Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO)

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

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

Patient Driven EKG Device Performance in Adults with Fontan Palliation

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

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, to improve continuous AF detection in ambulatory settings towards a population-wide screening use case, we face several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5 M 30-second records from 24,100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open-source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency.

Validation of a novel smartphone-based photoplethysmographic method for ambulatory heart rhythm diagnostics: the SMARTBEATS study

AIMS

In the current guidelines, smartphone photoplethysmography (PPG) is not recommended for diagnosis of atrial fibrillation (AF), without a confirmatory electrocardiogram (ECG) recording. Previous validation studies have been performed under supervision in healthcare settings, with limited generalizability of the results. We aim to investigate the diagnostic performance of a smartphone-PPG method in a real-world setting, with ambulatory unsupervised smartphone-PPG recordings, compared with simultaneous ECG recordings and including patients with atrial flutter (AFL).

METHODS AND RESULTS

Unselected patients undergoing direct current cardioversion for treatment of AF or AFL were asked to perform 1-min heart rhythm recordings post-treatment, at least twice daily for 30 days at home, using an iPhone 7 smartphone running the CORAI Heart Monitor PPG application simultaneously with a single-lead ECG recording (KardiaMobile). Photoplethysmography and ECG recordings were read independently by two experienced readers. In total, 280 patients recorded 18 005 simultaneous PPG and ECG recordings. Sufficient quality for diagnosis was seen in 96.9% (PPG) vs. 95.1% (ECG) of the recordings (P < 0.001). Manual reading of the PPG recordings, compared with manually interpreted ECG recordings, had a sensitivity, specificity, and overall accuracy of 97.7%, 99.4%, and 98.9% with AFL recordings included and 99.0%, 99.7%, and 99.5%, respectively, with AFL recordings excluded.

CONCLUSION

A novel smartphone-PPG method can be used by patients unsupervised at home to achieve accurate heart rhythm diagnostics of AF and AFL with very high sensitivity and specificity. This smartphone-PPG device can be used as an independent heart rhythm diagnostic device following cardioversion, without the requirement of confirmation with ECG.

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.

Detection of heart rate using smartphone gyroscope data: a scoping review

Heart rate (HR) is closely related to heart rhythm patterns, and its irregularity can imply serious health problems. Therefore, HR is used in the diagnosis of many health conditions. Traditionally, HR has been measured through an electrocardiograph (ECG), which is subject to several practical limitations when applied in everyday settings. In recent years, the emergence of smartphones and microelectromechanical systems has allowed innovative solutions for conveniently measuring HR, such as smartphone ECG, smartphone photoplethysmography (PPG), and seismocardiography (SCG). However, these measurements generally rely on external sensor hardware or are highly susceptible to inaccuracies due to the presence of significant levels of motion artifact. Data from gyrocardiography (GCG), however, while largely overlooked for this application, has the potential to overcome the limitations of other forms of measurements. For this scoping review, we performed a literature search on HR measurement using smartphone gyroscope data. In this review, from among the 114 articles that we identified, we include seven relevant articles from the last decade (December 2012 to January 2023) for further analysis of their respective methods for data collection, signal pre-processing, and HR estimation. The seven selected articles' sample sizes varied from 11 to 435 participants. Two articles used a sample size of less than 40, and three articles used a sample size of 300 or more. We provide elaborations about the algorithms used in the studies and discuss the advantages and disadvantages of these methods. Across the articles, we noticed an inconsistency in the algorithms used and a lack of established standardization for performance evaluation for HR estimation using smartphone GCG data. Among the seven articles included, five did not perform any performance evaluation, while the other two used different reference signals (HR and PPG respectively) and metrics for accuracy evaluation. We conclude the review with a discussion of challenges and future directions for the application of GCG technology.

Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment

BACKGROUND

Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision-making. There is an unmet need for medical-grade diagnostic wrist-worn devices that provide long-term, continuous AF monitoring.

METHODS AND RESULTS

We evaluated the performance of a wrist-worn device with lead-I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography-based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14-day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15-minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%-98.0%) and 98.1% specific (95% CI, 97.2%-99.1%) for interval-level AF detection. Photoplethysmography-derived AF burden estimation was highly correlated with the reference device burden (R2=0.986) with a mean difference of 0.8% (95% limits of agreement, -6.6% to 8.2%).

CONCLUSIONS

Continuous monitoring using a photoplethysmography-based convolutional neural network incorporated in a wrist-worn device has clinical-grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist-worn wearables for diagnosis and clinical management of AF.

REGISTRATION INFORMATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT04546763.

Determine atrial fibrillation burden with a photoplethysmographic mobile sensor: the atrial fibrillation burden trial: detection and quantification of episodes of atrial fibrillation using a cloud analytics service connected to a wearable with photoplethysmographic sensor

Aims

Recent studies suggest that atrial fibrillation (AF) burden (time AF is present) is an independent risk factor for stroke. The aim of this trial was to study the feasibility and accuracy to identify AF episodes and quantify AF burden in patients with a known history of paroxysmal AF with a photoplethysmography (PPG)-based wearable.

