Early Detection Of Atrial Fibrillation Research Articles

Predicting atrial fibrillation from ECG characteristics

Abstract Background/Introduction Atrial fibrillation (AF) is the most common cardiac arrythmia, and demographic shifts in age continues to increase the prevalence of atrial fibrillation on a global scale. With manifestations of short and intermittent duration, identifying and diagnosing AF often proves to be a challenge. As a consequence, patients who develop AF are at risk of experiencing stroke and other cardiac diseases such as heart failure (HF) before appropriate treatment is initialized. Despite the potential benefits of early AF detection, risk profiling based on electrocardiographic (ECG) findings is currently lacking. Several smaller studies have identified ECG characteristics associated with increased risk of AF, but these characteristics have not been individually evaluated in large-scale nationwide cohorts, and no evidence exists on the additive effect of these ECG characteristics. Purpose We aimed to evaluate the risk of new-onset AF, HF, and stroke according to five ECG characteristics previously linked to AF, in order to identify potential predictors of AF. Methods We used national registers and the Danish Nationwide ECG Cohort to include patients with ECG measurements taken between 2010 and 2022. Patients were grouped according to how many of the following ECG characteristics were present in their baseline ECG: Elevated heart rate (>80), prolonged PR-interval (≥196 ms for women and ≥204 ms for men), biphasic P-waves (positive and negative amplitude within same p-wave), abnormal P terminal force (≥ 6000 mV×ms), and prolonged P-wave duration(≥120 ms). Patients were followed for 2 years to evaluate the risk of atrial fibrillation, heart failure (HF), and stroke, according to baseline ECG characteristics. Results We included 1,505,474 patients of whom 594,702 had exactly one ECG characteristic, 197,367 had two characteristics, 44,983 had three characteristics, 6,390 had four characteristics, and 451 had all five characteristics on their baseline ECG. The risk of developing AF or HF increased with the number of ECG characteristics present on the baseline ECG. Patients with two ECG characteristics had a 2-fold increased risk of AF or HF compared to patients with no ECG characteristics (HR: 2.11 95%CI: 2.05-2.16). Patients with two or more ECG characteristics present on their baseline ECG exceeded a 5% 2-year absolute risk of developing either AF or HF. Among patients with only one ECG characteristic on their baseline ECG, elevated heart rate and prolonged PR interval were the factors most associated with an increased risk of AF compared to patients with none of the ECG characteristics on their baseline ECG. Conclusions The absolute and relative risk of atrial fibrillation, heart failure, and stroke increased with the number of ECG characteristics present on the baseline ECG. We suggest that the ECG characteristics evaluated in this study could be used in screening scenarios for atrial fibrillation to identify high risk patients.Absolute and relative 2-year risksRisk acc. to specific ECG characteristic

A deep learning method for beat-level risk analysis and interpretation of atrial fibrillation patients during sinus rhythm

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. The great majority of algorithms can only distinguish “AF rhythm in AF patients” from “sinus rhythm in healthy individuals” . However, AF patients do not always exhibit AF rhythm, most of the time they present with sinus rhythms, and there is also a potential risk of AF in sinus rhythms. How to detect AF from sinus rhythm is a challenge. To address this, this paper proposes a novel artificial intelligence (AI) algorithm to distinguish “sinus rhythm in AF patients” and “sinus rhythm in healthy individuals” in beat-level. We cut 1 s of sinus beats from single-lead Electrocardiogram (ECG) data and fed them to the Net1d model, a deep learning model that processes one-dimensional data, to obtain the risk probability of each beat. Besides, we have also introduced the beat-level risk interpreters, trend risk interpreters, addressing the interpretability issues of deep learning models and the difficulty in explaining AF risk trends. Additionally, the beat-level information fusion decision is presented to enhance model accuracy. The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314, with an average accuracy of 0.6606 and an F1 score of 0.6470. By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0.7591, while the average accuracy and F1 score improve to 0.6887 and 0.6749. Compared to previous segment-level algorithms, we utilized beats as input, reducing data dimensionality and making the model more lightweight, facilitating deployment on portable medical devices. Furthermore, we draw new and interesting findings through average beat analysis and subgroup analysis, considering varying risk levels. Our code is publicly available at https://github.com/leijsen/ECGBeat4AFSinus.

