Usefulness of continuous electrocardiographic monitoring for atrial fibrillation

Post-Ischemic Stroke Cardiovascular Risk Prevention and Management

Cardiac death is the second most common cause of death among patients with acute ischemic stroke (IS), following neurological death resulting directly from acute IS. Risk prediction models and screening tools including electrocardiograms can assess the risk of adverse cardiovascular events after IS. Prolonged heart rate monitoring and early anticoagulation therapy benefit patients with a higher risk of adverse events, especially stroke patients with atrial fibrillation. IS and cardiovascular diseases have similar risk factors which, if optimally managed, may reduce the incidence of recurrent stroke and other major cardiovascular adverse events. Comprehensive risk management emphasizes a healthy lifestyle and medication therapy, especially lipid-lowering, glucose-lowering, and blood pressure-lowering drugs. Although antiplatelet and anticoagulation therapy are preferred to prevent cardiovascular events after IS, a balance between preventing recurrent stroke and secondary bleeding should be maintained. Optimization of early rehabilitation care comprises continuous care across environments thus improving the prognosis of stroke survivors.

Novel interpretable Feature set extraction and classification for accurate atrial fibrillation detection from ECGs

OBJECTIVE

We present a novel method for detecting atrial fibrillation (AFib) by analyzing Lead II electrocardiograms (ECGs) using a unique set of features.

METHODS

For this purpose, we used specific signal processing techniques, such as proper orthogonal decomposition, continuous wavelet transforms, discrete cosine transform, and standard cross-correlation, to extract 48 features from the ECGs. Thus, our approach aims to more effectively capture AFib signatures, such as beat-to-beat variability and fibrillatory waves, than traditional metrics. Moreover, our features were designed to be physiologically interpretable. Subsequently, we incorporated an XGBoost-based ECG classifier to train and evaluate it using the publicly available 'Training' dataset of the 2017 PhysioNet Challenge, which includes 'Normal,' 'AFib,' 'Other,' and 'Noisy' ECG categories.

RESULTS

Our method achieved an accuracy of 96 % and an F1-score of 0.83 for 'AFib' detection and 80 % accuracy and 0.85 F1-score for 'Normal' ECGs. Finally, we compared our method's performance with the top-classifiers from the 2017 PhysioNet Challenge, namely ENCASE, Random Forest, and Cascaded Binary. As reported by Clifford et al., 2017, these three best performing models scored 0.80, 0.83, 0.82, in terms of F1-score for 'AFib' detection, respectively.

CONCLUSION

Despite using significantly fewer features than the competition's state-of-the-art ECG detection algorithms (48 vs. 150-622), our model achieved a comparable F1-score of 0.83 for successful 'AFib' detection.

SIGNIFICANCE

The interpretable features specifically designed for 'AFib' detection results in our method's adaptability in clinical settings for real-time arrhythmia detection and is even effective with short ECGs (<10 heartbeats).

Multiscale dilated convolutional neural network for Atrial Fibrillation detection

Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associated with an increased risk of stroke and mortality. In light of the urgent need for effective automated AF monitoring, existing methods often fall short in balancing accuracy and computational efficiency. To address this issue, we introduce a framework based on Multi-Scale Dilated Convolution (AF-MSDC), aimed at achieving precise predictions with low cost and high efficiency. By integrating Multi-Scale Dilated Convolution (MSDC) modules, our model is capable of extracting features from electrocardiogram (ECG) datasets across various scales, thus achieving an optimal balance between precision and computational savings. We have developed three MSDC modules to construct the AF-MSDC framework and assessed its performance on renowned datasets, including the MIT-BIH Atrial Fibrillation Database and Physionet Challenge 2017. Empirical results unequivocally demonstrate that our technique surpasses existing state-of-the-art (SOTA) methods in the AF detection domain. Specifically, our model, with only a quarter of the parameters of a Residual Network (ResNet), achieved an impressive sensitivity of 99.45%, specificity of 99.64% (on the MIT-BIH AFDB dataset), and an [Formula: see text] score of 85.63% (on the Physionet Challenge 2017 AFDB dataset). This high efficiency makes our model particularly suitable for integration into wearable ECG devices powered by edge computing frameworks. Moreover, this innovative approach offers new possibilities for the early diagnosis of AF in clinical applications, potentially improving patient quality of life and reducing healthcare costs.

Prevalence of atrial fibrillation on a 24-hour Holter in adult Indians

OBJECTIVE

To evaluate paroxysmal atrial fibrillation (AF) prevalence in Indian adults who completed 24-Hour Holter monitoring.

METHODS

A total of 23,847 patients (36.9 % women) were analyzed for AF duration using a software algorithm.

RESULTS

AF was diagnosed in 4153 (17.4 %) patients with a median AF duration of 13 min and 55 s.

CONCLUSION

AF prevalence was high and largely untreated. The short duration of AF episodes indicates a low likelihood of detection during clinical visits, highlighting its potential underestimation in Indian healthcare.

Association between serum cholinesterase and the prevalence of atrial fibrillation in Chinese hypertensive population: a cross-sectional study

BACKGROUND

Atrial fibrillation (AF) is a very common arrhythmia with significant incidence rate and mortality. Several studies have shown a notable correlation between non-alcoholic fatty liver disease (NAFLD) and AF. It has been observed that serum cholinesterase (SChE) levels are elevated in individuals with fatty liver. However, the relationship between the SChE index and AF is still unclear. Therefore, the purpose of this study is to explore the association between the SChE index and the prevalence of AF in patients with hypertension.

METHOD

We collected cross-sectional data from January 2018 to April 2021 based on a retrospective study of cardiovascular disease. A total of 748 patients with hypertension were included, of whom 165 had AF. We used logistic regression models to test the relationship between SChE and the prevalence of AF in hypertensive patients.

RESULT

In hypertensive patients, the SChE index was significantly associated with AF (OR = 0.723, P < 0.001). After adjusting for potential confounding factors, this correlation was still significant (OR = 0.778, P < 0.001). The stability of the model was verified by adjusting the variable type of SChE. The data were further stratified according to whether the patient had fatty liver. In the stratified data, the correlation between SChE and atrial fibrillation was still significant (P < 0.05).

CONCLUSION

Our study showed that SChE was significantly negatively correlated with the occurrence of AF in patients with hypertension. And this correlation was not affected by whether the patient had fatty liver.

Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study

BACKGROUND

While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation.

OBJECTIVE

We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates.

METHODS

In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ≥19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ≥2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists.

RESULTS

Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ≥4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively.

CONCLUSIONS

This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268.

A comparison of Atrial Fibrillation Detection Strategies After Ischemic Stroke-A Retrospective Study

Objective of this retrospective study was to determine if long-term continuous cardiac monitoring with Implantable loop recorder (ILR) in patients with Cryptogenic strokes or TIA is superior at detecting Atrial Fibrillation (AF) than 30-day Event Monitor (EM) and 48-hour Holter Monitor (HM). Furthermore, we aimed to deduce if uncovering AF leads to lower risk of future ischemic strokes, or reduction in mortality. In 20%-30% cases, the cause of stroke remained unexplained after diagnostic workup which has led to coining of the term, Cryptogenic Stroke (CS). Undiagnosed AF is a prime suspect in CS, but guidelines do not recommend initiation of anticoagulation unless AF has formally been detected. IRB approved retrospective study included patients with at least 1 episode of ischemic stroke or TIA without identifiable cause and was monitored with either HM, EM or ILR to diagnose any undiscovered AF. All patients (n = 531) had at least 1 year, and up to 3 years, of follow-up after device placement. Chi-Squared analysis and Multivariable logistic regression demonstrated no statistically significant difference among 3 devices for detection of AF within 1 month of index stroke but a significant difference in AF detection was observed at 6, 12 and 24 months. Cox proportional hazard model showed device type had no significant impact on secondary outcomes: Subsequent ischemic stroke or TIA, Initiation of anticoagulation, Mortality and Incidence of major bleeding. Despite the superiority of AF detection by ILR, it is not superior to HM or EM in lowering the risk of subsequent stroke or TIA, or in reducing mortality.

