Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation

State of the art of mobile health technologies use in clinical arrhythmia care

The rapid growth in consumer-facing mobile and sensor technologies has created tremendous opportunities for patient-driven personalized health management. The diagnosis and management of cardiac arrhythmias are particularly well suited to benefit from these easily accessible consumer health technologies. In particular, smartphone-based and wrist-worn wearable electrocardiogram (ECG) and photoplethysmography (PPG) technology can facilitate relatively inexpensive, long-term rhythm monitoring. Here we review the practical utility of the currently available and emerging mobile health technologies relevant to cardiac arrhythmia care. We discuss the applications of these tools, which vary with respect to diagnostic performance, target populations, and indications. We also highlight that requirements for successful integration into clinical practice require adaptations to regulatory approval, data management, electronic medical record integration, quality oversight, and efforts to minimize the additional burden to health care professionals.

Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias

PURPOSE OF REVIEW

To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias.

RECENT FINDINGS

Sensor-enabled strategies are critical for the detection, prediction and management of arrhythmias. In the last several years, great innovation has occurred in the types of devices (implanted and wearable) that are available and the data they collect. The integration of artificial intelligence solutions into sensor-enabled strategies has set the stage for a new generation of smart devices that augment the human clinician. Smart devices enhanced by new sensor technologies and Artificial Intelligence (AI) algorithms promise to reshape the care of cardiac arrhythmias.

The NORwegian atrial fibrillation self-SCREENing (NORSCREEN) trial: rationale and design of a randomized controlled trial

AIMS

Atrial fibrillation (AF) is a common arrhythmia, and many cases of AF may be undiagnosed. Whether screening for AF and subsequent treatment if AF is detected can improve long-term outcome remains an unsettled question. The primary aim of the NORwegian atrial fibrillation self-SCREENing (NORSCREEN) trial is to assess whether self-screening for AF with continuous electrocardiogram (ECG) for 3-7 days in individuals aged 65 years or older with at least one additional risk factor for stroke, and initiation of guideline-recommended therapy in patients with detected AF, will reduce the occurrence of stroke.

METHODS AND RESULTS

This study is a nationwide open, siteless, randomized, controlled trial. Individuals ≥65 years of age are randomly identified from the National Population Register of Norway and are invited to take a digital inclusion/exclusion test. Individuals passing the inclusion/exclusion test are randomized to either the intervention group or the control group. A total of 35 000 participants will be enrolled. In the intervention group, self-screening is performed continuously over 3-7 days at home with a patch ECG device (ECG247) at inclusion and after 12-18 months. If AF is detected, guideline-recommended therapy will be initiated. Patients will be followed up for 5 years through national health registries. The primary outcome is time to a first stroke (ischaemic or haemorrhagic stroke). The first participant in the NORSCREEN trial was enrolled on 1 September 2023.

CONCLUSION

The results from the NORSCREEN trial will provide new insights regarding the efficacy of digital siteless self-screening for AF with respect to stroke prevention in individuals at an increased risk of stroke.

TRIAL REGISTRATION

Clinical trials: NCT05914883.

Austrian consensus statement on the diagnosis and management of hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease that is characterized by left ventricular hypertrophy unexplained by secondary causes. Based on international epidemiological data, around 20,000-40,000 patients are expected to be affected in Austria. Due to the wide variety of clinical and morphological manifestations the diagnosis can be difficult and the disease therefore often goes unrecognized. HCM is associated with a substantial reduction in quality of life and can lead to sudden cardiac death, especially in younger patients. Early and correct diagnosis, including genetic testing, is essential for comprehensive counselling of patients and their families and for effective treatment. The latter is especially true as an effective treatment of outflow tract obstruction has recently become available in the form of a first in class cardiac myosin ATPase inhibitor, as a noninvasive alternative to established septal reduction therapies. The aim of this Austrian consensus statement is to summarize the recommendations of international guidelines with respect to the genetic background, pathophysiology, diagnostics and management in the context of the Austrian healthcare system and resources, and to present them in easy to understand algorithms.

2024 Chinese Expert Consensus Guidelines on the Diagnosis and Treatment of Atrial Fibrillation in the Elderly, Endorsed by Geriatric Society of Chinese Medical Association (Cardiovascular Group) and Chinese Society of Geriatric Health Medicine (Cardiovascular Branch): Executive Summary

The consensus guidelines of the Geriatric Society of Chinese Medical Association on the management of atrial fibrillation (AF) in the elderly was first published in 2011 and updated in 2016, with endorsement by Chinese Society of Geriatric Health Medicine. Since then, many important studies regarding the screening and treatment in the elderly population have been reported, necessitating this updated expert consensus guideline. The writing committee members comprehensively reviewed updated evidence pertaining to elderly patients with AF, and formulated this 2024 update. The highlighted issues focused on the following: screening for AF, geriatric comprehensive assessment, use of the Atrial fibrillation Better Care (ABC) pathway for the elderly patients, and special clinical settings related to elderly patients with AF. New recommendations addressing smart technology facilitated AF screening, ABC pathway based management, and optimal anticoagulation were developed, with a focus on the elderly.

