Validation and clinical use of a novel diagnostic device for screening of atrial fibrillation

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.

Supermarket/hypermarket opportunistic screening for atrial fibrillation (SHOPS-AF) using sensors embedded in the handles of supermarket trolleys: A feasibility study

BACKGROUND

Atrial fibrillation (AF) increases the risk of death, stroke, heart failure, cognitive decline, and healthcare costs but is often asymptomatic and undiagnosed. There is currently no national screening program for AF. The advent of validated hand-held devices allows AF to be detected in non-healthcare settings, enabling screening to be undertaken within the community.

METHOD AND RESULTS

In this novel observational study, we embedded a MyDiagnostick single lead ECG sensor into the handles of shopping trolleys in four supermarkets in the Northwest of England: 2155 participants were recruited. Of these, 231 participants either activated the sensor or had an irregular pulse, suggesting AF. Some participants agreed to use the sensor but refused to provide their contact details, or consent to pulse assessment. In addition, some data were missing, resulting in 203 participants being included in the final analyses. Fifty-nine participants (mean age 73.6 years, 43% female) were confirmed or suspected of having AF; 20 were known to have AF and 39 were previously undiagnosed. There was no evidence of AF in 115 participants and the remaining 46 recordings were non-diagnostic, mainly due to artefact. Men and older participants were significantly more likely to have newly diagnosed AF. Due to the number of non-diagnostic ECGs (n = 46), we completed three levels of analyses, excluding all non-diagnostic ECGs, assuming all non-diagnostic ECGs were masking AF, and assuming all non-diagnostic ECGs were not AF. Based on the results of the three analyses, the sensor's sensitivity (95% CI) ranged from 0.70 to 0.93; specificity from 0.15 to 0.97; positive predictive values (PPV) and negative predictive values (NPV) ranged from 0.24 to 0.56 and 0.55 to 1.00, respectively. These values should be interpreted with caution, as the ideal reference standard on 1934 participants was imperfect.

CONCLUSION

The study demonstrates that the public will engage with AF screening undertaken as part of their daily routines using hand-held devices. Sensors can play a key role in identifying asymptomatic patients in this way, but the technology must be further developed to reduce the quantity of non-diagnostic ECGs.

Being precise with anticoagulation to reduce adverse drug reactions: are we there yet?

Anticoagulants are potent therapeutics widely used in medical and surgical settings, and the amount spent on anticoagulation is rising. Although warfarin remains a widely prescribed oral anticoagulant, prescriptions of direct oral anticoagulants (DOACs) have increased rapidly. Heparin-based parenteral anticoagulants include both unfractionated and low molecular weight heparins (LMWHs). In clinical practice, anticoagulants are generally well tolerated, although interindividual variability in response is apparent. This variability in anticoagulant response can lead to serious incident thrombosis, haemorrhage and off-target adverse reactions such as heparin-induced thrombocytopaenia (HIT). This review seeks to highlight the genetic, environmental and clinical factors associated with variability in anticoagulant response, and review the current evidence base for tailoring the drug, dose, and/or monitoring decisions to identified patient subgroups to improve anticoagulant safety. Areas that would benefit from further research are also identified. Validated variants in VKORC1, CYP2C9 and CYP4F2 constitute biomarkers for differential warfarin response and genotype-informed warfarin dosing has been shown to reduce adverse clinical events. Polymorphisms in CES1 appear relevant to dabigatran exposure but the genetic studies focusing on clinical outcomes such as bleeding are sparse. The influence of body weight on LMWH response merits further attention, as does the relationship between anti-Xa levels and clinical outcomes. Ultimately, safe and effective anticoagulation requires both a deeper parsing of factors contributing to variable response, and further prospective studies to determine optimal therapeutic strategies in identified higher risk subgroups.

Sensitivity and specificity of handheld one lead ECG detecting atrial fibrillation in an outpatient clinic setting

AIMS

To validate the sensitivity and specificity of the Zenicor One handheld ECG device for detection of atrial fibrillation in an outpatient clinical setting.

METHODS AND RESULTS

Patients attending outpatient clinics at Danderyd Hospital (n = 220) were examined with one lead handheld ECG immediately after standard care 12‑lead ECG recording. Twelve recordings were excluded (atrial flutter or pacing) or missing. The recordings were dichotomously categorized as "atrial fibrillation" or "not atrial fibrillation" by two senior cardiologists. In cases of diverging interpretations, a third senior cardiologist had the deciding vote. Sensitivity and specificity in diagnosing atrial fibrillation was calculated with 12‑lead ECG as gold standard. Sensitivity and specificity for diagnosis of atrial fibrillation with one lead handheld ECG and 12‑lead ECG as gold standard was 98% and 99% respectively.

CONCLUSION

In a health-care outpatient setting, Zenicor One handheld ECG had high sensitivity and specificity for detection of atrial fibrillation when compared with 12‑lead ECG.

Portuguese Heart Failure Prevalence Observational Study (PORTHOS) rationale and design - A population-based study

INTRODUCTION AND OBJECTIVES

Current epidemiological data on heart failure (HF) in Portugal derives from studies conducted two decades ago. The main aim of this study is to determine HF prevalence in the Portuguese population. Using current standards, this manuscript aims to describe the methodology and research protocol applied.