Development and external validation of a prognostic model for occult atrial fibrillation in patients with ischemic stroke

Objective

Currently, the risk of occult atrial fibrillation (AF) could not be predicted in patients with acute ischemic stroke (AIS) using a simple scoring system. Therefore, in this study, we developed and externally validated a nomogram to predict occult AF in patients with AIS.

Methods

In this study, we prospectively conducted a development cohort study with data collected at our stroke center from July 2017 to February 2018, and an external validation cohort from March 2019 to December 2019.

Results

Follow-up data were collected from 177 participants (56.5% older than 65 years, 29.4% female) for generating the nomogram model. Multivariate logistic regression analysis was performed with AF as the dependent variable indicated that age >65 years, heart rate >100, C-reactive protein (CRP), N-terminal pro-B-type natriuretic peptide (NT-proBNP) >270, hemorrhagic transformation (HT) as independent variables for predicting the development of AF, and a nomogram was generated based on these factors. The area under the receiver operating characteristic curve (AUC-ROC) for the model was 0.937, the C-index was 0.926, and the AUC-ROC for the validation cohort was 0.913.

Conclusion

To our knowledge, this is the first nomogram developed and externally validated in a stroke center cohort for individualized prediction of risk of developing AIS in patients with occult AF. This nomogram could provide valuable information for the screening of occult AF after a stroke.

Prognostic impact of catheter ablation in patients with asymptomatic atrial fibrillation

BACKGROUND

Catheter ablation for asymptomatic atrial fibrillation (AF) remains controversial. The aim of the present study was to explore the prognostic impact of catheter ablation in asymptomatic AF patients.

METHODS

We performed a post-hoc analysis of 537 risk-matched pairs of AF patients receiving first-time catheter ablation or conservative management. The primary outcome measure was a composite of cardiovascular death, heart failure (HF) hospitalization, ischemic stroke, or major bleeding. The study patients were divided into asymptomatic and symptomatic patients, and were further divided according to the presence or absence of previous AF-related complications (ischemic stroke or HF hospitalization).

RESULTS

Most baseline characteristics were well balanced between the catheter ablation versus conservative management groups. The median follow-up period was 5.3 years. Catheter ablation as compared to conservative management was associated with significantly lower incidence of the primary outcome measure in the asymptomatic AF patients (14.7% versus 25.4% at 8-year, log-rank P = 0.008). However, the advantage of catheter ablation was significant only in the high-risk subset of patients with the previous AF-related complications (19.2% versus 55.6% at 8-year, log-rank P = 0.006), but not in those without (13.9% and 17.3%, P = 0.08). On the other hand, among the symptomatic AF patients, catheter ablation was associated with significantly lower incidence of the primary outcome measure regardless of the previous AF-related complications.

CONCLUSIONS

In the post-hoc analysis of the matched AF cohort, catheter ablation as compared with conservative management was associated with better long-term clinical outcomes among asymptomatic AF patients only when the previous AF-related complications were present.

Detection of Atrial Fibrillation Episodes based on 3D Algebraic Relationships between Cardiac Intervals

In this study, the notion of perfect matrices of Lagrange differences is employed to detect atrial fibrillation episodes based on three ECG parameters (JT interval, QRS interval, RR interval). The case study comprised 8 healthy individuals and 7 unhealthy individuals, and the mean and standard deviation of age was 65.84 ± 1.4 years, height was 1.75 ± 0.12 m, and weight was 79.4 ± 0.9 kg. Initially, it was demonstrated that the sensitivity of algebraic relationships between cardiac intervals increases when the dimension of the perfect matrices of Lagrange differences is extended from two to three. The baseline dataset was established using statistical algorithms for classification by means of the developed decision support system. The classification helps to determine whether the new incoming candidate has indications of atrial fibrillation or not. The application of probability distribution graphs and semi-gauge indicator techniques aided in visualizing the categorization of the new candidates. Though the study's data are limited, this work provides a strong foundation for (1) validating the sensitivity of the perfect matrices of Lagrange differences, (2) establishing a robust baseline dataset for supervised classification, and (3) classifying new incoming candidates within the classification framework. From a clinical standpoint, the developed approach assists in the early detection of atrial fibrillation in an individual.

Validation of an algorithm for atrial fibrillation detection with an analog smartwatch: prospective interventional clinical study

BACKGROUND

Atrial Fibrillation (AF) affects about 4% of the World's population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation.

OBJECTIVE

To validate an algorithm for the automatic detection of AF from a single-lead electrocardiogram (ECG) taken with a smartwatch.

METHODS

Eligible patients were recruited from 4 sites in Paris, France. Twelve-lead reference ECGs and single-lead ECG were captured simultaneously. The ECGs were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect AF and normal sinus rhythm (NSR) were calculated. The quality of single-lead ECGs (visibility and polarity of waves, interval durations, heart rate) was assessed by comparison to the gold standard.

RESULTS

Two hundred and sixty two patients were included in the final analysis: 100 AF, 113 NSR, 45 Other arrhythmia, 4 presented unreadable ECGs. Mean age was of 74.3 years ± 12.3 in the AF group versus 61.8 years old ± 14.3 and 66.9 years old ± 15.2 in the NSR and other arrhythmia groups respectively. 6.9% (18/262) were classified as "Noise" by the algorithm. Excluding "Other" arrhythmia and "Noise", the sensitivity to detect AF was of SeAF/NSR = 0.963 (0.894), and specificity of SpAF/NSR = 1.000 (0.967). Visibility and polarity accuracies (1-lead ECG vs 12-lead ECG) were respectively: P-waves: 96.9%/100%, QRS-complexes: 99.2%/98.8%, and T-waves: 91.2%/99.5% . P-wave visibility accuracy was of 99% (99/100) in AF patients and of 95.7% (155/162) when excluding AF patients. The absolute values of the mean difference in PR duration and QRS width were below 3ms, and more than 97% of these differences were below 40ms. The mean difference between the HR calculated by the algorithm and the device 1-lead ECG read by cardiologists was 0.55 bpm.

CONCLUSIONS

Withings algorithm demonstrated great diagnostic performance for AF detection. The smartwatch single-lead ECGs also demonstrated good quality for physician use in daily routine care.

CLINICALTRIAL

ClinicalTrials.gov registration number: NCT04351386.

Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.

Epidemiology and Prognostic Factors in Acute Lower Limb Ischaemia: A Population Based Study

OBJECTIVE

The aim of this study was to evaluate the contemporary population based incidence of acute lower limb ischaemia (ALI) and factors associated with major amputation/death at one year.

METHODS

In this retrospective observational study, in hospital, operation, radiological, and autopsy registries were scrutinised to capture 161 citizens of Malmö, Sweden, with ALI between 2015 and 2018. Age and sex specific incidence rates were calculated in the population of Malmö between 2015 and 2018, expressed as number of patients per 100 000 person years (PY). Independent risk factors for major amputation/death at one year were identified by multivariable logistic regression analysis and expressed as odds ratios (ORs) with 95% confidence intervals (CIs).

