Mobile health solutions for atrial fibrillation detection and management: a systematic review

Use of wearable technology in cardiac monitoring after cryptogenic stroke or embolic stroke of undetermined source: a systematic review

INTRODUCTION

Prolonged cardiac monitoring after cryptogenic stroke or embolic stroke of undetermined source (ESUS) is necessary to identify atrial fibrillation (AF) that requires anticoagulation. Wearable devices may improve AF detection compared to conventional management. We aimed to review the evidence for the use of wearable devices in post-cryptogenic stroke and post-ESUS monitoring.

METHODS

We performed a systematic search of PubMed, EMBASE, Scopus and clinicaltrials.gov on 21 July 2022, identifying all studies that investigated the use of wearable devices in patients with cryptogenic stroke or ESUS. The outcomes of AF detection were analysed. Literature reports on electrocardiogram (ECG)-based (external wearable, handheld, patch, mobile cardiac telemetry [MCT], smartwatch) and photoplethysmography (PPG)-based (smartwatch, smartphone) devices were summarised.

RESULTS

A total of 27 relevant studies were included (two randomised controlled trials, seven prospective trials, 10 cohort studies, six case series and two case reports). Only four studies compared wearable technology to Holter monitoring or implantable loop recorder, and these studies showed no significant differences on meta-analysis (odds ratio 2.35, 95% confidence interval [CI] 0.74-7.48, I 2 = 70%). External wearable devices detected AF in 20.7% (95% CI 14.9-27.2, I 2 = 76%) of patients and MCT detected new AF in 9.6% (95% CI 7.4%-11.9%, I 2 = 56%) of patients. Other devices investigated included patch sensors, handheld ECG recorders and PPG-based smartphone apps, which demonstrated feasibility in the post-cryptogenic stroke and post-ESUS setting.

CONCLUSION

Wearable devices that are ECG or PPG based are effective for paroxysmal AF detection after cryptogenic stroke and ESUS, but further studies are needed to establish how they compare with Holter monitors and implantable loop recorder.

Comparing the outcomes and costs of cardiac monitoring with implantable loop recorders and mobile cardiac outpatient telemetry following stroke using real-world evidence

Aim: Patients with ischemic stroke (IS) commonly undergo monitoring to identify atrial fibrillation with mobile cardiac outpatient telemetry (MCOT) or implantable loop recorders (ILRs). The authors compared readmission, healthcare cost and survival in patients monitored post-stroke with either MCOT or ILR. Materials & methods: The authors used claims data from Optum's de-identified Clinformatics® Data Mart Database to identify patients with IS hospitalized from January 2017 to December 2020 who were prescribed ambulatory cardiac monitoring via MCOT or ILR. They compared the costs associated with the initial inpatient visit as well as the rate and causes of readmission, survival and healthcare costs over the following 18 months. Datasets were balanced using patient baseline and hospitalization characteristics. Multivariable generalized linear gamma regression was used for cost comparisons. Cox proportional hazard regression was used for survival and readmission analysis. Sub-cohorts were analyzed based on the severity of the index IS. Results: In 2244 patients, readmissions were significantly lower in the MCOT monitored group (30.2%) compared with the ILR group (35.4%) (hazard ratio [HR] 1.23; 95% CI: 1.04-1.46). Average cost over 18 months starting with the index IS was $27,429 (USD) lower in the MCOT group (95% CI: $22,353-$32,633). Survival difference bordered on statistical significance and trended to lower mortality in MCOT (8.9%) versus ILR (11.3%) (HR 1.30; 95% CI: 1:00-1.69), led by significance in patients with complications or comorbidities with the index event (MCOT 7.5%, ILR 11.5%; HR 1.62; 95% CI: 1.11-2.36). Conclusion: The use of MCOT versus ILR as the primary monitor following IS was associated with significant decreases in readmission, lower costs for the initial IS and total care over the next 18 months, significantly lower mortality for patients with complications and comorbidities at the index stroke, and a trend toward improved survival across all patients.

