Mobile Phone-Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

[MVP Risk score and new atrial fibrillation diagnosis: Prospective cohort PREFATE study]

OBJECTIVE

To assess the association between electrocardiogram (ECG) patterns according to the MVP ECG risk score (morphology-voltage-P-wave duration) and a diagnosis of Atrial Fibrillation (AF).

DESIGN

Prospective observational cohort study (1/01/2023-31/12/2024). SITE: Primary care.

PARTICIPANTS

Randomized sample of 150 patients aged 65-85 years without prior diagnosis of AF, stroke, or current anticoagulant treatment; high risk of future AF; CHA2DS2-VASc ≥2; and ability to use the FibricheckR application (App).

MEASUREMENTS

At baseline, a standard ECG, MVP risk score assessment, and cardiac rhythm monitoring for 15 days using the FibricheckR App were performed. The dependent variables were the presence of P-wave patterns on the electrocardiogram according to MVP risk score and a new diagnosis of AF.

RESULTS

The diagnosis of AF was confirmed in 14 cases (9.3%, 95% CI 5.6-15.1), 3 men and 11 women. In 3 cases, the arrhythmia was diagnosed on the baseline ECG, and in 11 cases by Holter after being reported as possible AF by the FibricheckR App. A higher prevalence of atypical advanced interatrial block (A-AIB) (p 0.007) was detected among participants with AF, as well as the prevalence of P-wave <0.1mV. (p=0.006). All new diagnoses of AF were made at scores ≥4 in the MVP risk score.

CONCLUSIONS

Using scales for identifying ECG patterns in high-risk subjects in primary care can facilitate the diagnosis of unknown AF.

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.

Approaches of wearable and implantable biosensor towards of developing in precision medicine

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

Remote rhythm monitoring using a photoplethysmography smartphone application after cardioversion for atrial fibrillation

Aims

Direct current cardioversion (DCCV) is a commonly utilized rhythm control technique for atrial fibrillation. Follow-up typically comprises a hospital visit for 12-lead electrocardiogram (ECG) two weeks post-DCCV. We report the feasibility, costs, and environmental benefit of remote photoplethysmography (PPG) monitoring as an alternative.

Methods and results

We retrospectively analysed DCCV cases at our centre from May 2020 to October 2022. Patients were stratified into those with remote PPG follow-up and those with traditional 12-lead ECG follow-up. Monitoring type was decided by the specialist nurse performing the DCCV at the time of the procedure after discussing with the patient and offering them both options if appropriate. Outcomes included the proportion of patients who underwent PPG monitoring, patient compliance and experience, and cost, travel, and environmental impact. Four hundred sixteen patients underwent 461 acutely successful DCCV procedures. Two hundred forty-six underwent PPG follow-up whilst 214 underwent ECG follow-up. Patient compliance was high (PPG 89.4% vs. ECG 89.8%; P > 0.999) and the majority of PPG users (90%) found the app easy to use. Sinus rhythm was maintained in 71.1% (PPG) and 64.7% (ECG) of patients (P = 0.161). Twenty-nine (11.8%) PPG patients subsequently required an ECG either due to non-compliance, technical failure, or inconclusive PPG readings. Despite this, mean healthcare costs (£47.91 vs. £135 per patient; P < 0.001) and median cost to the patient (£0 vs. £5.97; P < 0.001) were lower with PPG. Median travel time per patient (0 vs. 44 min; P < 0.001) and CO2 emissions (0 vs. 3.59 kg; P < 0.001) were also lower with PPG. No safety issues were identified.

Conclusion

Remote PPG monitoring is a viable method of assessing for arrhythmia recurrence post-DCCV. This approach may save patients significant travel time, reduce environmental CO2 emission, and be cost saving in a publicly-funded healthcare system.

[Characterization of atrial fibrillation burden using wearables]

The characterization of atrial fibrillation (AF) according to current guidelines categorically refers to the differentiation between paroxysmal, persistent, and permanent AF. A more precise characterization of AF, including the evaluation of AF burden, is playing an increasingly significant role in both scientific research and clinical practice. Digital devices, especially those with the capability of passive (semi-)continuous recording, can contribute to a more accurate quantification of AF burden. Particularly in patients with an already established diagnosis of AF, the evaluation of AF burden can be used to monitor the success of antiarrhythmic therapy including antiarrhythmic drugs or pulmonary vein isolation. However, important questions remain unanswered: In addition to a uniform, evidence-based definition of AF burden, clinically relevant cut-offs for AF burden and resulting therapeutic consequences (e.g., subclinical AF) need to be elaborated. Furthermore, the establishment and evaluation of care structures for assessing and integrating AF burden in clinical care, especially by incorporating data from wearable medical devices, should take place.