Management of atrial fibrillation in older adults

Most people with atrial fibrillation are older adults, in whom atrial fibrillation co-occurs with other chronic conditions, polypharmacy, and geriatric syndromes such as frailty. Yet most randomized controlled trials and expert guidelines use an age agnostic approach. Given the heterogeneity of aging, these data may not be universally applicable across the spectrum of older adults. This review synthesizes the available evidence and applies rigorous principles of aging science. After contextualizing the burden of comorbidities and geriatric syndromes in people with atrial fibrillation, it applies an aging focused approach to the pillars of atrial fibrillation management, describing screening for atrial fibrillation, lifestyle interventions, symptoms and complications, rate and rhythm control, coexisting heart failure, anticoagulation therapy, and left atrial appendage occlusion devices. Throughout, a framework is suggested that prioritizes patients' goals and applies existing evidence to all older adults, whether atrial fibrillation is their sole condition, one among many, or a bystander at the end of life.

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.

Atrial Fibrillation Burden on a 14-Day ECG Monitor: Findings From the GUARD-AF Trial Screening Arm

BACKGROUND

The "burden" of atrial fibrillation (AF) detected by screening likely influences stroke risk, but the distribution of burden is not well described.

OBJECTIVES

This study aims to determine the frequency of AF and the distribution of AF burden found when screening individuals ≥70 years of age with a 14-day electrocardiograph monitor.

METHODS

This is a cohort study of the screening arm of a randomized AF screening trial among those ≥70 years of age without a prior AF diagnosis (between 2019 and 2021). Screening was performed with a 14-day continuous electrocardiogram patch monitor.

RESULTS

Analyzable patches were returned by 5,684 (95%) of screening arm participants; the median age was 75 years (Q1-Q3: 72-78 years), 57% were female, and the median CHA2DS2-VASc score was 3 (Q1-Q3: 2-4). AF was detected in 252 participants (4.4%); 29 (0.5%) patients had continuous AF and 223 (3.9%) had paroxysmal AF. Among those with paroxysmal AF, the average indices of AF burden were of low magnitude with right-skewed distributions. The median percent time in AF was 0.46% (Q1-Q3: 0.02%-2.48%), or 75 (Q1-Q3: 3-454) minutes, and the median longest episode was 38 (Q1-Q3: 2-245) minutes. The upper quartile threshold of 2.48% time in AF corresponded to 7.6 hours. Age greater than 80 years was associated with screen-detected AF in our multivariable model (OR: 1.46; 95% CI: 1.06-2.02).

CONCLUSIONS

Most AF detected in these older patients was very low burden. However, one-quarter of those with AF had multiple hours of AF, raising concern about stroke risk. These findings have implications for targeting populations for AF screening trials and for responding to heart rhythm alerts from mobile devices (GUARD-AF [A Study to Determine if Identification of Undiagnosed Atrial Fibrillation in People at least 70 Years of Age Reduces the Risk of Stroke]; NCT04126486).

Evaluating the impact of automatic positive airway pressure therapy on cardiovascular risk index and vascular behavior in patients with obstructive sleep apnea: a study on heterogeneity in the therapeutic response

STUDY OBJECTIVES

This study investigated the impact of automatic positive airway pressure (APAP) therapy on vascular behavior and its potential to lower cardiovascular risk in patients with obstructive sleep apnea (OSA), as well as differentiating APAP therapy heterogeneity.

METHODS

All participants were diagnosed with OSA by portable monitoring, and pulse wave parameters and cardiac risk composite parameter index were obtained by photoplethysmography before and after APAP. Clustering analysis of pulse wave parameters before APAP in the high-risk population was performed using k-means clustering. Linear regression was used to assess the associations of changes in cardiac risk composite parameter index and pulse wave parameters with clinical characteristics.

RESULTS

Eighty-two patients with OSA underwent APAP therapy. The cardiac risk composite parameter index after APAP was significantly lower than before APAP (0.38 ± 0.33 and 0.58 ± 0.31, respectively; P < .001). All pulse wave parameters (except irregular pulse) were significantly different (P < .001) in patients with OSA and in the high-risk responders group after vs before APAP. The differences in pulse wave parameters after vs before APAP were not significant in the high-risk nonresponders group, except for the difference between the pulse rate acceleration index and the oxygen saturation index and pulse rate variability. Four clusters were obtained from the clustering analysis of pulse wave parameters before APAP in the high-risk responders group.

CONCLUSIONS

APAP reduces the cardiac risk composite parameter index in patients with OSA by altering vascular behavior. Overnight photoplethysmography monitoring of pulse wave parameters can be used to assess whether patients with OSA will benefit from APAP.

CITATION

Zhu W, Xiang L, Cao L, Tian Y, Li W, He L. Evaluating the impact of automatic positive airway pressure therapy on cardiovascular risk index and vascular behavior in patients with obstructive sleep apnea: a study on heterogeneity in the therapeutic response. J Clin Sleep Med. 2024;20(9):1435-1444.

Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023

Background

In the study of atrial fibrillation (AF), a prevalent cardiac arrhythmia, the utilization of artificial intelligence (AI) in diagnostic and therapeutic strategies holds the potential to address existing limitations. This research employs bibliometrics to objectively investigate research hotspots, development trends, and existing issues in the application of AI within the AF field, aiming to provide targeted recommendations for relevant researchers.