METHODS

The Portuguese Heart Failure Prevalence Observational Study (PORTHOS) is a large, three-stage, population-based, nationwide, cross-sectional study. Community-dwelling citizens aged 50 years and older will be randomly selected via stratified multistage sampling. Eligible participants will be invited to attend a screening visit at a mobile clinic for HF symptom assessment, anthropomorphic assessment, N-terminal pro-B-type natriuretic peptide (NT-proBNP) testing, one-lead electrocardiogram (ECG) and a sociodemographic and health-related quality of life questionnaire (EQ-5D). All subjects with NT-proBNP ≥125 pg/mL or with a prior history of HF will undergo a diagnostic confirmatory assessment at the mobile clinic composed of a 12-lead ECG, comprehensive echocardiography, HF questionnaire (KCCQ) and blood sampling. To validate the screening procedure, a control group will undergo the same diagnostic assessment. Echocardiography results will be centrally validated, and HF diagnosis will be established according to the European Society of Cardiology HF guidelines. A random subsample of patients with an equivocal HF with preserved ejection fraction diagnosis based on the application of the Heart Failure Association preserved ejection fraction diagnostic algorithm will be invited to undergo an exercise echocardiography.

CONCLUSIONS

Through the application of current standards, appropriate methodologies, and a strong research protocol, the PORTHOS study will determine the prevalence of HF in mainland Portugal and enable a comprehensive characterization of HF patients, leading to a better understanding of their clinical profile and health-related quality of life.

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review

BACKGROUND

Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments.

OBJECTIVE

This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence.

METHODS

We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases.

RESULTS

From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation.

CONCLUSIONS

ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.

Validation of MyDiagnostick tool to identify atrial fibrillation in a multi-ethnic Asian population

Introduction

MyDiagnostick is an atrial fibrillation (AF) screening tool that has been validated in the Caucasian population in the primary care setting.

Methods

In our study, we compared MyDiagnostick with manual pulse check for AF screening in the community setting.

Results

In our cohort of 671 candidates from a multi-ethnic Asian population, AF prevalence was found to be 1.78%. Of 12 candidates, 6 (50.0%) had a previous history of AF and another 6 (50.0%) were newly diagnosed with AF. Candidates found to have AF during the screening were older (72.0 ± 11.7 years vs. 56.0 ± 13.0 years, P < 0.0001) and had a higher CHADSVASC risk score (2.9 ± 1.5 vs. 1.5 ± 1.1, P = 0.0001). MyDiagnostick had a sensitivity of 100.0% and a specificity of 96.2%. In comparison, manual pulse check had a sensitivity of 83.3% and a specificity of 98.9%.

Conclusion

MyDiagnostick is a simple AF screening device that can be reliably used by non-specialist professionals in the community setting. Its sensitivity and specificity are comparable and validated across various studies performed in different population cohorts.

Protocol for a Systematic Review and Individual Participant Data Meta-Analysis of Randomized Trials of Screening for Atrial Fibrillation to Prevent Stroke

INTRODUCTION

 Atrial fibrillation (AF) is a common cause of stroke. Timely diagnosis of AF and treatment with oral anticoagulation (OAC) can prevent up to two-thirds of AF-related strokes. Ambulatory electrocardiographic (ECG) monitoring can identify undiagnosed AF in at-risk individuals, but the impact of population-based ECG screening on stroke is uncertain, as ongoing and published randomized controlled trials (RCTs) have generally been underpowered for stroke.

METHODS AND ANALYSIS

 The AF-SCREEN Collaboration, with support from AFFECT-EU, have begun a systematic review and individual participant data meta-analysis of RCTs evaluating ECG screening for AF. The primary outcome is stroke. Secondary outcomes include AF detection, OAC prescription, hospitalization, mortality, and bleeding.After developing a common data dictionary, anonymized data will be collated from individual trials into a central database. We will assess risk of bias using the Cochrane Collaboration tool, and overall quality of evidence with the Grading of Recommendations Assessment, Development and Evaluation approach.We will pool data using random effects models. Prespecified subgroup and multilevel meta-regression analyses will explore heterogeneity. We will perform prespecified trial sequential meta-analyses of published trials to determine when the optimal information size has been reached, and account for unpublished trials using the SAMURAI approach.

IMPACT AND DISSEMINATION

 Individual participant data meta-analysis will generate adequate power to assess the risks and benefits of AF screening. Meta-regression will permit exploration of the specific patient, screening methodology, and health system factors that influence outcomes.

TRIAL REGISTRATION NUMBER

 PROSPERO CRD42022310308.

Validation of diagnostic accuracy of a handheld, smartphone-based rhythm recording device

AIM

Evaluation of the diagnostic accuracy of a rhythm recording device, for detection of atrial tachyarrhythmia (ATA) and atrial fibrillation (AF) compared to 12-lead-electrocardiogram (12-L-ECG).

RESEARCH DESIGN AND METHODS

Two hundred 12-L-ECGs (reference standard) and Coala Heart Monitor (CHM) recordings (index test) were collected from 189 patients. Two electrophysiologists independently performed manual analysis of all 12-L-ECGs and CHM recordings in random order. The CHM recordings were also analyzed by an automatic algorithm and compared to the results of the reference standard.

RESULTS

Manual analysis of CHM for ATA showed a sensitivity of 98.9% (95% confidence interval (CI): 94.0-100) and a specificity of 100% (CI: 96.6-100). Manual analysis for AF had a sensitivity of 100% (CI: 95.3-100) and a specificity of 97.5% (CI: 93.0-99.5). Automatic analysis for ATA showed a sensitivity of 93.5% (CI: 86.3-97.6) and a specificity of 92.6% (CI: 85.9-96.7). Automatic analysis for AF showed a sensitivity of 97.4% (CI: 91.0-99.7) and a specificity of 86.1% (CI: 78.6-91.7).

CONCLUSION

CHM has a very high accuracy for ATA and AF in manual analysis and a high accuracy for ATA and AF in automatic analysis, making the device suitable for screening.

