Triage tests for identifying atrial fibrillation in primary care: a diagnostic accuracy study comparing single-lead ECG and modified BP monitors

Atrial Fibrillation Screening During Routine Automated Office, Home, and Ambulatory Blood Pressure Measurement: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis

BACKGROUND

Atrial fibrillation (AF) is often asymptomatic and undiagnosed. As AF and hypertension often coexist, opportunistic AF detection during routine automated blood pressure (BP) measurement appears to be an attractive screening method.

METHODS

A systematic literature search was conducted to identify studies assessing the diagnostic test accuracy of office, home, or 24-hour ambulatory BP measuring devices with AF detection algorithms versus reference electrocardiography. Analyses were performed per participant (AF status based on several BP readings; most office/home devices) or per reading (AF status based on individual readings; all ambulatory devices). A meta-analysis stratified by device type (office/home/ambulatory) was conducted to calculate pooled measures of diagnostic accuracy. Sensitivity/meta-regression analyses were also performed.

RESULTS

Among 3096 records initially retrieved, 23 diagnostic test accuracy studies were included. Data derived from 11 093 individuals (weighted age 69 years, males 56%, hypertensives 79%, diabetics 24%, and AF prevalence 17%) indicated a pooled sensitivity 0.97 (95% CI, 0.92-0.99), specificity 0.93 (95% CI, 0.90-0.95), and accuracy 0.93 (95% CI, 0.89-0.95), with generally consistent results using office, home, or ambulatory BP devices (slightly lower specificity with the latter). The positive and negative predictive values were 0.70 (95% CI, 0.60-0.80) and 0.99 (95% CI, 0.98-1.00), respectively. Sensitivity analyses indicated lower specificity in studies implementing reading versus participant analyses. Most studies presented a low risk of bias and minor applicability concerns.

CONCLUSIONS

There is considerable and consistent evidence suggesting high diagnostic accuracy of AF detection algorithms implemented in automated BP monitors during routine BP measurements in and out of the office. AF diagnosis requires verification (electrocardiography) before treatment is administered.

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.

Mobile Electrocardiograms in the Detection of Subclinical Atrial Fibrillation in High-Risk Outpatient Populations: Protocol for an Observational Study

BACKGROUND

Single-lead, smartphone-based mobile electrocardiograms (ECGs) have the potential to provide a noninvasive, rapid, and cost-effective means of screening for atrial fibrillation (AFib) in outpatient settings. AFib has been associated with various comorbid diseases that prompt further investigation and screening methodologies for at-risk populations. A simple 30-second sinus rhythm strip from the KardiaMobile ECG (AliveCor) can provide an effective screen for cardiac rhythm abnormalities.

OBJECTIVE

The aim of this study is to demonstrate the feasibility of performing Kardia-enabled ECG recordings routinely in outpatient settings in high-risk populations and its potential use in uncovering previous undiagnosed cases of AFib. Specific aim 1 is to determine the feasibility and accuracy of performing routine cardiac rhythm sampling in patients deemed at high risk for AFib. Specific aim 2 is to determine whether routine rhythm sampling in outpatient clinics with high-risk patients can be used cost-effectively in an outpatient clinic without increasing the time it takes for the patient to be seen by a physician.

METHODS

Participants were recruited across 6 clinic sites across the University of Florida Health Network: University of Florida Health Nephrology, Sleep Center, Ophthalmology, Urology, Neurology, and Pre-Surgical. Participants, aged 18-99 years, who agreed to partake in the study were given a consent form and completed a questionnaire regarding their past medical history and risk factors for cardiovascular disease. Single-lead, 30-second ECGs were taken by the KardiaMobile ECG device. If patients are found to have newly diagnosed AFib, the attending physician is notified, and a 12-lead ECG or standard ECG equivalent will be ordered.

RESULTS

As of March 1, 2024, a total of 2339 participants have been enrolled. Of the data collected thus far, the KardiaMobile rhythm strip reported 381 abnormal readings, which are pending analysis from a cardiologist. A total of 78 readings were labeled as possible AFib, 159 readings were labeled unclassified, and 49 were unreadable. Of note, the average age of participants was 61 (SD 10.25) years, and the average self-reported weight was 194 (SD 14.26) pounds. Additionally, 1572 (67.25%) participants report not regularly seeing a cardiologist. Regarding feasibility, the average length of enrolling a patient into the study was 3:30 (SD 0.5) minutes after informed consent was completed, and medical staff across clinic sites (n=25) reported 9 of 10 level of satisfaction with the impact of the screening on clinic flow.

CONCLUSIONS

Preliminary data show promise regarding the feasibility of using KardiaMobile ECGs for the screening of AFib and prevention of cardiological disease in vulnerable outpatient populations. The use of a single-lead mobile ECG strip can serve as a low-cost, effective AFib screen for implementation across free clinics attempting to provide increased health care accessibility.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52647.

Diagnostic accuracy for detecting atrial fibrillation using a novel machine learning algorithm in a blood pressure monitor

BACKGROUND

Early detection of atrial fibrillation (AF) is key for preventing strokes. Blood pressure monitors (BPMs) with built-in AF screening features have the potential for early detection at home. Recently, 2 BPMs (HEM-7371T1-AZ and HEM-7372T1-AZAZ, Omron Healthcare Co., Ltd.) that share a novel AF screening feature have been developed. Their AF screening feature utilizes an algorithm that incorporates machine learning, with the potential to improve diagnostic accuracy.

OBJECTIVE

The purpose of this study was to evaluate the performance of this AF screening feature in a multicenter, prospective clinical study at 5 sites in the United States.

METHODS

A total of 559 subjects were enrolled for this study: 267 in AF cohort and 292 in the non-AF cohort. AF screening was performed in all subjects by the 2 Omron BPMs and by 1 Microlife BPM (BP 3MX1-3, WatchBP Home A, Microlife Corp.), and a simultaneous 12-lead electrocardiogram (ECG) was recorded for comparison. All 12-lead ECGs were interpreted by a board-certified cardiologist who was blinded to the BPM results. Sensitivity, specificity, and accuracy for the diagnosis of AF were calculated.