Biocompatible Electronic Skins for Cardiovascular Health Monitoring

Cardiovascular diseases represent a significant threat to the overall well-being of the global population. Continuous monitoring of vital signs related to cardiovascular health is essential for improving daily health management. Currently, there has been remarkable proliferation of technology focused on collecting data related to cardiovascular diseases through daily electronic skin monitoring. However, concerns have arisen regarding potential skin irritation and inflammation due to the necessity for prolonged wear of wearable devices. To ensure comfortable and uninterrupted cardiovascular health monitoring, the concept of biocompatible electronic skin has gained substantial attention. In this review, biocompatible electronic skins for cardiovascular health monitoring are comprehensively summarized and discussed. The recent achievements of biocompatible electronic skin in cardiovascular health monitoring are introduced. Their working principles, fabrication processes, and performances in sensing technologies, materials, and integration systems are highlighted, and comparisons are made with other electronic skins used for cardiovascular monitoring. In addition, the significance of integrating sensing systems and the updating wireless communication for the development of the smart medical field is explored. Finally, the opportunities and challenges for wearable electronic skin are also examined.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

H. Lip G, Thomas GN, J. Moore D, Nirantharakumar K, Kumarendran B, Subaschandran K, Kanesamoorthy S, Uruthirakumar P, Guruparan M. Diagnostic accuracy of digital technologies compared with 12-lead ECG in the diagnosis of atrial fibrillation in adults: A protocol for a systematic review

BACKGROUND

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in the world. AF increases the risk of stroke 5-fold, though the risk can be reduced with appropriate treatment. Therefore, early diagnosis is imperative but remains a global challenge. In low-and middle-income countries (LMICs), a lack of diagnostic equipment and under-resourced healthcare systems generate further barriers. The rapid development of digital technologies that are capable of diagnosing AF remotely and cost-effectively could prove beneficial for LMICs. However, evidence is lacking on what digital technologies exist and how they compare in regards to diagnostic accuracy. We aim to systematically review the diagnostic accuracy of all digital technologies capable of AF diagnosis.

METHODS

MEDLINE, Embase and Web of Science will be searched for eligible studies. Free text terms will be combined with corresponding index terms where available and searches will not be limited by language nor time of publication. Cohort or cross-sectional studies comprising adult (≥18 years) participants will be included. Only studies that use a 12-lead ECG as the reference test (comparator) and report outcomes of sensitivity, specificity, the diagnostic odds ratio (DOR) or the positive and negative predictive value (PPV and NPV) will be included (or if they provide sufficient data to calculate these outcomes). Two reviewers will independently assess articles for inclusion, extract data using a piloted tool and assess risk of bias using the QUADAS-2 tool. The feasibility of a meta-analysis will be determined by assessing heterogeneity across the studies, grouped by index device, diagnostic threshold and setting. If a meta-analysis is feasible for any index device, pooled sensitivity and specificity will be calculated using a random effect model and presented in forest plots.

DISCUSSION

The findings of our review will provide a comprehensive synthesis of the diagnostic accuracy of available digital technologies capable for diagnosing AF. Thus, this review will aid in the identification of which devices could be further trialed and implemented, particularly in a LMIC setting, to improve the early diagnosis of AF.

TRIAL REGISTRATION

Systematic review registration: PROSPERO registration number is CRD42021290542. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290542.

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.

Adherence to a handheld device-based atrial fibrillation screening protocol is associated with clinical outcomes

OBJECTIVE

To evaluate adherence and adherence consistency to the handheld ECG device-based screening protocol and their association with adverse cerebral and cardiovascular outcomes in two systematic atrial fibrillation (AF) screening programmes.

METHODS

In 2012 (Systematic ECG Screening for Atrial Fibrillation Among 75-Year Old Subjects in the Region of Stockholm and Halland, Sweden (STROKESTOP) study) and 2016 (Stepwise mass screening for atrial fibrillation using N-terminal pro b-type natriuretic peptide (STROKESTOP II) study), half of all 75- and 76-year-old inhabitants of up to two Swedish regions were invited to participate in a systematic AF screening programme. Participants were instructed to perform 30-second measurements twice daily in STROKESTOP and four times daily in STROKESTOP II for 2 weeks. Adherence was defined as the number of measurements performed divided by the number of measurements asked, whereas adherence consistency was defined as the number of days with complete registrations.