Use of digital health applications for the detection of atrial fibrillation

The advances in health care technologies over the last decade have led to improved capabilities in the use of digital health applications (DiHA) for the detection of atrial fibrillation (AFib). Thus, home-based remote heart rhythm monitoring is facilitated by smartphones or smartwatches alone or combined with external sensors. The available products differ in terms of type of application (wearable vs. handheld) and the technique used for rhythm detection (electrocardiography [ECG] vs. photoplethysmography [PPG]). While ECG-based algorithms often require additional sensors, PPG utilizes techniques integrated in smartphones or smartwatches. Algorithms based on artificial intelligence allow for the automated diagnosis of AFib, enabling high diagnostic accuracy for both ECG-based and PPG-based DiHA. Advantages for clinical use result from the widespread accessibility of rhythm monitoring, thereby permitting earlier diagnosis and higher AFib detection rates. DiHA are also useful for the follow-up of patients with known AFib by monitoring the success of therapeutic interventions to restore sinus rhythm, e.g. catheter ablation. Although some studies strongly suggest a potential benefit for the use of DiHA in the setting of AFib, the overall evidence for an improvement in hard, clinical endpoints and positive effects on clinical care is scarce. To enhance the acceptance of DiHA use in daily practice, more studies evaluating their clinical benefits for the detection of AFib are required. Moreover, most of the applications are still not reimbursable, although the German Digital Health Care Act (Digitale-Versorgung-Gesetz, DVG) made reimbursement possible in principle in 2019.

Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation

BACKGROUND

Atrial fibrillation affects approximately 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

We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch.

METHODS

Eligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram).

RESULTS

A total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm.

CONCLUSIONS

The algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch's single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386.

Regional implementation of atrial fibrillation screening: benefits and pitfalls

Aims

Despite general awareness that screening for atrial fibrillation (AF) could reduce health hazards, large-scale implementation is lagging behind technological developments. As the successful implementation of a screening programme remains challenging, this study aims to identify facilitating and inhibiting factors from healthcare providers' perspectives.

Methods and results

A mixed-methods approach was used to gather data among practice nurses in primary care in the southern region of the Netherlands to evaluate the implementation of an ongoing single-lead electrocardiogram (ECG)-based AF screening programme. Potential facilitating and inhibiting factors were evaluated using online questionnaires (N = 74/75%) and 14 (of 24) semi-structured in-depth interviews (58.3%). All analyses were performed using SPSS 26.0. In total, 16 682 screenings were performed on an eligible population of 64 000, and 100 new AF cases were detected. Facilitating factors included 'receiving clear instructions' (mean ± SD; 4.12 ± 1.05), 'easy use of the ECG-based device' (4.58 ± 0.68), and 'patient satisfaction' (4.22 ± 0.65). Inhibiting factors were 'time availability' (3.20 ± 1.10), 'insufficient feedback to the practice nurse' (2.15 ± 0.89), 'absence of coordination' (54%), and the 'lack of fitting policy' (32%).

Conclusion

Large-scale regional implementation of an AF screening programme in primary care resulted in a low participation of all eligible patients. Based on the perceived barriers by healthcare providers, future AF screening programmes should create preconditions to fit the intervention into daily routines, appointing an overall project lead and a General Practitioner (GP) as a coordinator within every GP practice.

The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.

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.

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study

Background

The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated.

Objective

The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being.

Methods

Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics.

Results

Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively.

Conclusions

The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening.

Trial Registration

ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335

Performance of the imPulse device for the detection of atrial fibrillation in hospital settings

Background

Atrial fibrillation (AF) increases thromboembolism and stroke risk; this can be reduced by oral anticoagulation, but only if AF is detected. A portable, point-of-care device, capable of accurately detecting and identifying AF, could reduce workload and diagnostic delay by minimizing need for follow-up 12-lead electrocardiogram (ECGs).

Objective

To assess the diagnostic performance of the Plessey imPulse lead I ECG device compared with a 12-lead ECG in detecting AF.

Methods

Cross-sectional diagnostic accuracy study. Participants underwent simultaneous 12-lead ECG and imPulse device recordings. The imPulse device reports AF to be “probable,” “possible,” “unlikely,” or “uncontrolled AF unlikely.” imPulse and ECG reference results were cross-tabulated; sensitivity, specificity, positive/negative predictive values, and positive/negative likelihood ratios with 95% confidence interval (CI) were estimated based on different imPulse device report categorizations and heart rate subgroups.

Results

A total of 217 participants were recruited (mean age 70.2 [standard deviation 12.7]), 56% male, 57% outpatients, 43% inpatients) and 199 were included in analyses. AF was diagnosed on ECG for 41 of 199 (20.6%) participants and reported by imPulse as possible, probable, or uncontrolled AF unlikely present for 49 of 199 (24.6%). Sensitivity and specificity for imPulse detection of possible, probable, or uncontrolled AF unlikely vs unlikely, compared with ECG, were 80.5% (95% CI, 65.1%–91.2%) and 89.9% (84.1%–94.1%), respectively. When probable or uncontrolled AF unlikely were compared vs possible or unlikely AF, sensitivity and specificity were 63.4% (46.9%–77.9%) and 98.1% (94.6%–99.6%), respectively.

Conclusion

The imPulse device has moderate sensitivity and good specificity compared with ECG AF detection in a hospital setting.

End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography

OBJECTIVE

The purpose of this research is to develop a new deep learning framework for detecting atrial fibrillation (AFib), one of the most common heart arrhythmias, by analyzing the heart's mechanical functioning as reflected in seismocardiography (SCG) and gyrocardiography (GCG) signals. Jointly, SCG and GCG constitute the concept of mechanocardiography (MCG), a method used to measure precordial vibrations with the built-in inertial sensors of smartphones.