Methods and results

In this prospective, single-centre trial, the PPG-based estimation of AF burden was compared with measurements of a conventional 48 h Holter electrocardiogram (ECG), which served as the gold standard. An automated algorithm performed PPG analysis, while a cardiologist, blinded for the PPG data, analysed the ECG data. Detected episodes of AF measured by both methods were aligned timewise.Out of 100 patients recruited, 8 had to be excluded due to technical issues. Data from 92 patients were analysed [55.4% male; age 73.3 years (standard deviation, SD: 10.4)]. Twenty-five patients presented AF during the study period. The intraclass correlation coefficient of total AF burden minutes detected by the two measurement methods was 0.88. The percentage of correctly identified AF burden over all patients was 85.1% and the respective parameter for non-AF time was 99.9%.

Conclusion

Our results demonstrate that a PPG-based wearable in combination with an analytical algorithm appears to be suitable for a semiquantitative estimation of AF burden in patients with a known history of paroxysmal AF.

Trial Registration number

NCT04563572.

Atrial fibrillation and stroke prevention: 25 years of research at EP Europace journal

Stroke prevention in patients with atrial fibrillation (AF) is one pillar of the management of this common arrhythmia. Substantial advances in the epidemiology and associated pathophysiology underlying AF-related stroke and thrombo-embolism are evident. Furthermore, the introduction of the non-vitamin K antagonist oral anticoagulants (also called direct oral anticoagulants) has clearly changed our approach to stroke prevention in AF, such that the default should be to offer oral anticoagulation for stroke prevention, unless the patient is at low risk. A strategy of early rhythm control is also beneficial in reducing strokes in selected patients with recent onset AF, when compared to rate control. Cardiovascular risk factor management, with optimization of comorbidities and attention to lifestyle factors, and the patient's psychological morbidity are also essential. Finally, in selected patients with absolute contraindications to long-term oral anticoagulation, left atrial appendage occlusion or exclusion may be considered. The aim of this state-of-the-art review article is to provide an overview of the current status of AF-related stroke and prevention strategies. A holistic or integrated care approach to AF management is recommended to minimize the risk of stroke in patients with AF, based on the evidence-based Atrial fibrillation Better Care (ABC) pathway, as follows: A: Avoid stroke with Anticoagulation; B: Better patient-centred, symptom-directed decisions on rate or rhythm control; C: Cardiovascular risk factor and comorbidity optimization, including lifestyle changes.

Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis

OBJECTIVE

The aim of this review was to evaluate the overall diagnostic performance of e-devices for detection of health problems in older adults at home.

METHODS

A systematic review was conducted following the PRISMA-DTA guidelines.

RESULTS

31 studies were included with 24 studies included in meta-analysis. The included studies were divided into four categories according to the signals detected: physical activity (PA), vital signs (VS), electrocardiography (ECG) and other. The meta-analysis showed the pooled estimates of sensitivity and specificity were 0.94 and 0.98 respectively in the 'VS' group. The pooled sensitivity and specificity were 0.97 and 0.98 respectively in the 'ECG' group.

CONCLUSIONS

All kinds of e-devices perform well in diagnosing the common health problems. While ECG-based health problems detection system is more reliable than VS-based ones. For sole signal detection system has limitation in diagnosing specific health problems, more researches should focus on developing new systems combined of multiple signals.

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.

A Contact-Free Optical Device for the Detection of Pulmonary Congestion-A Pilot Study

BACKGROUND

The cost of heart failure hospitalizations in the US alone is over USD 10 billion per year. Over 4 million Americans are hospitalized every year due to heart failure (HF), with a median length of stay of 4 days and an in-hospital mortality rate that exceeds 5%. Hospitalizations of patients with HF can be prevented by early detection of lung congestion. Our study assessed a new contact-free optical medical device used for the early detection of lung congestion.

METHODS

The Gili system is an FDA-cleared device used for measuring chest motion vibration data. Lung congestion in the study was assessed clinically and verified via two cardiologists. An algorithm was developed using machine learning techniques, and cross-validation of the findings was performed to estimate the accuracy of the algorithm.

RESULTS

A total of 227 patients were recruited (101 cases vs. 126 controls). The sensitivity and specificity for the device in our study were 0.91 (95% CI: 0.86-0.93) and 0.91 (95% CI: 0.87-0.94), respectively. In all instances, the observed estimates of PPVs and NPVs were at least 0.82 and 0.90, respectively. The accuracy of the algorithm was not affected by different covariates (including respiratory or valvular conditions).

CONCLUSIONS

This study demonstrates the efficacy of a contact-free optical device for detecting lung congestion. Further validation of the study results across a larger and precise scale is warranted.

Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

OBJECTIVES

Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection.

METHODS

This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool.

RESULTS

28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias.

CONCLUSION

PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection.

Prediction of atrial fibrillation using a home blood pressure monitor with a high-resolution system

Objective

The usefulness of screening for atrial fibrillation (AF) using several home blood pressure (BP) monitors has been reported. We evaluated the accuracy of a high-resolution system (HiRS) for AF prediction and its usefulness when installed in home BP monitors.

Methods

In patients with paroxysmal, persistent or permanent AF, ECG recording and BP measurements were performed simultaneously. The relationship between ECG rhythm diagnosis and pulse irregularity recognition, using a home BP monitor with HiRS, was investigated. The severity of a pulse disturbance during BP measurement was displayed as an irregular pulse rhythm symbol (IPRS) in three instances. The IPRS was not displayed if the pulse was regular, turned on if there was a weak variation in the pulse, and blinked if there was a strong variation in the pulse.