RESULTS

Omron HEM-7371T1-AZ BPM had sensitivity of 95.1% (95% confidence interval [CI] 91.8%-97.4%), specificity 98.6% (95% CI 96.6%-99.7%), and accuracy of 97.0% (95% CI 95.2%-98.2%). Equivalent results were obtained with the Omron HEM-7371T1-AZAZ BPM. This compared favorably to the Microlife BPM (sensitivity 78.5%, 95% CI 73.1%-83.3%; specificity 97.6%, 95% CI 95.1%-99.0%; accuracy 88.4%, 95% CI 85.5%-91.0%).

CONCLUSION

These data support both home and professional use of these novel Omron BPMs for the detection of AF.

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.

Anxiety and heart rate in a real-life class test in undergraduates choosing real-time or prerecorded oral presentations

Most studies on acute stress stem from works conducted under artificial laboratory conditions. Inducing stress for experimental scrutiny is problematic and can be unethical. In this study, a 'research methods' course's curriculum included a demonstration study testing anxiety and heart rate responses to the midterm test. Fifty-four university students (35 males and 19 females) presented a research topic in-person (n = 14) or prerecorded while being present (n = 40). Students selected a test format they could change until the week before the midterm test. The measures were trait anxiety, test anxiety, state anxiety, heart rate (HR), the last two being measured before and after examination, and grades. All students manifested decreased state anxiety and increased HR from before to after the test. Females exhibited higher HR and state anxiety than males. Real-time presentations were associated with higher HRs but not higher state anxiety. Those who changed their planned presentation mode from in-person to prerecorded exhibited higher test anxiety but not trait anxiety than those who presented as planned. Students who presented in-person obtained lower grades than those who prerecorded their presentations. Grades were negatively correlated with state anxiety after the test, but test anxiety did not significantly mediate the grades. Pre-test state anxiety was positively associated with both trait anxiety and test anxiety. The findings suggest that test anxiety is unlikely to affect grades, but test-anxious students need more control over academic evaluation. A choice in test format could be helpful in this regard.

Insights into the Role of Galectin-3 as a Diagnostic and Prognostic Biomarker of Atrial Fibrillation

Atrial fibrillation (AF) is an irregular atrial activity and the most prevalent type of arrhythmia. Although AF is easily diagnosed with an electrocardiogram, there is a keen interest in identifying an easy-to-dose biomarker that can predict the prognosis of AF and its recurrence. Galectin-3 (Gal-3) is a beta-galactoside binding protein from the lectin family with pro-fibrotic and -inflammatory effects and a pivotal role in a variety of biological processes, cell proliferation, and differentiation; therefore, it is implicated in the pathogenesis of many cardiovascular (e.g., heart failure (HF)) and noncardiovascular diseases. However, its specificity and sensitivity as a potential marker in AF patients remain debated and controversial. This article comprehensively reviewed the evidence regarding the interplay between Gal-3 and patients with AF. Clinical implications of measuring Gal-3 in AF patients for diagnosis and prognosis are mentioned. Moreover, the role of Gal-3 as a potential biomarker for the management of AF recurrence is investigated. The association of Gal-3 and AF in special populations (coronary artery disease, HF, metabolic syndrome, chronic kidney disease, and diabetes mellitus) has been explored in this review. Overall, although further studies are needed to enlighten the role of Gal-3 in the diagnosis and treatment of AF, our study demonstrated the high potential of this molecule to be used and focused on by researchers and clinicians.

Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Blood-biomarkers and devices for atrial fibrillation screening: Lessons learned from the AFRICAT (Atrial Fibrillation Research In CATalonia) study

Background and objective

AFRICAT is a prospective cohort study intending to develop an atrial fibrillation (AF) screening program through the combination of blood markers, rhythm detection devices, and long-term monitoring in our community. In particular, we aimed to validate the use of NT-proBNP, and identify new blood biomarkers associated with AF. Also, we aimed to compare AF detection using various wearables and long-term Holter monitoring.

Methods

359 subjects aged 65–75 years with hypertension and diabetes were included in two phases: Phase I (n = 100) and Phase II (n = 259). AF diagnosis was performed by baseline 12-lead ECG, 4 weeks of Holter monitoring (NuuboTM), and/or medical history. An aptamer array including 1310 proteins was measured in the blood of 26 patients. Candidates were selected according to p-value, logFC and biological function to be tested in verification and validation phases. Several screening devices were tested and compared: AliveCor, Watch BP, MyDiagnostick and Fibricheck.

Results

AF was present in 34 subjects (9.47%). The aptamer array revealed 41 proteins with differential expression in AF individuals. TIMP-2 and ST-2 were the most promising candidates in the verification analysis, but none of them was further validated. NT-proBNP (log-transformed) (OR = 1.934; p<0.001) was the only independent biomarker to detect AF in the whole cohort. Compared to an ECG, WatchBP had the highest sensitivity (84.6%) and AUC (0.895 [0.780–1]), while MyDiagnostick showed the highest specificity (97.10%).

Conclusion

The inclusion and monitoring of a cohort of primary care patients for AF detection, together with the testing of biomarkers and screening devices provided useful lessons about AF screening in our community. An AF screening strategy using rhythm detection devices and short monitoring periods among high-risk patients with high NT-proBNP levels could be feasible.

Sensitivity and specificity of automated blood pressure devices to detect atrial fibrillation: A systematic review and meta-analysis of diagnostic accuracy

Background

Atrial fibrillation (AF) is a prevalent and preventable cause of stroke and mortality.

Aim

This systematic review and meta-analysis aimed to investigate the sensitivity and specificity of office and out-of-office automated blood pressure (BP) devices to detect AF.

Methods

Diagnostic studies, extracted from databases such as Ovid Medline and Embase, on AF detection by BP device(s), electrocardiography, and reported sensitivity and specificity, were included. Screening of abstracts and full texts, data extraction, and quality assessment were conducted independently by two investigators using Covidence software. The sensitivity and specificity of the BP devices were pooled using a random-effects model.