APPROACH

We present a modified deep residual neural network model for the classification of sinus rhythm (SR), AFib, and Noise categories from tri-axial SCG and GCG data derived from smartphones. In the model presented, pre-processing including automated early sensor fusion and spatial feature extraction are carried out using attention-based convolutional and residual blocks. Additionally, we use bidirectional long short-term memory layers on top of fully-connected layers to extract both spatial and spatiotemporal features of the multidimensional SCG and GCG signals. The dataset consisted of 728 short measurements recorded from 300 patients. Further, the measurements were divided into disjoint training, validation, and test sets, respectively, of 481 measurements, 140 measurements, and 107 measurements. Prior to ingestion by the model, measurements were split into 10-second segments with 75 percent overlap, pre-processed, and augmented.

MAIN RESULTS

On the unseen test set, the model delivered average micro- and macro-F1-score of 0.88 (0.87-0.89; 95% CI) and 0.83 (0.83-0.84; 95% CI) for the segment-wise classification as well as 0.95 (0.94-0.96; 95% CI) and 0.95 (0.94-0.96; 95% CI) for the measurement-wise classification, respectively.

SIGNIFICANCE

Our method not only can effectively fuse SCG and GCG signals but also can identify heart rhythms and abnormalities in the MCG signals with remarkable accuracy.

Supermarket/Hypermarket Opportunistic Screening for Atrial Fibrillation (SHOPS-AF): A Mixed Methods Feasibility Study Protocol

Aims: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a key risk factor for ischaemic stroke. Following AF detection, treatment with oral anticoagulation can significantly lower mortality and morbidity rates associated with this risk. The availability of several hand-held devices which can detect AF may enable trained health professionals to adopt AF screening approaches which do not interfere with people’s daily routines. This study aims to investigate the effectiveness of a hand-held device (the MyDiagnostick single-lead Electrocardiogram (ECG) sensor) in screening for AF when embedded into the handles of supermarket trolleys. Methods: A mixed methods two-phase approach will be taken. The quantitative first phase will involve the recruitment of 2000 participants from a convenience sample at four large supermarkets with pharmacies. Prospective participants will be asked to conduct their shopping using a trolley embedded with a MyDiagnostick sensor. If the device identifies a participant with AF, the in-store pharmacist will be dispatched to take a manual pulse measurement and a static control sensor reading and offer a cardiologist consultation referral. When the sensor does not detect AF, a researcher will confirm the reading with a manual pulse measurement. ECGs will be compiled, and the sensitivity, specificity and positive and negative predictive values will be determined. A qualitative second phase will consist of semi-structured interviews carried out with those pharmacists and store managers in-store during the running of the trial period. These will explore the perceptions of staff regarding the merits of embedding sensors in the handles of supermarket trolleys to detect AF. Conclusion: This feasibility study will inform a larger future definitive trial.

[Mobile health for detection of atrial fibrillation-Status quo and perspectives]

Mobile health (mHealth) for the detection of atrial fibrillation is an innovative domestic monitoring of the heart rhythm. The use of mHealth in the context of atrial fibrillation increases the availability of diagnostic technologies and facilitates the integration into telemedical treatment concepts as well as the active participation of patients in the treatment process. The detection of atrial fibrillation with mHealth applications is usually based on electrocardiography (ECG) or by detection of the pulse wave using photoplethysmography (PPG). Some applications require additional sensors, others make use of sensors integrated into smartphones or smartwatches. A high diagnostic accuracy for the detection of atrial fibrillation has been shown for most mHealth applications regardless of the underlying technology (analytical validation); however, the evidence on positive care effects and improvement of medical endpoints (clinical validation) is so far scarce. Screening of symptomatic or asymptomatic patients and the follow-up care after antiarrhythmic measures are possibilities for the integration into the reality of care. The preventive detection of atrial fibrillation is an attractive field of application for mHealth with great potential for the future. Nevertheless, at present mHealth is only integrated to a limited extent into the reality of patient care. Adequate reimbursement and medical remuneration as well as opportunities to derive information and qualification are prerequisites in order to be able to guarantee a comprehensive implementation in the future. The Digital Health Care Act passed in 2019, regulates the reimbursement of digital healthcare applications but issues of primary preventive applications have not yet been included.

Smartphone and new tools for Atrial Fibrillation diagnosis: evidence for clinical applicability

Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia in adults. AF increases the risk of heart failure, cardiac ischemic disease, dementia and Alzheimer's disease. Either clinical and subclinical AF increase the risk of stroke and worsen the patients' clinical outcome. The early diagnosis of AF episodes, even if asymptomatic or clinically silent, is of pivotal importance to ensure prompt and adequate thromboembolic risk prevention therapies. The development of technology is allowing new systematic mass screening possibilities, especially in patients with higher stroke risk. The mobile health devices available for AF detection are: smartphones, wristworn, earlobe sensors and handheld ECG. These devices showed a high accuracy in AF detection especially when a combined approach with single-Lead ECG and photoplethysmography algorithms is used. The use of wearable devices for AF screening is a feasible method but more head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness across different study populations.

2021 Asia Pacific Heart Rhythm Society (APHRS) practice guidance on atrial fibrillation screening

In this paper, the Asia Pacific Heart Rhythm Society (APHRS) sought to provide practice guidance on AF screening based on recent evidence, with specific considerations relevant to the Asia-Pacific region. A key recommendation is opportunistic screening for people aged ≥65 years (all countries), with systematic screening to be considered for people aged ≥75 years or who have additional risk factors (all countries).

Atrial fibrillation: a geriatric perspective on the 2020 ESC guidelines

BACKGROUND

The Task Force for the diagnosis and management of atrial fibrillation (AF) of the European Society of Cardiology (ESC) published in 2020 the updated Guidelines for the Diagnosis and Management of Atrial Fibrillation with the contribution of the European Heart Rhythm Association (EHRA) of the ESC and the European Association for Cardiothoracic Surgery (EACTS).