Results

One hundred and seven patients (44 paroxysmal AF, 63 persistent or permanent AF) were enrolled, and a total of 333 recordings were analysed. The rhythms recorded by each ECG were 73 sinus regular rhythms, 35 extrasystoles, 222 AFs and 3 atrial flutters. Sensitivity and specificity for the prediction of any arrhythmia by the IPRS display of the BP monitor were 95.8% (95% CI 92.6% to 97.6%) and 96.8% (95% CI 92.6% to 100%), respectively. In addition, sensitivity and specificity for the prediction of AF were 100% (95% CI 97.5% to 100%) and 74.8% (95% CI 65.6% to 82.5%), respectively. Sensitivity and specificity for the prediction of AF by the IPRS blinking display were 88.3% (95% CI 83.3% to 92.2%) and 94.6% (95% CI 88.6% to 98.0%%), respectively. IPRS exhibited lighting or blinking during AF occurrence; however, during sinus rhythm, IPRS was not displayed in 72 out of 73 recordings.

Conclusion

The IPRS device predicted AF with precision and may be particularly useful for predicting an arrhythmia attack in patients with paroxysmal AF.

Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial

Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial ( NCT04250220 ), we randomly assigned 5,551 policyholders of a German health insurance company who were free of AF at baseline (age 65 years (median; interquartile range (11) years, 31% females)) to digital screening (n = 2,860) or usual care (n = 2,691). In this siteless trial, for digital screening, participants used a certified app on their own smartphones to screen for irregularities in their pulse waves. Abnormal findings were evaluated by 14-day external electrocardiogram (ECG) loop recorders. The primary endpoint was newly diagnosed AF within 6 months treated with oral anti-coagulation by an independent physician not involved in the study. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment for secondary analyses. The primary endpoint of the trial was met, as digital screening more than doubled the detection rate of treatment-relevant AF in both phases of the trial, with odds ratios of 2.12 (95% confidence interval (CI), 1.19-3.76; P = 0.010) and 2.75 (95% CI, 1.42-5.34; P = 0.003) in the first and second phases, respectively. This digital screening technology provides substantial benefits in detecting AF compared to usual care and has the potential for broad applicability due to its wide availability on ordinary smartphones. Future studies are needed to test whether digital screening for AF leads to better treatment outcomes.

Accuracy of wristwatch-type photoplethysmography in detecting atrial fibrillation in daily life. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022;3:455-464. [PMID: 36712156 PMCID: PMC9707983 DOI: 10.1093/ehjdh/ztac041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Aims

Detection of asymptomatic paroxysmal atrial fibrillation is challenging. Smartphone- or smartwatch-based photoplethysmography is efficient at detecting irregular rhythms using pulse waves but is too complex for older patients. We aimed to evaluate the detection accuracy of atrial fibrillation by a wristwatch-type continuous pulse wave monitor (PWM) in daily life.

Methods and results

Patients at high risk of atrial fibrillation but with no history of atrial fibrillation (n = 163; mean CHADS₂ score, 1.9) and patients with known atrial fibrillation (n = 123, including 34 with persistent atrial fibrillation) underwent PWM and telemetry electrocardiogram recording for 3 days. Risk of atrial fibrillation was judged using the 'Kyorin Atrial Fibrillation Risk Score', a scoring system based on previously reported atrial fibrillation risk scoring systems. The PWM assessed the presence of atrial fibrillation at 30 min intervals, and the results were compared with the telemetry electrocardiogram findings. The PWMs accurately diagnosed two patients with paroxysmal atrial fibrillation in the high-risk group. The PWMs accurately diagnosed 48 of the 55 patients with atrial fibrillation in the known-atrial fibrillation group. The PWM accuracy in detecting patients with atrial fibrillation was as follows: sensitivity, 98.0%; specificity, 90.6%; positive predictive value, 69.4%; negative predictive value, 99.5%. The respective values for intervals with atrial fibrillation were 86.9%, 98.8%, 89.6%, and 98.5%.

Conclusion

The wristwatch-type PWM has shown feasibility in detecting atrial fibrillation in daily life and showed the possibility of being used as a screening tool.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Trial Registration

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

Is machine learning the future for atrial fibrillation screening?

Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.

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.

The Potential and Limitations of Mobile Health and Insertable Cardiac Monitors in the Detection of Atrial Fibrillation in Cryptogenic Stroke Patients: Preliminary Results From the REMOTE Trial

Aim

This paper presents the preliminary results from the ongoing REMOTE trial. It aims to explore the opportunities and hurdles of using insertable cardiac monitors (ICMs) and photoplethysmography-based mobile health (PPG-based mHealth) using a smartphone or smartwatch to detect atrial fibrillation (AF) in cryptogenic stroke and transient ischemic attack (TIA) patients.

