidence that can guide the clinician in answering these questions. After discussing the merits of available therapy, including medications aimed at controlling rate, rhythm, or both, we focus on the present status of ablative therapy for AF. Catheter ablation, particularly targeting the pulmonary veins, is being increasingly performed, although the precise indications for this approach and its effectiveness and safety are being actively investigated. We briefly discuss other invasive options that are less frequently used, such as pacemakers, defibrillators, left atrial appendage closure devices, and the surgical maze procedure.

The progressive nature of atrial fibrillation: a rationale for early restoration and maintenance of sinus rhythm

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting young as well as elderly patients and presenting a major therapeutic challenge for clinical cardiologists. Recent research has elucidated the progressive nature of AF, including the structural and electrical remodelling that may become manifest if normal sinus rhythm is not restored, and the serious morbidities associated with long-term disease. The controversy over the merits of ventricular rate control vs. the restoration and maintenance of normal sinus rhythm in the treatment of AF has been explored in a number of large-scale, randomized clinical trials. The results of these trials suggest that whereas the two strategies may be equivalent for some patient populations, with both approaches requiring accompanying anticoagulation therapy, the restoration and maintenance of sinus rhythm provide important haemodynamic as well as subjective benefits not afforded by rate control. Although early intervention to limit the progression of this arrhythmia is hindered by the limitations of existing anti-arrhythmic therapies, it is nevertheless a critical goal.

Prevention of atrial fibrillation

PURPOSE OF REVIEW

This review summarizes current concepts on the pathophysiology of atrial fibrillation, identifying predisposing factors to guide primary and secondary preventive approaches.

RECENT FINDINGS

Many factors contribute to the development and progression of atrial fibrillation, including cardiovascular diseases, age, neurohormones, genetics, diet, autonomic influences, and inflammation. Therapeutic efforts have been directed to modify this altered milieu and prevent the development of atrial electrical and structural remodeling. This nonconventional antiarrhythmic management appears to have an important role also in secondary prevention of atrial fibrillation; the indications for conventional antiarrhythmic agents are decreasing because of side effects and limited efficacy. Interventional electrophysiology techniques have been developed to target the arrhythmia substrate responsible for the initiation or maintenance of atrial fibrillation, achieving high success rates.

SUMMARY

Atrial fibrillation is the most commonly treated arrhythmia and its incidence is predicted to increase. It is associated with significant morbidity and mortality. Preventive efforts should be initiated early and include diversified interventions to correct predisposing factors and modify the altered atrial substrate.