Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results

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.

Patient Driven EKG Device Performance in Adults with Fontan Palliation

The aim of this study was to evaluate the accuracy of the KardiaMobile (KM) device in adults with a Fontan palliation, and to assess the KM function as a screening tool for atrial arrhythmias. While patient driven electrocardiogram (EKG) devices are becoming a validated way to evaluate cardiac arrhythmias, their role for patients with congenital heart disease is less clear. Patients with single ventricle Fontan palliation have a high prevalence of atrial arrhythmias and represent a unique cohort that could benefit from early detection of atrial arrhythmias. This single center prospective study enrolled adult patients with Fontan palliation to use the KM heart rhythm monitoring device for both symptomatic episodes and asymptomatic weekly screening over a 1-year period. Accuracy was assessed by comparing the automatic KM interpretation (KM-auto) to an electrophysiologist overread (KM-EP) and traditional EKG. Fifty patients were enrolled and 510 follow-up transmissions were received. The sensitivity and specificity of enrollment KM-auto compared to EKG was 65% and 100%, respectively. The sensitivity and specificity of enrollment KM-auto compared to the KM-EP was 75% and 96%, respectively. In the adult Fontan palliation, the accuracy of the KM device to detect a normal rhythm was reliable and best with a physician overread. Abnormal or uninterpretable KM-auto device interpretations, symptomatic transmissions, and any transmissions with a high heart rate compared to a patient's normal baseline should warrant further review.

QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms

BACKGROUND AND OBJECTIVES

A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs.

METHODS

The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation (AF). Performance was assessed against manual annotations.

RESULTS

A total of 12 QRS detectors performed well on ECGs collected under clinical supervision (F1 score ≥0.96). However, fewer performed well on telehealth ECGs: five performed well on the TELE ECG Database; six performed well on high-quality SAFER data; and performance was poorer on low-quality SAFER data (three QRS detectors achieved F1 of 0.78-0.84). The presence of AF had little impact on performance.

CONCLUSIONS

The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society (HRS), the Asia Pacific HRS, and the Latin American HRS.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society, the Asia Pacific Heart Rhythm Society, and the Latin American Heart Rhythm Society.

A Scoping Review of Varying Mobile Electrocardiographic Devices

The electrocardiogram (ECG) can now be measured using mobile devices. Mobile ECG devices, which are defined as devices capable of recording and transmitting non-standard ECGs, offer numerous advantages such as cost-effectiveness and being user-friendly. Mobile ECG can also extend recording lengths (e.g., 2 days, 14 days), which is necessary to capture important intermittent events (e.g., cardiac arrhythmias) and evaluate prognostic risk markers (e.g., prolonged corrected QT (QTc) interval). Some mobile ECG devices can even connect to broadband networks allowing patients to remotely transmit their ECG to a clinician. This article systematically examines different mobile ECG devices used in prior studies and provides a detailed assessment of five diverse yet commonly used mobile ECG devices: AliveCor KardiaMobile; AliveCor KardiaMobile 6L; iRhythm ZioPatch; Apple Smartwatch ECG; and CardioSecur System. These mobile ECG devices are diverse in the number of leads measured and the duration of monitoring. Similar to their diversity, there has been a wide range of clinical applications of mobile ECG devices. Despite significant progress, questions regarding data quality, and clinican and patient acceptance and compliance persist.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society, the Asia Pacific Heart Rhythm Society, and the Latin American Heart Rhythm Society .

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation.

METHODS

A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate.

STRUCTURE

Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Patients With Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation.

METHODS

A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate.

STRUCTURE

Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

Contemporary wearable and handheld technology for the diagnosis of cardiac arrhythmias in Singapore

Twelve-lead electrocardiography (ECG) remains the gold standard for the diagnosis of cardiac arrhythmias. It provides a snapshot of the cardiac electrical activity while the leads are attached to the patient. As medical training is required to use the ECG machine, its use remains restricted to the clinic and hospital settings. These aspects limit the usefulness of 12-lead ECG in the diagnosis of cardiac arrhythmias, especially in individuals with short-lasting and infrequent paroxysmal symptoms. The introduction of ECG recording features in wearable and handheld smart devices has changed the paradigm of cardiac arrhythmia diagnosis, empowering patients to record their ECG as and when symptoms occur. This review describes contemporary ambulatory heart rhythm monitors commonly available in Singapore and their expanding role in the diagnosis of cardiac rhythm abnormalities.

