The WATCH AF Trial: SmartWATCHes for Detection of Atrial Fibrillation

Smartphone-Based Applications for Atrial Fibrillation Detection: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Background: Atrial fibrillation (AF) burden is strongly associated with an increased risk of stroke, which, in most cases, can be prevented through earlier detection of AF and the timely initiation of anticoagulation therapy. Smartphone devices can provide a simple, non-invasive, cost-effective early AF detection solution. Methods: PubMed, Embase, and Scopus databases were searched for studies comparing smartphone-based photoplethysmography (PPG) with standard electrocardiogram for AF detection. A bivariate random-effects model with a 95% confidence interval (CI) was applied to generate the summary receiver operating characteristic (SROC) curve. Results: Fourteen studies were included, comprising 5,090 patients with an AF prevalence of 31.6%. The pooled sensitivity and specificity were 0.96 (95% CI, 0.93-0.97) and 0.97 (95% CI, 0.95-0.98). The area under the SROC curve was 0.98 (95% CI, 0.94-0.99). The diagnostic odds ratio was 960 (95% CI, 439-2,104), with significant heterogeneity (I2 = 51%). The projected positive and negative predictive values were 66.5% and 99.7%, respectively, in the elderly population aged >65 years and 39.2% and 99.9% in the general population. Conclusion: Smartphone-based PPG demonstrated relatively high sensitivity and specificity and appears capable of ruling out AF. Patients aged >65 are more likely to benefit from AF screening.

Fully Automated Photoplethysmography-Based Wearable Atrial Fibrillation Screening in a Hospital Setting

Background/Objectives: Atrial fibrillation (AF) remains a major risk factor for stroke. It is often asymptomatic and paroxysmal, making it difficult to detect with conventional electrocardiography (ECG). While photoplethysmography (PPG)-based devices like smartwatches have demonstrated efficacy in detecting AF, they are rarely integrated into hospital infrastructure. The study aimed to establish a seamless system for real-time AF screening in hospitalized high-risk patients using a wrist-worn PPG device integrated into a hospital's data infrastructure. Methods: In this investigator-initiated prospective clinical trial conducted at the University Hospital Basel, patients with a CHA2DS2-VASc score ≥ 2 and no history of AF received a wristband equipped with a PPG sensor for continuous monitoring during their hospital stay. The PPG data were automatically transmitted, analyzed, stored, and visualized. Upon detection of an absolute arrhythmia (AA) in the PPG signal, a Holter ECG was administered. Results: The analysis encompassed 346 patients (mean age 72 ± 10 years, 175 females (50.6%), mean CHA2DS2-VASc score 3.5 ± 1.3)). The mean monitoring duration was 4.3 ± 4.4 days. AA in the PPG signal was detected in twelve patients (3.5%, CI: 1.5-5.4%), with most cases identified within 24 h (p = 0.004). There was a 1.3 times higher AA burden during the nighttime compared to daytime (p = 0.03). Compliance was high (304/346, 87.9%). No instances of AF were confirmed in the nine patients undergoing Holter ECG. Conclusions: This study successfully pioneered an automated infrastructure for AF screening in hospitalized patients through the use of wrist-worn PPG devices. This implementation allowed for real-time data visualization and intervention in the form of a Holter ECG. The high compliance and early AA detection achieved in this study underscore the potential and relevance of this novel infrastructure in clinical practice.

Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review

Neurocardiology is an evolving field focusing on the interplay between the nervous system and cardiovascular system that can be used to describe and understand many pathologies. Acute ischemic stroke can be understood through this framework of an interconnected, reciprocal relationship such that ischemic stroke occurs secondary to cardiac pathology (the Heart-Brain axis), and cardiac injury secondary to various neurological disease processes (the Brain-Heart axis). The timely assessment, diagnosis, and subsequent management of cerebrovascular and cardiac diseases is an essential part of bettering patient outcomes and the progression of medicine. Artificial intelligence (AI) and machine learning (ML) are robust areas of research that can aid diagnostic accuracy and clinical decision making to better understand and manage the disease of neurocardiology. In this review, we identify some of the widely utilized and upcoming AI/ML algorithms for some of the most common cardiac sources of stroke, strokes of undetermined etiology, and cardiac disease secondary to stroke. We found numerous highly accurate and efficient AI/ML products that, when integrated, provided improved efficacy for disease prediction, identification, prognosis, and management within the sphere of stroke and neurocardiology. In the focus of cryptogenic strokes, there is promising research elucidating likely underlying cardiac causes and thus, improved treatment options and secondary stroke prevention. While many algorithms still require a larger knowledge base or manual algorithmic training, AI/ML in neurocardiology has the potential to provide more comprehensive healthcare treatment, increase access to equitable healthcare, and improve patient outcomes. Our review shows an evident interest and exciting new frontier for neurocardiology with artificial intelligence and machine learning.

A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring

The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps ('clinical artificial-intelligence operations'), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson's disease. By enabling more frequent and accurate measurements of a patient's health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.

Comparison of diagnostic accuracy of electrocardiogram-based versus photoplethysmography-based smartwatches for atrial fibrillation detection: A Systematic Review and Meta-Analysis

Background

Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, significantly affects morbidity and mortality, making early detection crucial for preventing stroke and heart failure. Recent advancements in wearable technology have introduced smartwatches as potential tools for continuous non-invasive AF detection.