RESULTS

One hundred and sixty-one patients with ALI gave an overall incidence of 12.2/100 000 PY (95% CI 10.3 - 14.1), with no sex related differences. Embolism (42.2%) was the most common cause of ALI. Among 52 patients with atrial fibrillation, 38.5% were on anticoagulant medication. Endovascular or open vascular revascularisation was performed in 54.7% of patients. The total cause specific mortality ratio was 2.63 (95% CI 1.66 - 3.61)/1 000 deaths, without no sex related differences. The combined major amputation/mortality rate at one year for the whole cohort was 46.6%. Rutherford ≥ IIb ALI (OR 4.19, 95% CI 1.94 - 9.02; p < .001), age (OR 1.03/year, 95% CI 1.00 - 1.06; p = .036), female sex (OR 2.37, 95% 1.07 - 5.26; p = .034), and anaemia (OR 2.46, 95% CI 1.08 - 5.62; p = .033) were associated with an increased risk of major amputation/death at one year. The major amputation/mortality rate at one year was 100% (n = 14/14) for patients living in a nursing home on admission.

CONCLUSION

The incidence of ALI appears to be unchanged, and major amputation and mortality at one year remain high. It is necessary to include the substantial proportion of patients with ALI that do not undergo revascularisation in epidemiological studies. There is room for improvement in anticoagulation therapy in patients with atrial fibrillation to prevent ALI due to embolism. Research on gender inequalities in patients with ALI is warranted.

Mobile Cardiac Outpatient Telemetry Patch vs Implantable Loop Recorder in Cryptogenic Stroke Patients in the US - Cost-Minimization Model

Purpose

The aim of this study was to compare costs and outcomes of mobile cardiac outpatient telemetry (MCOT) patch followed by implantable loop recorder (ILR) compared to ILR alone in cryptogenic stroke patients from the US health-care payors’ perspective.

Patients and Methods

A quantitative decision tree cost-minimization simulation model was developed. Eligible patients were 18 years of age or older and were diagnosed with having a cryptogenic stroke, without previously documented atrial fibrillation (AF). All patients were assigned first to one then to the alternative monitoring strategies. Following AF detection, patients were initiated on oral anticoagulants (OAC). The model assessed direct costs for one year attributed to MCOT patch followed by ILR or ILR alone using a monitoring duration of 30 days post-cryptogenic stroke.

Results

In the base case modeling, the MCOT patch arm detected 4.6 more patients with AFs compared to the ILR alone arm in a cohort of 1000 patients (209 vs 45 patients with detected AFs, respectively). Using MCOT patch followed by ILR in half of the patients initially undiagnosed with AF leads to significant cost savings of US$4,083,214 compared to ILR alone in a cohort of 1000 patients. Cost per patient with detected AF was significantly lower in the MCOT patch arm $29,598 vs $228,507 in the ILR only arm.

Conclusion

An initial strategy of 30-day electrocardiogram (ECG) monitoring with MCOT patch in diagnosis of AF in cryptogenic stroke patients realizes significant cost-savings compared to proceeding directly to ILR only. Almost 8 times lower costs were achieved with improved detection rates and reduction of secondary stroke risk due to new anticoagulant use in subjects with MCOT patch detected AF. These results strengthen emerging recommendations for prolonged ECG monitoring in secondary stroke prevention.

Diagnostic performance of a wearing dynamic ECG recorder for atrial fibrillation screening: the HUAMI heart study

BACKGROUND

Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia with high morbidity and mortality rate. Evidence shows that in every three patients with AF, one is asymptomatic. The asymptomatic and paroxysmal nature of AF is the reason for unsatisfactory and delayed detection using traditional instruments. Research indicates that wearing a dynamic electrocardiogram (ECG) recorder can guide accurate and safe analysis, interpretation, and distinction of AF from normal sinus rhythm. This is also achievable in an upright position and after exercises, assisted by an artificial intelligence (AI) algorithm.

METHODS

This study enrolled 114 participants from the outpatient registry of our institution from June 24, 2020 to July 24, 2020. Participants were tested with a wearable dynamic ECG recorder and 12-lead ECG in a supine, an upright position and after exercises for 60 s.

RESULTS

Of the 114 subjects enrolled in the study, 61 had normal sinus rhythm and 53 had AF. The number of cases that could not be determined by the wristband of dynamic ECG recorder was two, one and one respectively. Case results that were not clinically objective were defined as false-negative or false-positive. Results for diagnostic accuracy, sensitivity, and specificity tested by wearable dynamic ECG recorders in a supine position were 94.74% (95% CI% 88.76-97.80%), 88.68% (95% CI 77.06-95.07%), and 100% (95% CI 92.91-100%), respectively. Meanwhile, the diagnostic accuracy, sensitivity and specificity in an upright position were 97.37% (95% CI 92.21-99.44%), 94.34% (95% CI 84.03-98.65%), and 100% (95% CI 92.91-100%), respectively. Similar results as those of the upright position were obtained after exercise.

CONCLUSION

The widely accessible wearable dynamic ECG recorder integrated with an AI algorithm can efficiently detect AF in different postures and after exercises. As such, this tool holds great promise as a useful and user-friendly screening method for timely AF diagnosis in at-risk individuals.

Determination of Parameters for an Entropy-Based Atrial Fibrillation Detector

Entropy algorithm is an important nonlinear method for cardiovascular disease detection due to its power in analyzing short-term time series. In previous a study, we proposed a new entropy-based atrial fibrillation (AF) detector, i.e., Entropy_AF, which showed a high classification accuracy in identifying AF and non-AF rhythms. As a variation of entropy measures, Entropy_AF has two parameters that need to be initialized before the calculation: (1) tolerance threshold r and (2) similarity weight n. In this study, a comprehensive analysis for the two parameters determination was presented, aiming to achieve a high detection accuracy for AF events. Data were from the MIT-BIH AF database. RR interval recordings were segmented using a 30-beat time window. The parameters r and n were initialized from a relatively small value, then gradually increased, and finally the best parameter combination was determined using grid searching. AUC (area under curve) values from the receiver operator characteristic curve (ROC) were compared under different parameter combinations of parameters r and n, and the results demonstrated that the selection of these two parameters plays an important role in AF/non-AF classification. Small values of parameters r and n can lead to a better detection accuracy than other selections. The best AUC value for AF detection was 98.15%, and the corresponding parameter combinations for Entropy_AF were as follows: r = 0.01, n = 0.0625, 0.125, 0.25, or 0.5; r = 0.05 and n = 0.0625, 0.125, or 0.25; and r = 0.10 and n = 0.0625 or 0.125.

Detection of occult atrial fibrillation with 24-hour ECG after cryptogenic acute stroke or transient ischaemic attack: A retrospective cross-sectional study in a primary care database in Israel

Background

Ischaemic stroke or cerebrovascular accident (CVA) due to occult atrial fibrillation (AF) may cause severe morbidity and mortality. Diagnosing occult AF can be challenging and there is no consensus regarding the optimal duration of screening. A 24-hour Holter electrocardiogram (ECG) is frequently employed to detect occult AF following ischaemic CVA.

Objectives

Demonstration of occult AF detection rate using a 24-hour Holter ECG in a primary care setting with descriptive analyses of independent variables to compare AF detected and non-detected patients.

Methods

This retrospective cross-sectional study utilised primary care data and included patients 50 years and older with a new CVA or transient ischaemic attack (TIA) diagnosis followed by a 24-hour Holter examination within 6 months, between 01 January 2013 and 01 June 2019. The analyses included descriptive statistics comparing demographics and clinical characteristics in patients who had AF or Atrial Flutter (AFL) detection to those who did not.

Results

Out of 5015 eligible patients, 66 (1.3%) were diagnosed with AF/AFL, with a number needed to screen of 88.5. Compared with those without AF/AFL detection, those diagnosed were older (75.42 ± 7.89 vs. 69.89 ± 9.88, p = 0.050), had a higher prevalence of hypertension (80.3% vs. 66.8%, p = 0.021) and chronic kidney disease (CKD) (71.2% vs. 44.2%, p < 0.001).

Conclusion

24-hour Holter has a low AF/AFL detection rate. Older persons and those with hypertension or CKD are more likely to be detected with AF/AFL using this method.