Methods and Results

Cryptogenic stroke or TIA patients (n = 39) received an ICM to search for AF and were asked to use a blinded PPG-based mHealth application for 6 months simultaneously. They were randomized to smartphone or smartwatch monitoring. In total, 68,748 1-min recordings were performed using PPG-based mHealth. The number of mHealth recordings decreased significantly over time in both smartphone and smartwatch groups (p < 0.001 and p = 0.002, respectively). Insufficient signal quality was more frequently observed in smartwatch (43.3%) compared to smartphone recordings (17.8%, p < 0.001). However, when looking at the labeling of the mHealth recordings on a patient level, there was no significant difference in signal quality between both groups. Moreover, the use of a smartwatch resulted in significantly more 12-h periods (91.4%) that were clinically useful compared to smartphone users (84.8%) as they had at least one recording of sufficient signal quality. Simultaneously, continuous data was collected from the ICMs, resulting in approximately 6,660,000 min of data (i.e., almost a 100-fold increase compared to mHealth). The ICM algorithm detected AF and other cardiac arrhythmias in 10 and 19 patients, respectively. However, these were only confirmed after adjudication by the remote monitoring team in 1 (10%) and 5 (26.3%) patients, respectively. The confirmed AF was also detected by PPG-based mHealth.

Conclusion

Based on the preliminary observations, our paper illustrates the potential as well as the limitations of PPG-based mHealth and ICMs to detect AF in cryptogenic stroke and TIA patients in four elements: (i) mHealth was able to detect AF in a patient in which AF was confirmed on the ICM; (ii) Even state-of-the-art ICMs yielded many false-positive AF registrations; (iii) Both mHealth and ICM still require physician revision; and (iv) Blinding of the mHealth results impairs compliance and motivation.

Premature Beats Rejection Strategy on Paroxysmal Atrial Fibrillation Detection

Paroxysmal atrial fibrillation (PAF) may related to the risk of thromboembolism and is the most common cardiac risk factor of cryptogenic stroke (CS). Due to its paroxysmal characteristics, it is usually diagnosed by continuous long-term ECG. Patients with paroxysmal atrial fibrillation usually have premature beats at the same time which is easy to be confused with the rhythm of atrial fibrillation. Therefore, in this article, we designed a screening algorithm for single premature beat, multi premature beats, bigeminy and trigeminy premature beats, according to their rhythm characteristics to reduce false detection caused by premature beats during the PAF detection process. The proposed elimination method was verified on ECG segments with different types of premature beats, and tested on long-term ECG data of PAF patients. ECG segments of different kinds of premature beats were selected from MIT Atrial Fibrillation database (MIT-AFDB), MIT-BIH Arrhythmia database (MIT-AR) and wearable ECG data from the China Physiological Signal Challenge 2021 (CPSC 2021). The proposed method can effectively eliminate single premature beat segments with 99.5% accuracy, and it also can eliminate more than 95% of ECG segments with other types of premature beats. We designed PAF-score as a new index to evaluate the accuracy of detection, and we also calculate the misjudged and missed segments to comprehensively evaluate the PAF detection algorithm. The proposed method get a PAF-score of 0.912 on MIT-AFDB. The proposed method also has the potential to implant low computing power wearable devices for real-time analysis.

Analyzability of Photoplethysmographic Smartwatch Data by the Preventicus Heartbeats Algorithm During Everyday Life: Feasibility Study

BACKGROUND

Continuous heart rate monitoring via mobile health technologies based on photoplethysmography (PPG) has great potential for the early detection of sustained cardiac arrhythmias such as atrial fibrillation. However, PPG measurements are impaired by motion artifacts.

OBJECTIVE

The aim of this investigation was to evaluate the analyzability of smartwatch-derived PPG data during everyday life and to determine the relationship between the analyzability of the data and the activity level of the participant.

METHODS

A total of 41 (19 female and 22 male) adults in good cardiovascular health (aged 19-79 years) continuously wore a smartwatch equipped with a PPG sensor and a 3D accelerometer (Cardio Watch 287, Corsano Health BV) for a period of 24 hours that represented their individual daily routine. For each participant, smartwatch data were analyzed on a 1-minute basis by an algorithm designed for heart rhythm analysis (Preventicus Heartbeats, Preventicus GmbH). As outcomes, the percentage of analyzable data (PAD) and the mean acceleration (ACC) were calculated. To map changes of the ACC and PAD over the course of one day, the 24-hour period was divided into 8 subintervals comprising 3 hours each.

RESULTS

Univariate analysis of variance showed a large effect (η_p²> 0.6; P<.001) of time interval (phase) on the ACC and PAD. The PAD ranged between 34% and 100%, with an average of 71.5% for the whole day, which is equivalent to a period of 17.2 hours. Between midnight and 6 AM, the mean values were the highest for the PAD (>94%) and the lowest for the ACC (<6×10^-3 m/s²). Regardless of the time of the day, the correlation between the PAD and ACC was strong (r=-0.64). A linear regression analysis for the averaged data resulted in an almost perfect coefficient of determination (r²=0.99).

CONCLUSIONS

This study showed a large relationship between the activity level and the analyzability of smartwatch-derived PPG data. Given the high yield of analyzable data during the nighttime, continuous arrhythmia screening seems particularly effective during sleep phases.

Remote Design of a Smartphone and Wearable Detected Atrial Arrhythmia in Older Adults Case Finding Study: Smart in OAC – AFNET 9

Introduction

Screening for atrial fibrillation and timely initiation of oral anticoagulation, rhythm management, and treatment of concomitant cardiovascular conditions can improve outcomes in high-risk populations. Whether wearables can facilitate screening in older adults is not known.