Objective

This systematic review and meta-analysis aimed to evaluate and compare the diagnostic accuracy of electrocardiography (ECG)-and photoplethysmography (PPG)-based smartwatches in detecting AF.

Methodology

A comprehensive search was conducted on PubMed, Google Scholar, and other databases from 18 August to 23 September 2024, to fetch original studies that evaluated performance metrics of ECG and PPG smartwatches in AF detection. The obtained literature was screened according to preset inclusion and exclusion criteria. For included studies, the random-effects model was used to calculate their pooled sensitivity and specificity in AF detection using Jamovi 2.3.28 software. A significance threshold of P <0.05 was applied to all statistical analyses.

Results

Out of the 2564 studies screened, 25 met the inclusion criteria: 11 on PPG and 14 on ECG smartwatches. PPG smartwatches exhibited higher diagnostic performance with a pooled sensitivity of 97.4% (95% CI: 96.5-98.3) and specificity of 96.6% (95% CI: 94.9-98.3). Conversely, ECG smartwatches showed a pooled sensitivity of 83% (95% CI: 78-88) and specificity of 88.4% (95% CI: 84.5-92.2), lower than PPG smartwatches.

Conclusion

PPG-based smartwatches outperformed ECG-based devices in AF detection, offering higher sensitivity and specificity. Even though both modalities are effective in AF detection, the considerable variability in ECG smartwatch performance highlights the need for further research and standardization.

Detection of atrial fibrillation from pulse waves using convolution neural networks and recurrence-based plots

We propose a classification method for distinguishing atrial fibrillation from sinus rhythm in pulse-wave measurements obtained with a blood pressure monitor. This method combines recurrence-based plots with convolutional neural networks. Moreover, we devised a novel plot, with which our classification achieved specificity of 97.5%, sensitivity of 98.4%, and accuracy of 98.6%. These criteria are higher than previously reported results for measurements obtained with blood pressure monitors and are almost equal to statistical measures for methods based on electrocardiographs and photoplethysmographs.

Diagnostic accuracy of ECG smart chest patches versus PPG smartwatches for atrial fibrillation detection: a systematic review and meta-analysis

INTRODUCTION

Atrial fibrillation (AF), the most common form of cardiac arrhythmia, is associated with significant morbidity, mortality, and financial burden. Traditional diagnostic methods, such as 12-lead electrocardiograms (ECG), have limitations in detecting intermittent AF episodes. Consequently, smart wearables have been introduced to enhance continuous AF monitoring. This systematic review and meta-analysis aimed to evaluate and compare the diagnostic accuracy of ECG smart chest patches and photoplethysmography (PPG)- based smartwatches in AF detection.

METHODS

From august 16-20, 2024, a comprehensive search was conducted across PubMed/MEDLINE, DOAJ, AJOL, and the Cochrane Library. Original studies assessing the performance of ECG smart chest patches and PPG smartwatches in detecting AF were included. Studies were screened based on predefined inclusion and exclusion criteria, and the most relevant were finally included. For ECG smart chest patches and PPG smartwatches groups, random-effects model was used to pool these performance metrics. Statistical analyses were performed using Jamovi 2.3.28, with a significance threshold of p < 0.05.

RESULTS

A total of 15 studies were included in the current systematic review and meta-analysis. ECG smart chest patches demonstrated a pooled sensitivity of 96.1% [(95% CI: 91.3-100.8), (I² = 94.59%)], and a pooled specificity of 97.5% [(95% CI: 94.7-100.2), (I² = 79.1%)]. PPG smartwatches showed a pooled sensitivity of 97.4% [(95% CI: 96.5-98.3), (I² = 3.16%)], and a pooled specificity of 96.6% [(95% CI: 94.9-98.3), (I² = 75.94%)]. Comparatively, both ECG smart chest patches and PPG smartwatches exhibited excellent performance in atrial fibrillation detection, with PPG smartwatches showing slightly higher sensitivity and ECG chest patches exhibiting marginally greater specificity.

CONCLUSION

Both ECG smart chest patches and PPG smartwatches are highly effective for detecting atrial fibrillation. However, further advancements are needed to match their accuracy with that of standard diagnostic methods and achieve comprehensive digital cardiac monitoring.

The role of atrial fibrillation in vascular cognitive impairment and dementia: epidemiology, pathophysiology, and preventive strategies