Methods

Relevant publications on the application of AI in AF field were retrieved from the Web of Science Core Collection (WoSCC) database from 2013 to 2023. The bibliometric analysis was conducted by the R (4.2.2) "bibliometrix" package and VOSviewer(1.6.19).

Results

Analysis of 912 publications reveals that the field of AI in AF is currently experiencing rapid development. The United States, China, and the United Kingdom have made outstanding contributions to this field. Acharya UR is a notable contributor and pioneer in the area. The following topics have been elucidated: AI's application in managing the risk of AF complications is a hot mature topic; AI-electrocardiograph for AF diagnosis and AI-assisted catheter ablation surgery are the emerging and booming topics; smart wearables for real-time AF monitoring and AI for individualized AF medication are niche and well-developed topics.

Conclusion

This study offers comprehensive analysis of the origin, current status, and future trends of AI applications in AF, aiming to advance the development of the field.

Diagnostic accuracy of Apple Watch Series 6 recorded single-lead ECGs for identifying supraventricular tachyarrhythmias: a comparative analysis with invasive electrophysiological study

BACKGROUND

The advancements in wearable technology have made the detection of arrhythmias more accessible. While smartwatches are commonly used to detect patients with atrial fibrillation, their effectiveness in the differential diagnosis of supraventricular tachycardias (SVT) lacks consensus.

METHODS

A study was conducted on 47 patients with documented SVTs on a 12-lead ECG. All patients in the cohort underwent electrophysiology study with induction of SVT. A 6th generation Apple Watch was used to record ECG tracings during baseline sinus rhythm and during induced SVT. Cardiology residents and attending cardiologists evaluated these recordings to diagnose the differential diagnosis of SVT.

RESULTS

The evaluation revealed 27 cases of typical atrioventricular nodal reentrant tachycardia (AVNRT), 11 cases of atrioventricular reentrant tachycardia (AVRT), and 9 cases of atrial tachycardia/atrial flutter (AT/AFL) among the induced tachycardias. Attending physicians achieved an accuracy of 66.0 to 76.6%, and residents demonstrated accuracy rates between 68.1 and 74.5%. Interrater reliability was assessed using Fleiss's Kappa method, resulting in a moderate level of agreement between residents (Kappa = 0.465, p < 0.001, 95% CI 0.30-0.63) and attendings (Kappa = 0.519, p < 0.001, 95% CI 0.35-0.68). The overall Kappa value was 0.417 (p < 0.001, 95% CI 0.34-0.49).

CONCLUSIONS

Smartwatch recordings demonstrate moderate feasibility in diagnosing SVT when following a pre-specified algorithm. However, this diagnostic performance was lower than the accuracy obtained from 12-lead ECG tracings when blinded to procedure outcomes.

Novel stroke prevention strategies following catheter ablation for atrial fibrillation

Stroke prevention following successful catheter ablation of atrial fibrillation remains a controversial topic. Oral anticoagulation is associated with a significant reduction in stroke risk in the general atrial fibrillation population but may be associated with an increased risk of major bleeding, and the benefit: risk ratio must be considered. Improvement in successful catheter ablation and widespread use of cardiac monitoring devices may allow for novel anticoagulation strategies in a subset of patients with atrial fibrillation, which may optimize stroke prevention while minimizing bleeding risk. In this review, we discuss stroke risk in atrial fibrillation and the effects of successful catheter ablation on thromboembolic risk. We also explore novel strategies for stroke prevention following successful catheter ablation.

Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives

Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.

The Risk of Atrial Fibrillation and Previous Ischemic Stroke in Cognitive Decline

Objectives: Our study investigated the inverse relationship between cognitive decline (CD) and the presence of documented atrial fibrillation (AFib), ischemic stroke, heart failure, lower extremity peripheral artery disease, and diabetes mellitus. Methods: We conducted a retrospective cross-sectional study between December 2016 and November 2019. A total of 469 patients were enrolled who underwent cognitive evaluation with three cognitive tests (Montreal Cognitive Assessment-MOCA, Mini-Mental State Examination-MMSE, and General Practitioner Assessment of Cognition-GPCOG). We used the standard cut-off values, and the optimal thresholds were obtained from the receiver operating characteristic curves. Results: The standard cut-off level of the MOCA (<26 points) was associated with the presence of AFib (OR: 1.83, 95% CI: 1.11-3.01) and the optimal cut-off level with <23 points with ischemic stroke (OR: 2.64, 95% CI: 1.47-4.74; p = 0.0011). The optimal cut-off value of the MMSE (<28 points) was associated with the presence of ischemic stroke (OR: 3.07, 95% CI: 1.56-6.07; p = 0.0012), AFib (OR: 1.65, 95% CI: 1.05-2.60; p = 0.0287), and peripheral artery disease (OR: 2.72, 95% CI: 1.38-5.36; p = 0.0039). GPCOG < 8 points were associated with ischemic stroke (OR: 2.18, 95% CI: 1.14-4.14; p = 0.0176) and heart failure (OR: 1.49, 95% CI: 1.01-2.21; p = 0.0430). Conclusions: Our research highlighted the broader utility of cognitive assessment. The MOCA and MMSE scores proved to be associated with documented AFib. Higher cognitive test results than the standard threshold for CD of the MMSE, GPCOG, and lower MOCA scores represented risk factors for the presence of previous ischemic stroke.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