METHODS AND RESULTS

In this narrative viewpoint, we approach AF from the perspective of aging medicine and try to provide the readers with information usually neglected in clinical routine, mainly due to the fact that while the large majority of AF patients in real life are older, frail and cognitively impaired, these are mostly excluded from clinical trials, and physicians' attitudes often prevail over standardized algorithms.

CONCLUSIONS

On the basis of existing evidence, (1) opportunistic AF screening by pulse palpation or ECG rhythm strip is cost-effective, and (2) whereas advanced chronological age by itself is not a contraindication to AF treatment, a Comprehensive Geriatric Assessment (CGA) including frailty, cognitive impairment, falls and bleeding risk may assist in clinical decision making to provide the best individualized treatment.

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.

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 paroxysmal atrial fibrillation in primary care using Holter monitoring and intermittent, ambulatory single-lead electrocardiography

BACKGROUND

Timely detection of atrial fibrillation (AF) is important because of its increased risk of thrombo-embolic events. Single time point screening interventions fall short in detection of paroxysmal AF, which requires prolonged electrocardiographic monitoring, usually using a Holter. However, traditional 24-48 h Holter monitoring is less appropriate for screening purposes because of its low diagnostic yield. Intermittent, ambulatory screening using a single-lead electrocardiogram (1 L-ECG) device can offer a more efficient alternative.

METHODS

Primary care patients of ≥65 years participated in an opportunistic screening study for AF. We invited patients with a negative 12 L-ECG to wear a Holter monitor for two weeks and to use a MyDiagnostick 1 L-ECG device thrice daily. We report the yield of paroxysmal AF found by Holter monitoring and calculate the diagnostic accuracy of the 1 L-ECG device's built-in AF detection algorithm with the Holter monitor as reference standard.

RESULTS

We included 270 patients, of whom four had AF in a median of 8.0 days of Holter monitoring, a diagnostic yield of 1.5% (95%-CI: 0.4-3.8%). In 205 patients we performed simultaneous 1 L-ECG screening. For diagnosing AF based on the 1 L-ECG device's AF detection algorithm, sensitivity was 66.7% (95%-CI: 9.4-99.2%), specificity 68.8% (95%-CI: 61.9-75.1%), positive predictive value 3.1% (95%-CI: 1.4-6.8%) and negative predictive value 99.3% (95%-CI: 96.6-99.9%).

CONCLUSION

We found a low diagnostic yield of paroxysmal AF using Holter monitoring in elderly primary care patients with a negative 12 L-ECG. The diagnostic accuracy of an intermittently, ambulatory used MyDiagnostick 1 L-ECG device as interpreted by its built-in AF detection algorithm is limited.

Screening for Atrial Fibrillation in Sub-Saharan Africa: A Health Economic Evaluation to Assess the Feasibility in Nigeria

Background:

Cardiovascular disease reflects a major burden of non-communicable disease in Sub-Saharan Africa (SSA). Early detection and treatment of atrial fibrillation (AF), as a preventive measure against stroke, is currently not in the scope of the World Health Organization recommendation to reduce cardiovascular disease.

Objective:

We hypothesized that screening for AF would be an important approach to determine the true AF prevalence in the general population in African countries and to identify asymptomatic AF patients at risk for stroke to optimize prevention.

Methods:

A decision analytic model was developed to study the health-economic impact of AF screening in Nigeria over a life-time horizon. The patient population explored in the model was a population of newly detected AF cases that would be diagnosed with a one-time systematic screening for AF with a single lead ECG device in community health centres across Nigeria.

Conclusions:

The health gain per newly detected AF patient (N = 31,687) was 0.41 QALY at a cost of $5,205 per patient with 100% NOAC use, leading to an ICER of $12,587 per QALY gained. The intervention was cost-effective with a 99.9% warfarin use with an ICER of $1,363 per QALY gained. The total cost of a single screening session was $7.3 million for the total screened population in Nigeria or $1.60 per patient screened. Screening for AF to detect AF patients in need for stroke prevention can be a cost-effective intervention in the Sub-Saharan region, depending on type of anticoagulant used and drug costs.

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.

Manual QT interval measurement with a smartphone-operated single-lead ECG versus 12-lead ECG: a within-patient diagnostic validation study in primary care

OBJECTIVE

To determine the accuracy of QT measurement in a smartphone-operated, single-lead ECG (1L-ECG) device (AliveCor KardiaMobile 1L).

DESIGN

Cross-sectional, within-patient diagnostic validation study.

SETTING/PARTICIPANTS

Patients underwent a 12-lead ECG (12L-ECG) for any non-acute indication in primary care, April 2017-July 2018.

INTERVENTION

Simultaneous recording of 1L-ECGs and 12L-ECGs with blinded manual QT assessment. OUTCOMES OF INTEREST: (1) Difference in QT interval in milliseconds (ms) between the devices; (2) measurement agreement between the devices (excellent agreement <20 ms and clinically acceptable agreement <40 ms absolute difference); (3) sensitivity and specificity for detection of extreme QTc (short (≤340 ms) or long (≥480 ms)), on 1L-ECGs versus 12L-ECGs as reference standard. In case of significant discrepancy between lead I/II of 12L-ECGs and 1L-ECGs, we developed a correction tool by adding the difference between QT measurements of 12L-ECG and 1L-ECGs.