Secondary Stroke Prevention Following Embolic Stroke of Unknown Source in the Absence of Documented Atrial Fibrillation: A Clinical Review

Approximately one-third of ischemic strokes are classified as cryptogenic strokes. The risk of stroke recurrence in these patients is significantly elevated with up to one-third of patients with cryptogenic stroke experiencing a further stroke within 10 years. While anticoagulation is the mainstay of treatment for secondary stroke prevention in the context of documented atrial fibrillation (AF), it is estimated that up to 25% of patients with cryptogenic stroke have undiagnosed AF. Furthermore, the historical acceptance of a causal relationship between AF and stroke has recently come under scrutiny, with evidence to suggest that embolic stroke risk may be elevated even in the absence of documented atrial fibrillation attributable to the presence of electrical and structural changes constituting an atrial cardiomyopathy. More recently, the term embolic stroke of unknown source has garnered increasing interest as a subset of patients with cryptogenic stroke in whom a minimum set of diagnostic investigations has been performed, and a nonlacunar infarct highly suspicious of embolic etiology is suspected but in the absence of an identifiable secondary cause of stroke. The ongoing ARCADIA (Atrial Cardiopathy and Antithrombotic Drugs in Prevention After Cryptogenic Stroke) randomized trial and ATTICUS (Apixiban for Treatment of Embolic Stroke of Undetermined Source) study seek to further define this novel term. This review summarizes the relationship between AF, embolic stroke, and atrial cardiomyopathy and provides an overview of the clinical relevance of cardiac imaging, electrocardiographic, and serum biomarkers in the assessment of AF and secondary stroke risk. The implications of these findings on therapeutic considerations is considered and gaps in the literature identified as areas for future study in risk stratifying this cohort of patients.

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. In this paper, state-of-the-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. This is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis in one place. It is observed that there is a dire need for low energy and low cost but accurate auto diagnosis solutions for the proactive management of AF.

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

Cost-effectiveness of an insertable cardiac monitor to detect atrial fibrillation in patients with cryptogenic stroke

Background: We assessed cost-effectiveness of insertable cardiac monitors (ICMs) in a US cryptogenic stroke population. Materials & methods: We modelled lifetime costs and quality-adjusted life years for three monitoring strategies post cryptogenic stroke: ICM starting immediately, ICM starting after Holter monitoring (delayed ICM) and standard of care involving intermittent ECG and Holter monitoring. Patient characteristics and detection efficacy were based on the CRYSTAL-AF trial. AF detection altered the modelled anticoagulation therapy and subsequent stroke and bleed risks. Results & conclusion: Immediate ICM was found to be cost-effective versus standard of care and cost-saving versus delayed ICM. Results were robust to sensitivity analyses. ICMs are a cost-effective diagnostic tool for the prevention of recurrent stroke in a US cryptogenic stroke population.

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.

A Batteryless Motion-Adaptive Heartbeat Detection System-on-Chip Powered by Human Body Heat

This paper presents a batteryless heartbeat detection system-on-chip (SoC) powered by human body heat. An adaptive threshold generation architecture using pulse-width locked loop (PWLL) is developed to detect heartbeats from electrocardiogram (ECG) in the presence of motion artifacts. The sensing system is autonomously powered by harvesting thermal energy from human body heat using a thermoelectric generator (TEG) coupled to a low-voltage, self-starting boost converter and integrated power management system. The SoC was implemented in a 0.18 μm CMOS process and is fully functional with a minimum input power of 20 μW, provided by a portable TEG at 20 mV with a ~0.5 °C temperature gradient. The complete system demonstrates motion-adaptive, power-autonomous heartbeat detection for sustainable healthcare using wearable devices.

Circadian variation and seasonal distribution of implantable defibrillator detected new onset atrial fibrillation

BACKGROUND

We aimed to characterize the hourly, daily, and seasonally variations in the detection of new atrial fibrillation (AF) in heart failure patients implanted with a defibrillator.

METHODS

In 1309 patients enrolled in MADIT-RIT without AF at baseline, atrial arrhythmia data were analyzed from device interrogations. The circadian, weekly, and seasonal distribution of device detected AF was evaluated. The morning period was defined as 06:00-11:59, afternoon as 12:00-16:59, evening as 17:00-22:59, and the nighttime as 23:00-05:59.

RESULTS

During 17 months of follow-up, 66 (5%) patients developed new device-detected AF. AF patients were less likely to have ischemic cardiomyopathy and were more likely to have received an implantable cardioverter defibrillator rather than a cardiac resynchronization therapy with defibrillator. The highest number of AF occurred during the evening hours (25 patients [38%]) followed by a second peak in AF detection during the afternoon hours (21 patients [32%]). Importantly during the nighttime, new AF occurred only in three patients (4%). In comparison with the nighttime period, the odds ratio (OR) of developing AF during the evening time period was 8.5-fold higher (95% CI 7.3-9.7, P < .01). Detection of AF during the spring and winter seasons accounted for 67% of all new device-detected AF.

CONCLUSIONS

There is diurnal and seasonal variation in new onset AF. A double peak in the incidence of AF is observed during the afternoon and evening hours, and during the spring and winter seasons. This information may be useful when deciding when to screen at-risk patients for new AF.

The symptom experience of early and late treatment seekers before an atrial fibrillation diagnosis

BACKGROUND

Atrial fibrillation is a complex condition associated with a broad spectrum of symptoms, coupled with variability in the frequency, duration and severity of symptoms. Early treatment seeking is important to reduce the risk of stroke, heart failure and dementia. Despite the increasing prevalence, there remains a limited understanding of the symptom experience prior to an atrial fibrillation diagnosis, and how these experiences influence treatment-related decisions and time frames.

AIMS

This qualitative study aimed to explore the symptom experiences of patients receiving an early diagnosis of less than 48 hours and a late diagnosis of 48 hours or more after symptom awareness.

METHODS

Twenty-six adults were interviewed guided by the symptom experience model. The symptom checklist was used to probe patient's symptoms further. Data were analysed using a two-step approach to thematic analysis utilising concepts from the symptom experience model.

RESULTS

The two groups differed in their perception, evaluation and response to symptoms. The early diagnosis group (n = 6) experienced traumatic, severe and persistent symptoms, evoking concern and urgent treatment seeking. Conversely, the late diagnosis group (n = 20) reported more vague, paroxysmal symptoms that were readily ignored, self-theorised as non-illness related, and engaged in non-treatment strategies. Healthy self-perceptions, past experiences, atrial fibrillation knowledge and healthcare provider interactions influenced early or late treatment seeking.

CONCLUSION

For many, the atrial fibrillation pre-diagnosis was a tumultuous period, requiring prolonged periods to recognise symptoms and formulate treatment-seeking responses. This study may promote future research and strategies aimed at facilitating the early identification and response to symptoms among atrial fibrillation patients.

Trends in Atrial Fibrillation Incidence Rates Within an Integrated Health Care Delivery System, 2006 to 2018

IMPORTANCE

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and multiple studies have reported increasing AF incidence rates over time, although the underlying explanations remain unclear.

OBJECTIVES

To estimate AF incidence rates from 2006 to 2018 in a community-based setting and to investigate possible explanations for increasing AF by evaluating the changing features of incident AF cases and the pool of patients at risk for AF over time.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 500 684 patients who received primary care and other health care services for more than 2 years through a single integrated health care delivery network in Pennsylvania. Data collection was conducted from January 2003 to December 2018. The base study population had no documentation of AF in the electronic medical record for at least 2 years prior to baseline. Data analysis was conducted from May to December 2019.

MAIN OUTCOMES AND MEASURES

Incident AF cases were identified through diagnostic codes recorded at inpatient or outpatient encounters. Age- and sex-adjusted AF incidence rates were estimated by calendar year from 2006 to 2018 both overall and across subgroups, including according to diagnostic setting (inpatient vs outpatient) and priority (primary vs secondary diagnosis).