Results

Sixteen studies including 10,158 participants were included. Only a few studies were conducted in primary care (n = 3) or with a low risk of bias (n = 5). Office BP devices, which utilised different algorithms to detect AF, had a sensitivity and specificity of 96.2 and 94%, respectively. Specificity was reduced when only one positive result was considered among consecutive BP measurements. Only a few studies (n = 3) investigated out-of-office BP. Only one study (n = 100) suggested the use of ≥79 and ≥26% of positive readings on 24-h ambulatory BP measurements to detect AF and paroxysmal AF, respectively.

Conclusions

Office BP devices can be used clinically to screen for AF in high-risk populations. Clinical trials are needed to determine the effect of AF screening using office BP devices in reducing stroke risk and mortality. Further studies are also required to guide out-of-office use of BP devices for detecting paroxysmal AF or AF.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022319541, PROSPERO CRD42022319541.

Clinical relevance of double-arm blood pressure measurement and prevalence of clinically important inter-arm blood pressure differences in Indian primary care

Hypertension guidelines recommend measuring blood pressure (BP) in both arms at least once. However, this is seldom done due to uncertainties regarding measurement procedure and the implications of finding a clinically important inter‐arm BP difference (IAD). This study aimed to provide insight into the prevalence of clinically important IADs in a large Indian primary care cohort.

A number of 134 678 (37% female) unselected Indian primary care participants, mean age 45.2 (SD 11.9) years, had BP measured in both arms using a standardized, triplicate, automated simultaneous measurement method (Microlife WatchBP Office Afib).

On average, there were clinically minor differences in right and left arm BP values: systolic BP 134.4 vs 134.2 mmHg (p < .01) and diastolic BP 82.7 vs 82.6 mmHg (p < .01), respectively.

Prevalence of significant mean systolic IAD between 10 and 15 mmHg was 7,813 (5.8%). Systolic IAD ≥ 15 mmHg 2,980 (2.2%) and diastolic IAD ≥ 10 mmHg 7,151 (5.3%). In total, there were 7,595 (5.6%) and 8,548 (6.3%) participants with BP above the 140/90 mmHg threshold in only the left or right arm, respectively. Prevalence of participants with elevated BP on one arm only was highest in patients with a systolic IAD ≥ 15 mmHg; 19.1% and 13.7%, for left and right arm, respectively.

This study shows that a substantial prevalence of IAD exists in Indian primary care patients. BP is above the diagnostic threshold for hypertension in one arm only for 6% of participants. These findings emphasize the importance of undertaking bilateral BP measurement in routine clinical practice.

Improving Knowledge and Early Detection of Atrial Fibrillation through a Community-Based Opportunistic Screening Program: What's Your Beat?

A community-based opportunistic screening program was implemented to (i) improve atrial fibrillation (AF) awareness and detection and (ii) assess the performance of the Microlife WatchBP Home A for detecting AF when used in community screening. Screening sessions were conducted among people aged ≥ 65 years with no history of AF at public events across Tasmania, Australia. Participants with positive screening results were referred to their general medical practitioner for assessment. The device’s performance was assessed using the positive predictive value. A total of 1704 eligible participants were screened at 79 sessions. Of these people, 50 (2.9%) had a positive screening result. The device correctly identified AF in 22 (46.8%) participants with positive results. Among those with subsequently confirmed AF, 6 (27.3%) had a history of AF but were not aware of the diagnosis, and 16 (72.7%) were identified to have previously undiagnosed AF, with an overall prevalence of 0.9% (95% CI, 0.58 to 1.52). Oral anticoagulation therapy was initiated in 12 (87.5%) eligible participants. The positive predictive value of the device was 46.8% (95% CI, 33.3 to 60.7). Given the relatively low performance of the device, its application in community-based opportunistic screening programs for AF is unlikely to be cost-effective.

Inter-arm blood pressure difference and cardiovascular risk estimation in primary care

BACKGROUND

Systolic inter-arm differences (IAD) in blood pressure (BP) contribute independently to cardiovascular risk estimates; this can be used to refine predicted risk and guide personalised interventions.

AIM

To model the effect of accounting for IAD in cardiovascular risk estimation in a primary care population free of pre-existing cardiovascular disease.

DESIGN AND SETTING

Cross-sectional analysis of people aged 40-75 years attending National Health Service (NHS) Health Checks in one general practice in England.

METHOD

Simultaneous bilateral BP measurements were made during Health Checks. QRISK2, ASCVD and Framingham cardiovascular risk scores were calculated before and after adjustment for IAD using previously published hazard ratios. Reclassification across guideline-recommended intervention thresholds was analysed.

RESULTS

Data for 334 participants were analysed. Mean (standard deviation) QRISK2, ASCVD and Framingham scores were 8.0 (6.9), 6.9 (6.5) and 10.7 (8.1) respectively rising to 8.9 (7.7), 7.1 (6.7) and 11.2 (8.5) after adjustment for IAD. 13 (3.9%) participants were reclassified from below to above the 10% QRISK2 threshold, 3 (0.9%) for the ASCVD 10% threshold and 9 (2.7%) for the Framingham 15% threshold.

CONCLUSION

Knowledge of IAD can be used to refine cardiovascular risk estimates in primary care. By accounting for IAD, recommendations of interventions for primary prevention of cardiovascular disease can be personalised and treatment offered to those at greater than average risk. When assessing elevated clinic BP readings, both arms should be measured to allow fuller estimation of cardiovascular risk.

Screening for Atrial Fibrillation: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

IMPORTANCE

Atrial fibrillation (AF), the most common arrhythmia, increases the risk of stroke.

OBJECTIVE

To review the evidence on screening for AF in adults without prior stroke to inform the US Preventive Services Task Force.

DATA SOURCES

PubMed, Cochrane Library, and trial registries through October 5, 2020; references, experts, and literature surveillance through October 31, 2021.

STUDY SELECTION

Randomized clinical trials (RCTs) of screening among asymptomatic persons without known AF or prior stroke; test accuracy studies; RCTs of anticoagulation among persons with AF; systematic reviews; and observational studies reporting harms.