RESULTS

In total, 6436 participants (55.7% female) from STROKESTOP and 3712 (59.8% female) from STROKESTOP II were included. Median adherence and adherence consistency were 100 (92-100)% and 12 (11-13) days in STROKESTOP and 90 (75-98)% and 8 (3-11) days in STROKESTOP II. Female sex and lower education were factors associated with both optimal adherence and adherence consistency in both studies. In STROKESTOP, low adherence and adherence consistency were associated with higher risk of adverse cerebral and cardiovascular outcomes (HR for composite primary endpoint 1.30 (1.11 to 1.51), p=0.001), including stroke (HR 1.68 (1.22 to 2.32), p=0.001) and dementia (1.67 (1.27 to 2.19), p<0.001).

CONCLUSIONS

Adherence to twice daily handheld ECG measurements in STROKESTOP was higher than to four times daily measurements in STROKESTOP II. Female sex and lower educational attainment were associated with ≥100% adherence and adherence consistency. Low adherence and adherence consistency were associated with a higher risk of adverse outcomes.

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.

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.

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.

Changes in healthcare utilisation during implementation of remote atrial fibrillation management: TeleCheck-AF project

AIM

To evaluate changes in healthcare utilisation and comprehensive packages of care activities and procedures (referred in the Netherlands to as 'diagnose-behandelcombinatie (DBC) care products) during the implementation of the TeleCheck-AF approach (teleconsultation supported by app-based heart rate/rhythm monitoring) in a Dutch atrial fibrillation (AF) clinic.

METHODS AND RESULTS

In the Maastricht University Medical Centre+ AF Clinic, data on healthcare utilisation and DBC care products for patients consulted by both a conventional approach in 2019 and the TeleCheck-AF approach in 2020 were analysed. A patient experience survey was performed. Thirty-seven patients (median age 68 years; 40% women) were analysed. With the conventional approach, 35 face-to-face consultations and 0 teleconsultations were conducted. After the implementation of TeleCheck-AF, the number of face-to-face consultations dropped by 80% (p < 0.001) and teleconsultations increased to 45 (p < 0.001). While 42 electrocardiograms (ECGs) and 25 Holter ECGs or echocardiograms were recorded when using the conventional approach, the number of ECGs decreased by 71% (p < 0.001) and Holter ECGs or echocardiograms by 72% (p < 0.001) with the TeleCheck-AF approach. The emergency department patient presentations showed no statistically significant change (p = 0.33). Overall, 57% of medium-weight DBC care products were changed to light-weight ones during implementation of the TeleCheck-AF approach. Patient satisfaction with the TeleCheck-AF approach was high.

CONCLUSION

The implementation of TeleCheck-AF led to a change in healthcare utilisation, a change from medium-weight to light-weight DBC care products and a reduction in patient burden. These results created the basis for a new reimbursement code for the TeleCheck-AF approach in the Netherlands.

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.

Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region

Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.

Atrial fibrillation: epidemiology, screening and digital health

Atrial fibrillation (AF) is highly prevalent with a lifetime risk of about 1 in 3-5 individuals after the age of 45 years. Between 2010 and 2019, the global prevalence of AF has risen markedly from 33.5 million to 59 million individuals living with AF. Early detection of AF and implementation of appropriate treatment could reduce the frequency of complications associated with AF. International AF management guidelines recommend opportunistic and systematic screening for AF, but additional data are needed. Digital approaches and pathways have been proposed for early detection and for the transition to early AF management. Mobile health (mHealth) devices provide an opportunity for digital screening and should be part of novel models of care delivery based on integrated AF care pathways. For a broad implementation of mHealth-based, integrated care for patients with chronic diseases as AF, further high quality evidence is necessary. In this review, we present an overview of the present data on epidemiology, screening techniques, and the contribution of digital health solutions to the integrated management of AF. We also provide a systemic review on current data of digital and integrated AF management.

Digital patient twins for personalized therapeutics and pharmaceutical manufacturing

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

Anticoagulation-Associated Bleeding in Patients Screened for Atrial Fibrillation versus Usual Care-A Post Hoc Analysis from the LOOP Study