RESULTS

Among 514 293 patients meeting criteria for the base study population, the mean (SD) age at baseline was 47 (18) years and 282 103 (54.9%) were women; 13 609 (2.6%) met AF diagnostic criteria on or prior to the baseline date and were excluded. Among 500 684 patients free of AF at baseline, standardized AF incidence rates from 2006 to 2018 increased from 4.74 (95% CI, 4.58-4.90) to 6.82 (95% CI, 6.65-7.00) cases per 1000 person-years, increasing significantly over time (P < .001). Incidence rates increased in all age and sex subgroups, although absolute rate increases were largest among those aged 85 years or older. The fraction of incident AF cases among individuals aged 85 years or older increased from 135 of 1075 (12.6%) in 2006 to 451 of 2427 (18.6%) in 2017. Patients with incident AF were more likely over time to have high body mass index (1351 of 3389 patients [39.9%] in 2006-2008 vs 4504 of 9214 [48.9%] in 2015-2018; P < .001), hypertension (2764 [81.6%] in 2006-2008 vs 7937 [86.1%] in 2015-2018; P < .001), and ischemic stroke (328 [9.7%] in 2006-2008 vs 1455 [15.8%] in 2015-2018; P < .001), but less likely to have coronary artery disease (1533 [45.2%] in 2006-2008 vs 3810 [41.4%] in 2015-2018; P < .001). Among 22 077 new cases of AF, 9146 (41.4%) were diagnosed as inpatients and 5731 (26.0%) as the primary diagnosis. Incidence rates of AF increased significantly in all diagnostic setting and priority pairings (eg, inpatient, primary: rate ratio, 1.07; 95% CI, 1.06-1.08; P < .001). Among patients at risk for AF, high BMI and hypertension increased over time (BMI: 71 433 of 198 245 [36.0%] in 2007 to 130 218 of 282 270 [46.1%] in 2017; hypertension: 79 977 [40.3%] in 2007 to 134 404 [47.6%] in 2017). Documentation of short-term ECG increased over time (23 297 of 207 349 [11.2%] in 2008 to 45 027 [16.0%] in 2017); however, long-term ECG monitoring showed no change (1871 [0.9%] in 2007 to 4036 [1.4%] in 2017).

CONCLUSIONS AND RELEVANCE

In this community-based study, AF incidence rates increased significantly during the study period. Concurrent increases were observed in AF risk factors in the at-risk population and short-term ECG use.

Real-world performance of the atrial fibrillation monitor in patients with a subcutaneous ICD

Introduction

The third‐generation subcutaneous implantable cardioverter‐defibrillator (S‐ICD) (EMBLEM™ A219, Boston Scientific) contains a new diagnostic tool to detect atrial fibrillation (AF) in S‐ICD patients, without the use of an intracardiac lead. This is the first study to evaluate the performance of the S‐ICD AF monitor (AFM).

Methods

The AFM algorithm analyzes a subcutaneous signal for the presence of AF, similar to the signals collected by implantable and wearable diagnostic devices. The AFM algorithm combines heart rate (HR) scatter analysis with an HR histogram. The algorithm was tested against publicly available electrocardiogram databases (simulated performance). Real‐world performance of the algorithm was evaluated by using the S‐ICD LATITUDE remote monitoring (RM) database.

Results

The simulated performance of the AFM algorithm resulted in a sensitivity of 95.0%, specificity of 100.0%, and positive predictive value (PPV) of 100.0%. To evaluate the real‐world performance of the AFM, 7744 S‐ICD devices were followed for up to 30 months by RM, whereof 99.5% had the AFM enabled. A total of 387 AF episodes were randomly chosen for adjudication, resulting in a PPV of 67.7%. The main cause of misclassification was atrial and ventricular ectopy.

Conclusion

The AFM exhibited a very high sensitivity and specificity in a simulated setting, designed to maximize PPV in order to minimize the clinical burden of reviewing falsely detected AF events. The real‐world performance of the AFM, enabled in 99.5% of S‐ICD patients, is a PPV of 67.7%.

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.

Prognostic indicators of new onset atrial fibrillation in patients with acute coronary syndrome

Background

This study aims to estimate prognostic indicators of new onset atrial fibrillation (AF) in patients with acute coronary syndrome (ACS) through 3 to 5 years of follow‐up.

Hypothesis

For patients with ACS, some prognostic indicators can be used to predict new onset AF.

Methods

The Improving Care for Cardiovascular Disease in China‐ACS (CCC‐ACS) program was launched in 2014 by a collaborative initiative of the American Heart Association and Chinese Society of Cardiology. We enrolled 866 patients with ACS in a telephone follow‐up program. We inquired about each patient's general health and invited each patient to our hospital for further consultation. We also performed ambulatory electrocardiography and other relevant examinations.

Results

A total of 743 ACS patients were included in the study. After 3 to 5 years, 50 (0.67%) patients developed AF. In multivariable Cox models adjusting for AF risk factors in ACS patients, we found that NT‐proBNP [hazard ratio (HR) 2.625, 1.654‐4.166, P < .05], creatine kinase‐MB (CK‐MB) (HR 4.279, 1.887‐9.703, P < .05), and left ventricular ejection fraction (LVEF) (HR 0.01, 0.001‐0.352, P < .05) were significantly associated with AF receiver operating characteristic (ROC) curves were used to determine a cutoff level for AF screening. NT‐proBNP using a cutoff of 1705 ng/L resulted in a sensitivity of 58% and a specificity of 89.8%. CK‐MB using a cutoff of 142.5 ng/L resulted in a sensitivity of 73.3% and a specificity of 58.3%.

Conclusion

For patients with ACS, NT‐proBNP, CK‐MB, and LVEF have a considerable prognostic value for predicting whether AF would be detected during follow‐up.

Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study

Background

Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke.

Objective

This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF.

Methods

A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF.

Results

Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout [W] mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R²) was 0.685 and 0.057, respectively (P<.001 and .29, respectively).

Conclusions

In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT.

Relationship between Atrial Tachyarrhythmias and Intrathoracic Impedance in Patients with a Pacemaker and Preserved Ejection Fraction

Atrial fibrillation (AF) is responsible for significant morbidity and mortality in patients with heart failure (HF). Modern pacemakers provide an index of intrathoracic fluid status (OptiVol fluid index-OVFI) by measuring daily intrathoracic impedance. This study aimed to determine whether OVFI is associated with increased atrial tachycardia/fibrillation (AT/AF) events in patients with a preserved ejection fraction (EF). We retrospectively reviewed data from patients with Medtronic Advisa pacemakers between 2012 and 2014 in our hospital. The association and temporal relationship between OVFI and AT/AF events were determined. A total of 150 patients with 211 follow-up visits (mean 1.4 visits per patient) were evaluated. The device-detected AT/AF prevalence was 47%. Device-measured OVFI ≥ 20 Ω-days was significantly associated with the onset of AT/AF ≥ 4 h. OVFI threshold crossing preceded AT/AF events in 55.1% of cases, followed by AT/AF events in only 18.7%. Fluid overload represented by OVFI may trigger AT/AF episodes in patients with a preserved EF more often than that previously reported in patients with a reduced EF. Our findings support the view that worsening pulmonary congestion is associated with increased AT/AF frequency and suggests that fluid overload could trigger and perpetuate AT/AF events in patients with a preserved EF.

Prevalence, risk factors, and type of sleep apnea in patients with paroxysmal atrial fibrillation

Background

Recent studies have suggested an association between sleep apnea (SA) and atrial fibrillation (AF). We aimed to study the prevalence, characteristics, risk factors and type of sleep apnea (SA) in ablation candidates with paroxysmal AF.