DATA EXTRACTION AND SYNTHESIS

Two reviewers assessed titles/abstracts, full-text articles, and study quality and extracted data; when at least 3 similar studies were available, meta-analyses were conducted.

MAIN OUTCOMES AND MEASURES

Detection of undiagnosed AF, test accuracy, mortality, stroke, stroke-related morbidity, and harms.

RESULTS

Twenty-six studies (N = 113 784) were included. In 1 RCT (n = 28 768) of twice-daily electrocardiography (ECG) screening for 2 weeks, the likelihood of a composite end point (ischemic stroke, hemorrhagic stroke, systemic embolism, all-cause mortality, and hospitalization for bleeding) was lower in the screened group over 6.9 years (hazard ratio, 0.96 [95% CI, 0.92-1.00]; P = .045), but that study had numerous limitations. In 4 RCTs (n = 32 491), significantly more AF was detected with intermittent and continuous ECG screening compared with no screening (risk difference range, 1.0%-4.8%). Treatment with warfarin over a mean of 1.5 years in populations with clinical, mostly persistent AF was associated with fewer ischemic strokes (pooled risk ratio [RR], 0.32 [95% CI, 0.20-0.51]; 5 RCTs; n = 2415) and lower all-cause mortality (pooled RR, 0.68 [95% CI, 0.50-0.93]) compared with placebo. Treatment with direct oral anticoagulants was also associated with lower incidence of stroke (adjusted odds ratios range, 0.32-0.44) in indirect comparisons with placebo. The pooled RR for major bleeding for warfarin compared with placebo was 1.8 (95% CI, 0.85-3.7; 5 RCTs; n = 2415), and the adjusted odds ratio for major bleeding for direct oral anticoagulants compared with placebo or no treatment ranged from 1.38 to 2.21, but CIs did not exclude a null effect.

CONCLUSIONS AND RELEVANCE

Although screening can detect more cases of unknown AF, evidence regarding effects on health outcomes is limited. Anticoagulation was associated with lower risk of first stroke and mortality but with increased risk of major bleeding, although estimates for this harm are imprecise; no trials assessed benefits and harms of anticoagulation among screen-detected populations.

Screening for Atrial Fibrillation: US Preventive Services Task Force Recommendation Statement

IMPORTANCE

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The prevalence of AF increases with age, from less than 0.2% in adults younger than 55 years to about 10% in those 85 years or older, with a higher prevalence in men than in women. It is uncertain whether the prevalence of AF differs by race and ethnicity. Atrial fibrillation is a major risk factor for ischemic stroke and is associated with a substantial increase in the risk of stroke. Approximately 20% of patients who have a stroke associated with AF are first diagnosed with AF at the time of the stroke or shortly thereafter.

OBJECTIVE

To update its 2018 recommendation, the US Preventive Services Task Force (USPSTF) commissioned a systematic review on the benefits and harms of screening for AF in older adults, the accuracy of screening tests, the effectiveness of screening tests to detect previously undiagnosed AF compared with usual care, and the benefits and harms of anticoagulant therapy for the treatment of screen-detected AF in older adults.

POPULATION

Adults 50 years or older without a diagnosis or symptoms of AF and without a history of transient ischemic attack or stroke.

EVIDENCE ASSESSMENT

The USPSTF concludes that evidence is lacking, and the balance of benefits and harms of screening for AF in asymptomatic adults cannot be determined.

RECOMMENDATION

The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening for AF. (I statement).

A programme for early diagnosis of atrial fibrillation: a multi-centre study in primary care

BACKGROUND

Atrial fibrillation (AF) is a morbid disease whose complications can be prevented if prompt and correctly treated.

OBJECTIVE

To assess the usefulness of an early AF diagnosis programme in at-risk individuals in primary care centres.

METHODS

In an open-label, multi-centre, controlled interventional study, individuals with one or more risk factors for AF but without known AF were enrolled. They were allocated to intervention and control groups in a 1:2 ratio. Participants in the intervention group had three clinical and educational visits (0, 6 and 12 months). In intervention subgroup A, an electrocardiogram (ECG) was performed at each visit and in subgroup B, only if arrhythmia was detected on auscultation. After 2 years, the medical records of all participants were reviewed. Participants diagnosed with AF were followed for two additional years.

RESULTS

Of the total 2231 participants enrolled, 1503 (67.36%) were allocated to the control group and 728 (32.63%) to the intervention groups (355 in subgroup A, 373 subgroup B). The groups showed similar clinical characteristics. New-onset AF was diagnosed in 38 patients. Early detection in subgroup B was similar to subgroup A and superior to control group (3.2% versus 1.2%, hazard ratio 3.149, 95% confidence interval 1.503-6.597, P = 0.002). AF patients in subgroups A and B had similar long-term complications and a tendency for fewer complications than AF patients in the control group.

CONCLUSIONS

An intervention programme consisting of health education, systematic auscultation and opportunistic ECG by a primary care provider is a useful method for the early diagnosis of AF.

Mobile health technologies in the diagnosis and management of atrial fibrillation

PURPOSE OF REVIEW

Atrial fibrillation is the most common sustained rhythm abnormality and is associated with stroke, heart failure, cognitive decline, and premature death. Digital health technologies using consumer-grade mobile technologies (i.e. mHealth) capable of recording heart rate and rhythm can now reliably detect atrial fibrillation using single lead or multilead ECG or photoplethysmography (PPG). This review will discuss how these developments are being used to detect and manage atrial fibrillation.

RECENT FINDINGS

Studies have established the accuracy of mHealth devices for atrial fibrillation detection. The feasibility of using mHealth technology to screen for atrial fibrillation has also been established, though the utility of screening is controversial. In addition to screening, key aspects of atrial fibrillation management can also be performed remotely and effectively using mHealth, though with some important limitations.

SUMMARY

mHealth technologies have proven disruptive in the diagnosis and management of atrial fibrillation. Healthcare providers can leverage these advances to better care for their atrial fibrillation patients whenever necessary.