Background  Atrial fibrillation (AF) prevalence is rising; however, data on the bleeding risks associated with the detection of subclinical AF are needed. Objective  Our objective was to determine the bleeding increment associated with implantable loop recorder (ILR) screening for subclinical AF and subsequent anticoagulation initiation compared with usual care. Methods  This post hoc study utilized LOOP trial data from 6,004 elderly patients with stroke risks randomized to either ILR ( n  = 1,503) or usual care ( n  = 4,503). The mean follow-up time was 64.5 months, and none were lost to follow-up. The primary exposure was the initiation of oral anticoagulation, and the main outcome was the risk of major bleeding events following initiation of oral anticoagulants (OACs), determined by time-dependent cox regression. Second, we investigated antithrombotic prescription patterns and major bleeding events after antiplatelet treatment and in subgroups. Results  OAC was initiated in 1,019 participants with a mean age (years) of 78.8 (± 4.67) in control versus 77.0 (± 4.84) in ILR, p  < 0.0001. Altogether did 202 participants end or pause OAC treatment. Among AF patients (n = 910) had 40 (28%) completely ended OAC and 105 (72%) temporarily paused OAC during follow-up. Major bleeding events totaled 221 (3.7%). Forty-seven major bleeding events followed an OAC initiation in 1,019 participants (4.6%); 26 versus 21 events in the control and ILR groups, respectively. The hazard ratio (HR) for major bleeding after OAC initiation compared with before initiation was 2.08 (1.50-2.90) p  < 0.0001 overall, 2.81 (1.82-4.34) p  < 0.0001 for control and 1.32 (0.78-2.23) p  = 0.31 for the ILR group ( p  = 0.07 for interaction). Antiplatelet treatment resulted in an overall adjusted HR of 1.3 (0.96-1.75) p  = 0.09. For OAC users aged ≥75 years in the ILR group, the rate of major bleeding was 1.73 (0.92-2.96) compared with 0.84 (0.36-1.66) for an age <75 years, and the rate of the corresponding control subgroup aged ≥75 years was 2.20 (1.23-3.63) compared with 1.64 (0.82-2.93) for an age <75 years. Conclusion  The individual risk of major bleeding increased twofold after initiation of oral anticoagulation for all patients in this study. However, the patients screened for subclinical AF did not have a higher bleeding risk after initiation of anticoagulation compared with those in usual care. Trial Registration:  The LOOP study is registered at ClinicalTrials.gov, identifier: NCT020364 50.

Characterizing Real-World Implementation of Consumer Wearables for the Detection of Undiagnosed Atrial Fibrillation in Clinical Practice: Targeted Literature Review

BACKGROUND

Atrial fibrillation (AF), the most common cardiac arrhythmia, is often undiagnosed because of lack of awareness and frequent asymptomatic presentation. As AF is associated with increased risk of stroke, early detection is clinically relevant. Several consumer wearable devices (CWDs) have been cleared by the US Food and Drug Administration for irregular heart rhythm detection suggestive of AF. However, recommendations for the use of CWDs for AF detection in clinical practice, especially with regard to pathways for workflows and clinical decisions, remain lacking.

OBJECTIVE

We conducted a targeted literature review to identify articles on CWDs characterizing the current state of wearable technology for AF detection, identifying approaches to implementing CWDs into the clinical workflow, and characterizing provider and patient perspectives on CWDs for patients at risk of AF.

METHODS

PubMed, ClinicalTrials.gov, UpToDate Clinical Reference, and DynaMed were searched for articles in English published between January 2016 and July 2023. The searches used predefined Medical Subject Headings (MeSH) terms, keywords, and search strings. Articles of interest were specifically on CWDs; articles on ambulatory monitoring tools, tools available by prescription, or handheld devices were excluded. Search results were reviewed for relevancy and discussed among the authors for inclusion. A qualitative analysis was conducted and themes relevant to our study objectives were identified.

RESULTS

A total of 31 articles met inclusion criteria: 7 (23%) medical society reports or guidelines, 4 (13%) general reviews, 5 (16%) systematic reviews, 5 (16%) health care provider surveys, 7 (23%) consumer or patient surveys or interviews, and 3 (10%) analytical reports. Despite recognition of CWDs by medical societies, detailed guidelines regarding CWDs for AF detection were limited, as was the availability of clinical tools. A main theme was the lack of pragmatic studies assessing real-world implementation of CWDs for AF detection. Clinicians expressed concerns about data overload; potential for false positives; reimbursement issues; and the need for clinical tools such as care pathways and guidelines, preferably developed or endorsed by professional organizations. Patient-facing challenges included device costs and variability in digital literacy or technology acceptance.

