Early Detection Of Atrial Fibrillation Research Articles

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.

Diagnosis and management of atrial fibrillation in primary care: a case study

This critically reflective case study will look at a 65-year-old male presenting with dyspnoea and an incidental diagnosis of atrial fibrillation (AF) in a primary care setting. The article critically analyses the diagnosis and management of the patient, considers potential differential diagnoses, current guidelines, and areas for service improvement. The author, a trainee advanced clinical practitioner (ACP) prescriber with a paramedic background, discusses the complexity of managing AF and the need for collaboration with a general practitioner. AF is a common cardiac arrhythmia associated with a significantly increased risk of stroke. The primary focus of AF management is stroke prevention. This article delves into the pathophysiology of AF, discussing its various causes and classifications, emphasising the role of left atrial appendage (LAA) in stroke risk. The limitations of AF screening methods, especially for paroxysmal AF, are also discussed. Recent advancements in opportunistic screening, including the use of artificial intelligence (AI) and smartwatches, show promise but require further research and validation. This article explores stroke risk assessment tools like CHA2DS2VASc and ATRIA, bleeding risk assessment using HAS-BLED and ORBIT scores, as well as rhythm control in newly diagnosed AF cases. Finally, the importance of lifestyle modifications and regular patient follow-up are underscored. AF diagnosis and management are multifaceted, involving stroke prevention, rate or rhythm control, and lifestyle modifications. Technological advancements hold promise in improving early AF detection. Collaborative efforts between healthcare providers and adherence to current guidelines are crucial in achieving optimal patient outcomes, while future practice may benefit from enhanced diagnostic screening and treatment options as further research unfolds.

Protocol for a multicentre and prospective follow-up cohort study of early detection of atrial fibrillation, silent stroke and cognitive impairment in high-risk primary care patients: the PREFA-TE study

BackgroundAtrial fibrillation (AF) is the most common type of cardiac arrhythmia. Future estimations suggest an increase in global burden of AF greater than 60% by 2050. Numerous studies provide growing...

Does atrial fibrillation still increase the risk of death? One-year follow-up results of the NOMED-AF study.

Although atrial fibrillation (AF) is a well‑known risk factor for ischemic stroke and hospitalization, its effect on mortality has not been clearly established. We aimed to assess whether AF is an independent risk factor for death. A secondary objective was to evaluate the role of oral anticoagulation in the prevention of stroke and death in 1‑year follow‑up of patients included in the NOMED‑AF (Noninvasive Monitoring for Early Detection of Atrial Fibrillation) study. The NOMED‑AF study included 3014 patients. The participants underwent continuous long‑term electrocardiographic monitoring using a wearable vest for up to 30 days. The present analysis involved 2795 patients who completed the 1‑year follow‑up. The median (interquartile range) follow‑up time was 365 (365-365) days. AF was diagnosed in 617 participants. Independent risk factors for death in the patients who completed the 1‑year follow‑up were AF, age equal to or above 65 years, and chronic kidney disease. The individuals with diagnosed AF had an almost 2‑fold higher risk of death (odds ratio [OR], 1.7; 95% CI, 1.18-2.44; P <0.001) and a 2.5‑fold higher risk of stroke (OR, 2.53; 95% CI, 1.41-4.44; P <0.001), as compared with those without an AF diagnosis. The participants with AF who received oral anticoagulants had an almost 5‑fold lower risk of death than those who were not on anticoagulation (2.9% vs 14.2%, respectively; P <0.001). AF is an independent risk factor for death and cardiovascular hospitalization. The risk of death and stroke in patients with AF is significantly higher than in the patients without this arrhythmia. Oral anticoagulation in patients with AF significantly reduces the rates of death and stroke; however, its use is suboptimal in this group of patients.

Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments

Early detection of atrial fibrillation (AF) is crucial for its effective management and prevention. Various methods for detecting AF using deep learning (DL) based on supervised learning with a large labeled dataset have a remarkable performance. However, supervised learning has several problems, as it is time-consuming for labeling and has a data dependency problem. Moreover, most of the DL methods do not provide any clinical evidence to physicians regarding the analysis of electrocardiography (ECG) for classification or detection of AF. To address these limitations, in this study, we proposed a novel AF diagnosis system using unsupervised learning for anomaly detection with three segments, PreQ, QRS, and PostS, based on the normal ECG. Two independent datasets, PTB-XL and China, were used in three experiments. We used a long short-term memory (LSTM)-based autoencoder to train the segments of the normal ECG. Based on the threshold of anomaly scores using mean squared error (MSE), it distinguished between normal and AF segments. In Experiment A, the best score was that of PreQ, which detected AF with an AUROC score of 0.96. In Experiment B and C for cross validation of each dataset, the best scores were also of PreQ, with AUROC scores of 0.9 and 0.95, respectively. To verify the significance of the anomaly score in distinguishing between AF and normal segments, we utilized an XG-Boosted model after generating anomaly scores in the three segments. The XG-Boosted model achieved an AUROC score of 0.98 and an F1 score of 0.94. AF detection using DL has been controversial among many physicians. However, our study differentiates itself from previous studies in that we can demonstrate evidence that distinguishes AF from normal segments based on the anomaly score.

Atrial Fibrillation Prevalence and Its Management in Transfusion-Dependent Thalassemias: The Fathal Study

Atrial fibrillation (AF) represents an emerging issue in the thalassemia population related to increased life expectancy. However, data on AF in these patients are limited, and the current management is based on the guidelines for the general population. Nevertheless, AF in these patients has distinctive features that could require specific management. We conducted a multicentric retrospective study supported by the Italian Society of Thalassemia and Hemoglobinopathies (SITE) to elucidate the prevalence of AF in transfusion-dependent thalassemia (TDT) adults in Italy and to study its management, particularly the use of transcatheter ablation and anticoagulation management. Patients followed between June 1, 2003 and May 31, 2023 with a diagnosis of alpha- or beta-transfusion-dependent thalassemia with at least one episode of AF were included in the study. Clinical data of patients have been retrospectively collected with dedicated digital case report form (CRF). So far, 3 centers (Cagliari, Ferrara, Milano) have participated in the study. Eight-hundred-sixty-one adult TDT patients are regularly followed in the participating centers. Data from 130 adult TDT with AF were included. At enrollment, 119 patients were alive (71 males, 60%), the mean age at enrollment was 51.9 years, and 71% were splenectomized. Almost all (98%) had beta-thalassemia, of which 20% had thalassemia intermedia and became transfusion dependent during their life. The mean age at the first AF episode was 39.7 yr. Overall, AF prevalence in TDT was 13.8%, with a significant rise above the age of 50 (age 51-65: prevalence 26.4%, age&amp;gt;65 33.3%), differently from the general population in which the prevalence is 2-4%, and patients are older. As in the general population, prevalence was higher in males (18.2% vs 10.2%). Among the known risk factors for AF, the most frequent were diabetes (16%, n=111) and previous heart failure (22%, n=111). Regarding disease-specific factors contributing to the pathophysiology of AF, cardiac iron overload, defined as at least one between T2*&amp;lt;20 ms and serum ferritin&amp;gt;2500 ng/ml, was present in half of the patients (48.7%, n=76) at any time before AF and in approximately one-third (29%, n=93) at the time of the first event. Thirty-eight patients did not show cardiac iron overload before or at the time of the first AF episode. Most subjects had left atrial dilatation (58.1%), which can result from chronically increased cardiac output due to anemia. In most patients (87%), a rhythm control strategy was chosen at any time. Amiodarone was the most widely used drug. Transcatheter ablation was performed in 29 patients (22%), with a total of 39 procedures, and 20 (77%) patients reported improvement in symptoms after the last ablation. CHA 2DS 2-VASc score at the first AF episode detected a low risk of thromboembolism in 66 (50.8%) patients and a high risk in 19 (14.6%). However, 72 patients, including 40 low-risk subjects, initiated anticoagulation after the first episode. Four patients had a hemorrhagic event (3.1%), but only one had major bleeding during anticoagulant therapy without significant sequelae. Interestingly, management was heterogenous between different centers for both transcatheter ablation use and anticoagulation strategy. The prevalence of stroke in TDT AF patients was 5%, rising to 7.6% when also considering transient ischemic attacks. Compared to the literature, this prevalence is at least 10-fold higher than the general TDT population. Of note, splenectomy, a well-known risk factor for thrombosis, was present in all patients with stroke at the time of the event. In conclusion, AF represents significant morbidity in adult TDT, and its management is heterogeneous. It generally occurs at a younger age than the general population, and iron overload and atrial dilatation play a central role in its genesis. For symptomatic patients, we propose a rhythm control strategy to improve their quality of life, among which ablation could be the best option. Moreover, because stroke represents a major and frequent complication in TDT patients with AF, early detection of AF and prompt initiation of anticoagulant therapy, irrespective of CHA 2DS 2-VASc, which does not capture potential disease-specific risk factors, including splenectomy, should be considered. Further studies to support our approach are needed.