A Comprehensive Review of Cryptogenic Stroke and Atrial Fibrillation: Real-World Insights Into the Role of Insertable Cardiac Monitors.

Cryptogenic stroke, a subtype of ischemic stroke with no identifiable cause after comprehensive evaluation, presents a unique challenge in stroke prevention. Atrial fibrillation (AF), a common risk factor for ischemic stroke, is often underdiagnosed in these patients due to its intermittent, asymptomatic nature. Early detection of AF is critical, as anticoagulation therapy significantly reduces the risk of recurrent stroke in AF patients. However, traditional short-term monitoring methods frequently fail to identify paroxysmal AF. Insertable cardiac monitors (ICMs) offer a novel solution by providing continuous, long-term heart rhythm monitoring, which has proven effective in detecting occult AF. Real-world data further support the clinical value of ICMs in guiding the initiation of anticoagulation therapy, ultimately improving stroke prevention strategies. Despite some limitations, such as false positives and the invasive nature of the device, ICMs have emerged as a critical tool in modern stroke management. As technology evolves, future advancements may further enhance AF detection by integrating artificial intelligence and wearable devices. This review provides a comprehensive overview of the role of AF in cryptogenic stroke, the importance of early detection, and the growing significance of ICMs in clinical practice.

Increasing the reach: optimizing screening for atrial fibrillation-the STROKESTOP III study.

Atrial fibrillation (AF) is the most common type of cardiac arrythmia and is an important risk factor for ischaemic stroke. Many cases of AF remain undiagnosed due to its paroxysmal, intermittent, and often asymptomatic nature. Early detection of AF through screening and initiation of treatment with oral anticoagulants can prevent stroke, increase life expectancy, and decrease the cost of healthcare for the society. However, participation has been low in previous AF screening studies employing population screening. The aim of this study is to determine whether opportunistic screening is a superior method to increase participation in comparison to population screening. We hypothesize that opportunistic screening will significantly increase participation. In our study, STROKESTOP III, a randomized prospective cohort study, we compare two different methods of AF screening in high-risk individuals: population screening vs. opportunistic screening. Sixteen different primary clinics in Värmland, Sweden, serving 75-76-year-old individuals (n = 2954), will be randomized to either population screening or opportunistic screening. The individuals will be instructed to record electrocardiogram (ECG) for 30 s, 3 times daily for 2 weeks, using a handheld one-lead ECG device. Patients with detected AF will be referred to their primary healthcare physician and offered treatment. The main objective of the study is to determine the rate of participation in opportunistic screening in comparison to population screening. The STROKESTOP III study will provide valuable information on which screening method to use for improved participation in atrial fibrillation screening.

Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.

Could the Early Detection of Atrial Fibrillation Reduce the Risk of Developing Dementia?

Atrial fibrillation (AF) and dementia are major global public health issues and share common risk factors, especially after the age of 65 and regardless of the presence of stroke. Despite accounting for potential confounders, AF appears to be an independent risk factor for cognitive decline and dementia. The mechanisms are likely to be multifactorial and may include AF-related ischemic stroke, cerebral hypoperfusion, microbleeds, systemic inflammation, genetic factors, and small vessel disease, leading to brain atrophy and white matter damage. The early aggressive management of AF and comorbidities may reduce the risk of dementia. Indeed, the early detection of AF-related cognitive impairment should allow for the early implementation of measures to prevent the development of dementia, mainly through integrative approaches involving the correction of risk factors and maintenance of rhythm control. Well-designed prospective studies are needed to determine whether early detection and AF treatment can prevent dementia and identify whether optimal integrative measures are effective in preventing cognitive impairment and dementia.

A clinical validation of AViTA home blood pressure monitor for atrial fibrillation detection in adults.