NICE atrial fibrillation guideline snubs wearable technology: a missed opportunity?

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing public health epidemic. In the UK, over 1.3 million people have a diagnosis of AF and an estimated 400,000 remain undiagnosed. AF-related strokes account for a quarter of all strokes and, as AF episodes are often asymptomatic, are still often the first manifestation of AF. Early diagnosis and initiation of oral anticoagulation, where appropriate, may prevent some of these thromboembolic strokes. Public Health England is committed to decrease the incidence of AF-related strokes and has sponsored initiatives aimed at improving AF detection by promoting the uptake of wearable technologies. However, the National Institute for Health and Care Excellence (NICE) has not recommended wearable technology in their recent AF diagnosis and management guidelines (NG196). Diagnostic accuracy of single-lead electrocardiography (ECG) generated by the latest iteration of wearable devices is excellent and, in many cases, superior to general practitioner interpretation of the 12-lead ECG. High-quality ECG from wearable devices that unequivocally shows AF can expedite AF detection. Otherwise, there is a real risk of delaying AF diagnosis with the potential of devastating consequences for patients and their families.

Mobile Health for Arrhythmia Diagnosis and Management

Palpitations are a common symptom managed by general practitioners and cardiologists; atrial fibrillation (AF) is the most common arrhythmia in adults. The recent commercial availability of smartphone-based devices and wearable technologies with arrhythmia detection capabilities has revolutionized the diagnosis and management of these common medical issues, as it has placed the power of arrhythmia detection into the hands of the patient. Numerous mobile health (mHealth) devices that can detect, record, and automatically interpret irregularities in heart rhythm and abrupt changes in heart rate using photoplethysmography (PPG)- and electrocardiogram-based technologies are now commercially available. As opposed to prescription-based external rhythm monitoring approaches, these devices are more inexpensive and allow for longer-term monitoring, thus increasing sensitivity for arrhythmia detection, particularly for patients with infrequent symptoms possibly due to cardiac arrhythmias. These devices can be used to correlate symptoms with cardiac arrhythmias, assess efficacy and toxicities of arrhythmia therapies, and screen the population for serious rhythm disturbances such as AF. Although several devices have received clearance for AF detection from the United States Food & Drug Administration, limitations include the need for ECG confirmation for arrhythmias detected by PPG alone, false positives, false negatives, charging requirements for the battery, and financial cost. In summary, the growth of commercially available devices for remote, patient-facing rhythm monitoring represents an exciting new opportunity in the care of patients with palpitations and known or suspected dysrhythmias. Physicians should be familiar with the evidence that underlies their added value to patient care and, importantly, their current limitations.

Screening for Atrial Fibrillation in Community and Primary CareSettings: A Scoping Review

Background

Atrial Fibrillation (AF) is the most common tachyarrhythmia and is associated with increased risk of stroke, morbidity and mortality. AF is responsible for up to a quarter of all strokes and is often asymptomatic until a stroke occurs.Screening for AF is a valuable approach to reduce the burden of stroke in the population.

Objectives

The motivation for this review was to synthesise and appraise the evidence for screening for AF in the community. The aims of this scoping review are 1). To describe the prevalence of newly diagnosed AF in screening programmes 2). Identify which techniques/ tools are employed for AF screening 3). To describe the setting and personnel involved in screening for AF.

Eligibility Criteria

All forms of AF screening in adults (≥18 years) in primary and community care settings.

Methods

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping reviews (PRISMA-ScR).

Results

Fifty-nine papers were included; most were cross-sectional studies (n=41) and RCTs (n=7). Prevalence of AF ranged from 0-34.5%. Screening tools and techniquesincluded the 12-lead ECG (n=33), the 1-lead ECG smartphone based Alivecor® (n=14) and pulse palpation (n=12). Studies were undertaken in community settings (n=30) or in urban/rural primary care (n=28). Personnel collecting research data were in the main members of the research team (n=31), GPs (n=16), practice nurses (n=10), participants (n=8) and pharmacists (n=4).

Conclusion

Prevalence of AF increased with advancing age. AF screening should target individuals at greatest risk of the condition including older adults≥65 years of age. Emerging novel technologies may increase the accessibility of AF screening in community and home settings. There is a need for high quality research to investigate AF prevalence and establish accuracy and validity for traditional versus novel screening tools used to screen for AF.

Accuracy of pulsatile photoplethysmography applications or handheld devices vs. 12-lead ECG for atrial fibrillation screening: a systematic review and meta-analysis

BACKGROUND

The relative accuracy of pulsatile photoplethysmography applications (PPG) or handheld (HH) devices compared with the gold standard 12-lead electrocardiogram (ECG) for the diagnosis of atrial fibrillation is unknown.

METHODS

Digital databases were searched to identify relevant articles. Raw data were pooled using a bivariate model to calculate diagnostic accuracy measures and estimate Hierarchical Summary Receiver Operating Characteristic (HSROC).

RESULTS

A total of 10 articles comprising 4296 patients (mean age 68.9 years, with 56% males) were included in the analysis. Compared with EKG, the pooled sensitivity of PPG and HH devices in AF detection was 0.93 (95% CI 0.87-0.96; p < 0.05) and 0.87 (95% CI. 0.74-0.94; p < 0.05), respectively. The pooled specificity of PPG and HH devices in AF detection was 0.91 (95% CI 0.88-0.94; p < 0.05) and 0.96 (95% CI 0.90-0.98; p < 0.05), respectively. The diagnostic odds ratio was 129 and 144 for PPG and HH devices, respectively. Fagan's nomogram showed the probability of a patient having AF and normal rhythm on PPG or HH devices was 2-3%, while the post-test probability of having AF with an irregular R-R interval on PPG or HH devices was 73% and 82%, respectively. The scatter plot of positive and negative likelihood ratio showed high confirmation of AF and reliability of exclusion of absence of irregular R-R intervals (positive likelihood ratio > 10, and negative likelihood ratio < 0.1) on HH devices while PPG was used as confirmation only.