RESULTS

250 ECGs of 125 patients were included. The mean QTc interval, using Bazett's formula (QTcB), was 393±25 ms (mean±SD) in 1L-ECGs and 392±27 ms in lead I of 12L-ECGs, a mean difference of 1±21 ms, which was not statistically different (paired t-test (p=0.51) and Bland Altman method (p=0.23)). In terms of agreement between 1L-ECGs and lead I, QTcB had excellent agreement in 66.9% and clinically acceptable agreement in 93.4% of observations. The sensitivity and specificity of detecting extreme QTc were 0% and 99.2%, respectively. The comparison of 1L-ECG QTcB with lead II of 12L-ECGs showed a significant difference (p=<0.01), but when using a correction factor (+9 ms) this difference was cancelled (paired t-test (p=0.43) or Bland Altman test (p=0.57)). Moreover, it led to improved rates of excellent (71.3%) and clinically acceptable (94.3%) agreement.

CONCLUSION

Smartphone-operated 1L-ECGs can be used to accurately measure the QTc interval compared with simultaneously obtained 12L-ECGs in a primary care population. This may provide an opportunity for monitoring the effects of potential QTc-prolonging medications.

The effect of a case-finding app on the detection rate of atrial fibrillation compared with opportunistic screening in primary care patients: protocol for a cluster randomized trial

Background

Atrial fibrillation is a cardiac arrhythmia commonly encountered in a primary care setting. Current screening is limited to pulse palpation and ECG confirmation when an irregular pulse is found. Paroxysmal atrial fibrillation will, however, still be difficult to pick up. With the advent of smartphones, screening could be more cost-efficient by making use of simple applications, lowering the need for intensive screening to discover (paroxysmal) atrial fibrillation.

Methods/design

This cluster randomized trial will examine the effect of using a smartphone-based application such as FibriCheck® on the detection rate of atrial fibrillation in a Flemish general practice population. This study will be conducted in 22 primary care practices across the Flanders region of Belgium and will last 12 months. Patients above 65 years of age will be divided in control and intervention groups on the practice level. The control group will be subjected to standard opportunistic screening only, while the intervention group will be prescribed the FibriCheck® app on top of this opportunistic screening. The difference in detection rate between control and intervention groups will be calculated at the end of the study.

We will use the online platform INTEGO for pseudonymized data collection and analysis, and risk calculation.

Discussion

Smartphone applications might offer a way to cost-effectively screen for (paroxysmal) atrial fibrillation in a primary care setting. This could open the door for the update of future screening guidelines.

Trial registration

ClinicalTrials.gov NCT04545723. Registered on September 10, 2020.

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.

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.

Clinical Factors Associated with Atrial Fibrillation Detection on Single-Time Point Screening Using a Hand-Held Single-Lead ECG Device

Our aim was to assess the prevalence of unknown atrial fibrillation (AF) among adults during single-time point rhythm screening performed during meetings or social recreational activities organized by patient groups or volunteers. A total of 2814 subjects (median age 68 years) underwent AF screening by a handheld single-lead ECG device (MyDiagnostick). Overall, 56 subjects (2.0%) were diagnosed with AF, as a result of 12-lead ECG following a positive/suspected recording. Screening identified AF in 2.9% of the subjects ≥ 65 years. None of the 265 subjects aged below 50 years was found positive at AF screening. Risk stratification for unknown AF based on a CHA2DS2VASc > 0 in males and >1 in females (or CHA2DS2VA > 0) had a high sensitivity (98.2%) and a high negative predictive value (99.8%) for AF detection. A slightly lower sensitivity (96.4%) was achieved by using age ≥ 65 years as a risk stratifier. Conversely, raising the threshold at ≥75 years showed a low sensitivity. Within the subset of subjects aged ≥ 65 a CHA2DS2VASc > 1 in males and >2 in females, or a CHA2DS2VA > 1 had a high sensitivity (94.4%) and negative predictive value (99.3%), while age ≥ 75 was associated with a marked drop in sensitivity for AF detection.

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

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: 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.

Screen-detected atrial fibrillation predicts mortality in elderly subjects

AIMS

Current guidelines recommend opportunistic screening for atrial fibrillation (AF) but the prognosis of individuals is unclear. The aim of this investigation is to determine prevalence and 1-year outcome of individuals with screen-detected AF.

METHODS AND RESULTS

We performed a prospective, pharmacy-based single time point AF screening study in 7107 elderly citizens (≥65 years) using a hand-held, single-lead electrocardiogram (ECG) device. Prevalence of AF was assessed, and data on all-cause death and hospitalization for cardiovascular (CV) causes were collected over a median follow-up of 401 (372; 435) days. Mean age of participants was 74 ± 5.9 years, with 58% (N = 4130) of female sex. Automated heart rhythm analyses identified AF in 432 (6.1%) participants, with newly diagnosed AF in 3.6% of all subjects. During follow-up, 62 participants (0.9%) died and 390 (6.0%) were hospitalized for CV causes. Total mortality was 2.3% in participants with a screen-detected AF and 0.8% in subjects with a normal ECG [hazard ratio (HR) 2.94; 95% confidence interval (CI) 1.49-5.78; P = 0.002]; hospitalization for CV causes occurred in 10.6% and 5.5%, respectively (HR 2.08; 95% CI 1.52-2.84; P < 0.001). Compared with subjects without a history of AF at baseline and a normal ECG, participants with newly diagnosed or known AF had a significantly higher mortality risk with HRs of 2.64 (95% CI 1.05-6.66; P = 0.04) and 2.68 (95% CI 1.44-4.97; P = 0.002), respectively. After multivariable adjustment, screen-detected AF remained a significant predictor of death or hospitalization for CV causes.

CONCLUSION

Pharmacy-based, automated AF screening in elderly citizens identified subjects with unknown AF and an excess mortality risk over the next year.