Data-Driven and Technology-Enabled Trial Innovations Toward Decentralization of Clinical Trials: Opportunities and Considerations

Traditional trial designs have well-recognized inefficiencies and logistical barriers to participation. Decentralized trials and digital health solutions have been suggested as potential solutions and have certainly risen to the challenge during the pandemic. Clinical trial designs are now increasingly data driven. The use of distributed clinical data networks and digitization has helped to fundamentally upgrade existing research systems. A trial design may vary anywhere from fully decentralized to hybrid to traditional on-site. Various decentralization components are available for stakeholders to increase the reach and pace of their trials, such as electronic informed consent, remote interviews, administration, outcome assessment, monitoring, and laboratory and imaging modalities. Furthermore, digital health technologies can be included to enrich study conduct. However, careful consideration is warranted, including assessing verification and validity through usability studies and having various contingencies in place through dedicated risk assessment. Selecting the right combination depends not just on the ability to handle patient care and the medical know-how but also on the availability of appropriate technologic infrastructure, skills, and human resources. Throughout this process, quality of evidence generation and physician-patient relation must not be undermined. Here we also address some knowledge gaps, cost considerations, and potential impact of decentralization and digitization on inclusivity, recruitment, engagement, and retention. Last, we mention some future directions that may help drive the necessary change in the right direction.

Mobile electrocardiographic devices and healthcare utilization in post-atrial fibrillation ablation patients

BACKGROUND

The use of mobile electrocardiogram (mECG) devices is becoming more prevalent. mECG devices allow instant access to recording arrhythmias and enable automatic rhythm interpretation. Providers can remotely evaluate patients and this may reduce in-person healthcare utilization. We sought to evaluate the utility of mECG devices in reducing healthcare utilization among patients who underwent atrial fibrillation (AF) ablation.

METHODS

We identified a population of patients with paroxysmal or persistent AF presenting for their first AF ablation. Patients were divided into two groups: KardiaMobile (AliveCor, Mountain View, CA) mECG users and non-KardiaMobile users. Healthcare utilization was compared between the two groups for one year post-ablation.

RESULTS

184 patients were studied (76 KardiaMobile users, 108 non-KardiaMobile users). There was no difference in the number of office visits (p = 0.59), cardiac-specific emergency department visits (p = 0.26), cardiac-specific hospital admissions (p = 0.13), ablations or cardioversions completed (p = 0.24), telephone encounters (p = 0.05), patient electronic health record messages (p = 0.40), or cardiac imaging (transthoracic or transesophageal echocardiograms) tests ordered (p = 0.36). Exposure to the device was associated with a reduction in ambulatory cardiac monitor use (p = 0.04). There was no difference in sinus rhythm maintenance over 12 months by Kaplan-Meier survival analysis (log rank test p = 0.05) between groups.

CONCLUSION

Mobile technology is available for heart rhythm monitoring and can give instant feedback to the user. mECG use is associated with a significant reduction in ambulatory cardiac monitor use in the post-ablation period. There was no difference in other AF-related healthcare utilization.

Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its early detection is critical for preventing complications and optimizing treatment. In this study, a novel AF prediction method is proposed, which is based on investigating a subset of the 12-lead ECG data using a recurrent plot and ParNet-adv model. The minimal subset of ECG leads (II &V1) is determined via a forward stepwise selection procedure, and the selected 1D ECG data is transformed into 2D recurrence plot (RP) images as an input to train a shallow ParNet-adv Network for AF prediction. In this study, the proposed method achieved F1 score of 0.9763, Precision of 0.9654, Recall of 0.9875, Specificity of 0.9646, and Accuracy of 0.9760, which significantly outperformed solutions based on single leads and complete 12 leads. When studying several ECG datasets, including the CPSC and Georgia ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the new method achieved F1 score of 0.9693 and 0.8660, respectively. The results suggested a good generalization of the proposed method. Compared with several state-of-art frameworks, the proposed model with a shallow network of only 12 depths and asymmetric convolutions achieved the highest average F1 score. Extensive experimental studies proved that the proposed method has a high potential for AF prediction in clinical and particularly wearable applications.

Health Techequity: Opportunities for Digital Health Innovations to Improve Equity and Diversity in Cardiovascular Care

Purpose of Review

In this review, we define health equity, disparities, and social determinants of health; the different components of digital health; the barriers to digital health equity; and cardiovascular digital health trials and possible solutions to improve health equity through digital health.

Recent Findings

Digital health interventions show incredible potential to improve cardiovascular diseases by obtaining longitudinal, continuous, and actionable patient data; increasing access to care; and by decreasing delivery barriers and cost. However, certain populations have experienced decreased access to digital health innovations and decreased representation in cardiovascular digital health trials.

Summary

Special efforts will need to be made to expand access to the different elements of digital health, ensuring that the digital divide does not exacerbate health disparities. As the expansion of digital health technologies continues, it is vital to increase representation of minoritized groups in all stages of the process: product development (needs findings and screening, concept generation, product creation, and testing), clinical research (pilot studies, feasibility studies, and randomized control trials), and finally health services deployment.

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.

Clinical Validation of a Smartphone-based Handheld ECG Device: A Validation Study

BACKGROUND

Remote cardiac monitoring and screening have already become an integral telemedicine component. The wide usage of several different wireless electrocardiography (ECG) devices warrants a validation study on their accuracy and reliability.

