Observations on the transition from intermittent to permanent atrial fibrillation

doi:10.1067/mhj.2000.107547

American Heart Journal

Volume 140, Issue 1, July 2000, Pages 142-145

https://doi.org/10.1067/mhj.2000.107547 Get rights and content

Abstract

Background Quantitative data on the frequency with which transition from intermittent to permanent atrial fibrillation occurs are lacking. We conducted this study to determine the proportion of patients with intermittent atrial fibrillation who progress to permanent atrial fibrillation and to investigate baseline clinical characteristics that might predict such a progression. Methods This retrospective cohort study included 231 patients who were seen with intermittent atrial fibrillation at a university hospital-based clinic from January 1978 through December 1997. Patients’ medical records and electrocardiograms were reviewed and data were collected for all clinic visits through May 1998. The proportion of patients who remained free of transition from intermittent to permanent atrial fibrillation was calculated by the Kaplan-Meier method. A Cox proportional hazards model was used to determine the effect of some baseline characteristics on this transition. Results The number of patients who remained free of transition from intermittent to permanent atrial fibrillation was 92% (95% confidence interval 88%-96%) at 1 year and 82% (95% confidence interval 75%-88%) at 4 years. Among 5 baseline characteristics (age, sex, structural heart disease, atrial fibrillation at presentation, and use of an antiarrhythmic medicine before presentation), the 2 significant predictors of progression from intermittent to permanent atrial fibrillation were age (P =.0003) and being in atrial fibrillation at presentation (P =.0006). The hazard ratio associated with 10 years of advancing age was 1.82 (95% confidence interval 1.31-2.51), and the hazard ratio associated with atrial fibrillation at presentation was 3.56 (95% confidence interval 1.73-7.34). Conclusions Approximately 18% of patients who had intermittent atrial fibrillation were permanently in atrial fibrillation after 4 years of follow-up. Age and being in atrial fibrillation at presentation were the only 2 important clinical variables identified in predicting such a progression. (Am Heart J 2000;140:142-5.)

Section snippets

Patients

The population of this study was a cohort of patients seen for atrial fibrillation in a hospital-based clinic by the same cardiologist from January 1978 through December 1997. To be considered for inclusion in the study, a patient had to have at least one episode of atrial fibrillation documented by a 12-lead electrocardiogram or by a transtelephonic rhythm strip. The electrocardiographic criteria for atrial fibrillation were a grossly irregular rhythm and the absence of discernible P waves.

Results

Three hundred thirty-one patients seen between January 1978 and December 1997 had documented atrial fibrillation. Some of the patients were excluded; 59 patients had permanent atrial fibrillation at presentation, 19 had Wolff-Parkinson-White syndrome, 2 had a history of His bundle ablation, 13 had pure atrial flutter, 6 had atrial fibrillation attributable to reversible causes, and 1 had concomitant ventricular arrhythmias requiring antiarrhythmic therapy. The remaining 231 patients were

Discussion

This study provides a quantitative estimate of the proportion of unselected patients with intermittent atrial fibrillation who progressed to permanent atrial fibrillation. This report shows that age and being in atrial fibrillation at presentation are strong predictors of such a progression. Despite the increasing incidence and high morbidity rates of atrial fibrillation, there are few studies that have addressed this aspect of atrial fibrillation in a quantitative fashion.

One previous natural

References (10)

WB Kannel et al.
Coronary heart disease and atrial fibrillation: The Framingham study
Am Heart J
(1983)
PA Wolf et al.
Atrial fibrillation: a major contributor to stroke in the elderly. The Framingham study
Arch Intern Med
(1987)
PA Wolf et al.
Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: The Framingham study
Neurology
(1978)
PA Wolf et al.
Atrial fibrillation as an independent risk factor for stroke: The Framingham study
Stroke
(1991)
MM Gallagher et al.
Classification of atrial fibrillation
PACE
(1998)

There are more references available in the full text version of this article.