Atrial fibrillation (AF) is a stroke and heart complication riskbut is often overlooked due to subtle symptoms. The accessibility of sphygmomanometers that can detect AF, may play crucial roles in identifying asymptomatic patients. AViTA BPM63B is an automatic blood pressure (BP) monitor for atrial fibrillation detection. This study validated the performance of BPM63B for AF detection using two novel algorithms of time-domain analysis and frequency-domain analysis which evaluate multiple statistics. The study included 100 subject, with 47 participants being male (47%) and ages ranging from 24 to 89 years (mean ± SD: 59 ± 17 years). Each participant received three consecutive readings from the subject device and a 12-lead ECG assessment. The pulse wave data from subject device was analyzed using the two algorithms. The atrial fibrillation status thus determined was compared to the ECG results interpreted by a physician. Among the 100 participants, 52 patients had chronic atrial fibrillation, along with 48 outpatients exhibiting sinus rhythm or non-atrial fibrillation arrhythmias. Using the time-domain analysis method for atrial fibrillation detection, we achieved a sensitivity of 94.8% [95% confidence interval (CI), 90.08-97.75%] and a specificity of 98.6% (95% CI, 95.07-99.83%). Similarly, utilizing the frequency-domain analysis method resulted in a sensitivity of 91.6% (95% CI, 86.08-95.46%) and specificity of 94.4% (95% CI, 89.35-97.57%). These findings suggest that AViTA BPM63B, which integrates two novel algorithms for atrial fibrillation diagnosis, demonstrates high sensitivity and specificity. Utilizing the AViTA BPM63B for BP monitoring could aid in the early detection of atrial fibrillation among outpatients in cardiology clinics.

Delayed diagnosis and missed opportunities in the early detection of atrial fibrillation: a cross-sectional study

IntroductionWe analysed the frequency of atrial fibrillation (AF) delayed diagnosis and the factors associated with it in newly diagnosed patients. MethodsThis was a descriptive, cross-sectional, multicentre study. Data were collected from newly diagnosed patients with AF through medical records review and interviews during cardiology, internal medicine, primary care and emergency department consultations in Spain. ResultsA total of 201 physicians participated in the study (64.2% cardiologists, 21.4% internists). 948 patients (58% men; mean age 72.8 years) were included. In 41.8% of patients, AF was classified as paroxysmal at diagnosis, 30.9% as persistent and 27.3% as permanent. The diagnosis was coincidental in 37%. It was considered that a delayed diagnosis occurred in 49.3% of patients. This delay was associated with the presence of permanent or persistent AF, older age or valvular disease. 74.8% of patients had some contact with the healthcare system in the preceding year. The diagnosis could have been established between 1 and 6 months earlier in 50.7% of cases and more than six months earlier in 20.1%. 54.4% of the patients had experienced AF compatible symptomatology previously. Of these, 32.6% had a consultation without a diagnosis. ConclusionsIn a significant proportion of AF cases, there is a diagnostic delay. Many people with compatible symptoms neither seek consultations nor contact the healthcare system facilities. Consequently, the opportunity for early diagnosis is lost.

Controversies in Diagnosis and Management of Atrial Fibrillation.

Early detection of atrial fibrillation (AF) plays an important role in decreasing adverse cardiovascular outcomes. It is estimated, however, that one-third of those with AF are asymptomatic and may experience the adverse effects of the arrhythmia prior to being detected clinically. In the past, AF was diagnosed on 12-lead electrocardiogram or medically prescribed external monitors. The development of device-monitoring technologies capable of recording AF or AF-surrogates such as atrial high-rate episodes on cardiovascular implantable electronic devices or photoplethysmography/electrocardiogram on consumer-grade wearable devices, has resulted in increased recognition of device-detected, subclinical, AF. Recent studies reveal information about the stroke risk associated with these subclinical events and the response to anticoagulation and raise important questions about the use of both medical and direct-to-consumer AF detection devices for screening purposes. In addition to screening and detection of AF, emerging studies are also being conducted on different strategies for maintenance of sinus rhythm and stroke prevention including catheter ablation and left atrial appendage occlusion. This review aims to highlight recent developments and future studies in these areas.