SEA 2024 Standards for Global Control of Vascular Risk

One of the objectives of the Spanish Society of Arteriosclerosis is to contribute to the knowledge, prevention and treatment of vascular diseases, which are the leading cause of death in Spain and entail a high degree of disability and health expenditure. Atherosclerosis is a multifactorial disease and its prevention requires a global approach that takes into account the associated risk factors. This document summarises the current evidence and includes recommendations for patients with established vascular disease or at high vascular risk: it reviews the symptoms and signs to evaluate, the laboratory and imaging procedures to request routinely or in special situations, and includes the estimation of vascular risk, diagnostic criteria for entities that are vascular risk factors, and general and specific recommendations for their treatment. Finally, it presents aspects that are not usually referenced in the literature, such as the organisation of a vascular risk consultation.

Remote monitoring of atrial fibrillation recurrence using mHealth technology (REMOTE-AF)

Aims

This proof-of-concept study sought to evaluate changes in heart rate (HR) obtained from a consumer wearable device and compare against implantable loop recorder (ILR)-detected recurrence of atrial fibrillation (AF) and atrial tachycardia (AT) after AF ablation.

Methods and results

REMOTE-AF (NCT05037136) was a prospectively designed sub-study of the CASA-AF randomized controlled trial (NCT04280042). Participants without a permanent pacemaker had an ILR implanted at their index ablation procedure for longstanding persistent AF. Heart rate and step count were continuously monitored using photoplethysmography (PPG) from a commercially available wrist-worn wearable. Photoplethysmography-recorded HR data were pre-processed with noise filtration and episodes at 1-min interval over 30 min of HR elevations (Z-score = 2) were compared with corresponding ILR data. Thirty-five patients were enrolled, with mean age 70.3 ± 6.8 years and median follow-up 10 months (interquartile range 8-12 months). Implantable loop recorder analysis revealed 17 out of 35 patients (49%) had recurrence of AF/AT. Compared with ILR recurrence, wearable-derived elevations in HR ≥ 110 beats per minute had a sensitivity of 95.3%, specificity 54.1%, positive predictive value (PPV) 15.8%, negative predictive value (NPV) 99.2%, and overall accuracy 57.4%. With PPG-recorded HR elevation spikes (non-exercise related), the sensitivity was 87.5%, specificity 62.2%, PPV 39.2%, NPV 92.3%, and overall accuracy 64.0% in the entire patient cohort. In the AF/AT recurrence only group, sensitivity was 87.6%, specificity 68.3%, PPV 53.6%, NPV 93.0%, and overall accuracy 75.0%.

Conclusion

Consumer wearable devices have the potential to contribute to arrhythmia detection after AF ablation.

Study Registration

ClinicalTrials.gov Identifier: NCT05037136 https://clinicaltrials.gov/ct2/show/NCT05037136.

Digital recruitment and compliance to treatment recommendations in the Norwegian Atrial Fibrillation self-screening pilot study

Aims

Atrial fibrillation (AF) is prevalent, undiagnosed in approximately one-third of cases, and is associated with severe complications. Guidelines recommend screening individuals at increased risk of stroke. This report evaluated the digital recruitment procedure and compliance with the follow-up recommendations in participants with screen-detected AF in the Norwegian Atrial Fibrillation self-screening pilot study.

Methods and results

Norwegians ≥65 years were invited through Facebooks posts, web pages, and newspapers to participate in the study. Targeted Facebook posts promoted over 11 days reached 84 208 users and 10 582 visitors to the study homepage. This accounted for 51% of the total homepage visitors (n = 20 704). A total of 2118 (10%) of the homepage visitors provided digital consent to participate after they met the inclusion criteria. The mean (standard deviation) age of the participants was 70 (4) years, and the majority [n = 1569 (74%)] were women. A total of 1849 (87%) participants completed the electrocardiogram self-screening test, identifying AF in 41 (2.2%) individuals. Of these, 39 (95%) participants consulted a general practitioner, and 34 (83%) participants initiated anticoagulation therapy.

Conclusion

Digital recruitment and inclusion in digital AF screening with a high rate of initiation of anticoagulation therapy in AF positive screening cases are feasible. However, digital recruitment and inclusion may introduce selection bias with regard to age and gender. Larger studies are needed to determine the efficacy and cost-effectiveness of a fully digital AF screening.

Trial registration

Clinical trials: NCT04700865.

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.