Research Priorities in Atrial Fibrillation Screening: A Report From a National Heart, Lung, and Blood Institute Virtual Workshop

Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications, including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute's virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: (1) role of opportunistic screening; (2) AF as a risk factor, risk marker, or both; (3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; (4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and (5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource-efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.

Patient-reported feasibility of chest and thumb ECG after cryptogenic stroke in Sweden: an observational study

OBJECTIVES

The aim of this study was to assess the feasibility, based on a questionnaire, of the chest and thumb ECG system Coala Heart Monitor in patients who recently had a stroke.

DESIGN

Observational study.

SETTING

Two stroke units, Region Gävleborg, Sweden.

PARTICIPANTS AND INTERVENTIONS

This study, Transient ECG Assessment in Stroke Evaluation (TEASE), included patients who had a stroke between 2017 and 2019. Patients eligible for anticoagulation in the presence of atrial fibrillation were scheduled for 28 days monitoring.

PRIMARY AND SECONDARY OUTCOME MEASURES

The questionnaire regarding feasibility of monitoring included seven questions, using a 100 mm Visual Analogue Scale which covered overall satisfaction, technical feasibility, remember to monitor, physical application, feeling of security, help from others and recommendation to others. A lower score indicated better outcome.

RESULTS

The prespecified number of 100 patients underwent the monitoring and 83 out of the 97 alive patients returned the questionnaire (response rate 85.6%). The median age was 69.5 years, mean CHA₂DS₂-VASc score was 4.4±1.3 points and 59.0% were men (n=49). The median time from index stroke to start of monitoring was 7.0 days. Patients performed on average 90.1%±15.0% of scheduled ECG-transmissions. In all seven questions, the median score ranged from 4 to 8. The vast majority reported acceptable outcomes, that is, the 95th percentile ranged from 30 to 54. There was no significant difference between men and women with regard to any of the seven questions (p values ranging from 0.117 to 0.849). Two of the seven outcome scores correlated significantly to patient age (Spearman's r=-0.238 and r=-0.308, and p values 0.031 and 0.005 for 'overall satisfaction' and 'remember to monitor', respectively).

CONCLUSION

In stroke survivors, chest and thumb ECG two times per day over a period of 4 weeks is feasible from a patient's perspective. The Coala Heart Monitor provides a valuable and convenient tool for monitoring after stroke.

TRIAL REGISTRATION NUMBER

NCT03301662.

Experience in screening for atrial fibrillation and monitoring arrhythmia using a single-lead ECG stick

Atrial fibrillation (AF) is the most common arrhythmia and is highly associated with increased morbidity and mortality. Since many AF episodes remain subclinical, screening for AF is considered a desirable approach for timely diagnosis, prevention of sequelae and effective treatment. Recently, devices for AF detection-stand-alone or integrated in mobile health technology-have become available and show promising preliminary results in the detection and monitoring of arrhythmia. This review describes the technical aspects of a single-lead ECG stick and summarizes the current literature, experience in large-scale screening for AF in pharmacies and potential fields of application.

Screening of unknown atrial fibrillation through handheld device in the elderly

OBJECTIVE

To estimate the prevalence of unknown atrial fibrillation (AF) in the elderly population of the Veneto Region, Italy.

METHODS

1820 patients aged ≥ 65 years with no history of AF and not anticoagulated were enrolled in primary-care settings. They underwent an opportunistic electrocardiogram screening with a handheld device (MyDiagnostick) designed to specifically detect AF. The electrocardiogram recordings were reviewed by the researchers, who confirmed the presence of AF.

RESULTS

The device detected an arrhythmia in 143 patients, which was confirmed as AF in 101/143 (70.6%), with an overall prevalence of AF of 5.5% (101/1820). Prevalence of unknown AF resulted in 3.6% in patients aged 65-74 years, and 7.5% in patients age 75 or older, and increased according to CHA₂DS₂-VASc score: 3.5% in patients with a score of 1 or 2, 5.6% in patients with a score of 3, 7.0% in patients with a score of 4, and 7.2% in patients with a score ≥ 5. The detection rate was significantly higher in patients with mild symptoms compared to asymptomatic counterparts (24.1% vs. 4.0%, P < 0.0001). At multivariate analysis, congestive heart failure and age ≥ 75 years-old were independent predictors for screen-detected AF.

CONCLUSIONS

An opportunistic screening with handheld device revealed an unexpectedly high prevalence of unknown AF in elderly patients with mild symptoms. Prevalence increased with age and CHA₂DS₂-VASc score.

72 hour Holter monitoring, 7 day Holter monitoring, and 30 day intermittent patient-activated heart rhythm recording in detecting arrhythmias in cryptogenic stroke patients free from arrhythmia in a screening 24 h Holter

Introduction

According to recent studies, silent atrial fibrillation (AF) is a common cause of cryptogenic ischemic stroke (CIS). 12-lead electrocardiogram (ECG) and 24 h Holter are not efficient to reveal an occult arrhythmic cause of stroke.

Objectives

The aim of the study was to evaluate 72 h Holter, 7 day Holter monitoring, and intermittent single-lead ECG recording in patients with CIS to identify cases with the arrhythmic cause of stroke in patients with CIS in whom 24 h ECG Holter was free from arrhythmia.

Methods

72 patients (aged 60 ± 9 years, 44 males) with CIS and no arrhythmic findings in 24 h Holter were enrolled. All patients had 7 day Holter monitoring and received handheld ECG recorder (CheckMe, Viatom) for ambulatory 30 ± 3 days ECG recording. AF, supraventricular tachycardia (SVT runs of ≥5 QRS), and other arrhythmias were assessed in the first 72 h of Holter recording, in 7 day-recording, and in handheld ECG strips.