Methods/Results

We prospectively studied 579 patients with paroxysmal AF, including 157 women (27.1%) and 422 men (72.9%). Mean age was 59.9 ± 9.6 years and mean body mass index (BMI) 28.5 ± 4.5 kg/m². SA was diagnosed using polygraphy for two nights at home. The Epworth Sleepiness Scale (ESS), STOP-Bang Questionnaire, and Berlin Questionnaire (BQ) assessed the degree of SA symptoms. A total of 479 (82.7%) patients had an apnea-hypopnea index (AHI) ≥ 5, whereas moderate-severe SA (AHI ≥ 15) was diagnosed in 244 patients (42.1%). The type of SA was predominantly obstructive, with a median AHI of 12.1 (6.7–20.6) (range 0.4–85.8). The median central apnea index was 0.3 (0.1–0.7). AHI increased with age, BMI, waist and neck circumference, body and visceral fat. Using the Atrial Fibrillation Severity Scale and the SF-36, patients with more severe SA had a higher AF burden, severity and symptom score and a lower Physical-Component Summary score. Age, male gender, BMI, duration of AF, and habitual snoring were independent risk factors in multivariate analysis (AHI ≥ 15). We found no association between ESS and AHI (R² = 0.003, p = 0.367).

Conclusions

In our AF population, SA was highly prevalent and predominantly obstructive. The high prevalence of SA detected in this study may indicate that SA is under-recognized in patients with AF. None of the screening questionnaires predicted SA reliably.

Applying the Capability, Opportunity, and Motivation Behaviour Model (COM-B) to Guide the Development of Interventions to Improve Early Detection of Atrial Fibrillation

Objective:

The primary objective of this study is to use the Capability, Opportunity, and Motivation Behaviour (COM-B) model to identify potential strategies aimed at improving the early detection of atrial fibrillation (AF) in the general population.

Methods:

We undertook a review of the literature to identify factors associated with participation in community-based screening for AF, followed by mapping of the factors generated into the components of the COM-B model, and validation of the model by an expert panel. The Behaviour Change Wheel (BCW) was used to nominate potential intervention strategies and steps to guide the design and implementation of community-based screening for AF.

Results:

A total of 28 factors from 21 studies were mapped into the COM-B model. Based on the BCW approach, 24 intervention strategies and 7 steps that could guide the design and implementation of community-based screening for AF were recommended.

Conclusion:

The application of the COM-B model demonstrated how factors influencing the participation of individuals with undiagnosed AF in community-based screening could be identified. The model could also serve as a guide for the design and implementation of interventions for improving AF detection in the general population.

How useful is the smartwatch ECG?

Apple launched a novel feature of the Apple Watch (Apple Inc.) series 4 that enables consumers to record a rhythm strip and assist with self-diagnosis of atrial fibrillation (AF). The watch is paired with an app that provides automatic classification of the rhythm. Ability of the algorithm to identify AF has received Food and Drug Administration clearance. Given increasing use of direct-to-consumer devices, important questions regarding the utilization of such devices and their features in clinical practice arise. It is unclear how the data obtained from these devices can be optimally incorporated in patient care and what it means for patients. Safety and security of using wearables are also of concern. Furthermore, whether data generated from the Electrocardiogram (ECG) feature will be beneficial to public health is to be determined. We discuss possible uses and challenges of Apple's (Apple Inc.) newly launched ECG feature and review an upcoming trial looking at clinical applications and outcomes using this technology. We also review the literature on the Kardia (AliveCor Inc.) mobile and smartwatch ECG technology and briefly discuss Apple Watch irregular heartbeat notifications along with the Apple Heart Study.

GWAS contribution to atrial fibrillation and atrial fibrillation-related stroke: pathophysiological implications

Atrial fibrillation (AF) and stroke are included in a group of complex traits that have been approached regarding of their study by susceptibility genetic determinants. Since 2007, several genome-wide association studies (GWAS) aiming to identify genetic variants modulating AF risk have been conducted. Thus, 11 GWAS have identified 26 SNPs (p < 5 × 10^-2), of which 19 reached genome-wide significance (p < 5 × 10^-8). From those variants, seven were also associated with cardioembolic stroke and three reached genome-wide significance in stroke GWAS. These associations may shed a light on putative shared etiologic mechanisms between AF and cardioembolic stroke. Additionally, some of these identified variants have been incorporated in genetic risk scores in order to elucidate new approaches of stroke prediction, prevention and treatment.

Continuous monitoring after atrial fibrillation ablation: the LINQ AF study

Aims

To study device performance, arrhythmia recurrence characteristics, and methods of outcome assessment using a novel implantable cardiac monitor (ICM) in patients undergoing ablation for atrial fibrillation (AF).

Methods and results

In 419 consecutive patients undergoing first-time catheter ablation for symptomatic paroxysmal (n = 224) or persistent (n = 195) AF an ICM was injected at the end of the procedure. Telemedicine staff ensured full episode transmission coverage and manually evaluated all automatic arrhythmia episodes. Device detection metrics were calculated for ≥2, ≥6, and ≥10 min AF detection durations. Four methods of outcome assessment were studied: continuous recurrence analysis, discontinuous recurrence analysis, AF-burden analysis, and analysis of individual rhythm profiles. A total of 43 673 automatic AF episodes were transmitted over a follow-up of 15 ± 6 months. Episode-based positive predictive values changed significantly with longer AF detection durations (70.5% for ≥2 min, 81.8% for ≥6 min, and 85.9% for ≥10 min). Patients with exclusive short episode recurrences (≥2 to <6 min) were rare and their arrhythmia detection was clinically irrelevant. Different methods of outcome assessment showed a large variation (46–79%) in ablation success. Individual rhythm characteristics and subclinical AF added to this inconsistency. Analysis of AF-burden and individual rhythm profiles were least influenced and showed successful treatment in 60–70% of the patients.

Conclusion

We suggest AF detection duration >6 min and AF burden >0.1% as a standardized outcome definition for AF studies to come in the future.

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Background

Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or handcrafted machine learning techniques that are limited in terms of diagnostic accuracy and reliability.

Objective

This study aimed to develop deep learning (DL) classifiers using PPG data to detect AF from the sinus rhythm (SR) in the presence of PACs after successful cardioversion.

Methods

We examined 75 patients with AF who underwent successful elective direct-current cardioversion (DCC). Electrocardiogram and pulse oximetry data over a 15-min period were obtained before and after DCC and labeled as AF or SR. A 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) were chosen as the 2 DL architectures. The PAC indicator estimated the burden of PACs on the PPG dataset. We defined a metric called the confidence level (CL) of AF or SR diagnosis and compared the CLs of true and false diagnoses. We also compared the diagnostic performance of 1D-CNN and RNN with previously developed AF detectors (support vector machine with root-mean-square of successive difference of RR intervals and Shannon entropy, autocorrelation, and ensemble by combining 2 previous methods) using 10 5-fold cross-validation processes.

Results

Among the 14,298 training samples containing PPG data, 7157 samples were obtained during the post-DCC period. The PAC indicator estimated 29.79% (2132/7157) of post-DCC samples had PACs. The diagnostic accuracy of AF versus SR was 99.32% (70,925/71,410) versus 95.85% (68,602/71,570) in 1D-CNN and 98.27% (70,176/71,410) versus 96.04% (68,736/71,570) in RNN methods. The area under receiver operating characteristic curves of the 2 DL classifiers was 0.998 (95% CI 0.995-1.000) for 1D-CNN and 0.996 (95% CI 0.993-0.998) for RNN, which were significantly higher than other AF detectors (P<.001). If we assumed that the dataset could emulate a sufficient number of patients in training, both DL classifiers improved their diagnostic performances even further especially for the samples with a high burden of PACs. The average CLs for true versus false classification were 98.56% versus 78.75% for 1D-CNN and 98.37% versus 82.57% for RNN (P<.001 for all cases).