Early Diagnosis of Atrial Fibrillation Episodes: Comparative Analysis of Different Matrix Architectures

This study presents three different matrix architectures for the analysis of ECG parameters, aimed at detecting atrial fibrillation episodes. The evaluation involves a cohort of 15 individuals, utilizing these matrix architectures across various orders. The findings reveal that the matrix norm delivers significantly better results compared to the large discriminant of the matrix. Detailed analysis of the spatial expansion of each matrix structure indicates that the PMLD architecture excels in terms of expandability compared to the MA1 and MA2 matrices. Consequently, third- and fifth-order PMLD matrix architectures are employed for classification techniques, demonstrating enhanced sensitivity with increased matrix order. These results are validated through the classification of several test candidates, confirming the efficacy of the proposed method. The study suggests that the developed approach holds substantial potential for clinical diagnostics in the early detection of atrial fibrillation.

Early detection of atrial fibrillation in the digital era, risk factors, treatment options, and the need for new definitions.

Graphical abstract.

The Role of 24-Hour Holter Electrocardiogram in the Early Detection of Atrial Fibrillation in Newly Diagnosed Acute Ischemic Stroke Patients.

Introduction Stroke is a leading cause of death and disability globally, with atrial fibrillation (AF) recognized as a significant risk factor due to its association with increased stroke recurrence and mortality. Timely detection of AF is crucial to prevent recurrent strokes and improve outcomes. This study primarily aimed to evaluate the utility of 24-hour Holter monitoring for AF detection in acute ischemic stroke patients. Methods This retrospective observational study examined data from 207 patients admitted with acute ischemic stroke to a tertiary-care hospital over a two-year period. Patients with pre-existing AF, transient ischemic attacks, unconfirmed diagnoses, and missing Holter reports were excluded. A total of 140 patients were included in the analysis. The study investigated AF detection rates, the relationship between AF and stroke risk factors, other Holter findings, and the time delay in attaching Holter monitors. Results Of the 140 patients evaluated, AF was detected in nine (6.4%), exclusively in those aged ≥65 years. The most prevalent risk factors among the study participants were hypertension (74.3%) and diabetes (61.4%). No significant correlations were observed between AF and the analyzed stroke risk factors. The median delay for Holter device attachment was 3,503 minutes (approximately two days and 10 hours), with longer delays noted in males (4,084 mins (approximately two days and 20 hours) vs. 2,565 mins (approximately one day and 18 hours), p=0.005). Premature atrial complexes(PACs) were notably associated with the absence of AF, suggesting their potential role as markers for undiagnosed AF. Conclusion The study highlights the limited utility of 24-hour Holter monitoring in detecting AF in acute ischemic stroke patients, advocating for extended monitoring durations, especially in older patients. To improve AF detection, potential strategies include using longer monitoring periods and optimizing hospital workflows to reduce delays in attaching Holter devices. These approaches can minimize the risk of underdiagnosing paroxysmal AF, thereby preventing recurrent strokes and improving patient outcomes. Further investigation into PACs as potential predictive markers for AF is warranted.

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to -16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.

Effectiveness of a stepwise approach for screening of atrial fibrillation after stroke: insights from the SAFAS study