Methods and Analyses

The multicenter, international, investigator-initiated, single-arm case-finding Smartphone and wearable detected atrial arrhythmia in older adults case finding study (Smart in OAC – AFNET 9) evaluates the diagnostic yield of a validated, cloud-based analysis algorithm detecting atrial arrhythmias via a signal acquired by a smartphone-coupled wristband monitoring system in older adults. Unselected participants aged ≥65 years without known atrial fibrillation and not receiving oral anticoagulation are enrolled in three European countries. Participants undergo continuous pulse monitoring using a wristband with a photo plethysmography (PPG) sensor and a telecare analytic service. Participants with PPG-detected atrial arrhythmias will be offered ECG loop monitoring. The study has a virtual design with digital consent and teleconsultations, whilst including hybrid solutions. Primary outcome is the proportion of older adults with newly detected atrial arrhythmias (NCT04579159).

Discussion

Smart in OAC – AFNET 9 will provide information on wearable-based screening for PPG-detected atrial arrhythmias in Europe and provide an estimate of the prevalence of atrial arrhythmias in an unselected population of older adults.

[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.

Cost-effectiveness of a photopethysmographic procedure for screening for atrial fibrillation in 6 European countries

BACKGROUND

Strokes cause an estimated annual health care burden of 170 billion euros across Europe. Atrial fibrillation is one of the major risk factors for stroke and increases the individual risk 4.2-fold. But prevention with anticoagulants may reduce this risk by 70%. Screening methods are employed to detect previously undetected atrial fibrillation. Screening studies in various European countries show a high degree of undetected atrial fibrillation. This study aims to assess the cost-effectiveness of systematic screening with a smartphone application, named Preventicus Heartbeats. It is a hands-on screening tool for use on smartphone to diagnose AF with high sensitivity and specificity.

METHODS

A previously published model for calculating screening cost-effectiveness was extended to 6 European countries covering a wide range in terms of treatment costs and epidemiologic parameters.

RESULTS

The use of screening lowers the cost per case in countries with comparatively high levels of health care costs (Switzerland: -€75; UK: -€7). Moderate higher costs per case were observed in 4 countries (Greece: €6; Netherlands: €15). Low levels of health care costs result in less or no potential for further cost reduction (Poland: €20; Serbia: €33). In all countries considered, the model showed an increase in effectiveness measures both in the number of strokes avoided and the quality adjusted life years. The number of strokes avoided per 1000 participants ranged from 2.52 (Switzerland) to 4.44 (Poland). Quality-adjusted life-years per case gained from screening ranged from 0.0105 (Switzerland) to 0.0187 (Poland). The screening procedure dominated in two countries (Switzerland, UK). For the remaining countries, the incremental cost effectiveness ratio ranged from €489/QALY (Greece) to €2548/QALY (Serbia).

CONCLUSION

The model results showed a strong dependence of the results on the country-specific costs for stroke treatment. The use of the investigated screening method is close to cost-neutral or cost-reducing in the Western European countries and Greece. In countries with low price levels, higher cost increases due to AF screening are to be expected. Lower costs of anticoagulation, which are expected due to the upcoming patent expiry of direct anticoagulants, have a positive effect on the cost result.

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

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

A Review on Atrial Fibrillation (Computer Simulation and Clinical Perspectives)

Atrial fibrillation (AF), a heart condition, has been a well-researched topic for the past few decades. This multidisciplinary field of study deals with signal processing, finite element analysis, mathematical modeling, optimization, and clinical procedure. This article is focused on a comprehensive review of journal articles published in the field of AF. Topics from the age-old fundamental concepts to specialized modern techniques involved in today’s AF research are discussed. It was found that a lot of research articles have already been published in modeling and simulation of AF. In comparison to that, the diagnosis and post-operative procedures for AF patients have not yet been totally understood or explored by the researchers. The simulation and modeling of AF have been investigated by many researchers in this field. Cellular model, tissue model, and geometric model among others have been used to simulate AF. Due to a very complex nature, the causes of AF have not been fully perceived to date, but the simulated results are validated with real-life patient data. Many algorithms have been proposed to detect the source of AF in human atria. There are many ablation strategies for AF patients, but the search for more efficient ablation strategies is still going on. AF management for patients with different stages of AF has been discussed in the literature as well but is somehow limited mostly to the patients with persistent AF. The authors hope that this study helps to find existing research gaps in the analysis and the diagnosis of AF.

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

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

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Mobile Health for Arrhythmia Diagnosis and Management

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

A novel contact-free atrial fibrillation monitor: a pilot study

Aims

Atrial fibrillation (AF) is a major cause of morbidity and mortality. Current guidelines support performing electrocardiogram (ECG) screenings to spot AF in high-risk patients. The purpose of this study was to validate a new algorithm aimed to identify AF in patients measured with a recent FDA-cleared contact-free optical device.

Methods and results

Study participants were measured simultaneously using two devices: a contact-free optical system that measures chest motion vibrations (investigational device, 'Gili') and a standard reference bed-side ECG monitor (Mindray^®). Each reference ECG was evaluated by two board certified cardiologists that defined each trace as: regular rhythm, AF, other irregular rhythm or indecipherable/missing. A total of 3582, 30-s intervals, pertaining to 444 patients (41.9% with a history of AF) were made available for analysis. Distribution of patients with active AF, other irregular rhythm, and regular rhythm was 16.9%, 29.5%, and 53.6% respectively. Following application of cross-validated machine learning approach, the observed sensitivity and specificity were 0.92 [95% confidence interval (CI): 0.91-0.93] and 0.96 (95% CI: 0.95-0.96), respectively.