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.

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.

Insights from a single centre implementation of a digitally-enabled atrial fibrillation virtual ward

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and poses a significant public health burden. Virtual wards are a novel approach utilising digital solutions to provide hospital-level care remotely; their rollout has become a key priority for the UK National Health Service to expand acute care capacity. We devised and implemented a digitally-enabled AF virtual ward to monitor patients being established onto medical therapy following an AF diagnosis or an AF-related hospitalisation. Patients were onboarded either as outpatients to avoid admission or on discharge after an acute AF hospitalisation. Remote monitoring was undertaken using a clinically validated photoplethysmography-based smartphone app. Over a 1-2 week period, patients performed twice daily measurements of heart rate and rhythm and provided corresponding symptoms. A traffic light system guided frequency of telephone assessments by specialist practitioners. Red flag symptoms or abnormal heart rate parameters prompted an urgent care escalation. We report our experience of the first 73 patients onboarded to the AF virtual ward from October 2022 to June 2023 (mean age 65 years, median 68 years, IQR range 27-101 years; 33 females). Thirty-nine (53%) patients had red flag features requiring care escalation, of whom 9 (23%) were advised to attend ED (emergency department) for urgent assessment, 10 (26%) attended for expedited review and 14 (36%) required medication changes. By 3 months post-monitoring, only 3 patients (4%) had re-attended ED with an arrhythmia-related presentation. Virtual ward patients had an average 3-day shorter inpatient stay (mean duration 4 days) compared with AF patients hospitalised prior to virtual ward implementation (mean duration 7 days). Overall, 22 arrhythmia-related readmissions were prevented via the virtual ward model. In this study, we present a novel implementation of a digitally-enabled virtual ward for the acute management of patients with newly diagnosed or poorly controlled AF. Our pilot data indicate that this model is feasible and is potentially cost-effective. Further longitudinal study is needed to definitively evaluate long-term clinical utility and safety.

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

Aims

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

Methods and results

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

Conclusion

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

The Utility of a Novel Electrocardiogram Patch Using Dry Electrodes Technology for Arrhythmia Detection During Exercise and Prolonged Monitoring: Proof-of-Concept Study

BACKGROUND

Accurate detection of myocardial ischemia and arrhythmias during free-living exercise could play a pivotal role in screening and monitoring for the prevention of exercise-related cardiovascular events in high-risk populations. Although remote electrocardiogram (ECG) solutions are emerging rapidly, existing technology is neither designed nor validated for continuous use during vigorous exercise.

OBJECTIVE

In this proof-of-concept study, we evaluated the usability, signal quality, and accuracy for arrhythmia detection of a single-lead ECG patch platform featuring self-adhesive dry electrode technology in individuals with chronic coronary syndrome. This sensor was evaluated during exercise and for prolonged, continuous monitoring.

METHODS

We recruited a total of 6 consecutive patients with chronic coronary syndrome scheduled for an exercise stress test (EST) as part of routine cardiac follow-up. Traditional 12-lead ECG recording was combined with monitoring with the ECG patch. Following the EST, the participants continuously wore the sensor for 5 days. Intraclass correlation coefficients (ICC) and Wilcoxon signed rank tests were used to assess the utility of detecting arrhythmias with the patch by comparing the evaluations of 2 blinded assessors. Signal quality during EST and prolonged monitoring was evaluated by using a signal quality indicator. Additionally, connection time was calculated for prolonged ECG monitoring. The comfort and usability of the patch were evaluated by a web-based self-assessment questionnaire.

RESULTS

A total of 6 male patients with chronic coronary syndrome (mean age 69.8, SD 6.2 years) completed the study protocol. The patch was worn for a mean of 118.3 (SD 5.6) hours. The level of agreement between the patch and 12-lead ECG was excellent for the detection of premature atrial contractions and premature ventricular contractions during the whole test (ICC=0.998, ICC=1.000). No significant differences in the total number of premature atrial contractions and premature ventricular contractions were detected neither during the entire exercise test (P=.79 and P=.18, respectively) nor during the exercise and recovery stages separately (P=.41, P=.66, P=.18, and P=.66). A total of 1 episode of atrial fibrillation was detected by both methods. Total connection time during recording was between 88% and 100% for all participants. There were no reports of skin irritation, erythema, or pain while wearing the patch.