Abstract Background Detection of atrial fibrillation (AF) is critical after ischemic stroke, providing information regarding the mechanism of the event and leading to modification in the antithrombotic strategy. While most guidelines recommend screening patients for AF with 12-lead ECG, telemetry, long-duration Holter monitoring and implantable cardiac monitor (ICM), the optimal timing and combination of such screening tools remain unclear. Objective This study aimed at investigating the suitability of a sequential combination of screening techniques (12-lead ECG, telemetry, in hospital long-lasting Holter monitoring, and ICM in the detection of AF after stroke. Methods Patients without previously known AF admitted to the Dijon University Hospital stroke unit for acute ischemic stroke were prospectively enrolled. After a stepwise screening approach for AF based on admission ECG, telemetry monitoring during the stroke unit stay and long-duration Holter monitoring during hospital stay, cryptogenic stroke patients were implanted of an ICM. Primary endpoint was the presence of AF detected during the 3-year period after stroke based on this sequential screening approach. Results A total of 240 patients were included. Among them, 104 (43.3%) patients had a documented cause of stroke non-related to AF. Among the remaining 136 patients (53.7% male, 70.8±13.7 yo), AF was detected in 82 (60%) patients over the acute screening phase or the 3-year follow-up with ICM. AF was diagnosed using 12-lead ECG, in-hospital telemetry, and in hospital long-lasting Holter monitoring in 17 (13%), 25 (18%), and 18 (13%) patients, respectively. AF was detected after the first 24 hours on the long-lasting Holter monitoring in 66% of patients. Among the 76 (56%) patients classified as cryptogenic after the complete stroke work-up and implanted from an ICM, AF was detected in 22 (29%) patients. AF occurred during the first, second, and third years of implantable monitoring in 14 (18.4%), 5 (6.6%), and 3 (3.9%) patients, respectively (Figure 1). Mean time from ICM implantation to AF diagnosis was 308+/-279 days. Finally, among all AF detected, 72% (60/83) were found during the initial intensive in-hospital screening. Conclusion A stepwise approach for AF screening after ischemic stroke allows the early detection of AF in a substantial number of patients during hospital stay. Even with such proactive initial monitoring strategy, invasive monitoring remains complementary to non-invasive screening tools not to overlook more distant AF episodes. Studies focusing on the relative risk of ischemic stroke recurrence according to AF timing and burden are needed.Figure 1

Machine Learning Approaches in Atrial Fibrillation Detection

Atrial fibrillation (AF) characterized by rapid and irregular electrical activity in the atria represents a prevalent form of cardiac arrhythmia that significantly challenges healthcare systems due to its links to heightened mortality and morbidity rates. Early detection of AF is critical for accurate and effective management and treatment. In response to this pressing need, numerous researchers have used machine learning (ML) to enhance the precision and efficiency of AF detection. By analyzing available datasets, signal lengths, preprocessing techniques, and a diverse array of ML approaches, this paper aims to cover methodologies of AF detection using electrocardiogram (ECG) data and ML.

Screening and diagnosis of atrial fibrillation using wearable devices.

In recent years, the development and use of various devices for the screening of atrial fibrillation (AF) have significantly increased. Such devices include 12-lead electrocardiogram (ECG), photoplethysmography systems, and single-lead ECG and ECG patches. This review outlines several studies that have focused on the feasibility and efficacy of such devices for AF screening, and summarizes the risks and benefits involved in the initiation of anticoagulant therapy after early detection of AF. We also describe several ongoing trials on unresolved issues associated with AF screening. Overall, this review provides a comprehensive summary of the current state of AF screening and its implications for patient care.

Diagnostic Performance of an Automated Blood Pressure Monitor With an Irregular Heartbeat Algorithm Designed to Detect Atrial Fibrillation.

Background: Early detection of atrial fibrillation (AF) remains an unsolved challenge and because the greatest risk factor for AF is hypertension, blood pressure (BP) monitors with AF detectors have been developed. We evaluated the clinical performance of an irregular heartbeat (IHB) algorithm built into an A&D automated BP monitor for AF diagnosis. Methods and Results: Each of the 239 enrolled patients underwent BP measurement 3 times using the A&D UM-212 with the IHB algorithm. Real-time 3-lead ECG was recorded using automated ECG analysis software. Independent of the ECG analysis software results, 2 cardiologists interpreted the ECG and made the final diagnosis. Of the 239 patients, 135 were in sinus rhythm, 31 had AF, and 73 had non-AF arrhythmias. The respective sensitivity, specificity, and accuracy of the IHB algorithm for AF diagnosis were 98.9%, 91.2%, and 92.2% for the per-measurement evaluation, and 96.8%, 95.7%, and 95.8% for the per-patient evaluation (3/3 positive measurements). The respective sensitivity, specificity, and accuracy of the ECG analysis software for AF diagnosis were 91.4%, 97.9%, and 97.1% for the per-measurement evaluation, and 77.4%, 99.5%, and 96.7% for the per-patient evaluation (3/3 positive measurements). Conclusions: The IHB algorithm built into an A&D automated BP monitor had high diagnostic performance for AF in general cardiology patients, especially when multiple measurements were obtained.