Atrial fibrillation burden and timing of anticoagulation therapy in patients with screen-detected and clinically-diagnosed AF

Abstract Purpose We evaluated the impact of atrial fibrillation (AF) burden and timing of oral anticoagulation (OAC) therapy initiation on stroke risk in patients with screen-detected and clinically-diagnosed AF. Methods Consecutive patients aged ≥65 years attending medical outpatient clinics with or without known AF were prospectively enrolled for AF-screening using handheld single-lead ECG (AliveCor) from 12/2014 to 12/2017 (NCT02409654). Repeated screening was performed in patients with &amp;gt;1 visit during this period. Patients who participated in repeat screening were included in final analysis. Patients with previously known AF who were in AF in the initial screening and those with repeat screen-positivity were considered probably persistent AF, otherwise paroxysmal AF (single screen-positivity) or no AF (never screen-positive). Time from AF detection to initiation of OAC (Dx-to-Rx) was divided into two exposure levels, Dx-to-Rx ≤ 1 and &amp;gt;1 month. Ischemic stroke risk was estimated using adjusted sub-distribution hazard ratios (aSHR) derived from Fine and Gray regression models, accounting for death as competing risk, adjusting for components of CHA2DS2VASC score and chronic renal disease, with no AF as reference and stratified according to AF burden and Dx-to-Rx. Results Of 11,972 patients enrolled, 3,853(32.2%) received repeated screening (mean age 76.7±7.3, female 47.5%), of which 3.7% (n=144) had screened-detected AF, 25.2% (n=972) previously known AF, 5.9% (n=226) clinically-diagnosed AF during follow-up, and 65.2% (n=2,511) no AF. Persistent AF was identified in 16.7% (n=644/3,853). Of patients with all AF categories, 55.1% (n=739/1,342) initiated OAC within 1 month after AF diagnosis. During a median follow-up period of 7.2(IQR: 6.1-7.5) years, compared to no AF, patients with paroxysmal AF and Dx-to-Rx ≤ 1m (aSHR=1.40, 95%CI:0.80-2.47) had similar low risk of ischemic stroke, while aSHR was highest for persistent AF and Dx-to-Rx &amp;gt;1m (aSHR=2.42, 95%CI:1.38-4.26), followed by persistent AF with Dx-to-Rx≤ 1m (aSHR=2.19,95%:1.47-3.28), and paroxysmal AF with Dx-to-Rx &amp;gt;1m (aSHR=2.00,95%CI:1.32-3.02) (Figure). Subgroup analysis revealed the effect of early initiation of OAC (P for interaction=0.936) and higher AF burden (P for interaction=0. 863) were consistent across screen-detected and clinically-diagnosed AF. Conclusion Among patients with screen-detected AF or clinically-diagnosed AF, lower AF burden and immediate initiation of OAC therapy were associated with reduction in risk of ischemic stroke. Early detection of AF by screening facilitating early OAC prophylaxis might improve clinical outcomes.Cumulative risk of ischemic stroke

Diagnosing atrial fibrillation early: the power of radiomics analysis on non-contrast cardiac CT