The aging population in Europe faces a substantial burden from dementia, with vascular cognitive impairment and dementia (VCID) being a preventable cause. Atrial fibrillation (AF), a common cardiac arrhythmia, increases the risk of VCID through mechanisms such as thromboembolism, cerebral hypoperfusion, and inflammation. This review explores the epidemiology, pathophysiology, and preventive strategies for AF-related VCID. Epidemiological data indicate that AF prevalence rises with age, affecting up to 12% of individuals over 80. Neuroimaging studies reveal chronic brain changes in AF patients, including strokes, lacunar strokes, white matter hyperintensities (WMHs), and cerebral microbleeds (CMHs), while cognitive assessments show impairments in memory, executive function, and attention. The COVID-19 pandemic has exacerbated the underdiagnosis of AF, leading to an increase in undiagnosed strokes and cognitive impairment. Many elderly individuals did not seek medical care due to fear of exposure, resulting in delayed diagnoses. Additionally, reduced family supervision during the pandemic contributed to missed opportunities for early detection of AF and related complications. Emerging evidence suggests that long COVID may also elevate the risk of AF, further complicating the management of this condition. This review underscores the importance of early detection and comprehensive management of AF to mitigate cognitive decline. Preventive measures, including public awareness campaigns, patient education, and the use of smart devices for early detection, are crucial. Anticoagulation therapy, rate and rhythm control, and addressing comorbid conditions are essential therapeutic strategies. Recognizing and addressing the cardiovascular and cognitive impacts of AF, especially in the context of the COVID-19 pandemic, is essential for advancing public health.

Smart watch applications in atrial fibrillation detection: Current state and future directions

INTRODUCTION

Atrial fibrillation (Afib) is a prevalent chronic arrhythmia associated with severe complications, including stroke, heart failure, and increased mortality. This review explores the use of smartwatches for Afib detection, addressing the limitations of current monitoring methods and emphasizing the potential of wearable technology in revolutionizing healthcare.

RESULTS/OBSERVATION

Current Afib detection methods, such as electrocardiography, have limitations in sensitivity and specificity. Smartwatches with advanced sensors offer continuous monitoring, improving the chances of detecting asymptomatic and paroxysmal Afib. The review meticulously examines major clinical trials studying Afib detection using smartwatches, including the landmark Apple Heart Study and ongoing trials such as the Heart Watch, Heartline, and Fitbit Heart Study. Detailed summaries of participant numbers, smartwatch devices used, and key findings are presented. It also comments on the cost-effectiveness and scalability of smartwatch-based screening, highlighting the potential to reduce healthcare costs and improve patient outcomes.

CONCLUSION/RELEVANCE

The integration of wearable technology into healthcare can lead to earlier diagnosis, improved patient engagement, and enhanced cardiac health monitoring. Despite ethical considerations and disparities, the potential benefits outweigh the challenges. This review calls for increased awareness, collaboration with insurance companies, and ongoing research efforts to optimize smartwatch accuracy and encourage widespread adoption of Afib detection. With insights from major trials, this review serves as a comprehensive reference for healthcare professionals and policymakers, guiding future strategies in the early diagnosis and management of atrial fibrillation.

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

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

Oxygen saturation measurement in cyanotic heart disease with the Apple watch

BACKGROUND

Accurate measurement of transcutaneous oxygen saturation is important for the assessment of cyanosis in CHD. Aim of this study was the evaluation of a supplementary transcutaneous oxygen saturation measurement with an Apple watch® in children with cyanotic heart disease.

MATERIAL AND METHODS

During a six-minute walk test, measurement of transcutaneous oxygen saturation was performed simultaneously with an Oximeter (Nellcor, Medtronic, USA) and an Apple watch® Series 7 (Apple inc, USA) in 36 children with cyanotic heart disease.

RESULTS

Median age was 9.2 (IQR 5.7-13.8) years. Transcutaneous oxygen saturation measurement with the Apple watch® was possible in 35/36 and 34/36 subjects before and after six-minute walk test. Children, in whom Apple watch® measurement was not possible, had a transcutaneous oxygen saturation < 85% on oximeter. Before six-minute walk test, median transcutaneous oxygen saturation was 93 (IQR 91-97) % measured by oximeter and 95 (IQR 93-96) % by the Apple watch®. After a median walking distance of 437 (IQR 360-487) m, transcutaneous oxygen saturation dropped to 92 (IQR 88-95, p < 0.001) % by oximeter and to 94 (IQR 90-96, p = 0.013) % measured with the Apple watch®.

CONCLUSION

In children with mild cyanosis measurement of transcutaneous oxygen saturation with an Apple watch® showed only valid results if transcutaneous oxygen saturation was > 85%, with higher values being measured with the smart watch. In children with moderate or severe cyanosis transcutaneous oxygen saturation, measurement with the Apple watch® was not reliable and cannot be recommended to monitor oxygen saturation at home.

Enhancing cardiac postoperative care: a smartwatch-integrated remote telemonitoring platform for health screening with ECG analysis

Aims

The integration of smartwatches into postoperative cardiac care transforms patient monitoring, systematically tracking vital signs and delivering real-time data to a centralized platform. This study focuses on developing a platform for seamless integration, assessing reliability, and evaluating the impact on post-cardiac surgery. The goal is to establish a robust foundation for understanding the efficacy and dependability of smartwatch-based telemonitoring, enhancing care for this population.

Methods and results

A total of 108 cardiac surgery patients were divided into telemonitoring (TLM) and control (CTL) groups. The TLM group utilized smartwatches for continuous monitoring of vital parameters (SpO2, HR, BP, ECG) over 30 ± 3 days. Statistical analyses (Pearson, Intraclass Correlation, Bland-Altman, Tost Test) were employed to compare smartwatch measurements with traditional methods. Significant correlations and concordance were observed, particularly in HR and BP measurements. Challenges were noted in SpO2 measurement. The ECG algorithm exhibited substantial agreement with cardiologists (Kappa: 0.794; p > 0.001), highlighting its reliability. The telemonitoring platform played a crucial role in early detection of clinical changes, including prompt Emergency Department (ED) visits, contributing significantly to preventing outcomes that could lead to mortality, such as asymptomatic Atrioventricular block. Positive patient responses affirmed technological efficacy, especially in identifying cardiac arrhythmias like atrial fibrillation.