METHODS

Totally, 300 inpatients with the Nabz Hooshmand-1 handheld ECG device and the GE MAC 1200 ECG system (as the reference) were studied to check the accuracy of the devices in 1 and 6-limb lead performance. Simultaneous 10-second resting ECGs were assessed for the most common ECG parameters in lead I. Afterward, 6-lead ECGs (limb leads), were performed immediately and studied for their morphologies.

RESULTS

Of the 300 patients, 297 had acceptable ECG quality in both devices for simultaneous lead I ECGs. The ECGs were inspected on-screen by a cardiologist for their rhythms, rates, axes, numbers, morphologies of premature atrial and ventricular beats, morphologies and amplitudes of PQRST waves, P-wave durations, QRS-wave durations, P-R intervals, and QT intervals. No significant differences were detected between the devices, and no major abnormalities were missed. Six-limb lead ECGs were obtained in 284 patients, of whom 281 had acceptable quality in ECGs by both devices. The morphology matching evaluation of the ECGs demonstrated an overall 98% compatibility rate, with the highest compatibility in lead I and the lowest in lead augmented vector foot.

CONCLUSIONS

The diagnosis of critical pathological rhythms, including atrial fibrillation and high-grade atrioventricular node block, was not missed by the Nabz Hooshmand-1 and GE MAC 1200 ECG devices. Accordingly, rhythm detection as the primary purpose of handheld ECG devices was highly accurate. Both devices had acceptable sensitivity to diagnose long P-R and long and short QT intervals. Although the modern technology of smartphones and the physical inability for the 6-limb mode might cause old patients difficulty in utilizing such devices, their use for screening and follow-up is safe.

Artificial intelligence in cardiology: Hope for the future and power for the present

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.

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.

Physical activity, patient-reported symptoms, and clinical events: Insights into postprocedural recovery from personal digital devices

Background

Personal digital devices may offer insights into patient recovery and an approach for remote monitoring after procedures.

Objective

To examine associations between activity measured using personal digital devices, patient-reported outcome measures (PROMs), and clinical events among patients after catheter ablation for atrial fibrillation (AF) or bariatric surgery.

Methods

We aggregated personal digital device, PROM, and electronic health record data in a study conducted at 2 health systems. We used Fitbit devices for step count assessments, KardiaMobile for cardiac rhythm assessments, and PROMs for pain and palpitations over 5 weeks.

Results

Among 59 patients, 30 underwent AF ablation and 29 bariatric surgery. Thirty-six patients (63%) reported pain. There was no difference in median [interquartile range] daily steps between patients with and those without pain (4419 [3286–7041] vs 3498 [2609–5888]; P = .23). Among AF ablation patients, 21 (70%) reported palpitations. Median daily steps were lower among those with palpitations than among those without (4668 [3021–6116] vs 8040 [6853–10,394]; P = .03). When accounting for within-subject correlation, recordings of AF were associated with a significant mean decrease in median daily steps (–351; 95% confidence interval –524 to –177; P <.01). Patients who received a new antiarrhythmic drug prescription had AF recorded in a median of 5 [5–5] of 5 total weeks, whereas patients who did not receive a new antiarrhythmic recorded AF in a median of 1 [0–3] week (P = .02).

Conclusion

Personal digital device and PROM data can provide insight into postprocedural recovery outside of usual clinical settings and may inform follow-up and clinical decision-making. (ClinicalTrials.gov Identifier: NCT03436082)

Remote and Wearable ECG Devices with Diagnostic Abilities in Adults: A State-of-the-Science Scoping Review

The electrocardiogram (ECG) records the electrical activity in the heart in real-time, providing an important opportunity to detecting various cardiac pathologies. The 12-lead ECG currently serves as the "standard" ECG acquisition technique for diagnostic purposes for many cardiac pathologies other than arrhythmias. However, the technical aspects of acquiring a 12-lead ECG are not easy and its usage is currently restricted to trained medical personnel, limiting the scope of its usefulness. Remote and wearable ECG devices have attempted to bridge this gap by enabling patients to take their own ECG using a simplified method at the expense of a reduced number of leads, usually a single-lead ECG. In this review article, we summarize the studies which investigate the use of remote ECG devices and their clinical utility in diagnosing cardiac pathologies. Eligible studies discussed FDA-cleared, commercially available devices that were validated on an adult population. We summarize technical logistics of signal quality and device reliability, dimensional and functional features, and diagnostic value. In summary, our synthesis shows that reduced-set ECG wearables have huge potential for long-term monitoring, particularly if paired with real-time notification techniques. Such capabilities make them primarily useful for abnormal rhythm detection and there is sufficient evidence that a remote ECG device can be more superior to traditional 12-lead ECG in diagnosing specific arrhythmias such as atrial fibrillation. However, this review identifies important challenges faced by this technology, highlighting the limited availability of clinical research examining their usefulness.

[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.