Conclusion

This study demonstrates for the first time the efficacy of a contact-free optical device for detecting AF.

Diagnostic Utility of Smartwatch Technology for Atrial Fibrillation Detection - A Systematic Analysis

Background

Smartphone technologies have been recently developed to assess heart rate and rhythm, but their role in accurately detecting atrial fibrillation (AF) remains unknown.

Objective

We sought to perform a meta-analysis using prospective studies comparing Smartwatch technology with current monitoring standards for AF detection (ECG, Holter, Patch Monitor, ILR).

Methods

We performed a comprehensive literature search for prospective studies comparing Smartwatch technology simultaneously with current monitoring standards (ECG, Holter, and Patch monitor) for AF detection since inception to November 25th, 2019. The outcome studied was the accuracy of AF detection. Accuracy was determined with concomitant usage of ECG monitoring, Holter monitoring, loop recorder, or patch monitoring.

Results

A total of 9 observational studies were included comparing smartwatch technology, 3 using single-lead ECG monitoring, and six studies using photoplethysmography with routine AF monitoring strategies. A total of 1559 patients were enrolled (mean age 63.5 years, 39.5% had an AF history). The mean monitoring time was 75.6 days. Smartwatch was non-inferior to composite ECG monitoring strategies (OR 1.06, 95% CI 0.93 - 1.21, p=0.37), composite 12 lead ECG/Holter monitoring (OR 0.90, 95% CI 0.62 - 1.30, p=0.57) and patch monitoring (OR 1.28, 95% CI 0.84 - 1.94, p=0.24) for AF detection. The sensitivity and specificity for AF detection using a smartwatch was 95% and 94%, respectively.

Conclusions

Smartwatch based single-lead ECG and photoplethysmography appear to be reasonable alternatives for AF monitoring.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Rationale and design of a digital trial using smartphones to detect subclinical atrial fibrillation in a population at risk: The eHealth-based bavarian alternative detection of Atrial Fibrillation (eBRAVE-AF) trial

Current guidelines recommend opportunistic screening for subclinical atrial fibrillation (AF) taking advantage of e-health-based technologies. However, the efficacy of a fully scalable e-health-based strategy for AF detection in a head-to-head comparison with routine symptom-based screening is unknown. eBRAVE-AF is an investigator-initiated, digital, prospective, randomized, siteless, open-label, cross-over study to evaluate an e-health-based strategy for detection of AF in a real-world setting. 67,488 policyholders of a large German health insurance company (Versicherungskammer Bayern, Germany) selected by age ≥ 50 years and a CHA₂DS₂-VASc score ≥ 1 (females ≥2) are invited to participate. Subjects with known AF or on treatment with oral anticoagulation are excluded. After obtaining electronic informed consent, at least 4,400 participants will be randomly assigned to an e-health-based screening strategy or routine symptom-based screening. The e-health-based strategy consists of repetitive one-minute photoplethysmographic (PPG) pulse wave assessments using a certified smartphone app (Preventicus Heartbeats, Preventicus, Jena, Germany), followed by a confirmatory 14-day ECG patch (CardioMem CM 100 XT, Getemed, Teltow, Germany) in case of abnormal findings. After 6 months, participants are crossed over to the other study arm. Primary endpoint is the incidence of newly diagnosed AF leading to oral anticoagulation indicated by an independent physician. Clinical follow-up will be at least 12 months. In both groups, follow-up is performed by 4-week app-based questionnaires, personal contact in case of abnormal findings, and matching with claim-based insurance data and medical reports. At time of writing enrollment is completed. First results are expected to be available in mid-2021.

Body composition analysis in patients with acute heart failure: the Scale Heart Failure trial

Aims

In this study, we aimed to investigate whether body composition analysis (BCA) derived from bioelectrical impedance vector analysis (BIVA) could be used to monitor the hydration status of patients with acute heart failure (AHF) during intensified diuretic therapy.

Methods and results

This observational, single‐centre study involved a novel, validated eight‐electrode segmental body composition analyser to perform BCA derived from BIVA with an alternating current of 100 μA at frequencies of 5, 7.5, 50, and 75 kHz. The BCA‐derived and BIVA‐derived parameters were estimated and compared with daily body weight measurements in hospitalized patients with AHF. A total of 867 BCA and BIVA assessments were conducted in 142 patients (56.3% men; age 76.8 ± 10.7 years). Daily changes in total body water (TBW) and extracellular water (ECW) were significantly associated with changes in body weight in 62.2% and 89.1% of all measurements, respectively (range, ±1 kg). Repeated measures correlation coefficients between weight loss and TBW loss resulted with rho 0.43, P < 0.01, confidence interval (CI) [0.36, 0.50] and rho 0.71, P > 0.01, CI [0.67, 0.75] for ECW loss. Between the first and last assessments, the mean weight loss was −2.5 kg, compared with the −2.6 L mean TBW loss and −1.7 L mean ECW loss. BIVA revealed an increase in mean Resistance R and mean Reactance X_c across all frequencies, with the subsequent reduction in body fluid (including corresponding body weight) between the first and last assessments.