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Comprehensive analysis of heart rate variability features for accurate prediction of paroxysmal atrial fibrillation
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Paroxysmal 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.
We 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.
The 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.
Our 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.
The 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.
Prediction of paroxysmal atrial fibrillation using new heart rate variability features
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Citation Excerpt :
AF patients often start with episodes of PAF before their condition escalates to a chronic stage. Furthermore, about 18% of PAF evolves to permanent AF (persistent or chronic) over 4 years [6]. AF can be treated by medication or electrical shock issued by the implantable defibrillator device (ICD) [7].
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.
Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal
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Citation Excerpt :
Nevertheless, it is often associated with a high risk of cardiovascular morbidity and mortality [9]. The aim of therapy is to prevent stroke and regain sinus rhythm [10], however, prevention and treatment of AF is still far from satisfactory. Clinically, AF presents itself in different forms, commonly starting as paroxysmal, and becoming more persistent with time.
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.
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.
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.
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.
Paroxysmal atrial fibrillation prediction method with shorter HRV sequences
2016, Computer Methods and Programs in Biomedicine
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.
Assessment of anticoagulation treatments in non-valvular atrial fibrillation patients diagnosed in a basic health area
2018, Clinica e Investigacion en Arteriosclerosis
. La fibrilación auricular (FA) es la arritmia cardiaca más frecuente. En su manejo, es clave valorar la necesidad de anticoagulación. Nuestro objetivo fue valorar en pacientes diagnosticados de FA no valvular si la indicación de anticoagulación es adecuada en función de la escala CHA2DS2-VASc y la adecuación del rango del International Normalizad Ratio (INR) en los pacientes en tratamiento con antivitamina K.
Estudio observacional, analítico transversal. Se seleccionaron 232 pacientes con diagnóstico de FA no valvular. Se han analizado variables demográficas, variables de la escala CHA2DS2-VASc, tratamiento prescrito y valores de INR durante 6 meses consecutivos. La comparación de variables se realizó con ji cuadrado y la tendencia lineal entre grupos por Mantel Haenzel, siendo calculadas las odds ratios.
La prevalencia total de FA no valvular en el área fue 1,05%. El 88,4% presentó un CHA2DS2-VASc ≥ 2. Un 71,1% de pacientes con fibrilación auricular estaban anticoagulados, de los que el 58,2% tomaban fármacos antivitamina K. El 46,7% de los pacientes en tratamiento con acenocumarol presentó un INR con un tiempo en rango terapéutico directo insuficiente. La prescripción de antivitamina K en los pacientes con FA permanente fue superior que en pacientes con FA paroxística (62,8 vs. 37,2%, p < 0,001). El consumo de fármacos que aumentan el sangrado se asoció a un peor control de INR (tras ajuste por las principales variables de relevancia clínica (odds ratio 2,17 [1,02-4,59], p = 0,043).
El control de la anticoagulación oral con antivitamina K fue subóptimo pese a la adecuada adherencia de los pacientes. Los pacientes con FA paroxística recibieron menos antivitamina-K que los de FA persistente/permanente.
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.
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.
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).
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.

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^☆: Dr Pritchett’s research in supraventricular arrhythmias has been supported by grant M01-RR00030 from the National Center for Research Resources, National Institutes of Health, Bethesda, Maryland.

^☆☆: Guest Editor for this manuscript was Gust H. Bardy, MD, University of Washington, Seattle.

^★: Reprint requests: Sana Al-Khatib, MD, Duke University Medical Center, Box 31071, Durham, NC 27710. E-mail: [email protected]

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Clinical Investigations: ElectrophysiologyObservations on the transition from intermittent to permanent atrial fibrillation☆,☆☆,★

Abstract

Section snippets

Patients

Results

Discussion

Am Heart J

Atrial fibrillation: a major contributor to stroke in the elderly. The Framingham study

Arch Intern Med

Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: The Framingham study

Neurology

Atrial fibrillation as an independent risk factor for stroke: The Framingham study

Stroke

Classification of atrial fibrillation

PACE

Clinical Investigations: Electrophysiology
Observations on the transition from intermittent to permanent atrial fibrillation☆,☆☆,★