CONCLUSIONS

The PPG or HH devices can serve as a reliable alternative for the detection of AF.

Atrial Fibrillation Population Screening

Atrial fibrillation (AF) is associated with adverse outcomes. Screening may lead to earlier recognition and treatment of asymptomatic AF. However, most evidence regarding AF applies to clinical AF, with symptoms or electrocardiographic diagnosis. Whether this evidence can be translated toward subclinical AF, without symptoms and detected by novel, more continuous screening devices is uncertain. The diagnostic yield of screening is determined by the screening population, tool, duration and frequency. Longer and more frequent screening in a higher risk population leads to more effective screening. New devices based on photoplethysmography and single-lead electrocardiography increase convenience and the likelihood of cost-effectiveness.

New Technologies for Detection and Management of Atrial Fibrillation

Atrial fibrillation (AF) is a common and prevalent form of arrhythmia. It is associated with various morbidities with stroke being the major hazard. Since AF is often reported to be asymptomatic, many individuals remain unaware of their condition and may not receive the requisite treatment. Hence, screening for AF has gained substantial attention recently. Growing advancement in technology has paved way for numerous approaches for AF screening using medical-prescribed devices as well as consumer electronic devices. However, there still lies scope for large-scale randomized trials which would explore additional aspects associated with AF. This review very concisely summarizes AF, screening, present technology, current literature and clinical studies associated with it.

Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation

Screening for atrial fibrillation (AF) with a handheld device for recording the ECG is becoming increasingly popular. The poorer signal quality of such ECGs may lead to false detection of AF, often caused by transient noise. Consequently, the need for expert review in AF screening can become extensive. A convolutional neural network (CNN) is proposed for transient noise identification in AF detection. The network is trained using the events produced by a QRS detector, classified into either true beat detections or false detections. The CNN and a low-complexity AF detector are trained and tested using the StrokeStop I database, containing 30-s ECGs from mass screening for AF in the elderly population. Performance evaluation of the CNN-based quality control using a subset of the database resulted in sensitivity, specificity, and accuracy of 96.4, 96.9, and 96.9%, respectively. By inserting the CNN before the AF detector, the false AF detections were reduced by 22.5% without any loss in sensitivity. The results show that the number of recordings calling for expert review can be significantly reduced thanks to the identification of transient noise. The reduction of false AF detections is directly linked to the time and cost spent on expert review.

Is Screening for Atrial Fibrillation and Its Risk Factors Useful and Cost-Effective?

Atrial fibrillation (AF) is a major, preventable cause of stroke, whose prevalence is increasing with the aging of the population. There are safe and effective anticoagulation therapies to prevent stroke and new technologies that can identify AF in asymptomatic individuals. Ongoing research will determine if AF screening is cost-effective and will define the best screening strategies. The effectiveness of AF screening can be enhanced by simultaneously screening for the cardiovascular conditions that predispose to the development and progression of AF and its complications. Future studies evaluating an integrated screening program on outcomes, health care utilization, and cost are needed.

2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society

Supplemental Digital Content is available in the text.

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society describes the current status of mobile health technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mobile health. The promises of predictive analytics but also operational challenges in embedding mobile health into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self‐management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health (“mHealth”) technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self‐management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

Advances in cardiovascular monitoring technologies have resulted in an influx of consumer-targeted wearable sensors that have the potential to detect numerous heart conditions. In this Review, Krittanawong and colleagues describe processes involved in biosignal acquisition and analysis of cardiovascular monitors, as well as their associated ethical, regulatory and legal challenges.

Advances in the use of cardiovascular monitoring technologies, such as the development of novel portable sensors and machine learning algorithms that can provide near-real-time diagnosis, have the potential to provide personalized care.

Wearable sensor technologies can detect numerous biosignals, such as cardiac output, blood-pressure levels and heart rhythm, and can integrate multiple modalities.

The use of novel biosignals for diagnosis raises concerns regarding accuracy and actionability within clinical guidelines, in addition to medical, legal and ethical issues.

Machine learning-based interpretation of biosensor data can facilitate rapid evaluation of the haemodynamic consequences of heart failure or arrhythmias, but is limited by the presence of noise and training data that might not be representative of the real-world clinical setting.

The use of data derived from cardiovascular monitoring devices is associated with numerous challenges, such as data security, accessibility and ownership, in addition to other ethical and regulatory concerns.

Blood pressure measurement in atrial fibrillation: review and meta-analysis of evidence on accuracy and clinical relevance

: Atrial fibrillation (AF) often coexists with hypertension in the elderly and multiplies the risk of stroke and death. Blood pressure (BP) measurement in patients with AF is difficult and uncertain and has been a classic exclusion criterion in hypertension clinical trials leading to limited research data. This article reviews the evidence on the accuracy of BP measurement in AF performed using different methods (office, ambulatory, home) and devices (auscultatory, oscillometric) and its clinical relevance in predicting cardiovascular damage. The current evidence suggests the following: (i) Interobserver and intra-observer variation in auscultatory BP measurement is increased in AF because of increased beat-to-beat BP variability and triplicate measurement is required; (ii) Data from validation studies of automated electronic BP monitors in AF are limited and methodologically heterogeneous and suggest reasonable accuracy in measuring SBP and a small yet consistent overestimation of DBP; (iii) 24-h ambulatory BP monitoring is feasible in AF, with similar proportion of errors as in individuals without AF; (iv) both auscultatory and automated oscillometric BP measurements appear to be clinically relevant in AF, providing similar associations with intra-arterial BP measurements and with indices of preclinical cardiac damage as in patients without AF, and predict cardiovascular events and death; (v) Screening for AF in the elderly using an AF-specific algorithm during routine automated office, home or ambulatory BP measurement has high diagnostic accuracy. In conclusion, in AF patients, BP measurement is important, reliable, and clinically relevant and should not be neglected in clinical research and in practice.