Conclusions

Body composition analysis derived from BIVA with a focus on ECW is a promising approach to detect changes in hydration status in patients undergoing intensified diuretic therapy. Defining personalized BIVA reference values using bioelectrical impedance devices is a promising approach to monitor hydration status.

Advances in telemedicine for the management of the elderly cardiac patient

Telemedicine is the use of information and communication technology to deliver healthcare at a distance. It has been resorted to during the COVID-19 pandemic to lessen the need for in-person patient care decreasing the risk of transmission, and it can be of benefit afterward in the management of cardiac disease. The elderly population has unique challenges concerning the use of telehealth technologies. We thus review the advances in telemedicine technologies in treating elderly cardiac patients including in our discussion only studies with a mean age of participants above 60. Remote monitoring of blood pressure, weight, and symptoms, along with home ECG recording has been found to be superior to usual in-clinic follow up. Combining remote monitoring with video conferencing with physicians, patient education websites, and applications is also of benefit. Remote monitoring of Implantable Cardioverter Defibrillators (ICD) and Cardiac Resynchronization Therapy Defibrillators (CRT-D) is also beneficial but can be at the cost of an increase in both appropriate and inappropriate interventions. Implantable sensing devices compatible with remote monitoring have been developed and have been shown to improve care and cost-effectiveness. New smartphone software can detect arrhythmias using home ECG recordings and can detect atrial fibrillation using smartphone cameras. Remote monitoring of implanted pacemakers has shown non-inferiority to in clinic follow up. On the other hand, small-scale questionnaire-based studies demonstrated the willingness of the elderly cardiac patients to use such technologies, and their satisfaction with their use and ease of use. Large-scale studies should further investigate useability in samples more representative of the general elderly population with more diverse socioeconomic and educational backgrounds. Accordingly, it seems that studying integrating multiple technologies into telehealth programs is of great value. Further efforts should also be put in validating the technologies for specific diseases along with the legal and reimbursement aspects of the use of telehealth.

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

Aim

We aimed to systematically review the available literature on mobile Health (mHealth) solutions, including handheld and wearable devices, implantable loop recorders (ILRs), as well as mobile platforms and support systems in atrial fibrillation (AF) detection and management.

Methods

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The electronic databases PubMed (NCBI), Embase (Ovid), and Cochrane were searched for articles published until 10 February 2021, inclusive. Given that the included studies varied widely in their design, interventions, comparators, and outcomes, no synthesis was undertaken, and we undertook a narrative review.

Results

We found 208 studies, which were deemed potentially relevant. Of these studies included, 82, 46, and 49 studies aimed at validating handheld devices, wearables, and ILRs for AF detection and/or management, respectively, while 34 studies assessed mobile platforms/support systems. The diagnostic accuracy of mHealth solutions differs with respect to the type (handheld devices vs wearables vs ILRs) and technology used (electrocardiography vs photoplethysmography), as well as application setting (intermittent vs continuous, spot vs longitudinal assessment), and study population.

Conclusion

While the use of mHealth solutions in the detection and management of AF is becoming increasingly popular, its clinical implications merit further investigation and several barriers to widespread mHealth adaption in healthcare systems need to be overcome.

Graphic abstract

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

Supplementary Information

The online version contains supplementary material available at 10.1007/s00392-021-01941-9.

[Interpretation of photoplethysmography: a step-by-step guide]

By applying photoplethysmography (PPG), the camera of the mobile phone can be used to remotely assess heart rate and rhythm, which was widely used in conjunction with teleconsultations within the TeleCheck-AF project during the coronavirus disease 2019 (COVID-19) pandemic. Herein, we provide an educational, structured, stepwise practical guide on how to interpret PPG signals. A better understanding of PPG recordings is critical for the implementation of this widely available technology into clinical practice.

A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations

Abstract

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care.

Lay summary

Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12265-021-10151-7.

Enhancing Healthcare Access-Smartphone Apps in Arrhythmia Screening: Viewpoint

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

Next-Generation Digital Biomarkers for Tuberculosis and Antibiotic Stewardship: Perspective on Novel Molecular Digital Biomarkers in Sweat, Saliva, and Exhaled Breath

The internet of health care things enables a remote connection between health care professionals and patients wearing smart biosensors. Wearable smart devices are potentially affordable, sensitive, specific, user-friendly, rapid, robust, lab-independent, and deliverable to the end user for point-of-care testing. The datasets derived from these devices are known as digital biomarkers. They represent a novel patient-centered approach to collecting longitudinal, context-derived health insights. Adding automated, analytical smartphone applications will enable their use in high-, middle-, and low-income countries. So far, digital biomarkers have been focused primarily on accelerometer data and heart rate due to well-established sensors originating from the consumer market. Novel emerging smart biosensors will detect biomarkers (or compounds) independent of a lab and noninvasively in sweat, saliva, and exhaled breath. These molecular digital biomarkers are a promising novel approach to reduce the burden from 2 major infectious diseases with urgent unmet needs: tuberculosis and infections with multidrug resistant pathogens. Active tuberculosis (aTbc) is one of the deadliest diseases from an infectious agent. However, a simple and reliable test for its detection is still missing. Furthermore, inappropriate antimicrobial use leads to the development of antimicrobial resistance, which is associated with high mortality and health care costs. From this perspective, we discuss the innovative approach of a noninvasive and lab-independent collection of novel biomarkers to detect aTbc, which at the same time may additionally serve as a scalable therapeutic drug monitoring approach for antibiotics. These molecular digital biomarkers are next-generation digital biomarkers and have the potential to shape the future of infectious diseases.