Conclusions

New DL classifiers could detect AF using PPG monitoring signals with high diagnostic accuracy even with frequent PACs and could outperform previously developed AF detectors. Although diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of the diagnosis could be indicated by the CL. Wearable devices sensing PPG signals with DL classifiers should be validated as tools to screen for AF.

Evaluation of a clinical score for predicting atrial fibrillation in cryptogenic stroke patients with insertable cardiac monitors: results from the CRYSTAL AF study

Background

The HAVOC score was previously developed to predict the risk of atrial fibrillation (AF) after cryptogenic stroke (CS) or transient ischemic attack (TIA). The purpose of this study was to apply the HAVOC score to patients who received insertable cardiac monitors (ICMs) in the CRYSTAL AF study.

Methods

All patients from the CRYSTAL AF study who received an ICM were included. HAVOC score (one point each for peripheral vascular disease and obesity with body mass index >30, two points each for hypertension, age ⩾ 75, valvular heart disease, and coronary artery disease, 4 points for congestive heart failure) was computed for all patients. The primary endpoint was AF detection by 12 months of ICM monitoring.

Results

A total of 214 patients who received ICM were included. AF was detected in 40 patients while the remaining 174 patients were AF negative. The HAVOC score was significantly higher among patients with AF [median 3.0 with interquartile range (IQR) 2-4] than those without AF [median 2.0 (IQR 0-3)], p = 0.01. AF increased significantly across the three HAVOC score groups: 11% in Group A (score 0-1), 18% in Group B (score 2-3), and 32 % in Group C (score ⩾ 4) with p = 0.02.

Conclusions

The HAVOC score was shown in this post hoc analysis of CRYSTAL AF to successfully stratify AF risk post CS or TIA. The 11% AF rate in the lowest HAVOC score group highlights the significance of nontraditional contributors to AF and ischemic stroke.

Risk Assessment of Stroke in Patients with Atrial Fibrillation: Current Shortcomings and Future Directions

Atrial fibrillation is a well-known risk factor for cardioembolic stroke; a number of risk stratification scoring systems have been developed to help differentiate which patients would stand to benefit from anticoagulation. However, these scoring systems cannot be utilized in patients whose atrial fibrillation has not been diagnosed. As implantable cardiac monitors become more prevalent, it becomes possible to identify occult, subclinical atrial fibrillation. With this data, it is also possible to examine the relationship between episodes of paroxysmal atrial fibrillation and thromboembolism and the total burden of paroxysmal atrial fibrillation and thromboembolic risk. The data gleaned from these devices provides insight and raises questions regarding the underlying mechanism of thromboembolism in atrial fibrillation, and in doing so, exposes shortcomings in the present clinical use of current risk scoring systems, specifically, the inability to account for atrial fibrillation burden and to apply scores at all in subclinical atrial fibrillation.

A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings

Entropy-based atrial fibrillation (AF) detectors have been applied for short-term electrocardiogram (ECG) analysis. However, existing methods suffer from several limitations. To enhance the performance of entropy-based AF detectors, we have developed a new entropy measure, named Entropy_AF, which includes the following improvements: (1) use of a ranged function rather than the Chebyshev function to define vector distance, (2) use of a fuzzy function to determine vector similarity, (3) replacement of the probability estimation with density estimation for entropy calculation, (4) use of a flexible distance threshold parameter, and (5) use of adjusted entropy results for the heart rate effect. Entropy_AF was trained using the MIT-BIH Atrial Fibrillation (AF) database, and tested on the clinical wearable long-term AF recordings. Three previous entropy-based AF detectors were used for comparison: sample entropy (SampEn), fuzzy measure entropy (FuzzyMEn) and coefficient of sample entropy (COSEn). For classifying AF and non-AF rhythms in the MIT-BIH AF database, Entropy_AF achieved the highest area under receiver operating characteristic curve (AUC) values of 98.15% when using a 30-beat time window, which was higher than COSEn with AUC of 91.86%. SampEn and FuzzyMEn resulted in much lower AUCs of 74.68% and 79.24% respectively. For classifying AF and non-AF rhythms in the clinical wearable AF database, Entropy_AF also generated the largest values of Youden index (77.94%), sensitivity (92.77%), specificity (85.17%), accuracy (87.10%), positive predictivity (68.09%) and negative predictivity (97.18%). COSEn had the second-best accuracy of 78.63%, followed by an accuracy of 65.08% in FuzzyMEn and an accuracy of 59.91% in SampEn. The new proposed Entropy_AF also generated highest classification accuracy when using a 12-beat time window. In addition, the results from time cost analysis verified the efficiency of the new Entropy_AF. This study showed the better discrimination ability for identifying AF when using Entropy_AF method, indicating that it would be useful for the practical clinical wearable AF scanning.

Usefulness of the Addition of Renal Function to the CHA2DS2-VASc Score as a Predictor of Thromboembolism and Mortality in Patients Without Atrial Fibrillation

Research is conflicting whether kidney function should be incorporated in thromboembolism risk prediction. Our published data showed that the CHA2DS2-VASc score predicts thromboembolism and mortality in those without atrial fibrillation. We used the Rochester Epidemiology Project medical records system to retrospectively evaluate whether adding renal impairment (1 point) to the CHA2DS2-VASc score (-R) enhances the score's prediction of mortality, thromboembolism, and atrial fibrillation in patients without atrial fibrillation. We identified patients that had an implantable cardiac monitoring device placed from January 1, 2004 to December 31, 2013, which was defined as the start date. Follow-up was through March 7, 2016. An implantable device was required to discern the absence of atrial fibrillation. Renal impairment was defined as chronic kidney disease stage 3 or greater. The population (n = 1,606) had a mean age of 69.8 years and median follow-up of 4.8 years. Baseline renal impairment was predictive of mortality (hazard ratio [HR] 2.06, 95% confidence interval [CI] 1.64 to 2.60, p <0.001), thromboembolism (HR 1.34, 95% CI 0.96 to 1.87, p = 0.09), and atrial fibrillation (HR 1.31, 95% CI 0.98 to 1.74, p = 0.07). Lower glomerular filtration rate correlated significantly with mortality. Increasing CHA2DS2-VASc-R score correlated significantly with mortality, thromboembolism, and incident atrial fibrillation. The addition of renal impairment to the CHA2DS2-VASc score improved the C-statistics for thromboembolism and survival from 0.72 to 0.73 (p = 0.01) and 0.70 to 0.72 (p <0.001). Adding renal impairment to the CHA2DS2-VASc score improves the score's prediction of thromboembolism and mortality in a population without atrial fibrillation, although the incremental benefit appears mild.

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo-Plethysmography at the Wrist

Background Long-term continuous cardiac monitoring would aid in the early diagnosis and management of atrial fibrillation ( AF ). This study examined the accuracy of a novel approach for AF detection using photo-plethysmography signals measured from a wrist-based wearable device. Methods and Results ECG and contemporaneous pulse data were collected from 2 cohorts of AF patients: AF patients (n=20) undergoing electrical cardioversion ( ECV ) and AF patients (n=40) that were prescribed for 24 hours ECG Holter in outpatient settings ( HOL ). Photo-plethysmography and acceleration data were collected at the wrist and processed to determine the inter-pulse interval and discard inter-pulse intervals in presence of motion artifacts. A Markov model was deployed to assess the probability of AF given irregular pattern in inter-pulse interval sequences. The AF detection algorithm was evaluated against clinical rhythm annotations of AF based on ECG interpretation. Photo-plethysmography recordings from apparently healthy volunteers (n=120) were used to establish the false positive AF detection rate of the algorithm. A total of 42 and 855 hours (AF: 21 and 323 hours) of photo-plethysmography data were recorded in the ECV and HOL cohorts, respectively. AF was detected with >96% accuracy ( ECV, sensitivity=97%; HOL , sensitivity=93%; both with specificity=100%). Because of motion artifacts, the algorithm did not provide AF classification for 44±16% of the monitoring period in the HOL group. In healthy controls, the algorithm demonstrated a <0.2% false positive AF detection rate. Conclusions A novel AF detection algorithm using pulse data from a wrist-wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