CONCLUSIONS

This proof-of-concept study showed that this innovative ECG patch based on self-adhesive dry electrode technology can potentially be used for arrhythmia detection during vigorous exercise. The results suggest that the wearable patch is also usable for prolonged continuous ECG monitoring in free-living conditions and can therefore be of potential use in cardiac rehabilitation and tele-monitoring for the prevention of exercise-related cardiovascular events. Future efforts will focus on optimizing signal quality over time and conducting a larger-scale validation study focusing on both arrhythmia and ischemia detection.

Personal recording devices for arrhythmia detection

Persistent cardiac arrhythmias are readily amenable to detection by performing a standard electrocardiogram (ECG), but detection of transient (paroxysmal) arrhythmias has long been a significant cause of frustration to both doctors and patients. Often a significantly symptomatic arrhythmia is experienced by the patient but terminates before an ECG can be recorded to allow diagnosis. Prognostically important treatment is often delayed, and recurrent symptomatic attacks represent a high morbidity in patients' lives and result in a burden on emergency services, who often arrive after the arrhythmia has terminated with no resultant progress in making a diagnosis. Another area of concern has been the presence of asymptomatic, but clinically important, arrhythmias that can go unnoticed by people experiencing them and may result in permanent harm; asymptomatic paroxysmal atrial fibrillation in patients with high CHA 2 DS 2-VASc scores being the most common example. Both these issues are now being importantly addressed by the widespread availability of portable ECG recording devices, which patients can either manually activate themselves or program to automatically detect abnormal arrhythmias. Information on the range of devices available and their strengths and weaknesses is limited. This article aims to provide a helpful overview for patients and doctors advising them.

Telemedicine in Adult Congenital Heart Disease: Usefulness of Digital Health Technology in the Assistance of Critical Patients

The number of adults with congenital heart disease (ACHD) has progressively increased in recent years to surpass that of children. This population growth has produced a new demand for health care. Moreover, the 2019 coronavirus pandemic has caused significant changes and has underlined the need for an overhaul of healthcare delivery. As a result, telemedicine has emerged as a new strategy to support a patient-based model of specialist care. In this review, we would like to highlight the background knowledge and offer an integrated care strategy for the longitudinal assistance of ACHD patients. In particular, the emphasis is on recognizing these patients as a special population with special requirements in order to deliver effective digital healthcare.

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.

Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation

AIMS

Although mobile health tools using photoplethysmography (PPG) technology have been validated for the detection of atrial fibrillation (AF), their utility for heart rate assessment during AF remains unclear. Therefore, we aimed to evaluate the accuracy of continuous PPG-based 1 min mean heart rate assessment during AF.

METHODS AND RESULTS

Persistent AF patients were provided with Holter electrocardiography (ECG) (for ≥24 h) simultaneously with a PPG-equipped smartwatch. Both the PPG-based smartwatch and Holter ECG automatically and continuously monitored patients' heart rate/rhythm. ECG and PPG recordings were synchronized and divided into 1 min segments, from which a PPG-based and an ECG-based average heart rate estimation were extracted. In total, 47 661 simultaneous ECG and PPG 1 min heart rate segments were analysed in 50 patients (34% women, age 73 ± 8 years). The agreement between ECG-determined and PPG-determined 1 min mean heart rate was high [root mean squared error (RMSE): 4.7 bpm]. The 1 min mean heart rate estimated using PPG was accurate within ±10% in 93.7% of the corresponding ECG-derived 1 min mean heart rate segments. PPG-based 1 min mean heart rate estimation was more often accurate during night-time (97%) than day-time (91%, P < 0.001) and during low levels (96%) compared to high levels of motion (92%, P < 0.001). A neural network with a 10 min history of the recording did not further improve the PPG-based 1 min mean heart rate assessment [RMSE: 4.4 (95% confidence interval: 3.5-5.2 bpm)]. Only chronic heart failure was associated with a lower agreement between ECG-derived and PPG-derived 1 min mean heart rates (P = 0.040).

CONCLUSION

During persistent AF, continuous PPG-based 1 min mean heart rate assessment is feasible in 60% of the analysed period and shows high accuracy compared with Holter ECG for heart rates <110 bpm.