Conclusion

The integration of smartwatches into remote telemonitoring for postoperative cardiac care demonstrates substantial potential, improving monitoring and early complication detection, thereby enhancing patient outcomes. The FAPO-X Study (Assisted Digital Telemonitoring with Wearables in Patients After Cardiovascular Surgery; NCT05966857) underscores the promising role of telemonitoring in postoperative cardiac care.

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.

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

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

The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers

Objectives

We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers.

Materials and Methods

The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification.

Results

By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention.

Discussion

We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection.

Conclusion

The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.

Early warning of atrial fibrillation using deep learning

Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.

Cardiac Health Assessment Using a Wearable Device Before and After Transcatheter Aortic Valve Implantation: Prospective Study

Background

Due to aging of the population, the prevalence of aortic valve stenosis will increase drastically in upcoming years. Consequently, transcatheter aortic valve implantation (TAVI) procedures will also expand worldwide. Optimal selection of patients who benefit with improved symptoms and prognoses is key, since TAVI is not without its risks. Currently, we are not able to adequately predict functional outcomes after TAVI. Quality of life measurement tools and traditional functional assessment tests do not always agree and can depend on factors unrelated to heart disease. Activity tracking using wearable devices might provide a more comprehensive assessment.

Objective

This study aimed to identify objective parameters (eg, change in heart rate) associated with improvement after TAVI for severe aortic stenosis from a wearable device.

Methods

In total, 100 patients undergoing routine TAVI wore a Philips Health Watch device for 1 week before and after the procedure. Watch data were analyzed offline-before TAVI for 97 patients and after TAVI for 75 patients.

Results

Parameters such as the total number of steps and activity time did not change, in contrast to improvements in the 6-minute walking test (6MWT) and physical limitation domain of the transformed WHOQOL-BREF questionnaire.

Conclusions

These findings, in an older TAVI population, show that watch-based parameters, such as the number of steps, do not change after TAVI, unlike traditional 6MWT and QoL assessments. Basic wearable device parameters might be less appropriate for measuring treatment effects from TAVI.

[Digital competence in rhythmology : Training and education]

BACKGROUND

The digital transformation in medicine, particularly in technology-orientated areas such as rhythmology, is leading to a rapid change in diagnostic and therapeutic options. Digital skills are helpful and need to keep up with this pace of change.

RESEARCH QUESTION

Which digital technologies and resources with rhythmological relevance play a role today and in the future?

METHODS

Review of the various digital technologies for rhythm detection and monitoring, as well as current digital resources for training and education.

RESULTS

Rhythm detection and monitoring can be optimized with smart devices and telemedicine, while digital platforms such as social media and virtual reality offer new perspectives in the training of rhythmology specialists.

CONCLUSION

Acquiring digital skills will be the basis for future work in rhythmology.

Detection of Arrhythmias Using Smartwatches-A Systematic Literature Review

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

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Health behavior outcomes in stroke survivors prescribed wearables for atrial fibrillation detection stratified by age

BACKGROUND

Smartwatches have become readily accessible tools for detecting atrial fibrillation (AF). There remains limited data on how they affect psychosocial outcomes and engagement in older adults. We examine the health behavior outcomes of stroke survivors prescribed smartwatches for AF detection stratified by age.

METHODS

We analyzed data from the Pulsewatch study, a randomized controlled trial that enrolled patients (≥ 50 years) with a history of stroke or transient ischemic attack and CHA2DS2-VASc ≥ 2. Intervention participants were equipped with a cardiac patch monitor and a smartwatch-app dyad, while control participants wore the cardiac patch monitor for up to 44 days. We evaluated health behavior parameters using standardized tools, including the Consumer Health Activation Index, the Generalized Anxiety Disorder questionnaire, the 12-Item Short Form Health Survey, and wear time of participants categorized into three age groups: Group 1 (ages 50-60), Group 2 (ages 61-69), and Group 3 (ages 70-87). We performed statistical analysis using a mixed-effects repeated measures linear regression model to examine differences amongst age groups.

RESULTS

Comparative analysis between Groups 1, 2 and 3 revealed no significant differences in anxiety, patient activation, perception of physical health and wear time. The use of smartwatch technology was associated with a decrease in perception of mental health for Group 2 compared to Group 1 (β = -3.29, P = 0.046).

CONCLUSION

Stroke survivors demonstrated a willingness to use smartwatches for AF monitoring. Importantly, among these study participants, the majority did not experience negative health behavior outcomes or decreased engagement as age increased.

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

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

Does asymptomatic atrial fibrillation exist?