Current perspectives on wearable rhythm recordings for clinical decision-making: the wEHRAbles 2 survey

Novel wearable devices for heart rhythm analysis using either photoplethysmography (PPG) or electrocardiogram (ECG) are in daily clinical practice. This survey aimed to assess impact of these technologies on physicians' clinical decision-making and to define, how data from these devices should be presented and integrated into clinical practice. The online survey included 22 questions, focusing on the diagnosis of atrial fibrillation (AF) based on wearable rhythm device recordings, suitable indications for wearable rhythm devices, data presentation and processing, reimbursement, and future perspectives. A total of 539 respondents {median age 38 [interquartile range (IQR) 34-46] years, 29% female} from 51 countries world-wide completed the survey. Whilst most respondents would diagnose AF (83%), fewer would initiate oral anticoagulation therapy based on a single-lead ECG tracing. Significantly fewer still (27%) would make the diagnosis based on PPG-based tracing. Wearable ECG technology is acceptable for the majority of respondents for screening, diagnostics, monitoring, and follow-up of arrhythmia patients, while respondents were more reluctant to use PPG technology for these indications. Most respondents (74%) would advocate systematic screening for AF using wearable rhythm devices, starting at patients' median age of 60 (IQR 50-65) years. Thirty-six percent of respondents stated that there is no reimbursement for diagnostics involving wearable rhythm devices in their countries. Most respondents (56.4%) believe that costs of wearable rhythm devices should be shared between patients and insurances. Wearable single- or multiple-lead ECG technology is accepted for multiple indications in current clinical practice and triggers AF diagnosis and treatment. The unmet needs that call for action are reimbursement plans and integration of wearable rhythm device data into patient's files and hospital information systems.

Mobile health applications for the detection of atrial fibrillation: a systematic review

AIMS

Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF.

METHODS AND RESULTS

We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients.

CONCLUSION

Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.

Feasibility of digital atrial fibrillation screening in an elderly population : The Ulm heart rhythm weeks

AIMS

Atrial fibrillation (AF) screening in risk populations has the potential to prevent strokes. The authors tested the feasibility of a digital program with initial photoplethysmographic (PPG) self-screening and cardiologist-attended electrocardiographic (ECG) confirmation of screen-positive cases.

METHODS

Inhabitants of the city of Ulm aged ≥ 65 years were invited to participate. After digital consent, participants were given access to a smartphone application for 14 days of self-screening (two recordings per day recommended). Screen-positive participants without known AF were invited to present to a cardiologist for AF confirmation with a 14-day ECG event recorder. PPG recordings were first analyzed by algorithm using a combination of linear and non-linear methods. The quality of pathological (classified by algorithm) PPG and all ECG recordings were checked by a telecare service. Primary outcomes included adherence to the screening protocol defined as the proportion of participants performing at least 14 PPG recordings (or until documentation of absolute arrhythmia) and the proportion of pathological PPG and all ECG recordings rejected by the telecare center.

RESULTS

A total of 215 participants registered. Of these, 204 (95%) performed at least one recording and 169 (79%) reached the performance target of two sufficient measurements per day; 75 PPG recordings were automatically classified as pathological by algorithm; 14 (19%) were rejected by the telecare service due to poor quality. Of the 12 participants with a suspected first diagnosis of AF, five visited a cardiologist as part of the study. Of 1090 ECG recordings obtained, 390 (36%) were qualified as non-diagnostic. AF was confirmed in three cases.

CONCLUSIONS

A digital AF screening program with initial self-screening and referral of screen-positive cases to a cardiologist-attended ECG-confirmation service is feasible with meaningful results in an elderly risk population. However, the availability of the target population of persons > 65 years of age for such a digital screening program appears to be limited despite extensive public relations activities.

The RITHMI study: diagnostic ability of a heart rhythm monitor for automatic detection of atrial fibrillation

INTRODUCTION AND OBJECTIVES

Early detection of atrial fibrillation (AF) is a priority to reduce embolic events by initiating oral anticoagulation therapy. The aim of this study was to evaluate the diagnostic ability of a wrist device designed for automatic AF detection.

METHODS

RITHMI is a prospective, comparative, observational study that included 167 patients referred to a cardiology outpatient clinic for a general consultation or for electrical cardioversion. The study evaluated the ability of a wrist monitor that uses a photoplethysmography (PPG) signal and an electrocardiographic lead to automatically detect AF compared with diagnosis established by 2 cardiologists using the 12-lead electrocardiogram.

RESULTS

The AF detection algorithm based on the PPG signal had a sensitivity of 91% and a specificity of 96% (diagnostic accuracy: 93%). The automatic algorithm based on the electrocardiographic signal had a sensitivity of 94% and a specificity of 96% (diagnostic accuracy: 95%). The 2 algorithms concurred in the diagnosis in 96% of the cases. Overall, the monitor had a sensitivity and specificity of 95% (diagnostic accuracy: 95% and Kappa index: 0.98).

CONCLUSIONS

This study shows that automatic AF detection through the use of a heart rhythm monitor incorporating sensors and algorithms that analyze the PPG signal and the electrocardiographic signal corresponding to lead I is feasible and has high diagnostic accuracy.