CONCLUSIONS

This targeted literature review highlights the lack of a comprehensive body of literature guiding real-world implementation of CWDs for AF detection and provides insights for informing additional research and developing appropriate tools and resources for incorporating these devices into clinical practice. The results should also provide an impetus for the active involvement of medical societies and other health care stakeholders in developing appropriate tools and resources for guiding the real-world use of CWDs for AF detection. These resources should target clinicians, patients, and health care systems with the goal of facilitating clinician or patient engagement and using an evidence-based approach for establishing guidelines or frameworks for administrative workflows and patient care pathways.

Recent Advances in Antiarrhythmic Drug Therapy

Cardiac arrhythmias remain a common cause of death and disability. Antiarrhythmic drugs (AADs) and antiarrhythmic agents remain a cornerstone of current cardiac arrhythmia management, despite moderate efficacy and the potential for significant adverse proarrhythmic effects. Due to conceptual, regulatory and financial considerations, the number of novel antiarrhythmic targets and agents in the development pipeline has decreased substantially during the last few decades. However, several promising candidates remain and there are exciting developments in repurposing and reformulating already existing drugs for indications related to cardiac arrhythmias. This review discusses the key conceptual considerations for the development of new antiarrhythmic agents, summarizes new compounds and formulations currently in clinical development for rhythm control of atrial fibrillation, and highlights the potential for drug repurposing. Finally, future directions in AAD development are discussed. Together with an ever-increasing understanding of the molecular mechanisms underlying cardiac arrhythmias, these components support a cautiously optimistic outlook towards improved pharmacological treatment opportunities for patients suffering from cardiac arrhythmias.

Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias

BACKGROUND

Smartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms.

OBJECTIVES

The aim of the study was to establish a novel, expanded approach of a semiautomated retrospective App Store analysis (SARASA) to identify and characterise health apps in the context of cardiac arrhythmias.

MATERIALS AND METHODS

An automated total read-out of the "Medical" category of Apple's German App Store was performed in December 2022 by analysing the developer-provided descriptions and other metadata using a semiautomated multilevel approach. Search terms were defined, based on which the textual information of the total extraction results was automatically filtered.

RESULTS

A total of 435 of 31,564 apps were identified in the context of cardiac arrhythmias. Of those, 81.4% were found to deal with education, decision support, or disease management, and 26.2% (additionally) provided the opportunity to derive information on heart rhythm. The apps were intended for healthcare professionals in 55.9%, students in 17.5%, and/or patients in 15.9%. In 31.5%, the target population was not specified in the description texts. In all, 108 apps (24.8%) provided a telemedicine treatment approach; 83.7% of the description texts did not reveal any information on medical product status; 8.3% of the apps indicated that they have and 8.0% that they do not have medical product status.

CONCLUSION

Through the supplemented SARASA method, health apps in the context of cardiac arrhythmias could be identified and assigned to the target categories. Clinicians and patients have a wide choice of apps, although the app description texts do not provide sufficient information about the intended use and quality.

Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller

At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller. We used the CMSIS-DSP library, which supports Naïve Bayes and Support Vector Machine classifiers, with different kernel functions. We also developed Python scripts to automatically transfer the Python model (trained in Scikit-learn) to the C environment. To train and evaluate the models, we used part of the data from the PhysioNet/Computing in Cardiology Challenge 2020 and performed simple classification of atrial fibrillation based on heart-rate irregularity. The performance of the classifiers was tested on a general-purpose ARM Cortex-M4 microcontroller (STM32WB55RG). Our study reveals that among the tested classifiers, the SVM classifier with RBF kernel function achieves the highest accuracy of 96.9%, sensitivity of 98.4%, and specificity of 95.8%. The execution time of this classifier was 720 μs per recording. We also discuss the advantages of moving computing tasks to edge devices, including increased power efficiency of the system, improved patient data privacy and security, and reduced overall system operation costs. In addition, we highlight a problem with false-positive detection and unclear significance of device-detected atrial fibrillation.