PRR30, pRR3.25% and Asymmetrical Entropy Descriptors in Atrial Fibrillation Detection.

Early detection of atrial fibrillation (AF) is essential to prevent stroke and other cardiac and embolic complications. We compared the diagnostic properties for AF detection of the percentage of successive RR interval differences greater than or equal to 30 ms or 3.25% of the previous RR interval (pRR30 and pRR3.25%, respectively), and asymmetric entropy descriptors of RR intervals. Previously, both pRR30 and pRR3.25% outperformed many other heart rate variability (HRV) parameters in distinguishing AF from sinus rhythm (SR) in 60 s electrocardiograms (ECGs). The 60 s segments with RR intervals were extracted from the publicly available Physionet Long-Term Atrial Fibrillation Database (84 recording, 24 h Holter ECG). There were 31,753 60 s segments of AF and 32,073 60 s segments of SR. The diagnostic properties of all parameters were analysed with receiver operator curve analysis, a confusion matrix and logistic regression. The best model with pRR30, pRR3.25% and total entropic features (H) had the largest area under the curve (AUC)-0.98 compared to 0.959 for pRR30-and 0.972 for pRR3.25%. However, the differences in AUC between pRR30 and pRR3.25% alone and the combined model were negligible from a practical point of view. Moreover, combining pRR30 and pRR3.25% with H significantly increased the number of false-negative cases by more than threefold. Asymmetric entropy has some potential in differentiating AF from SR in the 60 s RR interval time series, but the addition of these parameters does not seem to make a relevant difference compared to pRR30 and especially pRR3.25%.

Comparison of continuous 24-hour and 14-day ECG monitoring for the detection of cardiac arrhythmias in patients with ischemic stroke or syncope.

Previous studies show that using 12-lead electrocardiogram (ECG) or 24-h ECG monitor for the detection of cardiac arrhythmia events in patients with stroke or syncope is ineffective. The 14-day continuous ECG patch has higher detection rates of arrhythmias compared with conventional 24-h ECG monitoring in patients with ischemic stroke or syncope. This cross-sectional study of patients with newly diagnosed ischemic stroke or syncope received a 24-h ECG monitoring and 14-day continuous cardiac monitoring patch and the arrhythmia events were measured. This study enrolled 83 patients with ischemic stroke or syncope. The detection rate of composite cardiac arrhythmias was significantly higher for the 14-day ECG patch than 24-h Holter monitor (69.9% vs. 21.7%, p = .006). In patients with ischemic stroke, the detection rates of cardiac arrhythmias were 63.4% for supraventricular tachycardia (SVT), 7% for ventricular tachycardia (VT), 5.6% for atrial fibrillation (AF), 4.2% for atrioventricular block (AVB), and 1.4% for pause by 14-day ECG patch, respectively. The significant difference in arrhythmic detection rates were found for SVT (45.8%), AF (6%), pause (1.2%), AVB (2.4%), and VT (9.6%) by 14-day ECG patch but not by 24-h Holter monitor in patients with ischemic stroke or syncope. A 14-day ECG patch can be used on patients with ischemic stroke or syncope for the early detection of AF or other cardiac arrhythmia events. The patch can be helpful for physicians in planning medical or mechanical interventions of patients with ischemic stroke and occult AF.

Early Detection of Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients.

Atrial fibrillation (AF) is an important medical problem, as it significantly affects patients' quality of life and prognosis. AF often complicates the course of chronic obstructive pulmonary disease (COPD), a widespread disease with heavy economic and social burdens. A growing body of evidence suggests multiple links between COPD and AF. This review considers the common pathogenetic mechanisms (chronic hypoxia, persistent inflammation, endothelial dysfunction, and myocardial remodeling) of these diseases and describes the main risk factors for the development of AF in patients with COPD. The most effective models based on clinical, laboratory, and functional indices are also described, which enable the identification of patients suffering from COPD with a high risk of AF development. Thus, AF in COPD patients is a frequent problem, and the search for new tools to identify patients at a high risk of AF among COPD patients remains an urgent medical problem.