Abstract Background Often silent and asymptomatic, atrial fibrillation (AF) contributes significantly to morbidity and mortality. Early detection of AF could prevent and treat AF complications. Epicardial adipose tissue (EAT) is a unique fat tissue located on the surface of the myocardium that can be demonstrated on gated non-contrast cardiac CT (CCT). Radiomics, a method that translates medical images into quantitative data, can yield biological information and enable radiologic phenotypic profiling. Purpose This study aimed to identify radiomic features of both EAT and left atrium (LA)-EAT in patients with AF, based on non-contrast CCT, and utilize these features to construct a prediction model for the presence of AF. Methods We conducted a retrospective case control study of 180 patients who underwent cardiac CTA (CCTA) for clinical indications between 2018-2022 in Shaare Zedek Medical Center Jerusalem. The AF group contained 90 patients with known AF referred to CCTA prior to pulmonary vein isolation. The control group included 90 patients referred to CCTA for other indications, without a definite or suspected AF diagnosis. In all patients, the cardiac CT protocol included initial non-contrast CCT, which we used for radiomic analysis. The study dataset was trained on 60 patients from each group, with evaluation performed on a validation dataset of 30 patients each. Detection of the cardiac EAT was performed fully automatically using the cardiac risk analysis tool, SyngoVia, Research Frontier, Siemens Healthineers. Additional specific segmentation of the LA- EAT was performed semi-automatically using fixed anatomical landmarks. Radiomic analysis was performed via Radiomic tool, SyngoVia, Research Frontier, Siemens Healthineers. Results Both groups included in this research, study and control, had matching clinical characteristics such as age (average 63.2) , sex (female 50%), history of dyslipidemia, hypertension, diabetes, and smoking. A univariant statistical analysis for detection of AF of 1691 radiomic features in LA-EAT found an AUC of 0.866, p&amp;lt;&amp;lt;0.001 and 0.804, p&amp;lt;&amp;lt;0.001 for morphological and texture features, respectively. In a multivariate regression analysis, combining the top two radiomic features increased the AUC to 0.89, p = 0.004. Evaluation of linear regression and random forest model applications on a new validation cohort of 60 patients verified the results with AUC curves of 0.88 and 0.87 respectively. Compared to LA-EAT, applying total EAT prediction model on a new validation cohort produced inferior results, with AUC curves of 0.79 and 0.8 for linear regression and random forest model applications, respectively. Conclusion This proof-of-concept study demonstrated that radiomic features of LA-EAT are associated with and predictive of AF. Models based on radiomic features from gated non-contrast CCT of LA-EAT identified the presence of AF with excellent diagnostic performance. Models based on total EAT produced inferior results.Figure 1Heatmap of radiomic featuresFigure 2ROC curves

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learn more about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasive tools for rapid and continuous monitoring of body vitals that reflect the status of one’s overall health. In this backdrop, this work proposes a deep learning approach to turn a smartphone—the popular hand-held personal gadget—into a diagnostic tool to measure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), and respiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG) of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip by placing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filtered and/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks (CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise, the contribution of this paper is twofold: (1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); (2) a novel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to a single-lead ECG signal. The significance of this work is twofold: (i) it allows rapid self-testing of body vitals (e.g., self-monitoring for covid19 symptoms), (ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrial fibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases, and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead to reduction in health insurance costs.

Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation

Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases. To determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population. This prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm. A convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center. A total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater. Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation.

RECOGNITION OF ATRIAL FIBRILLATION BASED ON CNN-LSTM AND LAPLACIAN SUPPORT VECTOR MACHINE

Atrial fibrillation (AF) is a common arrhythmia associated with cardiac death and stroke. Therefore, the early detection of AF is of critical importance and the wearable long-term ECG monitoring system is one promising way. In order to assist the cardiologists in identifying potential AF in a tremendous amount of long-term ECG data, this study proposed an automatic detector combining deep learning and semi-supervised learning in view of the difficulty of obtaining a large number of labeled data in clinical practice. Three R-R interval features and two nonlinear features extracted from ECG samples, combined with 16-dimension deep learning features extracted by CNN-LSTM, are put into the semi-supervised machine learning model Laplacian Support Vector Machine (LapSVM) for AF detection. The proposed method showed very promising performance, with an accuracy of 99.63%, a sensitivity of 99.70%, a specificity of 99.57% and an F_score of 99.59% on the AFDB dataset. It still achieved an accuracy of 98% when the proportion of the training set was reduced, and achieved an accuracy of 96% on the SPHD collected clinically. The results show that the proposed method can classify AF and non-AF with a higher accuracy, and has excellent generalization performance in different categories of subjects, which is in line with clinical scenarios. The proposed method is also conducive to solving the clinical cases with little labeled data.