ECG Smart Monitoring versus Implantable Loop Recorders for Atrial Fibrillation Detection after Cryptogenic Stroke-An Overview for Decision Making

Up to 20% of patients with ischemic stroke or transient ischemic attack have a prior history of known atrial fibrillation (AF). Additionally, unknown AF can be detected by different monitoring strategies in up to 23% of patients with cryptogenic or non-cardioembolic stroke. However, most studies had substantial gaps in monitoring time, especially early after the index event. Following this, AF rates would be higher if patients underwent continuous monitoring early after stroke, avoiding any gaps in monitoring. The few existing randomized studies focused on patients with cryptogenic stroke but did not focus otherwise specifically on prevention strategies in patients at high risk for AF (patients at higher age or with high CHA2DS2-VASC scores). Besides invasive implantable loop recorders (ILRs), external loop recorders (ELRs) and mobile cardiac outpatient telemetry (MCOT) are non-invasive tools that are commonly used for long-term ECG monitoring in cryptogenic-stroke patients in the ambulatory setting. The role of MCOT and hand-held devices within ECG smart monitoring in the detection of AF for the prevention of and after cryptogenic stroke is currently unclear. This intense review provides an overview of current evidence, techniques, and gaps in knowledge and aims to advise which patients benefit most from the current available devices.

Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.

Nocturnal pulse wave amplitude attenuations are associated with long-term cardiovascular events

OBJECTIVES

Photoplethysmography (PPG) is an established technology for detecting pulse rate and pulse wave irregularities. However, whether temporal variations in pulse wave amplitudes, reflecting a combination of acute hemodynamic or autonomic responses to changes in overall vascular function, carry prognostic information remains unclear. To quantify nocturnal temporal pulse wave amplitude (PWA) attenuations and evaluate its association with long-term cardiovascular (CV) events in a large, racially diverse sample of men and women.

METHODS

Temporal PWA attenuations were determined based on the slopes between the upper and lower envelopes of PPGs derived from overnight polysomnography of 1957 participants (899 men, 1058 women, mean age 68.2 ± 9.1 years) of the Multi-Ethnic Study of Atherosclerosis. The nocturnal PWA attenuation index was defined as the cumulative duration of all PWA attenuation events relative to total sleep duration.

RESULTS

Nocturnal PWA attenuation index was greater in men than in women by almost 13% (16.3 ± 8.9% vs. 14.4 ± 7.9%, p < 0.001). The nocturnal PWA attenuation index was highest in Chinese-American participants (17.9 ± 9.2%) and lowest in African-Americans (13.5 ± 8.1%). During a median follow-up of 4.9 years, 94 CV events occurred. In multivariable Cox proportional hazard analysis adjusted for typical confounders, the nocturnal PWA attenuation index <15.2% was associated with CV events (HR = 1.58 [1.02-2.45], p = 0.042).

CONCLUSIONS

Nocturnal PWA attenuation index is inversely associated with the risk of CV events, particularly in men and African-Americans. The PPG-derived nocturnal PWA attenuation index could be simply obtained from smart wearable consumer devices and may provide a low-cost, accessible and scalable CV risk marker.

Efficacy of Individualized Sensory-Based mHealth Interventions to Improve Distress Coping in Healthcare Professionals: A Multi-Arm Parallel-Group Randomized Controlled Trial

Detrimental effects of chronic stress on healthcare professionals have been well-established, but the implementation and evaluation of effective interventions aimed at improving distress coping remains inadequate. Individualized mHealth interventions incorporating sensor feedback have been proposed as a promising approach. This study aimed to investigate the impact of individualized, sensor-based mHealth interventions focusing on stress and physical activity on distress coping in healthcare professionals. The study utilized a multi-arm, parallel group randomized controlled trial design, comparing five intervention groups (three variations of web-based training and two variations of an app training) that represented varying levels of individualization to a control group. Both self-reported questionnaire data (collected using Limesurvey) as well as electrocardiography and accelerometry-based sensory data (collected using Mesana Sensor) were assessed at baseline and post-intervention (after eight weeks). Of the 995 eligible participants, 170 (26%) completed the post-intervention measurement (Group 1: N = 21; Group 2: N = 23; Group 3: N = 7; Group 4: N = 34; Group 5: N = 16; Control Group: N = 69). MANOVA results indicated small to moderate time-by-group interaction effects for physical activity-related outcomes, including moderate to vigorous physical activity (F(1,5) = 5.8, p = ≤0.001, η2p = 0.057) and inactivity disruption (F(1,5) = 11.2, p = <0.001, η2p = 0.100), in the app-based intervention groups, but not for step counts and inactivity. No changes were observed in stress-related heart rate variability parameters over time. Despite a high dropout rate and a complex study design, the individualized interventions showed initial positive effects on physical activity. However, no significant changes in stress-related outcomes were observed, suggesting that the intervention duration was insufficient to induce physiological adaptations that would result in improved distress coping.