Population-Based Screening for Atrial Fibrillation

Atrial fibrillation (AF) is a common and morbid arrhythmia. Stroke is a major hazard of AF and may be preventable with oral anticoagulation. Yet since AF is often asymptomatic, many individuals with AF may be unaware and do not receive treatment that could prevent a stroke. Screening for AF has gained substantial attention in recent years as several studies have demonstrated that screening is feasible. Advances in technology have enabled a variety of approaches to facilitate screening for AF using both medical-prescribed devices as well as consumer electronic devices capable of detecting AF. Yet controversy about the utility of AF screening remains owing to concerns about potential harms resulting from screening in the absence of randomized data demonstrating effectiveness of screening on outcomes such as stroke and bleeding. In this review, we summarize current literature, present technology, population-based screening considerations, and consensus guidelines addressing the role of AF screening in practice.

How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.

Diagnostic Value of Atrial Fibrillation by Built-in Electrocardiogram Technology in a Blood Pressure Monitor

Background: Hypertension in patients with atrial fibrillation (AF) is a known independent risk factor for stroke. The Complete blood pressure (BP) monitor (Omron Healthcare, Kyoto, Japan) was developed as the first BP monitor with electrocardiogram (ECG) capability in a single device to simultaneously monitor ECG and BP readings. This study investigated whether the Complete can accurately differentiate sinus rhythm (SR) from AF during BP measurement.

Methods and Results: Fifty-six consecutive patients with persistent AF admitted for catheter ablation were enrolled in the study (mean age 65.8 years; 83.9% male). In all patients, 12-lead ECGs and simultaneous Complete recordings were acquired before and after ablation. The Complete interpretations were compared with physician-reviewed ECGs, whereas Complete recordings were reviewed by cardiologists in a blinded manner and compared with ECG interpretations. Sensitivity, specificity, and κ coefficient were also determined. In all, 164 Complete and ECG recordings were simultaneously acquired from the 56 patients. After excluding unclassified recordings, the Complete automated algorithm performed well, with 100% sensitivity, 86% specificity, and a κ coefficient of 0.87 compared with physician-interpreted ECGs. Physician-interpreted Complete recordings performed well, with 99% sensitivity, 85% specificity, and a κ coefficient of 0.85 compared with physician-interpreted ECGs.

Conclusions: The Complete, which combines BP and ECG monitoring, can accurately differentiate SR from AF during BP measurement.

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.

Machine learning detection of Atrial Fibrillation using wearable technology

BACKGROUND

Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF.

METHODS

We designed a novel AF detection algorithm using a de-correlated Lorenz plot of 60 consecutive RR intervals. In order to reduce the volume of data, the resulting images were compressed using a wavelet transformation (JPEG200 algorithm) and the compressed images were used as input data to a Support Vector Machine (SVM) classifier. We used the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) Atrial Fibrillation database and the MIT-BIH Arrhythmia database as training data and verified the algorithm performance using RR intervals collected using an inexpensive consumer heart rate monitor device (Polar-H7) in a case-control study.

RESULTS

The SVM algorithm yielded excellent discrimination in the training data with a sensitivity of 99.2% and a specificity of 99.5% for AF. In the validation data, the SVM algorithm correctly identified AF in 79/79 cases; sensitivity 100% (95% CI 95.4%-100%) and non-AF in 328/336 cases; specificity 97.6% (95% CI 95.4%-99.0%).

CONCLUSIONS

An inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burden and improved risk stratification in AF.

Uptake of a primary care atrial fibrillation screening program (AF-SMART): a realist evaluation of implementation in metropolitan and rural general practice

Background

Screening for atrial fibrillation (AF) in people aged ≥65 years is recommended by international guidelines. The Atrial Fibrillation Screen, Management And guideline-Recommended Therapy (AF-SMART) studies of opportunistic AF screening in 16 metropolitan and rural general practices were conducted from November 2016–June 2019. These studies trialled custom-designed eHealth tools to support all stages of AF screening in general practice.

Methods

A realist evaluation of the AF-SMART studies, which aimed to explain the circumstances in which the program worked (or not) to increase the proportion of people screened for AF. The initial program theory was based on our previous research, policy documents and screening studies. To test this, we conducted 45 semi-structured interviews with general practitioners (GPs), nurses and practice managers across all participating practices, and collected observational and quantitative screening data. These data were analysed and interpreted to refine the program theory.

Results

GPs/nurses liked the eHealth tools, although technical problems sometimes disrupted screening. Time was the main barrier to screening for GPs/nurses, so systems need to be very efficient. Practices with leadership from a senior GP ‘screening champion’ had broader uptake, especially from the nursing team. Providing regular feedback on screening data was beneficial for quality improvement and motivation. Clear protocols for follow-up of abnormal results were required for successful nurse-led screening in a hierarchical system. Participation in the program had broader benefits of improving AF knowledge and raising the profile of cardiovascular health in the practice. Screening for a shorter, more intense period (eg during influenza vaccination) worked well for practices where sufficient staff time was allocated.

Conclusions

Introducing an AF screening program is likely to be successful in contexts where there is a senior GP ‘screening champion’, a clear protocol exists for abnormal results, and there is regular data reporting to staff. These contexts link to mechanisms around motivation, leadership, empowerment of nurses, and efficient screening systems. The contexts and mechanisms contribute to the longer-term outcomes of increasing the proportion of people screened and treated for AF, which is recommended by guidelines as a key strategy for the prevention of AF-related stroke.

Trial registrations

AF SMART (metropolitan): ACTRN12616000850471 (Australia New Zealand Clinical Trials Registry).

AF SMART II (rural): ACTRN12618000004268 (Australia New Zealand Clinical Trials Registry).

Validation of the modified Microlife blood pressure monitor in patients with paroxysmal atrial fibrillation

AIMS

Undiagnosed atrial fibrillation (AF) accounts for 6% of all strokes, therefore early detection and treatment of the arrhythmia are paramount. Previous research has illustrated that the Microlife WatchBPO3 AFIB, an automated blood pressure (BP) monitor with an inbuilt AF algorithm, accurately detects permanent AF. Currently, limited data exist on whether the modified BP monitor is able to detect paroxysmal AF (PAF). Therefore, this study aims to assess the accuracy of the Microlife WatchBPO3 AFIB monitor to detect PAF against a pacemaker reference standard over a 24-h period.