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

Telemedicine and digitalised healthcare have recently seen exponential growth, led, in part, by increasing efforts to improve patient flexibility and autonomy, as well as drivers from financial austerity and concerns over climate change. Nephrology is no exception, and daily innovations are underway to provide digitalised alternatives to current models of healthcare provision. Wearable technology already exists commercially, and advances in nanotechnology and miniaturisation mean interest is also garnering clinically. Here, we outline the current existing wearable technology pertaining to the diagnosis and monitoring of patients with a spectrum of kidney disease, give an overview of wearable dialysis technology, and explore wearables that do not yet exist but would be of great interest. Finally, we discuss challenges and potential pitfalls with utilising wearable technology and the factors associated with successful implementation.

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.

Photoplethysmography-Based Smart Devices for Detection of Atrial Fibrillation

Atrial fibrillation is the most commonly experienced type of cardiac arrhythmia and is the most associated with substantial clinical occurrences and expenses. This arrhythmia often occurs in its "silent" asymptomatic form, revealed only after complications such as a stroke or congestive heart failure have transpired. New smart devices confer effective advantages in the detection of this heart arrhythmia, of which photoplethysmography-based smart devices have shown great potential, according to previous research. However, the solution becomes a problem as widespread use and high availability of various applications and smart devices may lead to substantial amounts of false and misleading recordings and information, causing unnecessary anxiety regarding arrhythmic occurrences diagnosed by the devices but not professionally confirmed. Thus, with most of the devices being photoplethysmography based for detection of atrial fibrillation, it is important to research devices studied up to this point to find the best smart device to detect the aforementioned arrhythmias.

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.

Blood-biomarkers and devices for atrial fibrillation screening: Lessons learned from the AFRICAT (Atrial Fibrillation Research In CATalonia) study

Background and objective

AFRICAT is a prospective cohort study intending to develop an atrial fibrillation (AF) screening program through the combination of blood markers, rhythm detection devices, and long-term monitoring in our community. In particular, we aimed to validate the use of NT-proBNP, and identify new blood biomarkers associated with AF. Also, we aimed to compare AF detection using various wearables and long-term Holter monitoring.

Methods

359 subjects aged 65–75 years with hypertension and diabetes were included in two phases: Phase I (n = 100) and Phase II (n = 259). AF diagnosis was performed by baseline 12-lead ECG, 4 weeks of Holter monitoring (NuuboTM), and/or medical history. An aptamer array including 1310 proteins was measured in the blood of 26 patients. Candidates were selected according to p-value, logFC and biological function to be tested in verification and validation phases. Several screening devices were tested and compared: AliveCor, Watch BP, MyDiagnostick and Fibricheck.

Results

AF was present in 34 subjects (9.47%). The aptamer array revealed 41 proteins with differential expression in AF individuals. TIMP-2 and ST-2 were the most promising candidates in the verification analysis, but none of them was further validated. NT-proBNP (log-transformed) (OR = 1.934; p<0.001) was the only independent biomarker to detect AF in the whole cohort. Compared to an ECG, WatchBP had the highest sensitivity (84.6%) and AUC (0.895 [0.780–1]), while MyDiagnostick showed the highest specificity (97.10%).

Conclusion

The inclusion and monitoring of a cohort of primary care patients for AF detection, together with the testing of biomarkers and screening devices provided useful lessons about AF screening in our community. An AF screening strategy using rhythm detection devices and short monitoring periods among high-risk patients with high NT-proBNP levels could be feasible.

Long-term outcome of a pragmatic trial of multifaceted intervention (STROKE-CARD care) to reduce cardiovascular risk and improve quality-of-life after ischaemic stroke and transient ischaemic attack: study protocol

Background

Patients with ischaemic stroke or transient ischaemic attack (TIA) are at high risk of incident cardiovascular events and recurrent stroke. Despite compelling evidence about the efficacy of secondary prevention, a substantial gap exists between risk factor management in real life and that recommended by international guidelines. We conducted the STROKE-CARD trial (NCT02156778), a multifaceted pragmatic disease management program between 2014 and 2018 with follow-up until 2019. This program successfully reduced cardiovascular risk and improved health-related quality of life and functional outcome in patients with acute ischaemic stroke or TIA within 12 months after the index event. To investigate potential long-term effects of STROKE-CARD care compared to standard care, an extension of follow-up is warranted.