Diagnostic Accuracy of a Novel Mobile Phone Application for the Detection and Monitoring of Atrial Fibrillation

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in adults, associated with significant morbidity, increased mortality, and rising health-care costs. Simple and available tools for the accurate detection of arrhythmia recurrence in patients after electrical cardioversion (CV) or ablation procedures for AF can help to guide therapeutic decisions. We conducted a prospective, single-center study to evaluate the accuracy of Cardiio Rhythm Mobile Application (CRMA) for AF detection. Patients >18 years of age who were scheduled for elective CV for AF were enrolled in the study. CRMA finger pulse recordings, utilizing an iPhone camera, were obtained before (pre-CV) and after (post-CV) the CV. The findings were validated against surface electrocardiograms. Ninety-eight patients (75.5% men), mean age of 67.7 ± 10.5 years, were enrolled. No electrocardiogram for validation was available in 1 case. Pre-CV CRMA readings were analyzed in 97 of the 98 patients. Post-CV CRMA readings were analyzed for 92 of 93 patients who underwent CV. One patient left before the recording was obtained. The Cardiio Rhythm Mobile Application correctly identified 94 of 101 AF recordings (93.1%) as AF and 80 of 88 non-AF recordings (90.1%) as non-AF. The sensitivity was 93.1% (95% confidence interval [CI] = 86.9% to 97.2%) and the specificity was 90.9% (95% CI = 82.9% to 96.0%). The positive predictive value was 92.2% (95% CI = 85.8% to 95.8%) and the negative predictive value was 92.0% (95% CI = 94.8% to 95.9%). In conclusion, the CRMA demonstrates promising potential in accurate detection and discrimination of AF from normal sinus rhythm in patients with a history of AF.

Characteristics and outcomes of atrial fibrillation patients with or without specific symptoms: results from the PREFER in AF registry

Aims

Atrial fibrillation (AF) is a common condition that is a major cause of stroke. A significant proportion of patients with AF are not classically symptomatic at diagnosis or soon after diagnosis. There is little information comparing their characteristics, treatment, and outcomes of patients with symptoms, which predominate in clinical trials to those without.

Methods and results

We analysed data from the Prevention of Thromboembolic Events-European Registry in Atrial Fibrillation. This was a prospective, real-world registry with a 12-month follow-up that included AF patients aged 18 years and over. Patients were divided into those with and without AF symptoms using the European Heart Rhythm Association (EHRA) score (Category I vs. Categories II-IV). Of the 6196 patients (mean age 72 years) with EHRA scores available, 501 (8.1%) were asymptomatic. A lower proportion of asymptomatic patients was female (22.8 vs. 41.2%), with less noted to have heart failure and coronary artery disease (P < 0.01 for all). There were no differences in terms of the prevalence of diabetes, obesity, or prior stroke. Asymptomatic patients had a lower CHA2DS2-VASc score (2.9 ± 1.7 vs. 3.4 ± 1.8; P < 0.01) and HAS-BLED score (1.8 ± 1.1 vs. 2.1 ± 1.2; P < 0.01). During the 1-year follow-up, adverse events occurred at similar frequencies in asymptomatic and symptomatic patients (1.6 vs. 0.8% for ischaemic stroke; P = 0.061; 1.4 vs. 1.3% for transient ischaemic attack; P = 0.840). Patients with higher CHA2DS2-VASc and HAS-BLED scores experienced more events, independent of symptoms. Antithrombotic therapy was comparable for both groups at baseline and at follow-up.

Conclusions

The similar clinical characteristics and frequency of adverse events between asymptomatic and symptomatic AF patients revives the question of whether screening programmes to detect people with asymptomatic AF are worthwhile, particularly in those aged 65 and over potentially likely to have clinical and economic benefits from anticoagulants. This evidence may be informative if clinicians may not be comfortable participating in future clinical trials, leaving asymptomatic patients with AF and high stroke risk without anticoagulation.

Monitoring and detecting atrial fibrillation using wearable technology

Atrial fibrillation (AFib) is diagnosed by analysis of the morphological and rhythmic properties of the electrocardiogram. It was recently shown that accurate detection of AFib is possible using beat-to-beat interval variations. This raises the question of whether AFib detection can be performed using a pulsatile waveform such as the Photoplethysmogram (PPG). The recent explosion in use of recreational and professional ambulatory wrist-based pulse monitoring devices means that an accurate pulse-based AFib screening algorithm would enable large scale screening for silent or undiagnosed AFib, a significant risk factor for multiple diseases. We propose a noise-resistant machine learning approach to detecting AFib from noisy ambulatory PPG recorded from the wrist using a modern research watch-based wearable device (the Samsung Simband). Ambulatory pulsatile and movement data were recorded from 46 subjects, 15 with AFib and 31 non symptomatic. Single channel electrocardiogram (ECG), multi-wavelength PPG and tri-axial accelerometry were recorded simultaneously at 128 Hz from the non-dominant wrist using the Simband. Recording lengths varied from 3.5 to 8.5 minutes. Pulse (beat) detection was performed on the PPG waveforms, and eleven features were extracted based on beat-to-beat variability and waveform signal quality. Using 10-fold cross validation, an accuracy of 95 % on out-of-sample data was achieved, with a sensitivity of 97%, specificity of 94%, and an area under the receiver operating curve (AUROC) of 0.99. The described approach provides a noise-resistant, accurate screening tool for AFib from PPG sensors located in an ambulatory wrist watch. To our knowledge this is the first study to demonstrate an algorithm with a high enough accuracy to be used in general population studies that does not require an ambulatory Holter electrocardiographic monitor.

ANMCO Position Paper: direct oral anticoagulants for stroke prevention in atrial fibrillation: clinical scenarios and future perspectives

It is now 4 years since the introduction of the new direct oral anticoagulants into clinical practice. Therefore, the Italian Association of Hospital Cardiologists (ANMCO) has deemed necessary to update the previous position paper on the prevention of thrombo-embolic complications in patients with non-valvular atrial fibrillation, which was published in 2013. All available scientific evidence has been reviewed, focusing on data derived from both clinical trials and observational registries. In addition, all issues relevant to the practical clinical management of oral anticoagulation with the new direct inhibitors have been considered. Specific clinical pathways for optimal use of oral anticoagulation with the new directly acting agents are also developed and proposed for clinical implementation. Special attention is finally paid to the development of clinical algorithms for medium and long-term follow-up of patients treated with new oral direct anticoagulants.

Heartbeat monitoring from adaptively down-sampled electrocardiogram

BACKGROUND AND OBJECTIVE

Heartbeats Holter monitoring is important for the detection of arrhythmias and possible anomalies, which are predictive of cardiovascular risks and infections. Reducing the number of acquired samples is useful to save energy and memory, but a proper down-sampling schedule is needed to record all useful information. Method An adaptive algorithm for the non-uniform down-sampling of data is used to reduce the mean sampling frequency of ECG data. The acquired data are processed to extract RR rhythm and to classify the heartbeats among a set of possible types of arrhythmias.

RESULTS

The proposed method is tested in terms of the ability to estimate the heart rate and to classify the heartbeats from the MIT-BIH Arrhythmia data down-sampled below the Nyquist limit. The mean accuracy in identifying the heartbeats was over the 98% and the RMS error in estimating the RR time series was lower than the 1%. Variability, spectral and complexity indexes extracted from RR series were estimated with a mean error that was lower than 10%. Classification accuracy was above the 95%.

CONCLUSIONS

An adaptive method to down-sample ECG data is discussed. It can be useful to save energy and to reduce memory occupation, while still preserving important information on the heartbeats.