Detection of Arrhythmias Using Smartwatches-A Systematic Literature Review

Smartwatches represent one of the most widely adopted technological innovations among wearable devices. Their evolution has equipped them with an increasing array of features, including the capability to record an electrocardiogram. This functionality allows users to detect potential arrhythmias, enabling prompt intervention or monitoring of existing arrhythmias, such as atrial fibrillation. In our research, we aimed to compile case reports, case series, and cohort studies from the Web of Science, PubMed, Scopus, and Embase databases published until 1 August 2023. The search employed keywords such as "Smart Watch", "Apple Watch", "Samsung Gear", "Samsung Galaxy Watch", "Google Pixel Watch", "Fitbit", "Huawei Watch", "Withings", "Garmin", "Atrial Fibrillation", "Supraventricular Tachycardia", "Cardiac Arrhythmia", "Ventricular Tachycardia", "Atrioventricular Nodal Reentrant Tachycardia", "Atrioventricular Reentrant Tachycardia", "Heart Block", "Atrial Flutter", "Ectopic Atrial Tachycardia", and "Bradyarrhythmia." We obtained a total of 758 results, from which we selected 57 articles, including 33 case reports and case series, as well as 24 cohort studies. Most of the scientific works focused on atrial fibrillation, which is often detected using Apple Watches. Nevertheless, we also included articles investigating arrhythmias with the potential for circulatory collapse without immediate intervention. This systematic literature review provides a comprehensive overview of the current state of research on arrhythmia detection using smartwatches. Through further research, it may be possible to develop a care protocol that integrates arrhythmias recorded by smartwatches, allowing for timely access to appropriate medical care for patients. Additionally, continuous monitoring of existing arrhythmias using smartwatches could facilitate the assessment of the effectiveness of prescribed therapies.

Photoplethysmography based atrial fibrillation detection: a continually growing field

Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field.Approach. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies.Significance. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.

Diagnostic Performance of an Automated Blood Pressure Monitor With an Irregular Heartbeat Algorithm Designed to Detect Atrial Fibrillation

Background: Early detection of atrial fibrillation (AF) remains an unsolved challenge and because the greatest risk factor for AF is hypertension, blood pressure (BP) monitors with AF detectors have been developed. We evaluated the clinical performance of an irregular heartbeat (IHB) algorithm built into an A&D automated BP monitor for AF diagnosis. Methods and Results: Each of the 239 enrolled patients underwent BP measurement 3 times using the A&D UM-212 with the IHB algorithm. Real-time 3-lead ECG was recorded using automated ECG analysis software. Independent of the ECG analysis software results, 2 cardiologists interpreted the ECG and made the final diagnosis. Of the 239 patients, 135 were in sinus rhythm, 31 had AF, and 73 had non-AF arrhythmias. The respective sensitivity, specificity, and accuracy of the IHB algorithm for AF diagnosis were 98.9%, 91.2%, and 92.2% for the per-measurement evaluation, and 96.8%, 95.7%, and 95.8% for the per-patient evaluation (3/3 positive measurements). The respective sensitivity, specificity, and accuracy of the ECG analysis software for AF diagnosis were 91.4%, 97.9%, and 97.1% for the per-measurement evaluation, and 77.4%, 99.5%, and 96.7% for the per-patient evaluation (3/3 positive measurements). Conclusions: The IHB algorithm built into an A&D automated BP monitor had high diagnostic performance for AF in general cardiology patients, especially when multiple measurements were obtained.

Thromboembolic risk and oral anticoagulation in subclinical atrial fibrillation

Availability of devices capable of continuous rhythm monitoring such as smartwatches, implantable loop recorders, or pacemakers/defibrillators is continuously increasing. Importantly, device detected "subclinical" atrial fibrillation seems to convey a significantly lower risk of thromboembolism than "clinical" atrial fibrillation verified by a conventional ECG recording. While current guidelines indicate a possible role of oral anticoagulation in selected high-risk patients with subclinical AF, recent trials show an ambiguous risk/benefit relationship of anticoagulation in this setting. The present review therefore summarizes current data on the role of oral anticoagulation in subclinical AF, aims at aiding in the decision process of anticoagulation, and illustrates current gaps in evidence regarding subclinical AF.

Evaluation of an algorithm-guided photoplethysmography for atrial fibrillation burden using a smartwatch

BACKGROUND

Wearable devices based on the PPG algorithm can detect atrial fibrillation (AF) effectively. However, further investigation of its application on long-term, continuous monitoring of AF burden is warranted.

METHOD

The performance of a smartwatch with continuous photoplethysmography (PPG) and PPG-based algorithms for AF burden estimation was evaluated in a prospective study enrolling AF patients admitted to Beijing Anzhen Hospital for catheter ablation from September to November 2022. A continuous Electrocardiograph patch (ECG) was used as the reference device to validate algorithm performance for AF detection in 30-s intervals.

RESULTS

A total of 578669 non-overlapping 30-s intervals for PPG and ECG each from 245 eligible patients were generated. An interval-level sensitivity of PPG was 96.3% (95% CI 96.2%-96.4%), and specificity was 99.5% (95% CI 99.5%-99.6%) for the estimation of AF burden. AF burden estimation by PPG was highly correlated with AF burden calculated by ECG via Pearson correlation coefficient (R2 = 0.996) with a mean difference of -0.59 (95% limits of agreement, -7.9% to 6.7%). The subgroup study showed the robust performance of the algorithm in different subgroups, including heart rate and different hours of the day.

CONCLUSION

Our results showed the smartwatch with an algorithm-based PPG monitor has good accuracy and stability in continuously monitoring AF burden compared with ECG patch monitors, indicating its potential for diagnosing and managing AF.

Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation

AIMS

Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions.