Methods

We aim to include all patients from the STROKE-CARD trial (n = 2149) for long-term follow-up between 2019 and 2021 with the study visit scheduled 3–6 years after the stroke/TIA event. The co-primary endpoint is the composite of major recurrent cardiovascular events (nonfatal stroke, nonfatal myocardial infarction, and vascular death) from hospital discharge until the long-term follow-up visit and health-related quality of life measured with the European Quality of Life-5 Dimensions (EQ-5D-3L) at the final visit. Secondary endpoints include overall mortality, long-term functional outcome, and target-level achievement in risk factor management.

Discussion

This long-term follow-up will provide evidence on whether the pragmatic post-stroke/TIA intervention program STROKE-CARD is capable of preventing recurrent cardiovascular events and improving quality-of-life in the long run.

Trial registration clinicaltrials.gov: NCT04205006 on 19 December 2019.

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

A Watch-Type Electrocardiography Is a Reliable Tool for Detecting Paroxysmal Cardiac Arrhythmias

Background: A substantial proportion of cardiac arrhythmias are paroxysmal in nature, and 12-lead electrocardiography (ECG) and Holter monitoring often fail to detect paroxysmal arrhythmias. We designed and evaluated a watch-type, electrocardiograph-recording, wearable device (w-ECG) to overcome the limitations of 12-lead ECG and Holter monitoring. Methods: We prospectively enrolled 96 patients with symptoms assumed to be related to cardiac arrhythmias. Electrocardiography recording was performed with both the w-ECG and Holter monitoring. Detection of any arrhythmia was the primary outcome endpoint and was compared between the w-ECG and Holter monitoring. Results: Any arrhythmia was detected in 51 (53.1%) and 27 (28.1%) patients by the w-ECG and Holter monitoring, respectively (odds ratio (OR) = 2.9, p < 0.001). The w-ECG was superior to Holter monitoring for the detection of clinically significant arrhythmias (excluding atrial premature contraction, ventricular premature contraction, and non-sustained atrial tachyarrhythmia) (OR = 2.34, p = 0.018). In 27 (28.1%) patients, cardiac arrhythmias were detected only by the w-ECG, with atrial fibrillation being the most frequent case (13 patients). Based on ECGs recorded by using the w-ECG, 17 patients (17.7%) received therapeutic interventions, including radiofrequency catheter ablation. Conclusions: The w-ECG is capable of recording ECGs of good quality, with a discernable P wave and distinguishable QRS morphology. The ability of the w-ECG to detect cardiac arrhythmias was significantly better than that of Holter monitoring, and a significant proportion of patients received therapeutic intervention based on ECGs recorded by the w-ECG.

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.

Samen sterk door persoonsgerichte en digitale zorg

Digitale zorg voor chronische patiënten heeft zich in de huisartsenpraktijk de afgelopen jaren stormachtig ontwikkeld, mede door COVID-19. Past die ontwikkeling wel bij persoonsgerichte zorg? Het NHG-Standpunt E-health voor huisarts en patiënt uit 2015 is positief-kritisch en ook de auteurs van deze beschouwing denken dat het kan. Ze geven nieuwe voorbeelden uit de praktijk die dat bevestigen, maar stippen ook de randvoorwaarden aan: training, toegankelijkheid en verder onderzoek naar de effecten.

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.

Will Smartphone Applications Replace the Insertable Cardiac Monitor in the Detection of Atrial Fibrillation? The First Comparison in a Case Report of a Cryptogenic Stroke Patient

Background and Case

This case report exemplifies the clinical application of non-invasive photoplethysmography (PPG)-based rhythm monitoring in the awakening mobile health (mHealth) era to detect symptomatic and asymptomatic paroxysmal atrial fibrillation (AF) in a cryptogenic stroke patient. Despite extensive diagnostic workup, the etiology remains unknown in one out of three ischemic strokes (i.e., cryptogenic stroke). Prolonged cardiac monitoring can reveal asymptomatic atrial fibrillation in up to one-third of this population. This case report describes a cryptogenic stroke patient who received prolonged cardiac monitoring with an insertable cardiac monitor (ICM) as standard of care. In the context of a clinical study, the patient simultaneously monitored his heart rhythm with a PPG-based smartphone application. AF was detected simultaneously on both the ICM and smartphone application after three days of monitoring. Similar AF burden was detected during follow-up (five episodes, median duration of 28 and 34 h on ICM and mHealth, respectively, p = 0.5). The detection prompted the initiation of oral anticoagulation and AF catheter ablation procedure.