Rhythm Monitoring Strategy and Arrhythmia Recurrence in Atrial Fibrillation Ablation Trials: A Systematic Review

Background

: The rhythm-monitoring strategy after catheter ablation (CA) for atrial fibrillation (AF) impacts the detection of atrial arrhythmia recurrence and is not well characterized. We performed a systematic review and meta-regression analysis to determine whether the duration and mode of rhythm monitoring after CA affects detection of atrial arrhythmia recurrence.

Methods

Databases were systematically searched for randomized controlled trials of adult patients undergoing first CA for AF from 2007 to 2021. Duration and strategy of rhythm monitoring were extracted. Meta-regression was used to identify any association between duration of monitoring and detection of atrial arrhythmia recurrence. The primary measure of outcome was single-procedure recurrence of atrial arrhythmia.

Results

The search strategy yielded 57 trial arms from 56 randomized controlled trials comprising 5322 patients: 36 arms of patients with paroxysmal AF (PAF), and 21 arms of patients with persistent AF (PeAF) or both PAF/PeAF. Intermittent monitoring was associated with detection of significantly less atrial arrhythmia recurrence than continuous monitoring in PAF arms (31.2% vs 46.9%, P = 0.001), but not in PeAF/PAF-PeAF combined arms (43.3% vs 63.6%, P = 0.12). No significant relationship was seen between the duration of intermittent rhythm monitoring and atrial arrhythmia recurrence detection in either the PAF (P = 0.93) or PeAF/PAF-PeAF combined arms (P = 0.20).

Conclusions

Continuous rhythm monitoring detected higher atrial arrhythmia recurrence rates, compared to intermittent rhythm monitoring, in patients with PAF. The duration of intermittent monitoring did not show a statistically significant relationship to the yield of arrhythmia detection, in near identical cohorts of trial subjects undergoing similar interventions, with clinical and research implications.

Mobile Single-Lead Electrocardiogram Technology for Atrial Fibrillation Detection in Acute Ischemic Stroke Patients

(1) Background: AliveCor KardiaMobile (KM) is a portable electrocardiography recorder for detection of atrial fibrillation (AF). The aim of the study was to define the group of acute ischemic stroke (AIS) patients who can use the KM device and assess the diagnostic test accuracy. (2) Methods: the AIS patients were recruited to the study. Thirty-second single-lead electrocardiogram (ECG) usages were recorded on demand for three days using KM portable device. Each KM ECG record was verified by a cardiologist. The feasibility was evaluated using operationalization criteria. (3) Results: the recruitment rate among AIS patients was 26.3%. The withdrawal rate before the start of the intervention was 26%. The withdrawal rate after the start of the intervention was 6%. KM device detected AF in 2.8% of AIS patients and in 2.2% of ECG records. Cardiologist confirmed the AF in 0.3% AIS patients. Sensitivity and specificity of KM for AF was 100% and 98.3%, respectively. (4) Conclusions: the results of this study suggest that it is feasible to use KM device to detect AF in the selected AIS patients (younger and in better neurological condition). KM detected AF in the selected AIS patients with high specificity and sensitivity.

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poor generalization. We, therefore, hypothesized that finding an optimal lead subset of the 12-lead ECG to eliminate the redundancy would help improve the generalizability of DL-based models. In this study, we developed and evaluated a DL-based model that has a feature extraction stage, an ECG-lead subset selection stage and a decision-making stage to automatically interpret multiple common ECG abnormality types. The data analysed in this study consisted of 6877 12-lead ECG recordings from CPSC 2018 (labelled as normal rhythm or eight types of ECG abnormalities, split into training (approx. 80%), validation (approx. 10%) and test (approx. 10%) sets) and 3998 12-lead ECG recordings from PhysioNet/CinC 2020 (labelled as normal rhythm or four types of ECG abnormalities, used as external text set). The ECG-lead subset selection module was introduced within the proposed model to efficiently constrain model complexity. It detected an optimal 4-lead ECG subset consisting of leads II, aVR, V1 and V4. The proposed model using the optimal 4-lead subset significantly outperformed the model using the complete 12-lead ECG on the validation set and on the external test dataset. The results demonstrated that our proposed model successfully identified an optimal subset of 12-lead ECG; the resulting 4-lead ECG subset improves the generalizability of the DL model in ECG abnormality interpretation. This study provides an outlook on what channels are necessary to keep and which ones may be ignored when considering an automated detection system for cardiac ECG abnormalities. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology

The artificial intelligence (AI) revolution is well underway, including in the medical field, and has dramatically transformed our lives. An understanding of the basics of AI applications, their development, and challenges to their clinical implementation is important for clinicians to fully appreciate the possibilities of AI. Such a foundation would ensure that clinicians have a good grasp and realistic expectations for AI in medicine and prevent discrepancies between the promised and real-world impact. When quantifying the track record for AI applications in cardiology, we found that a substantial number of AI systems are never deployed in clinical practice, although there certainly are many success stories. Successful implementations shared the following: they came from clinical areas where large amount of training data was available; were deployable into a single diagnostic modality; prediction models generally had high performance on external validation; and most were developed as part of collaborations with medical device manufacturers who had substantial experience with implementation of new technology. When looking into the current processes used for developing AI-based systems, we suggest that expanding the analytic framework to address potential deployment and implementation issues at project outset will improve the rate of successful implementation, and will be a necessary next step for AI to achieve its full potential in cardiovascular medicine.