Atrial fibrillation (AF) is currently defined as symptomatic by asking patients if they are aware of when they are in AF and if they feel better in sinus rhythm. However, this approach of defining AF as symptomatic and asymptomatic fails to adequately consider the adverse effects of AF in patients who are unaware of their rhythm including progression from paroxysmal to persistent AF, and the development of dementia, stroke, sinus node dysfunction, valvular regurgitation, ventricular dysfunction, and heart failure. Labeling these patients as asymptomatic falsely suggests that their AF requires less intense therapy and puts into question the notion of truly asymptomatic AF. Because focusing on patient awareness ignores other important consequences of AF, clinical endpoints that are independent of symptoms are being developed. The concept of AF burden has more recently been used as a clinical endpoint in clinical trials as a more clinically relevant endpoint compared to AF-related symptoms or time to first recurrence, but its correlation with symptoms and other clinical outcomes remains unclear. This review will explore the impact of AF on apparently asymptomatic patients, the use of AF burden as an endpoint for AF management, and potential refinements to the AF burden metric. The review is based on a presentation by the senior author during the 2023 16th annual European Cardiac Arrhythmia Society (ECAS) congress in Paris, France.

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

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

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

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

Anesthesia Patient Monitoring 2050

The monitoring of vital signs in patients undergoing anesthesia began with the very first case of anesthesia and has evolved alongside the development of anesthesiology ever since. Patient monitoring started out as a manually performed, intermittent, and qualitative assessment of the patient's general well-being in the operating room. In its evolution, patient monitoring development has responded to the clinical need, for example, when critical incident studies in the 1980s found that many anesthesia adverse events could be prevented by improved monitoring, especially respiratory monitoring. It also facilitated and perhaps even enabled increasingly complex surgeries in increasingly higher-risk patients. For example, it would be very challenging to perform and provide anesthesia care during some of the very complex cardiovascular surgeries that are almost routine today without being able to simultaneously and reliably monitor multiple pressures in a variety of places in the circulatory system. Of course, anesthesia patient monitoring itself is enabled by technological developments in the world outside of the operating room. Throughout its history, anesthesia patient monitoring has taken advantage of advancements in material science (when nonthrombogenic polymers allowed the design of intravascular catheters, for example), in electronics and transducers, in computers, in displays, in information technology, and so forth. Slower product life cycles in medical devices mean that by carefully observing technologies such as consumer electronics, including user interfaces, it is possible to peek ahead and estimate with confidence the foundational technologies that will be used by patient monitors in the near future. Just as the discipline of anesthesiology has, the patient monitoring that accompanies it has come a long way from its beginnings in the mid-19th century. Extrapolating from careful observations of the prevailing trends that have shaped anesthesia patient monitoring historically, patient monitoring in the future will use noncontact technologies, will predict the trajectory of a patient's vital signs, will add regional vital signs to the current systemic ones, and will facilitate directed and supervised anesthesia care over the broader scope that anesthesia will be responsible for.

Characterization of Atrial Fibrillation Episode Patterns: A Comparative Study

OBJECTIVE

The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors.

METHODS

To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality.

RESULTS

The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability.

CONCLUSIONS

Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.

Self-reported efficacy in patient-physician interaction in relation to anxiety, patient activation, and health-related quality of life among stroke survivors

BACKGROUND

Early detection of AF is critical for stroke prevention. Several commercially available smartwatches are FDA cleared for AF detection. However, little is known about how patient-physician relationships affect patients' anxiety, activation, and health-related quality of life when prescribed smartwatch for AF detection.

METHODS

Data were used from the Pulsewatch study (NCT03761394), which randomized adults (>50 years) with no contraindication to anticoagulation and a CHA2DS2-VASc risk score ≥2 to receive a smartwatch-smartphone app dyad for AF monitoring vs. conventional monitoring with an ECG patch (Cardea SoloTM) and monitored participants for up to 45 days. The Perceived Efficacy in Patient-Physician Interactions survey was used to assess patient confidence in physician interaction at baseline with scores ≥45 indicating high perceived efficacy in patient-provider interactions. Generalized Anxiety Disorder-7 Scale, Consumer Health Activation Index, and Short-Form Health Survey were utilized to examine anxiety, patient activation, and physical and mental health status, at baseline, 14, and 44 days, respectively. We used mixed-effects repeated measures linear regression models to assess changes in psychosocial outcomes among smartwatch users in relation to self-reported efficacy in physician interaction over the study period.

RESULTS

A total of 93 participants (average age 64.1 ± 8.9 years; 43.0% female; 88.2% non-Hispanic white) were included in this analysis. At baseline, fifty-six (60%) participants reported high perceived efficacy in patient-physician interaction. In the fully adjusted models, high perceived efficacy (vs. low) at baseline was associated with greater patient activation and perceived mental health (β 12.0, p-value <0.001; β 3.39, p-value <0.05, respectively). High perceived self-efficacy was not associated with anxiety or physical health status (β - 0.61, p-value 0.46; β 0.64, p-value 0.77) among study participants.

CONCLUSIONS

Higher self-efficacy in patient-physician interaction was associated with higher patient activation and mental health status among stroke survivors using smartwatches. Furthermore, we found no association between anxiety and smartwatch prescription for AF in participants with high self-efficacy in patient-physician interaction. Efforts to improve self-efficacy in patient-physician interaction may improve patient activation and self-rated health and subsequently may lead to better clinical outcomes.KEY MESSAGESHigher self-efficacy in patient-physician interaction was associated with higher patient activation and mental health status among stroke survivors using smartwatches.No association between anxiety and smartwatch prescription for AF in participants with high self-efficacy in patient-physician interaction.Efforts to improve self-efficacy in patient-physician interaction may improve patient activation and self-rated health and subsequently may lead to better clinical outcomes.