Detection of atrial fibrillation using an implantable loop recorder in patients with previous cryptogenic stroke: the SPIDER-AF registry (Stroke Prevention by Increasing DEtection Rates of Atrial Fibrillation).

The use of implantable loop recorder (ILR) to detect atrial fibrillation (AF) in patients with a history of cryptogenic stroke (CS) has seldom been investigated in "real-world" settings. This study aimed to present the results of the Stroke Prevention by Increasing DEtection Rates of Atrial Fibrillation (SPIDER-AF) registry. SPIDER is a multicentric, observational registry, including 35 facilities all over Germany. It enrolled outpatients without AF history and with CS before ILR implantation. We included 500 patients (mean age 63.1 ± 12.7years), 304 (60.8%) were males, with previous stroke (72.6%) and TIA (27.4%). After inclusion, all were followed for at least 12months. AF was detected in 133(26.8%), 25.3% of patients with previous stroke and 29.9% with TIA (p = 0.30). AF was detected after a median of 121.5days (IQR 40.5-223.0). AF was primarily paroxysmal (95.7% after stroke, 100% after TIA). In 19.5% (n = 26) anticoagulation was not administered after AF detection. Mean CHA2DS2-VASc score (OR 1.22; CI 1.01-1.49; p = 0.037) and age > 70years (OR 0.41; CI 0.19-0.88; p = 0.0020) correlated with AF. Repeat cerebrovascular events at follow-up (28; 5.6%) correlated with AF (OR = 2.17; CI 1.00-4.72; p = 0.049). AF (p = 0.91) and recurrent events (p = 0.43) occurred similarly in females and males. In the context of cryptogenic strokes ILRs are a valuable tool for early detection of AF. In a real-world scenario, the anticoagulation therapy remains inadequate despite diagnosed AF in a relevant subset of patients requiring optimization of patient management.

Trends, predictors and impact of atrial fibrillation on inpatient outcomes among malignant melanoma patients: A 5-year National Inpatient Sample (NIS) study.

e21525 Background: Malignant melanoma (MM), a very aggressive cancer, is associated with the highest mortality rate among dermatologic malignancies. Atrial fibrillation (AF) is the most common cardiac arrhythmia. Very little is reported in the literature on the association between both diseases, and there is marked paucity of studies comparing outcomes in patients with concomitant MM and AF. Methods: Retrospective cohort analyses were performed using the National Inpatient Sample (NIS) data collected between 2016 and 2020. Multivariable logistic and linear regression models were used to examine the predictors of AF and inpatient outcomes among MM patients. Results: Out of 9,760 hospitalizations with a diagnosis of MM, the overall prevalence of AF was 13.3% with an increasing trend from 10.1% in 2016 to 16.4% in 2020. MM patients with AF had similar odds (adjusted odds ratio (AOR) = 0.9; 95% confidence interval (CI) = 0.45–1.61) of inpatient all-cause mortality when compared to those without atrial fibrillation. However, they were more likely to have longer hospital stays (beta coefficient (β) = 1.27; 95% CI = 0.39–2.0) and total hospital charges (β = $12,830; 95% CI: $7041.4–$25426.4) when compared to their counterparts without atrial fibrillation. Furthermore, patients with AF had a double likelihood (AOR: 1.90; 95% CI: 1.25–2.90) of developing an acute kidney injury (AKI). We also found that female MM patients were 50% (AOR: 0.50; 95% CI: 0.36–0.68) less likely to have a diagnosis of AF relative to their male counterparts. Similarly, Hispanic MM patients were significantly less likely (AOR: 0.06; 95% CI: 0.008–0.459) to have AF in comparison to Non-Hispanic White patients. Conclusions: There is an increasing trend of comorbid atrial fibrillation in patients hospitalized for malignant melanoma, and this is associated with longer hospital stays and higher total hospital charges. These findings highlight the need for surveillance to enable early detection of atrial fibrillation in this population.