METHODS AND RESULTS

Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7-99.9%], specificity (99.9%; CI: 99.8-100.0%), positive predictive value (99.6%; CI: 99.1-100.0%), and negative predictive value (99.6%; CI: 99.0-100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland-Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m.

CONCLUSION

Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.

Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference

AIMS

Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA).

METHODS AND RESULTS

Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF.

CONCLUSIONS

Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study

BACKGROUND

Depression and anxiety are common and disabling mental health problems in children and young adults. Group cognitive behavioral therapy (GCBT) is considered that an efficient and effective treatment for these significant public health concerns, but not all participants respond equally well. The aim of this study was to examine the predictive ability of heart rate variability (HRV), based on sensor data from consumer-grade wearable devices to detect GCBT effectiveness in early intervention.

METHODS

In a study of 33 college students with depression and anxiety, participants were randomly assigned to either GCBT group or a wait-list control (WLC) group. They wore smart wearable devices to measure their physiological activities and signals in daily life. The HRV parameters were calculated and compared between the groups. The study also assessed correlations between participants' symptoms, HRV, and GCBT outcomes.

RESULTS

The study showed that participants in GCBT had significant improvement in depression and anxiety symptoms after four weeks. Higher HRV was associated with greater improvement in depressive and anxious symptoms following GCBT. Additionally, HRV played a noteworthy role in determining how effective GCBT was in improve anxiety(P = 0.002) and depression(P = 0.020), and its predictive power remained significant even when considering other factors.

CONCLUSION

HRV may be a useful predictor of GCBT treatment efficacy. Identifying predictors of treatment response can help personalize treatment and improve outcomes for individuals with depression and anxiety.

TRIAL REGISTRATION

The trial has been retrospectively registered on [22/06/2023] with the registration number [NCT05913349] in the ClinicalTrials.gov. Variations in heart rate variability (HRV) have been associated with depression and anxiety, but the relationship of baseline HRV to treatment outcome in depression and anxiety is unclear. This study predicted GCBT effectiveness using HRV measured by wearable devices. 33 students with depression and anxiety participated in a trial comparing GCBT and wait-list control. HRV parameters from wearables correlated with symptoms (PHQ, PSS) and GCBT effectiveness. Baseline HRV levels are strongly associated with GCBT treatment outcomes. HRV may serve as a useful predictor of efficacy of GCBT treatment,facilitating personalized treatment approaches for individuals with depression and anxiety.

Validation of Electrocardiogram Based Photoplethysmogram Generated Using U-Net Based Generative Adversarial Networks

Photoplethysmogram (PPG) performs an important role in alarming atrial fibrillation (AF). While the importance of PPG is emphasized, there is insufficient amount of openly available atrial fibrillation PPG data. We propose a U-net-based generative adversarial network (GAN) which synthesize PPG from paired electrocardiogram (ECG). To measure the performance of the proposed GAN, we compared the generated PPG to reference PPG in terms of morphology similarity and also examined its influence on AF detection classifier performance. First, morphology was compared using two different metrics against the reference signal: percent root mean square difference (PRD) and Pearson correlation coefficient. The mean PRD and Pearson correlation coefficient were 27% and 0.94, respectively. Heart rate variability (HRV) of the reference AF ECG and the generated PPG were compared as well. The p-value of the paired t-test was 0.248, indicating that no significant difference was observed between the two HRV values. Second, to validate the generated AF PPG dataset, four different datasets were prepared combining the generated PPG and real AF PPG. Each dataset was used to optimize a classification model while maintaining the same architecture. A test dataset was prepared to test the performance of each optimized model. Subsequently, these datasets were used to test the hypothesis whether the generated data benefits the training of an AF classifier. Comparing the performance metrics of each optimized model, the training dataset consisting of generated and real AF PPG showed a test accuracy result of 0.962, which was close to that of the dataset consisting only of real AF PPG data at 0.961. Furthermore, both models yielded the same F1 score of 0.969. Lastly, using only the generated AF PPG dataset resulted in test accuracy of 0.945, indicating that the trained model was capable of generating valuable AF PPG. Therefore, it can be concluded that the generated AF PPG can be used to augment insufficient data. To summarize, this study proposes a GAN-based method to generate atrial fibrillation PPG that can be used for training atrial fibrillation PPG classification models.

An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review

Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.

Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care: JACC Scientific Statement

Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.

Atrial fibrillation: stroke prevention

Stroke prevention is central to the management of patients with atrial fibrillation (AF) which has moved towards a more holistic or integrative care approach. The published evidence suggests that management of AF patients following such a holistic approach based on the Atrial fibrillation Better Care (ABC) pathway is associated with a lower risk of stroke and adverse events. Risk assessment, re-assessment and use of direct oral anticoagulants (DOACs) are important for stroke prevention in AF. The stroke and bleeding risks of AF patients are not static and should be re-assessed regularly. Bleeding risk assessment is to address and mitigate modifiable bleeding risk factors, and to identify high bleeding risk patients for early review and follow-up. Well-controlled comorbidities and healthy lifestyles also play an important role to achieve a better clinical outcome. Digital health solutions are increasingly relevant in the diagnosis and management of patients with AF, with the potential to improve stroke prevention. In this review, we provide an update on stroke prevention in AF, including importance of holistic management, risk assessment/re-assessment, and stroke prevention for special AF populations. Evidence-based and structured management of AF patients would reduce the risk of stroke and other adverse events.