METHODS AND RESULTS

Forty-eight patients with a pacemaker implanted for sick sinus syndrome and previously documented fast AF participated. Sensitivity of the atrial pacemaker lead was set to allow detection of signals of ≥ 0.5 mV. Patients engaged in their normal daily routine whilst wearing the modified BP monitor. The modified BP monitor demonstrated an overall sensitivity of 76.0% and specificity of 80.8% for detecting PAF. This sensitivity and specificity increased to 100% and 83.1%, respectively, for patients that achieved more than 80% successful BP readings. Compared to day-time readings, night-time readings also demonstrated a lower proportion of movement artefact (14.4% vs. 3.4%), and therefore, a higher sensitivity and specificity of 100% and 84.9%, respectively, for detecting PAF.

CONCLUSION

The Microlife WatchBPO3 AFIB device has an acceptable diagnostic accuracy to detect PAF; however, movement artefact affects the accuracy of the readings. This modified BP monitor may potentially be useful as a screening tool for AF in patients at high risk of developing stroke.

Agreement Between Automated and Human Measurements of Heart Rate in Patients With Atrial Fibrillation

Background:

The accuracy of heart rate (HR) measurement by automated blood pressure monitors in patients with atrial fibrillation (AF) remains unclear. The authors investigate the agreement between HR measurements by 2 automated devices and human counting in patients with AF.

Methods:

In 47 patients with persistent AF, HR was recorded using 2 automated blood pressure monitors: Omron M5-I and Microlife BPA100 Plus. Human counting of HR by a stethoscope was used as the reference. For each method, 3 readings were made and the mean was calculated for comparison. In addition to Wilcoxon signed rank test, the correlation between HR measurements by automated devices and human counting was determined using Spearman's rank correlation coefficient (r), and the agreement between HR measurements by both devices and human counting was validated by the Bland-Altman plot and intraclass correlation coefficient (ICC).

Results:

Overall, we found no significant difference in HR measurements between devices and human counting (Omron vs human counting, 81.1 ± 11.1 vs 80.2 ± 10.8 beats per minute [bpm]; P = .21, r = 0.911; ICC, 0.954; Microlife vs human counting, 81.3 ± 10.8 vs 80.2 ± 10.8 bpm; P = .22, r = 0.842; ICC, 0.912). However, in patients with HR greater than 80 bpm, the HR measured by the Microlife device was significantly higher than that measured by human counting (91.1 ± 5.2 vs 87.1 ± 8.6 bpm, P = .034).

Conclusion:

There was a high agreement between HR measurements by 2 automated devices and human counting, but the Microlife device may overestimate HR in AF patients with HR greater than 80 bpm.

Opportunistic Screening for Atrial Fibrillation in the Pharmacies: A Population-Based Cross-Sectional Study

INTRODUCTION

Opportunistic screening of atrial fibrillation is a valuable approach to the identification of subjects with unknown or non-symptomatic atrial fibrillation (AF) with the potential of reducing the burden of ischemic stroke in the population.

AIM

To evaluate the feasibility of a large-scale screening for atrial fibrillation using a blood pressure monitor (MicrolifeAFIB) endowed with a validated algorithm able to detect AF calculating the irregularity of interval times between heartbeats.

METHODS

In this cross-sectional study conducted in 74 pharmacies in Verona participated 3071 people aged 50 years or more. In 6 months, information about drugs, previous diagnoses of cardiovascular diseases, anthropometric and demographic data was recorded, together with the measurement of blood pressure and cardiac rhythm by using the MicrolifeAFIB device. Pharmacists also collected anthropometric and demographic data of the participants, along with information concerning their personal history of cardiovascular disease and the use of antihypertensive and antithrombotic agents. All those who were positive at the screening for atrial fibrillation were referred to their family doctor.

RESULTS

The screening revealed 98 subjects (3.2%) positive for AF; 44 of these reported a previous diagnosis of AF and were treated with anticoagulants (77%) or with antiplatelet agents (7%). By logistic regression analysis, age, male sex and heart failure were independently associated with positivity for AF. Association between positive test and previous stroke/TIA was found in the 54 subjects without a previous diagnosis of AF (9% had a previous stroke/TIA).

CONCLUSIONS

Opportunistic screening for atrial fibrillation in the pharmacies is feasible and allows to identify a number of subjects with silent, non-previously diagnosed AF, therefore is potentially useful in large-scale projects aimed at the prevention of cardiovascular morbidity and mortality.

Accuracy of automated blood pressure measurements in the presence of atrial fibrillation: systematic review and meta-analysis

Atrial fibrillation (AF) affects ~3% of the general population and is twice as common with hypertension. Validation protocols for automated sphygmomanometers exclude people with AF, raising concerns over accuracy of hypertension diagnosis or management, using out-of-office blood pressure (BP) monitoring, in the presence of AF. Some devices include algorithms to detect AF; a feature open to misinterpretation as offering accurate BP measurement with AF. We undertook this review to explore accuracy of automated devices, with or without AF detection, for measuring BP. We searched Medline and Embase to October 2018 for studies comparing automated BP measurement devices to a standard mercury sphygmomanometer contemporaneously. Data were extracted by two reviewers. Mean BP differences between devices and mercury were calculated, where not reported and compared; meta-analyses were undertaken where possible. We included 13 studies reporting 14 devices. Mean systolic and diastolic BP differences from mercury ranged from -3.1 to + 6.1/-4.6 to +9.0 mmHg. Considerable heterogeneity existed between devices (I²: 80 to 90%). Devices with AF detection algorithms appeared no more accurate for BP measurement with AF than other devices. A previous review concluded that oscillometric devices are accurate for systolic but not diastolic BP measurement in AF. The present findings do not support that conclusion. Due to heterogeneity between devices, they should be evaluated on individual performance. We found no evidence that devices with AF detection measure BP more accurately in AF than other devices. More home or ambulatory automated BP monitors require validation in populations with AF.