Use of wearables for monitoring cardiometabolic health: A systematic review

INTRODUCTION

Cardiometabolic disorders (CMD) such as hyperglycemia, obesity, hypertension, and dyslipidemia are the leading causes of mortality and significant public health concerns worldwide. With the advances in wireless technology, wearables have become popular for health promotion, but its impact on cardiometabolic health is not well understood.

PURPOSE

A systematic literature review aimed to describe the features of wearables used for monitoring cardiometabolic health and identify the impact of using wearables on those cardiometabolic health indicators.

METHODS

A systematic search of PubMed, CINAHL, Academic Search Complete, and Science and Technology Collection databases was performed using keywords related to CMD risk indicators and wearables. The wearables were limited to sensors for blood pressure (BP), heart rate (HR), electrocardiogram (ECG), glucose, and cholesterol.

INCLUDED STUDIES

1) were published from 2016 to March 2021 in English, 2) focused on wearables external to the body, and 3) examined wearable use by individuals in daily life (not by health care providers). Protocol, technical, and non-empirical studies were excluded.

RESULTS

Out of 53 studies, the types of wearables used were smartwatches (45.3%), patches (34.0%), chest straps (22.6%), wristbands (13.2%), and others (9.4%). HR (58.5%), glucose (28.3%), and ECG (26.4%) were the predominant indicators. No studies tracked BP or cholesterol. Additional features of wearables included physical activity, respiration, sleep, diet, and symptom monitoring. Twenty-two studies primarily focused on the use of wearables and reported direct impacts on cardiometabolic indicators; seven studies used wearables as part of a multi-modality approach and presented outcomes affected by a primary intervention but measured through CMD-sensor wearables; and 24 validated the precision and usability of CMD-sensor wearables.

CONCLUSION

The impact of wearables on cardiometabolic indicators varied across the studies, indicating the need for further research. However, this body of literature highlights the potential of wearables to promote cardiometabolic health.

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.

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.

An augmented reality-based method to assess precordial electrocardiogram leads: a feasibility trial

Aims

It has been demonstrated that several cardiac pathologies, including myocardial ischaemia, can be detected using smartwatch electrocardiograms (ECGs). Correct placement of bipolar chest leads remains a major challenge in the outpatient population.

Methods and results

In this feasibility trial, we propose an augmented reality-based smartphone app that guides the user to place the smartwatch in predefined positions on the chest using the front camera of a smartphone. A machine-learning model using MobileNet_v2 as the backbone was trained to detect the bipolar lead positions V1-V6 and visually project them onto the user's chest. Following the smartwatch recordings, a conventional 10 s, 12-lead ECG was recorded for comparison purposes. All 50 patients participating in the study were able to conduct a 9-lead smartwatch ECG using the app and assistance from the study team. Twelve patients were able to record all the limb and chest leads using the app without additional support. Bipolar chest leads recorded with smartwatch ECGs were assigned to standard unipolar Wilson leads by blinded cardiologists based on visual characteristics. In every lead, at least 86% of the ECGs were assigned correctly, indicating the remarkable similarity of the smartwatch to standard ECG recordings.

Conclusion

We have introduced an augmented reality-based method to independently record multichannel smartwatch ECGs in an outpatient setting.

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.

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

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

Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices

Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.

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.

The electrocardiogram on the wrist: a frightening experience to the untrained consumer: a case report

BACKGROUND

Smartwatches offering electrocardiogram recordings advertise the benefits of supporting an active and healthy lifestyle. More often, medical professionals are faced with privately acquired electrocardiogram data of undetermined quality recorded by smartwatches. This is boasted by results and suggestions for medical benefits, based on industry-sponsored trials and potentially biased case reports. Yet potential risks and adverse effects have been widely overlooked.

CASE PRESENTATION

This case report describes an emergency consultation of a 27-year-old Swiss-German man lacking known previous medical conditions who developed an episode of anxiety and panic due to pain in the left chest prompted by over-interpretation of unremarkable electrocardiogram readings of his smartwatch. Fearing acute coronary syndrome, he presented at the emergency department. His smartwatch electrocardiograms, as well as a 12-lead electrocardiogram, appeared normal. After extensive calming and reassuring, as well as symptomatic therapy with paracetamol and lorazepam, the patient was discharged with no indications for further treatment.

CONCLUSIONS

This case demonstrates the potential risks of anxiety from nonprofessional electrocardiogram recordings by smartwatches. Medico-legal and practical aspects of electrocardiogram recordings by smartwatches need to be further considered. The case shows the potential side effects of pseudo-medical recommendations for the untrained consumer, and may add to the discussion on the ethics of how to evaluate smartwatch electrocardiogram data as a medical professional.

Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting

Aims

The aim was to validate the performance of a monitoring system consisting of a wrist-worn device and a data management cloud service intended to be used by medical professionals in detecting atrial fibrillation (AF).