Pathophysiology of Atrial Fibrillation and Approach to Therapy in Subjects Less than 60 Years Old

Atrial fibrillation (AF) is an arrhythmia that affects the left atrium, cardiac function, and the patients' survival rate. Due to empowered diagnostics, it has become increasingly recognized among young individuals as well, in whom it is influenced by a complex interplay of autoimmune, inflammatory, and electrophysiological mechanisms. Deepening our understanding of these mechanisms could contribute to improving AF management and treatment. Inflammation is a complexly regulated process, with interactions among various immune cell types, signaling molecules, and complement components. Addressing circulating antibodies and designing specific autoantibodies are promising therapeutic options. In cardiomyopathies or channelopathies, the first manifestation could be paroxysmal AF; persistent forms tend not to respond to antiarrhythmic drugs in these conditions. Further research, both in vitro and in vivo, on the use of genomic biotechnology could lead to new therapeutic approaches. Additional triggers that can be encountered in AF patients below 60 years of age are systemic hypertension, overweight, diabetes, and alcohol abuse. The aims of this review are to briefly report evidence from basic science and results of clinical studies that might explain the juvenile burden of the most encountered sustained supraventricular tachyarrhythmias in the general population.

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.

Atrial fibrillation and stroke: State-of-the-art and future directions

Atrial fibrillation (AF) and stroke remain a major cause of morbidity and mortality. The two conditions shared common co-morbidities and risk factors. AF-related strokes are associated with worse clinical outcomes and higher mortality compared to non-AF-related. Early detection of AF is vital for prevention. While various scores have been developed to predict AF in such a high-risk group, they are yet to incorporated into clinical guidelines. Novel markers and predictors of AF including coronary and intracranial arterial calcification have also been studied. There are also ongoing debates on the management of acute stroke in patients with AF, and those who experienced breakthrough stroke while on oral anticoagulants. We provided an overview of the complex interplay between AF and stroke, as well as the treatment and secondary prevention of stroke in AF. We also comprehensively discussed the current evidence and the ongoing conundrums, and highlighted the future directions on the topic.

Eicosapentaenoic Acid: between Cardiovascular Benefits and the Risk of Atrial Fibrillation

In recent years, scientific research has increasingly focused on the cardiovascular benefits of omega-3 polyunsaturated fatty acids (n-3 PUFAs) supplements. The most promising results emerged from the new trials on a high-dose eicosapentaenoic acid (EPA)-only approach, instead of the previously prescribed therapy with EPA + docosahexaenoic acid (DHA). The evidence of the reduction of cardiovascular events in patients at high cardiovascular risk with EPA is intriguing. However, physicians have expressed concern about the potential high risk of atrial fibrillation (AF) occurrence due to such an approach. This study aims to investigate the current evidence on the cardiovascular benefits of EPA and its association with atrial arrhythmogenesis. Current guidelines consider EPA (as IPE) treatment for selected patients but with no specific indication regarding AF risk evaluation. We propose a flowchart that could be a starting point for the future development of an algorithm to help clinicians to prescribe EPA safely and effectively, especially in patients at high risk of incipient AF.

Smartwatch-based algorithm for early detection of pulmonary infection: Validation and performance evaluation

Background

The proliferation of smart devices provides the possibility of early detection of the signs of pulmonary infections (PI). This study validates a smartwatch-based algorithm to monitor the risk of PI in adults.

Methods

An algorithm that runs on smartwatches was developed and tested in 87 patients with PI and 408 healthy subjects. The algorithm examines heart rate variability, respiratory rate, oxygen saturation, body temperature, and cough sound. It was embedded into the Respiratory Health Study app for a smartwatch to detect the risk of PI and was further validated in the hospital. Doctors diagnosed PI using a clinical evaluation, lab tests, and imaging examination, the gold standard for diagnosis. The accuracy, sensitivity, and specificity of the algorithm predicting PI were evaluated.

Results

In all, 80 patients with PI and 85 healthy volunteers were recruited to validate the accuracy of the algorithm. The area under the curve of the algorithm for predicting PI was 0.86 (95% confidence interval: 0.82-0.91) (P < 0.001). Compared to the gold standard, the overall accuracy of the algorithm was 85.9%, the sensitivity was 81.4%, and the specificity was 90.4%. The algorithm for heart rate, respiratory rate, oxygen saturation, and body temperature had an accuracy of 68.2%, and the accuracy of the algorithm including cough sound was 82.6%.

Conclusion

Our wearable system facilitated the detection of risk of PI. Multi-source features were useful for enhancing the performance of the lung infection screening algorithm.

Trial Registration

Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR2100050843; https://www.chictr.org.cn/showproj.html?proj = 126556.

Smartphone-based atrial fibrillation screening in the general population: feasibility and impact on medical treatment

Aims

The aim of this study is to determine the feasibility, detection rate, and therapeutic implications of large-scale smartphone-based screening for atrial fibrillation (AF).