Ablation Index-guided point-by-point ablation versus Grid annotation-guided dragging for pulmonary vein isolation: A randomized controlled trial

Introduction

Radiofrequency (RF) atrial fibrillation (AF) ablation using a catheter dragging technique may shorten procedural duration and improve durability of pulmonary vein isolation (PVI) by creating uninterrupted linear ablation lesions. We compared a novel AF ablation approach guided by Grid annotation allowing for “drag lesions” with a standard point‐by‐point ablation approach in a single‐center randomized study.

Methods

Eighty‐eight paroxysmal or persistent AF patients were randomized 1:1 to undergo RF‐PVI with either a catheter dragging ablation technique guided by Grid annotation or point‐by‐point ablation guided by Ablation Index (AI) annotation. In the Grid annotation arm, ablation was visualized using 1 mm³ grid points coloring red after meeting predefined stability and contact force criteria. In the AI annotation arm, ablation lesions were created in a point‐by‐point fashion with AI target values set at 380 and 500 for posterior/inferior and anterior/roof segments, respectively. Patients were followed up for 12 months after PVI using ECGs, 24‐h Holter monitoring and a mobile‐based one‐lead ECG device.

Results

Procedure time was not different between the two randomization arms (Grid annotation 71 ± 19 min, AI annotation 72 ± 26 min, p = .765). RF time was significantly longer in the Grid annotation arm compared with the AI annotation arm (49 ± 8 min vs. 37 ± 8 min, respectively, p < .001). Atrial tachyarrhythmia recurrence was documented in 10 patients (23%) in the Grid annotation arm compared with 19 patients (42%) in the AI annotation arm with time to recurrence not reaching statistical significance (p = .074).

Conclusions

This study shows that a Grid annotation‐guided dragging approach provides an alternative to point‐by‐point RF‐PVI using AI annotation.

Risk factor management of atrial fibrillation using mHealth: The Atrial Fibrillation – Helping Address Care with Remote Technology (AF-HEART) Pilot Study

Background

Personalized treatment of atrial fibrillation (AF) risk factors using mHealth and telehealth may improve patient outcomes.

Objective

The purpose of this study was to assess the feasibility of the Atrial Fibrillation Helping Address Care with Remote Technology (AF-HEART) intervention on the following patient outcomes: (1) heart rhythm tracking; (2) weight, alcohol, blood pressure (BP), and sleep apnea reduction; (3) AF symptom reduction; and (4) quality-of-life (QOL) improvement.

Methods

A total of 20 patients with AF undergoing antiarrhythmic therapy, cardioversion, and/or catheter ablation were enrolled and followed for 6 months. The AF-HEART intervention included remote heart rhythm, weight, and BP tracking; televisits with a dietician focusing on AF risk factors; and referrals for sleep apnea and hypertension treatment.

Results

Patients transmitted a median of 181 rhythm recordings during the 6-month follow-up period. Patients lost an average of 3.5 kilograms at 6 months (P = .005). Patients had improved SF-12 scores (P = .01), AFSS score (P = .01), EQ-5D score (P = .006), and AFEQT Global Score (P = .03). There was significant correlation between weight loss and decrease in symptom severity (r = -0.45, P = .05), and between % weight loss and decrease in symptom severity (r = -0.49, P = .03).

Conclusion

This study described the feasibility of the AF-HEART intervention for (1) consistent remote tracking of heart rhythm, weight, and BP; (2) achievement of weight loss; (3) reduction of symptoms; and (4) improvement in QOL. Expansion to a larger randomized study is planned.

Exploring Depressive Symptoms and Anxiety Among Patients With Atrial Fibrillation and/or Flutter at the Time of Cardioversion or Ablation

BACKGROUND

Depression and anxiety in patients with atrial fibrillation (AF) and/or atrial flutter may influence the effectiveness of cardioversion and ablation. There is a lack of knowledge related to depressive symptoms and anxiety at the time of these procedures.

OBJECTIVE

We aimed to describe the prevalence and explore potential covariates of depressive symptoms and anxiety in patients with AF at the time of cardioversion or ablation. We further explored the influence of depressive symptoms and anxiety on quality of life at the time of procedure and 6-month AF recurrence.

METHODS

Depressive symptoms, anxiety, and quality of life were collected at the time of cardioversion or ablation using the Patient Health Questionnaire-9, State-Trait Anxiety Inventory, and Atrial Fibrillation Effect on Quality of Life questionnaire. Presence of AF recurrence within 6 months post procedure was evaluated.