Results

72 h-recording revealed AF in four cases (5.6%) and SVT in 18 (25%) cases. 7 day Holter confirmed AF in seven patients (10%) and SVT in 27 patients (37.5%). There was no difference in regards to CHADS2VASc score between patients with SVT and non-arrhythmic group (3.6 ± 1.1 vs 3.4 ± 1.6; p = NS). Symptoms did not correlate with findings. Patient-activated handheld ECG recorders were used with good compliance. The mean number of recordings was 49 ± 30. Except for PACs, there was only one case of AF documented in 3,531 strips.

Conclusions

7 day Holter performs better than 72 h and reveals supraventricular arrhythmias in every third and AF in 10% of CIS patients who were free from arrhythmia in 24 h ECG monitoring. 30 day intermittent ECG monitor does not yield diagnostic value in CIS.

Emerging Technologies for Identifying Atrial Fibrillation

Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.

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.

Opportunistic screening versus usual care for diagnosing atrial fibrillation in general practice: a cluster randomised controlled trial

BACKGROUND

Atrial fibrillation (AF) increases the risk of stroke, heart failure, and all-cause mortality. AF may be asymptomatic and therefore remain undiagnosed. Devices such as single-lead electrocardiographs (ECGs) may help GPs to diagnose AF.

AIM

To investigate the yield of opportunistic screening for AF in usual primary care using a single-lead ECG device.

DESIGN AND SETTING

A clustered, randomised controlled trial among patients aged ≥65 years with no recorded AF status in the Netherlands from October 2014 to March 2016.

METHOD

Fifteen intervention general practices used a single-lead ECG device at their discretion and 16 control practices offered usual care. The follow-up period was 1 year, and the primary outcome was the proportion of newly diagnosed cases of AF.

RESULTS

In total, 17 107 older people with no recorded AF status were eligible to participate in the study. In the intervention arm, 10.7% of eligible patients (n = 919) were screened over the duration of the study year. The rate of newly diagnosed AF was similar in the intervention and control practices (1.43% versus 1.37%, P = 0.73). Screened patients were more likely to have comorbidities, such as hypertension (60.0% versus 48.7%), type 2 diabetes (24.3% versus 18.6%), and chronic obstructive pulmonary disease (11.3% versus 7.4%), than eligible patients not screened in the intervention arm. Among patients with newly diagnosed AF in intervention practices, 27% were detected by screening, 23% by usual primary care, and 50% by a medical specialist or after stroke/transient ischaemic attack.

CONCLUSION

Opportunistic screening with a single-lead ECG at the discretion of the GP did not result in a higher yield of newly detected cases of AF in patients aged ≥65 years in the community than usual care. For higher participation rates in future studies, more rigorous screening methods are needed.

Diagnostic accuracy of handheld electrocardiogram devices in detecting atrial fibrillation in adults in community versus hospital settings: a systematic review and meta-analysis

With increasing use of handheld ECG devices for atrial fibrillation (AF) screening, it is important to understand their accuracy in community and hospital settings and how it differs among settings and other factors. A systematic review of eligible studies from community or hospital settings reporting the diagnostic accuracy of handheld ECG devices (ie, devices producing a rhythm strip) in detecting AF in adults, compared with a gold standard 12-lead ECG or Holter monitor, was performed. Bivariate hierarchical random-effects meta-analysis and meta-regression were performed using R V.3.6.0. The search identified 858 articles, of which 14 were included. Six studies recruited from community (n=6064 ECGs) and eight studies from hospital (n=2116 ECGs) settings. The pooled sensitivity was 89% (95% CI 81% to 94%) in the community and 92% (95% CI 83% to 97%) in the hospital. The pooled specificity was 99% (95% CI 98% to 99%) in the community and 95% (95% CI 90% to 98%) in the hospital. Accuracy of ECG devices varied: sensitivity ranged from 54.5% to 100% and specificity ranged from 61.9% to 100%. Meta-regression showed that setting (p=0.032) and ECG device type (p=0.022) significantly contributed to variations in sensitivity and specificity. The pooled sensitivity and specificity of single-lead handheld ECG devices were high. Setting and handheld ECG device type were significant factors of variation in sensitivity and specificity. These findings suggest that the setting including user training and handheld ECG device type should be carefully reviewed.

Opportunistic screening for atrial fibrillation with a single lead device in geriatric patients

OBJECTIVE

To determine the diagnostic yield of repeated screening for atrial fibrillation (AF) among geriatric patients.

METHODS

A pragmatic prospective cohort study into applying opportunistic screening for AF with a handheld single lead ECG device (SLD) in a geriatric cohort. Consecutive patients of 65 years old and older visiting the geriatric outpatient clinic were eligible for inclusion. A 12 lead ECG was performed, followed by measurements with the SLD during every visit to the geriatric outpatient clinic. A frailty index was based on the accumulation of deficits model.

RESULTS

478 patients were eligible. Patients were excluded if they did not give informed consent (17 patients), had a pacemaker or implantable cardioverter defibrillator (20 patients), or had incomplete medical files (two patients). After exclusion, 439 patients participated in this study. The mean age was 78 years (range 65 to 100 years), 54% were female. AF was known in 89 patients (20%), first detected on the baseline ECG in four patients (1%) and first detected with the SLD in 20 patients (5%) during follow up visits. Sensitivity of the SLD was 90.0%, specificity 99.0%, negative predictive value 99.7%, and positive predictive value 73.5%. Most patients (82%) with AF were frail and 53% were severely frail.

CONCLUSION

Repeated screening in geriatric patients has a five times higher diagnostic yield than usual care. It was easily combined with usual care. Because of the positive predictive value of 73.5%, it remains necessary to confirm AF with a 12 lead ECG or 24-h Holter monitoring.