Methods

Thirty adult patients diagnosed with AF alone or AF with concomitant flutter were recruited. Continuous photoplethysmogram (PPG) and intermittent 30 s Lead I electrocardiogram (ECG) recordings were collected over 48 h. The ECG was measured four times a day at prescheduled times, when notified due to irregular rhythm detected by PPG, and when self-initiated based on symptoms. Three-channel Holter ECG was used as the reference.

Results

The subjects recorded a total of 1,415 h of continuous PPG data and 3.8 h of intermittent ECG data over the study period. The PPG data were analyzed by the system's algorithm in 5-min segments. The segments containing adequate amounts, at least ~30 s, of adequate quality PPG data for rhythm assessment algorithm, were included. After rejecting 46% of the 5-min segments, the remaining data were compared with annotated Holter ECG yielding AF detection sensitivity and specificity of 95.6 and 99.2%, respectively. The ECG analysis algorithm labeled 10% of the 30-s ECG records as inadequate quality and these were excluded from the analysis. The ECG AF detection sensitivity and specificity were 97.7 and 89.8%, respectively. The usability of the system was found to be good by both the study subjects and the participating cardiologists.

Conclusion

The system comprising of a wrist device and a data management service was validated to be suitable for use in patient monitoring and in the detection of AF in an ambulatory setting.Clinical Trial Registration: ClinicalTrials.gov/, NCT05008601.

Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation

Background

Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored.

Objective

The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data.

Methods

We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0-2 days, ±3-7 days, and ±8-30 days from AF events to determine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively.

Results

We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence interval [CI] 0.759-0.760), sensitivity of 0.703 (95% CI 0.700-0.705), specificity of 0.684 (95% CI 0.678-0.685), and accuracy of 69.4% (95% CI 0.692-0.700). Model performance was better on ±0-2 day samples (sensitivity 0.711; 95% CI 0.709-0.713) and worse on the ±8-30 day window (sensitivity 0.688; 95% CI 0.685-0.690), with performance on the ±3-7 day window falling in between (sensitivity 0.708; 95% CI 0.704-0.710).

Conclusion

Neural networks can predict AF using a widely scalable and cost-effective mobile technology prospectively and retrospectively.

Improving the management of patients with chronic cardiac and respiratory diseases by extending pulse-oximeter uses: the dynamic pulse-oximetry

Respiratory and cardio-vascular chronic diseases are among the most common noncommunicable diseases (NCDs) worldwide, accounting for a large portion of health-care costs in terms of mortality and disability. Their prevalence is expected to rise further in the coming years as the population ages. The current model of care for diagnosing and monitoring NCDs is out of date because it results in late medical interventions and/or an unfavourable cost-effectiveness balance based on reported symptoms and subsequent inpatient tests and treatments. Health projects and programs are being implemented in an attempt to move the time of an NCD's diagnosis, as well as its monitoring and follow up, out of hospital settings and as close to real life as possible, with the goal of benefiting both patients' quality of life and health system budgets. Following the SARS-CoV-2 pandemic, this implementation received additional impetus. Pulseoximeters (POs) are currently used in a variety of clinical settings, but they can also aid in the telemonitoring of certain patients. POs that can measure activities as well as pulse rate and oxygen saturation as proxies of cardio-vascular and respiratory function are now being introduced to the market. To obtain these data, the devices must be absolutely reliable, that is, accurate and precise, and capable of recording for a long enough period of time to allow for diagnosis. This paper is a review of current pulse-oximetry (POy) use, with the goal of investigating how its current use can be expanded to manage not only cardio-respiratory NCDs, but also acute emergencies with telemonitoring when hospitalization is not required but the patients' situation is debatable. Newly designed devices, both "consumer" and "professional," will be scrutinized, particularly those capable of continuously recording vital parameters on a 24-hour basis and coupling them with daily activities, a practice known as dynamic pulse-oximetry.

Clinical applications of heart rhythm monitoring tools in symptomatic patients and for screening in high-risk groups

Recent technological advances have facilitated and diversified the options available for the diagnosis of cardiac arrhythmias. Ranging from simple resting or exercise electrocardiograms to more sophisticated and expensive smartphones and implantable cardiac monitors. These tests and devices may be used for varying periods of time depending on symptom frequency. The choice of the most appropriate heart rhythm test should be guided by clinical evaluation and optimized following accurate characterization of underlying symptoms, 'red flags', risk factors, and consideration of cost-effectiveness of the different tests. This review provides evidence-based guidance for assessing suspected arrhythmia in patients who present with symptoms or in the context of screening, such as atrial fibrillation or advanced conduction disturbances following transcatheter aortic valve implantation in high-risk groups. This is intended to help clinicians choose the most appropriate diagnostic tool to facilitate the management of patients with suspected arrhythmias.

Monitoring and diagnosis of intermittent arrhythmias: evidence-based guidance and role of novel monitoring strategies

Technological advances have made diagnosis of heart rhythm disturbances much easier, with a wide variety of options, including single-lead portable devices, smartphones/watches to sophisticated implantable cardiac monitors, allowing accurate data to be collected over different time periods depending on symptoms frequency. This review provides an overview of the novel and existing heart rhythm testing options, including a description of the supporting evidence for their use. A description of each of the tests is provided, along with discussion of their advantages and limitations. This is intended to help clinicians towards choosing the most appropriate test, thus improving diagnostic yield management of patients with suspected arrhythmias.