RESULTS

Participants (N = 171) had a mean (SD) age of 61.20 (11.23) years and were primarily male (80.1%) and white, non-Hispanic (81.4%). Moderate to severe depressive symptoms (17.2%) and clinically significant state (30.2%) and trait (23.6%) anxiety were reported. Mood/anxiety disorder diagnosis was associated with all 3 symptoms. Atrial fibrillation symptom severity was associated with both depressive symptoms and trait anxiety. Heart failure diagnosis and digoxin use were also associated with depressive symptoms. Trends toward significance between state and trait anxiety and participant race/ethnicity as well as depressive symptoms and body mass index were observed. Study findings support associations between symptoms and quality of life, but not 6-month AF recurrence.

CONCLUSION

Depressive symptoms and anxiety are common in patients with AF. Healthcare providers should monitor patients with AF for depressive symptoms and anxiety at the time of procedures and intervene when indicated. Additional investigations on assessment, prediction, treatment, and outcome of depressive symptoms and anxiety in patients with AF are warranted.

Enhancing Healthcare Access-Smartphone Apps in Arrhythmia Screening: Viewpoint

Atrial fibrillation is the most commonly reported arrhythmia and, if undiagnosed or untreated, may lead to thromboembolic events. It is therefore desirable to provide screening to patients in order to detect atrial arrhythmias. Specific mobile apps and accessory devices, such as smartphones and smartwatches, may play a significant role in monitoring heart rhythm in populations at high risk of arrhythmia. These apps are becoming increasingly common among patients and professionals as a part of mobile health. The rapid development of mobile health solutions may revolutionize approaches to arrhythmia screening. In this viewpoint paper, we assess the availability of smartphone and smartwatch apps and evaluate their efficacy for monitoring heart rhythm and arrhythmia detection. The findings obtained so far suggest they are on the right track to improving the efficacy of early detection of atrial fibrillation, thus lowering the risk of stroke and reducing the economic burden placed on public health.

Artificial intelligence in the diagnosis and management of arrhythmias

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.

Usefulness of Mobile Electrocardiographic Devices to Reduce Urgent Healthcare Visits

Mobile electrocardiogram (mECG) devices are being used increasingly, supplying recordings to providers and providing automatic rhythm interpretation. Given the intermittent nature of certain cardiac arrhythmias, mECGs allow instant access to a recording device. In the current COVID-19 pandemic, efforts to limit in-person patient interactions and avoid overwhelming emergency and inpatient services would add value. Our goal was to evaluate whether a mECG device would reduce healthcare utilization overall, particularly those of urgent nature. We identified a cohort of KardiaMobile (AliveCor, USA) mECG users and compared their healthcare utilization 1 year prior to obtaining the device and 1 year after. One hundred and twenty-eight patients were studied (mean age 64, 47% female). Mean duration of follow-up pre-intervention was 9.8 months. One hundred and twenty-three of 128 individuals completed post-intervention follow-up. Patients were less likely to have cardiac monitors ordered (30 vs 6; p <0.01), outpatient office visits (525 vs 382; p <0.01), cardiac-specific ED visits (51 vs 30; p <0.01), arrhythmia related ED visits (45 vs 20; p <0.01), and unplanned arrhythmia admissions (34 vs 11; p <0.01) in the year after obtaining a KardiaMobile device compared to the year prior to obtaining the device. Mobile technology is available for heart rhythm monitoring and can give feedback to the user. This study showed a reduction of in-person, healthcare utilization with mECG device use. In conclusion, this strategy would be expected to decrease the risk of exposure to patients and providers and would avoid overwhelming emergency and inpatient services.

Role of digital health in detection and management of atrial fibrillation

Atrial fibrillation is a common arrhythmia associated with significant morbidity, mortality and decreased quality of life. Mobile health devices marketed directly to consumers capable of detecting atrial fibrillation through methods including photoplethysmography, single-lead ECG as well as contactless methods are becoming ubiquitous. Large-scale screening for atrial fibrillation is feasible and has been shown to detect more cases than usual care-however, controversy still exists surrounding screening even in older higher risk populations. Given widespread use of mobile health devices, consumer-driven screening is happening on a large scale in both low-risk and high-risk populations. Given that young people make up a large portion of early adopters of mobile health devices, there is the potential that many more patients with early onset atrial fibrillation will come to clinical attention requiring possible referral to genetic arrythmia clinic. Physicians need to be familiar with these technologies, and understand their risks, and limitations. In the current review, we discuss current mobile health devices used to detect atrial fibrillation, recent and upcoming trials using them for diagnosis of atrial fibrillation, practical recommendations for patients with atrial fibrillation diagnosed by a mobile health device and special consideration in young patients.