Conclusion

This is the first report of the cryptogenic stroke patient in whom PPG-based mHealth was able to detect occurrence and burden of the symptomatic and asymptomatic paroxysmal AF episodes with similar precision as ICM. It accentuates the potential role of PPG-based mHealth in prolonged cardiac rhythm monitoring in cryptogenic stroke patients.

Photoplethysmogram Analysis and Applications: An Integrative Review

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

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

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

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

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

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.

NICE atrial fibrillation guideline snubs wearable technology: a missed opportunity?

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing public health epidemic. In the UK, over 1.3 million people have a diagnosis of AF and an estimated 400,000 remain undiagnosed. AF-related strokes account for a quarter of all strokes and, as AF episodes are often asymptomatic, are still often the first manifestation of AF. Early diagnosis and initiation of oral anticoagulation, where appropriate, may prevent some of these thromboembolic strokes. Public Health England is committed to decrease the incidence of AF-related strokes and has sponsored initiatives aimed at improving AF detection by promoting the uptake of wearable technologies. However, the National Institute for Health and Care Excellence (NICE) has not recommended wearable technology in their recent AF diagnosis and management guidelines (NG196). Diagnostic accuracy of single-lead electrocardiography (ECG) generated by the latest iteration of wearable devices is excellent and, in many cases, superior to general practitioner interpretation of the 12-lead ECG. High-quality ECG from wearable devices that unequivocally shows AF can expedite AF detection. Otherwise, there is a real risk of delaying AF diagnosis with the potential of devastating consequences for patients and their families.

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.

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.

Accuracy of Physicians Interpreting Photoplethysmography and Electrocardiography Tracings to Detect Atrial Fibrillation: INTERPRET-AF

Aims: This study aims to compare the performance of physicians to detect atrial fibrillation (AF) based on photoplethysmography (PPG), single-lead ECG and 12-lead ECG, and to explore the incremental value of PPG presentation as a tachogram and Poincaré plot, and of algorithm classification for interpretation by physicians. Methods and Results: Email invitations to participate in an online survey were distributed among physicians to analyse almost simultaneously recorded PPG, single-lead ECG and 12-lead ECG traces from 30 patients (10 in sinus rhythm (SR), 10 in SR with ectopic beats and 10 in AF). The task was to classify the readings as 'SR', 'ectopic/missed beats', 'AF', 'flutter' or 'unreadable'. Sixty-five physicians detected or excluded AF based on the raw PPG waveforms with 88.8% sensitivity and 86.3% specificity. Additional presentation of the tachogram plus Poincaré plot significantly increased sensitivity and specificity to 95.5% (P < 0.001) and 92.5% (P < 0.001), respectively. The algorithm information did not further increase the accuracy to detect AF (sensitivity 97.5%, P = 0.556; specificity 95.0%, P = 0.182). Physicians detected AF on single-lead ECG tracings with 91.2% sensitivity and 93.9% specificity. Diagnostic accuracy was also not optimal on full 12-lead ECGs (93.9 and 98.6%, respectively). Notably, there was no significant difference between the performance of PPG waveform plus tachogram and Poincaré, compared to a single-lead ECG to detect or exclude AF (sensitivity P = 0.672; specificity P = 0.536). Conclusion: Physicians can detect AF on a PPG output with equivalent accuracy compared to single-lead ECG, if the PPG waveforms are presented together with a tachogram and Poincaré plot and the quality of the recordings is high.

The photoplethysmography dictionary: practical guidance on signal interpretation and clinical scenarios from TeleCheck-AF

Aims

Within the TeleCheck-AF project, numerous centres in Europe used on-demand photoplethysmography (PPG) technology to remotely assess heart rate and rhythm in conjunction with teleconsultations. Based on the TeleCheck-AF investigator experiences, we aimed to develop an educational structured stepwise practical guide on how to interpret PPG signals and to introduce typical clinical scenarios how on-demand PPG was used.

Methods and results

During an online conference, the structured stepwise practical guide on how to interpret PPG signals was discussed and further refined during an internal review process. We provide the number of respective PPG recordings (FibriCheck®) and number of patients managed within a clinical scenario during the TeleCheck-AF project. To interpret PPG recordings, we introduce a structured stepwise practical guide and provide representative PPG recordings. In the TeleCheck-AF project, 2522 subjects collected 90 616 recordings in total. The majority of these recordings were classified by the PPG algorithm as sinus rhythm (57.6%), followed by AF (23.6%). In 9.7% of recordings, the quality was too low to interpret. The most frequent clinical scenarios where PPG technology was used in the TeleCheck-AF project was a follow-up after AF ablation (1110 patients) followed by heart rate and rhythm assessment around (tele)consultation (966 patients).