This is Your Brain, and This is Your Brain on Atrial Fibrillation: The Roles of Cardiac Malperfusion Events and Vascular Dysfunction in Cognitive Impairment

AF is an independent and strong predictor of long-term cognitive decline. However, the mechanism for this cognitive decline is difficult to define and likely multifactorial, leading to many different hypotheses. Examples include macro- or microvascular stroke events, biochemical changes to the blood-brain barrier related to anticoagulation, or hypo-hyperperfusion events. This review explores and discusses the hypothesis that AF contributes to cognitive decline and dementia through hypo-hyperperfusion events occurring during cardiac arrhythmias. We briefly explain several brain perfusion imaging techniques and further examine the novel findings associated with changes in brain perfusion in patients with AF. Finally, we discuss the implications and areas requiring more research to further understand and treat patients with cognitive decline related to AF.

Mobile health adherence for the detection of recurrent recent-onset atrial fibrillation

OBJECTIVE

The Rate Control versus Electrical Cardioversion Trial 7-Acute Cardioversion versus Wait and See trial compared early to delayed cardioversion for patients with recent-onset symptomatic atrial fibrillation (AF). This study aims to evaluate the adherence to a 4-week mobile health (mHealth) prescription to detect AF recurrences after an emergency department visit.

METHODS

After the emergency department visit, the 437 included patients, irrespective of randomisation arm (early or delayed cardioversion), were asked to record heart rate and rhythm for 1 min three times daily and in case of symptoms by an electrocardiography-based handheld device for 4 weeks (if available). Adherence was appraised as number of performed measurements per number of recordings asked from the patient and was evaluated for longitudinal adherence consistency. All patients who used the handheld device were included in this subanalysis.

RESULTS

335 patients (58% males; median age 67 (IQR 11) years) were included. The median overall adherence of all patients was 83.3% (IQR 29.9%). The median number of monitoring days was 27 out of 27 (IQR 5), whereas the median number of full monitoring days was 16 out of 27 (IQR 14). Higher age and a previous paroxysm of AF were identified as multivariable adjusted factors associated with adherence.

CONCLUSIONS

In this randomised trial, a 4-week mHealth prescription to monitor for AF recurrences after an emergency department visit for recent-onset AF was feasible with 85.7% of patients consistently using the device with at least one measurement per day. Older patients were more adherent.

TRIAL REGISTRATION NUMBER

NCT02248753.

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.

New concepts in atrial fibrillation pathophysiology

The current classification of atrial fibrillation (AF) is mainly focused on the clinical presentation according to the duration of AF episodes and the mode of termination, which incompletely reflect the severity and progressive nature of the underlying atrial disease. In this review article, "atrial cardiomyopathy" is discussed as a new concept in AF pathophysiology. Electrogram-, imaging-, and biomarker-derived measures and parameters to assess atrial cardiomyopathy, which will likely impact how AF is clinically classified and managed in the future, are presented.

Wearables in Cardiovascular Disease

Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.

A meta-analysis of extended ECG monitoring in detection of atrial fibrillation in patients with cryptogenic stroke

Objective

The aim of this systematic review is to evaluate the various modalities available for extended ECG monitoring in the detection of atrial fibrillation (AF) following a cryptogenic stroke.

Methods

MEDLINE (Ovid), EMBASE (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL) were searched from January 2011 to November 2021. All randomised controlled trials and prospective cohort studies including the use of extended ECG monitoring >24 hours with a minimum duration of AF of 30 s in patients with either cryptogenic strokes or transient ischaemic attacks were included. A random-effects model was used to pool effect estimates of AF detection rates from different ECG modalities.

Results

3924 studies were identified, of which 47 were included reporting on a pooled population of 6448 patients with cryptogenic stroke. The pooled AF rate for implantable loop recorders (ILRs) increased from 4.9% (3.0%–7.9%) at 1 month to 38.4% (20.4%–60.2%) at 36 months. Mobile cardiac outpatient telemetry (MCOT) had a significantly higher pooled AF detection rate of 12.8% (8.9%–17.9%) versus 4.9% (3.0%–7.9%) for ILR at 1 month (p<0.0001). Predictors for AF detection include duration of monitoring (p<0.0001) and age (p<0.0001) for ILRs, but only age for MCOTs (p<0.020).