Portable Device Improves the Detection of Atrial Fibrillation After Ablation

Asymptomatic recurrences of atrial fibrillation (AF) have been found to be common after ablation.A randomized controlled trial of AF screening using a handheld single-lead ECG monitor (BigThumb^®) or a traditional follow-up strategy was conducted in patients with non-valvular AF after catheter ablation. Consecutive patients were randomized to either BigThumb Group (BT Group) or Traditional Follow-up Group (TF Group). The ECGs collected via BigThumb were compared using the automated AF detection algorithm, artificial intelligence (AI) algorithm, and cardiologists' manual review. Subsequent changes in adherence to oral anticoagulation of patients were also recorded. In this study, we examined 218 patients (109 in each group). After a follow-up of 345.4 ± 60.2 days, AF-free survival rate was 64.2% in BT Group and 78.9% in TF Group (P = 0.0163), with more adherence to oral anticoagulation in BT Group (P = 0.0052). The participants in the BT Group recorded 26133 ECGs, among which 3299 (12.6%) were diagnosed as AF by cardiologists' manual review. The sensitivity and specificity of the AI algorithm were 94.4% and 98.5% respectively, which are significantly higher than the automated AF detection algorithm (90.7% and 96.2%).As per our findings, it was determined that follow-up after AF ablation using BigThumb leads to a more frequent detection of AF recurrence and more adherence to oral anticoagulation. AI algorithm improves the accuracy of ECG diagnosis and has the potential to reduce the manual review.

Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges

Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients can collect data through consumer devices and access data traditionally shared only with health care providers. These data may improve screening, self-management, and shared decision-making for cardiac arrhythmias, but challenges remain, including patient comprehension, communication with providers, and sustained engagement. Ways to address these challenges include leveraging visualizations that support comprehension, involving patients in designing and developing patient-facing digital tools, and establishing clear practices and goals for data exchange with health care providers.

Phenotypes of engagement with mobile health technology for heart rhythm monitoring

Objectives

Guided by the concept of digital phenotypes, the objective of this study was to identify engagement phenotypes among individuals with atrial fibrillation (AF) using mobile health (mHealth) technology for 6 months.

Materials and Methods

We conducted a secondary analysis of mHealth data, surveys, and clinical records collected by participants using mHealth in a clinical trial. Patterns of participants’ weekly use over 6 months were analyzed to identify engagement phenotypes via latent growth mixture model (LGMM). Multinomial logistic regression models were fitted to compute the effects of predictors on LGMM classes.

Results

One hundred twenty-eight participants (mean age 61.9 years, 75.8% male) were included in the analysis. Application of LGMM identified 4 distinct engagement phenotypes: “High-High,” “Moderate-Moderate,” “High-Low,” and “Moderate-Low.” In multinomial models, older age, less frequent afternoon mHealth use, shorter intervals between mHealth use, more AF episodes measured directly with mHealth, and lower left ventricular ejection fraction were more strongly associated with the High-High phenotype compared to the Moderate-Low phenotype (reference). Older age, more palpitations, and a history of stroke or transient ischemic attack were more strongly associated with the Moderate-Moderate phenotype compared to the reference.

Discussion

Engagement phenotypes provide a nuanced characterization of how individuals engage with mHealth over time, and which individuals are more likely to be highly engaged users.

Conclusion

This study demonstrates that engagement phenotypes are valuable in understanding and possibly intervening upon engagement within a population, and also suggests that engagement is an important variable to be considered in digital phenotyping work more broadly.

Cardiac symptom burden and arrhythmia recurrence drives digital health use: results from the iHEART randomized controlled trial

Aims

Digital health can transform the management of atrial fibrillation (AF) and enable patients to take a central role in detecting symptoms and self-managing AF. There is a gap in understanding factors that support sustained use of digital health tools for patients with AF. This study identified predictors of Alivecor^® KardiaMobile ECG monitor usage among patients with AF enrolled in the iPhone^®Helping Evaluate Atrial fibrillation Rhythm through Technology (iHEART) randomized controlled trial.

Methods and results

We analysed data from 105 English and Spanish-speaking adults with AF enrolled in the intervention arm of the iHEART trial. The iHEART intervention included smartphone-based electrocardiogram self-monitoring with Alivecor^® KardiaMobile and triweekly text messages for 6 months. The primary outcome was use of Alivecor^® categorized as: infrequent (≤5 times/week), moderate (>5 times and ≤11 times/week), and frequent (>11 times/week). We applied multinomial logistic regression modelling to characterize frequency and predictors of use. Of the 105 participants, 25% were female, 75% were White, and 45% were ≥65 years of age. Premature atrial contractions (PACs) [adjusted odds ratio (OR): 1.23, 1.08–1.40, P = 0.002] predicted frequent as compared to infrequent use. PACs (adjusted OR: 1.17, 95% confidence interval 1.06–1.30, P = 0.003), lower symptom burden (adjusted OR: 1.06, 1.01–1.11, P = 0.02), and less treatment concern (adjusted OR: 0.96, 0.93–0.99, P = 0.02) predicted moderate as compared to infrequent use.