Methods and results

Subjects from the general population in Belgium were recruited through a media campaign to perform AF screening during 8 consecutive days with a smartphone application. The application analyses photoplethysmography traces with artificial intelligence and offline validation of suspected signals to detect AF. The impact of AF screening on medical therapy was measured through questionnaires. Atrial fibrillation was detected in the screened population (n = 60.629) in 791 subjects (1.3%). From this group, 55% responded to the questionnaire. Clinical AF [AF confirmed on a surface electrocardiogram (ECG)] was newly diagnosed in 60 individuals and triggered the initiation of anti-thrombotic therapy in 45%, adjustment of rate or rhythm controlling strategies in 62%, and risk factor management in 17%. In subjects diagnosed with known AF before screening, a positive screening result led to these therapy adjustments in 9%, 39%, and 11%, respectively. In all subjects with clinical AF and an indication for oral anti-coagulation (OAC), OAC uptake increased from 56% to 74% with AF screening. Subjects with clinical AF were older with more co-morbidities compared with subclinical AF (no surface ECG confirmation of AF) (P < 0.001). In subjects with subclinical AF (n = 202), therapy adjustments were performed in only 7%.

Conclusion

Smartphone-based AF screening is feasible at large scale. Screening increased OAC uptake and impacted therapy of both new and previously diagnosed clinical AF but failed to impact risk factor management in subjects with subclinical AF.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial

BACKGROUND

Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers.

OBJECTIVE

This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors.

METHODS

Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period.

RESULTS

A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days.

CONCLUSIONS

Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1016/j.cvdhj.2021.07.002.

Non-invasive mid-term electrocardiogram patch monitoring is effective in detecting atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is a cause of serious morbidity such as stroke. Early detection and treatment of AF is important. Current guidelines recommend screening via opportunistic pulse taking or 12‑lead electrocardiogram. Mid-term ECG patch monitors increases the sensitivity of AF detection.

METHODS

The Singapore Atrial Fibrillation Study is a prospective multi-centre study aiming to study the incidence of AF in patients with no prior AF and a CHA2DS2-VASc score of at least 1, with the use of a mid-term continuous ECG monitoring device (Spyder ECG). Consecutive patients from both inpatient and outpatient settings were recruited from 3 major hospitals from May 2016 to December 2019.

RESULTS

Three hundred and fifty-five patients were monitored. 6 patients (1.7%) were diagnosed with AF. There were no significant differences in total duration of monitoring between the AF and non-AF group (6.39 ± 3.19 vs 5.42 ± 2.46 days, p = 0.340). Patients with newly detected AF were more likely to have palpitations (50.0% vs 11.8%, p = 0.027). Half of the patients (n = 3, 50.0%) were diagnosed on the first day of monitoring and the rest were diagnosed after 24 h. On univariate analysis, only hyperlipidemia was associated with reduced odds of being diagnosed with AF (OR HR 0.08 CI 0.01-0.74, p = 0.025). In a group of 128 patients who underwent coronary artery bypass grafting and had post-operative ECG monitoring, 9 patients (7.0%) were diagnosed with post-operative AF.

CONCLUSIONS

The use of non-invasive mid-term patch-based ECG monitoring is an effective modality for AF screening.

Feasibility and reliability of whintings scanwatch to record 4-lead Electrocardiogram: A comparative analysis with a standard ECG

The recent technological advances allowed us to produce some new wearable devices, such as smartphones and smartwatches (SW). These devices provide different services to their users through different software applications installed even in the SW or smartphones. Health monitoring functionalities, among these services, are nowadays the new technological fashion. In fact, the monitoring is ensured by the sensor incorporated in the SW. The SW allows the record of only one single lead Electrocardiogram (ECG), which is sufficient to screen or diagnosis of rhythm and conduction disorders, especially during the onset of cardiac symptoms, but insufficient for the detection of ischemic disease and cardiomyopathies. In this context, this paper aims to evaluate the feasibility, and reliability of a SW to obtain ECG recordings in comparison with a standard ECG. For that purpose, 140 patients were recruited for this analysis. At the first step, the 12 lead ECG followed with four lead SW-ECG; using the Withings Scanwatch device, were recorded in the same resting conditions. The four lead SW-ECG consists of Einthoven DI lead recorded with the SW, where the SW was on the left wrist and the right index finger on the crown, and three Wilson-type leads, in the which the V1 was recorded in the fourth right parasternal intercostal space, V3 was recorded in the fifth intercostal space on the midclavicular line, and V6 was recorded in the fifth intercostal space on the left midaxillary line with the right index finger placed on the crown and the left hand encompassing the right wrist. 700 ECGs recordings were collected and statistically analyzed in this study. In total, 97 % of the patients were able to obtain an ECG through the SW. A strong correlation was observed between the two recording methods concerning the duration of the studied parameters (r >90 %). The correlation coefficient showed that 33 out of 44 parameters have a strong correlation with the standard ECG. The similarity of the combined leads in the 4 established subgroups was significantly higher, meaning that increasing the number of leads would improve the detection of electrical anomalies. Our findings confirm the existing data on the high similarity between SW and standard 12-leads ECG. Despite SW not having the accuracy and utility of the standard ECG machine, they should be considered as an interesting screening tool for cardiac rhythm disorders, and a compelling solution to electrical documentation of general cardiac symptoms.