Smartphone and wearable detected atrial arrhythmias in Older Adults: Results of a fully digital European Case finding study

Aims

Simplified detection of atrial arrhythmias via consumer-electronics would enable earlier therapy in at-risk populations. Whether this is feasible and effective in older populations is not known.

Methods and results

The fully remote, investigator-initiated Smartphone and wearable detected atrial arrhythmia in Older Adults Case finding study (Smart in OAC-AFNET 9) digitally enrolled participants ≥65 years without known atrial fibrillation, not receiving oral anticoagulation in Germany, Poland, and Spain for 8 weeks. Participants were invited by media communications and direct contacts. Study procedures adhered to European data protection. Consenting participants received a wristband with a photoplethysmography sensor to be coupled to their smartphone. The primary outcome was the detection of atrial arrhythmias lasting 6 min or longer in the first 4 weeks of monitoring. Eight hundred and eighty-two older persons (age 71 ± 5 years, range 65-90, 500 (57%) women, 414 (47%) hypertension, and 97 (11%) diabetes) recorded signals. Most participants (72%) responded to adverts or word of mouth, leaflets (11%) or general practitioners (9%). Participation was completely remote in 469/882 persons (53%). During the first 4 weeks, participants transmitted PPG signals for 533/696 h (77% of the maximum possible time). Atrial arrhythmias were detected in 44 participants (5%) within 28 days, and in 53 (6%) within 8 weeks. Detection was highest in the first monitoring week [incidence rates: 1st week: 3.4% (95% confidence interval 2.4-4.9); 2nd-4th week: 0.55% (0.33-0.93)].

Conclusion

Remote, digitally supported consumer-electronics-based screening is feasible in older European adults and identifies atrial arrhythmias in 5% of participants within 4 weeks of monitoring (NCT04579159).

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.

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

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, wrist-worn, 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.

Management of atrial fibrillation: two decades of progress - a scientific statement from the European Cardiac Arrhythmia Society

BACKGROUND

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice. The aim of this review was to evaluate the progress made in the management of AF over the two last decades.

RESULTS

Clinical classification of AF is usually based on the presence of symptoms, the duration of AF episodes and their possible recurrence over time, although incidental diagnosis is not uncommon. The majority of patients with AF have associated cardiovascular diseases and more recently the recognition of modifiable risk factors both cardiovascular and non-cardiovascular which should be considered in its management. Among AF-related complications, stroke and transient ischaemic accidents (TIAs) carry considerable morbidity and mortality risk. The use of implantable devices such as pacemakers and defibrillators, wearable garments and subcutaneous cardiac monitors with recording capabilities has enabled to access the burden of "subclinical AF". The recent introduction of non-vitamin K antagonists has led to improve the prevention of stroke and peripheral embolism. Agents capable of reversing non-vitamin K antagonists have also become available in case of clinically relevant major bleeding. Transcatheter closure of left atrial appendage represents an option for patients unable to take oral anticoagulation. When treating patients with AF, clinicians need to select the most suitable strategy, i.e. control of heart rate and/or restoration and maintenance of sinus rhythm. The studies comparing these two strategies have not shown differences in terms of mortality. If an AF episode is poorly tolerated from a haemodynamic standpoint, electrical cardioversion is indicated. Otherwise, restoration of sinus rhythm can be obtained using intravenous pharmacological cardioversion and oral class I or class III antiarrhythmic is used to prevent recurrences. During the last two decades after its introduction in daily practice, catheter ablation has gained considerable escalation in popularity. Progress has also been made in AF associated with heart failure with reduced or preserved ejection fraction.

CONCLUSIONS

Significant progress has been made within the past 2 decades both in the pharmacological and non-pharmacological managements of this cardiac arrhythmia.

[Cardiovascular preventive recommendations. PAPPS 2022 thematic updates. Working groups of the PAPPS]

The recommendations of the semFYC's Program for Preventive Activities and Health Promotion (PAPPS) for the prevention of cardiovascular diseases (CVD) are presented. The following sections are included: epidemiological review, where the current morbidity and mortality of CVD in Spain and its evolution as well as the main risk factors are described; cardiovascular (CV) risk and recommendations for the calculation of CV risk; main risk factors such as arterial hypertension, dyslipidemia and diabetes mellitus, describing the method for their diagnosis, therapeutic objectives and recommendations for lifestyle measures and pharmacological treatment; indications for antiplatelet therapy, and recommendations for screening of atrial fibrillation, and recommendations for management of chronic conditions. The quality of testing and the strength of the recommendation are included in the main recommendations.

Smartphone in medicine — from a reference book to a diagnostic system. Overview of the current state of the issue

The paper provides a brief overview of the modern possibilities of using a smartphone as a diagnostic device of a wide profile. In some cases, additional specialized attachments are required. In others, the diagnostic algorithm uses only standard cameras, a microphone and various built-in smartphone sensors. The development of the smartphone integration into the healthcare system is modern, relevant and very promising, given the widespread use of smartphones among the global population.