Conclusions

Frequent use of AliveCor^® is associated with AF symptoms and potentially symptomatic cardiac events. Symptom burden and frequency should be measured and incorporated into analyses of future digital health trials for AF management.

Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

Background

Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate.

Objective

The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up—for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient’s chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates.

Methods

Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size.

Results

A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I² of 61.9, and therefore it was decided to not pool the results into a meta-analysis.

Conclusions

Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.

Predictors of clinical success after paroxysmal atrial fibrillation catheter ablation

INTRODUCTION

Contact force (CF) guided ablation of paroxysmal atrial fibrillation (PAF) with stable catheter-tissue contact optimizes clinical success and may increase an operator's ability to achieve pulmonary vein isolation (PVI) in a single encirclement. First pass PVI reduces procedure time but the relationship with long term clinical success is not well understood. This study evaluated patient characteristics and procedural details as predictors of 1-year clinical success after PAF ablation, including first pass isolation.

METHODS

Consecutive de novo PAF ablations were performed with a porous tip CF catheter in 2017 and 2018. All ablations used wide-area circumferential ablation, with first pass isolation captured separately for the left and right pulmonary veins (PVs). CF was held between 10 and 20 g and the catheter was moved every 10-20 s. Radiofrequency energy was set at 40-45 W throughout the atrium. Patient characteristics and procedural details were tested for association with clinical success, defined as freedom from recurrent atrial tachyarrhythmia through 1 year.

RESULTS

A total of 404 patients were included in the study. Clinical success at 1 year was 86.6%. Achieving first pass isolation on at least one ipsilateral PV pair was the most significant predictor of clinical success (p = .0126). After controlling for first pass isolation, only recurrence within the 90-day blanking period was independently predictive (p = .0015). First pass isolation was not associated with early recurrence (p = .2454).

CONCLUSION

In a real-world setting, first pass isolation was highly predictive of 12-month clinical success after CF-guided ablation in a PAF population.

What clinical trials of ablation for atrial fibrillation tell us - and what they do not

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in clinical practice. Radiofrequency and cryoballoon catheter ablation are therapeutic options in addition to antiarrhythmic drug therapy for the treatment of AF. Ablation is effective at reducing recurrent atrial arrhythmias and also in the reduction of AF burden. Besides arrhythmia control, improvement in quality of life and clinical outcomes are also desirable goals with AF treatment. Randomized clinical trials have evaluated ablation in several patient populations, including symptomatic patients as first-line or second-line therapy, asymptomatic patients, and patients with heart failure. These trials clarify the durability of ablation in arrhythmia control, clarify quality-of-life improvement, and identify patient populations in whom ablation may be expected to improve clinical outcomes. In this review, we summarize the major clinical trials involving ablation; discuss the strengths, weakness, and clinical implications of these trials; and highlight the knowledge gaps in our current understanding of AF ablation for future clinical studies.

Changes in the digital health landscape in cardiac electrophysiology: A pre-and peri-pandemic COVID-19 era survey

Background

Digital health is transforming healthcare delivery.

Objective

To compare the current digital health landscape in select groups of cardiac electrophysiology (EP) professionals prior to and during the COVID-19 era.

Methods

Two online surveys were emailed to 4 Heart Rhythm Society communities and tweeted out to Twitter EP, 1 before and 1 during the pandemic. Categorical variables were analyzed using the χ² test and reported as absolute numbers and percentages.

Results

There were 253 pre-pandemic (S1) and 273 follow-up surveys (S2) completed. The majority of respondents to both surveys were male, aged <55 years (70.6% vs 75.1%), university-affiliated (52.6% vs 55%), and physicians (83.3% vs 87.9%). Between S1 and S2, routine use of video-telehealth increased (5.9% vs 58.6%; P < .001) for all types of consultations (P < .001 for all). Wireless electrocardiogram prescribing was prevalent and similar (80.2% vs 81.0%), whereas wireless blood pressure monitoring (9.9% vs 18.3%) and wireless oximetry (1.6% vs 8.1%; P = .006 for both) prescribing both increased. For smartphone mobile applications (mApps), prescriptions for heart rate mApps decreased (50.6% vs 40.7%; P = .022), while vital sign (28.9% vs 37%; P = .04) and symptom trackers (15.8% vs 24.9%; P = .01) prescribing increased. A majority in both surveys (84.6% vs 75.5%) reported no workplace infrastructure or support for digital health with concerns for lack of parity in reimbursement.

Conclusion

Our results show an increase in adoption of digital health by EP during the COVID-19 pandemic. Concerns regarding a lack of supportive infrastructure persisted. Development of professional society guidelines on optimal clinical workflow, infrastructure, and reimbursement may help advance and sustain digital health integration in EP.

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.

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.