Conclusion

We introduce a newly developed structured stepwise practical guide on PPG signal interpretation developed based on presented experiences from TeleCheck-AF. The present clinical scenarios for the use of on-demand PPG technology derived from the TeleCheck-AF project will help to implement PPG technology in the management of AF patients.

[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.

Kardia Mobile and ISTEL HR applicability in clinical practice: a comparison of Kardia Mobile, ISTEL HR, and standard 12-lead electrocardiogram records in 98 consecutive patients of a tertiary cardiovascular care centre

Aims

Mobile, portable ECG-recorders allow the assessment of heart rhythm in out-of-hospital conditions and may prove useful for monitoring patients with cardiovascular diseases. However, the effectiveness of these portable devices has not been tested in everyday practice.

Methods and results

A group of 98 consecutive cardiology patients [62 males (63%), mean age 69 ± 12.9 years] were included in an academic care centre. For each patient, a standard 12-lead electrocardiogram (SE), as well as a Kardia Mobile 6L (KM) and Istel (IS) HR-2000 ECG were performed. Two groups of experienced physicians analysed obtained recordings. After analysing ECG tracings from SE, KM, and IS, quality was marked as good in 82%, 80%, and 72% of patients, respectively (P < 0.001). There were no significant differences between devices in terms of detecting sinus rhythm [SE (60%, n = 59), KM (58%, n = 56), and IS (61%, n = 60); SE vs. KM P = 0.53; SE vs. IS P = 0.76) and atrial fibrillation [SE (22%, n = 22), KM (22%, n = 21), and IS (18%, n = 18); (SE vs. KM P = 0.65; SE vs. IS = 0.1)]. KM had a sensitivity of 88.1% and a specificity of 89.7% for diagnosing sinus rhythm. IS showed 91.5% and 84.6% sensitivity and specificity, respectively. The sensitivity of KM in detecting atrial fibrillation was higher than IS (86.4% vs. 77.3%), but their specificity was comparable (97.4% vs. 98.7%).

Conclusion

Novel, portable devices are useful in showing sinus rhythm and detecting atrial fibrillation in clinical practice. However, ECG measurements concerning conduction and repolarization should be clarified with a standard 12-lead electrocardiogram.

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.

Remote Clinics and Investigations in Arrhythmia Services: What Have We Learnt During Coronavirus Disease 2019?

The coronavirus disease 2019 (COVID-19) pandemic has had a dramatic impact on the way that medical care is delivered. To minimise hospital attendance by both patients and staff, remote clinics, meetings and investigations have been used. Technologies including hand-held ECG monitoring using smartphones, patch ECG monitoring and sending out conventional Holter monitors have aided remote investigations. Platforms such as Google Meet and Zoom have allowed remote multidisciplinary meetings to be delivered effectively. The use of phone consultations has allowed outpatient care to continue despite the pandemic. The COVID-19 pandemic has resulted in a radical, and probably permanent, change in the way that outpatient care is delivered. Previous experience in remote review and the available technologies for monitoring have allowed the majority of outpatient care to be conducted without obviously compromising quality or safety.

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

Background

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

Methods/design

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

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

Discussion

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

Trial registration

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

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.

Adapting a Human Physiology Teaching Laboratory to the At-Home Education Setting

Teaching labs at the undergraduate level poses unique challenges to a school system forced online by COVID-19. We adapted physiology laboratories typically taught in-person to an online-only format, allowing students to measure personal health data alone. Students used available technology and low-cost devices for measuring respiratory and cardiovascular parameters and analyzed the data for differences in testing conditions such as posture and exertion. Students did not physically interact, which encouraged self-directed learning but disallowed peer-to-peer education. Pre-recorded data was utilized for ECG measurements, which streamlined the process but precluded the interactive act of experimentation. The use of low-cost devices empowered and encouraged students to take ownership of their health and form important connections between their own lives and theoretical physiology. Facilitating communication and TA preparedness is key to smoothly running the virtual lab. It will be important for future virtual labs to be designed to facilitate student interaction, include hands-on experimentation, and encourage personal investigation.