Conclusion

MCOT has a higher rate of detection at 1 month and is less invasive. Beyond 1 month, compliance becomes a significant limitation for MCOT. MCOT may be a reasonable alternative AF screening tool for patients with cryptogenic stroke if ILR is not available.

PROSPERO registration number

CRD42022297782.

Patient motivation and adherence to an on-demand app-based heart rate and rhythm monitoring for atrial fibrillation management: data from the TeleCheck-AF project

AIMS

The aim of this TeleCheck-AF sub-analysis was to evaluate motivation and adherence to on-demand heart rate/rhythm monitoring app in patients with atrial fibrillation (AF).

METHODS AND RESULTS

Patients were instructed to perform 60 s app-based heart rate/rhythm recordings 3 times daily and in case of symptoms for 7 consecutive days prior to teleconsultation. Motivation was defined as number of days in which the expected number of measurements (≥3/day) were performed per number of days over the entire prescription period. Adherence was defined as number of performed measurements per number of expected measurements over the entire prescription period.Data from 990 consecutive patients with diagnosed AF [median age 64 (57-71) years, 39% female] from 10 centres were analyzed. Patients with both optimal motivation (100%) and adherence (≥100%) constituted 28% of the study population and had a lower percentage of recordings in sinus rhythm [90 (53-100%) vs. 100 (64-100%), P < 0.001] compared with others. Older age and absence of diabetes were predictors of both optimal motivation and adherence [odds ratio (OR) 1.02, 95% coincidence interval (95% CI): 1.01-1.04, P < 0.001 and OR: 0.49, 95% CI: 0.28-0.86, P = 0.013, respectively]. Patients with 100% motivation also had ≥100% adherence. Independent predictors for optimal adherence alone were older age (OR: 1.02, 95% CI: 1.00-1.04, P = 0.014), female sex (OR: 1.70, 95% CI: 1.29-2.23, P < 0.001), previous AF ablation (OR: 1.35, 95% CI: 1.03-1.07, P = 0.028).

CONCLUSION

In the TeleCheck-AF project, more than one-fourth of patients had optimal motivation and adherence to app-based heart rate/rhythm monitoring. Older age and absence of diabetes were predictors of optimal motivation/adherence.

Cardiac Rhythm Monitoring Using Wearables for Clinical Guidance before and after Catheter Ablation

Mobile health technologies are gaining importance in clinical decision-making. With the capability to monitor the patient’s heart rhythm, they have the potential to reduce the time to confirm a diagnosis and therefore are useful in patients eligible for screening of atrial fibrillation as well as in patients with symptoms without documented symptom rhythm correlation. Such is crucial to enable an adequate arrhythmia management including the possibility of a catheter ablation. After ablation, wearables can help to search for recurrences, in symptomatic as well as in asymptomatic patients. Furthermore, those devices can be used to search for concomitant arrhythmias and have the potential to help improving the short- and long-term patient management. The type of wearable as well as the adequate technology has to be chosen carefully for every situation and every individual patient, keeping different aspects in mind. This review aims to describe and to elaborate a potential workflow for the role of wearables for cardiac rhythm monitoring regarding detection and management of arrhythmias before and after cardiac electrophysiological procedures.

Mobile health technology in atrial fibrillation

INTRODUCTION

Mobile health (mHealth) solutions in atrial fibrillation (AF) are becoming widespread, thanks to everyday life devices such as smartphones. Their use is validated both in monitoring and in screening scenarios. In the published literature, the diagnostic accuracy of mHealth solutions wide differs, and their current clinical use is not well established in principal guidelines.

AREAS COVERED

mHealth solutions have progressively built an AF-detection chain to guide patients from the device's alert signal to the health care practitioners' (HCPs) attention. This review aims to critically evaluate the latest evidence regarding mHealth devices and the future possible patient's uses in everyday life.

EXPERT OPINION

The patients are the first to be informed of the rhythm anomaly, leading to the urgency of increasing the patients' AF self-management. Furthermore, HCPs need to update themselves about mHealth devices use in clinical practice. Nevertheless, these are promising instruments in specific populations, such as post-stroke patients, to promote an early arrhythmia diagnosis in the post-ablation/cardioversion period, allowing checks on the efficacy of the treatment or intervention.

[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.

Artificial intelligence for the detection, prediction, and management of atrial fibrillation

The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.