Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology: Cardiovascular Round Table

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

Unlocking Tomorrow's Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence

As an integral aspect of health care, digital technology has enabled modelling of complex relationships to detect, screen, diagnose, and predict patient outcomes. With massive data sets, artificial intelligence (AI) can have marked effects on 3 levels: for patients, clinicians, and health systems. In this review, we discuss contemporary AI-enabled wearable devices undergoing research in the field of cardiovascular medicine. These include devices such as smart watches, electrocardiogram patches, and smart textiles such as smart socks and chest sensors for diagnosis, management, and prognostication of conditions such as atrial fibrillation, heart failure, and hypertension as well as monitoring for cardiac rehabilitation. We review the evolution of machine learning algorithms used in wearable devices from random forest models to the use of convolutional neural networks and transformers. We further discuss frameworks for wearable technologies such as the V3-stage process of verification, analytical validation, and clinical validation as well as challenges of AI integration in medicine such as data veracity, validity, and security and provide a reference framework to maintain fairness and equity. Last, clinician and patient perspectives are discussed to highlight the importance of considering end-user feedback in development and regulatory processes.

Heart Failure Drug Development Over the Eras: From the Heart Failure Collaboratory

Over the past decade, the field of heart failure (HF) has witnessed remarkable progress in drug development, resulting in the approval of numerous groundbreaking drugs by the U.S. Food and Drug Administration. To address some of these challenges, the U.S. Food and Drug Administration has issued guidance documents that have been critical in contemporary HF drug development; however, there are still many challenges in need of investigation. This article leverages efforts of the Heart Failure Collaboratory and the scientific community to discuss the critical need for innovative trial designs, important concepts in clinical trials in the modern era, and the utilization of big data to accelerate HF drug development. At this inflection point in HF drug development, it is imperative that, as a global scientific community, we foster increased collaboration among researchers, clinicians, patients, and regulatory bodies. Only through such unified efforts can we navigate the complexities of HF, accelerate the development process, and ultimately deliver effective therapies that transform patient outcomes.

Forecasting Atrial Fibrillation: The Predictive Power of N-terminal Prohormone of Brain Natriuretic Peptide in a Five-Year Study

Introduction Atrial fibrillation (AF) is a major global health concern, and early prediction is essential for managing high-risk individuals. N-terminal prohormone of brain natriuretic peptide (NT-proBNP) has emerged as a crucial biomarker for predicting AF. While most studies have concentrated on cohorts already diagnosed with AF or other cardiac diseases, this research investigates the predictive value of NT-proBNP for AF development in a population without prior AF diagnosis. Methods and materials A five-year prospective observational study was conducted on 4090 individuals aged 45 to 75 with no previous diagnosis of AF. Baseline demographic characteristics, comorbid conditions, cardiac-specific measures, and NT-proBNP levels were systematically recorded. The primary endpoint was the onset of AF, confirmed through annual 12-lead ECG or 24-hour Holter monitoring. Univariate and multivariate analyses identified factors associated with AF onset. Results Out of the total population, 16.6% (679 individuals) developed AF. Notably, increased NT-proBNP levels (P=0.001), older age (P=0.001), and hypertension (P=0.001) were significantly associated with the onset of AF. The mean NT-proBNP levels in the AF group were significantly higher than in the non-AF group (P<0.001). The AF group also showed a higher mean age and a greater prevalence of hypertension (P<0.001 for both). Conclusion This study confirms the predictive value of NT-proBNP for AF onset in a non-AF population, highlighting older age and hypertension as significant risk factors for AF development. The findings underscore the potential of NT-proBNP not only as a predictive biomarker but also as a therapeutic target. These insights emphasize the potential role of NT-proBNP in early intervention and management strategies for AF, suggesting that future research should include additional variables, such as lifestyle factors and genetic predisposition, in assessing AF risk.

Artificial Intelligence in Telemedicine: A Global Perspective Visualization Analysis

Background: The use of artificial intelligence (AI) in telemedicine has been a popular topic in academic research in recent years, resulting in a surge of literature publications. This study provides a scientific overview of AI in telemedicine through bibliometric and visualization analysis. Methods: The Web of Science Core Collection was used as the data source, and the search was conducted on June 1, 2023. A total of 2,860 articles and review studies published in English between 2010 and 2023 were included. This study analyzed general information on the field, trends in publication output, countries/regions, authors, journals, influential articles, keyword usage, and knowledge flows between disciplines. The Bibliometrix R package, VOSviewer, and CiteSpace were used for the analysis. Results: The rate of articles published on AI in telemedicine is increasing by ∼42.1% annually. The United States and China are the top two countries in terms of the number of articles published, accounting for 37.1% of the overall publication volume. In addition to AI and telemedicine, machine learning, digital health, and deep learning are the top three keywords in terms of frequency of occurrence. The keyword time trend graph shows that ChatGPT became one of the important keywords in 2023. The analysis of burst detection suggests that mobile health, based on mobile phones, may be a promising area for future research. Conclusions: This study systematically reviewed the development of AI in telemedicine and identified current research hotspots and future research directions. The results will provide impetus for the innovative development of this field.

Heart Failure-Related Death in Subjects With Atrial Fibrillation in the United States, 1999 to 2020

BACKGROUND

Population-based data on heart failure (HF)-related death in patients with atrial fibrillation (AF) are lacking. We assessed HF-related death in people with AF in the United States over the past 21 years and examined differences by age, sex, race, ethnicity, urbanization, and census region.

METHODS AND RESULTS

Data were extracted from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research to determine trends in age-adjusted mortality rates per 100 000 people, due to HF-related death among subjects with AF aged ≥15 years. To calculate nationwide annual trends, we assessed the average annual percent change (AAPC) and annual percent change with relative 95% CIs using joinpoint regression. Between 1999 and 2020, 916 685 HF-related deaths (396 205 men and 520 480 women) occurred among US adults having a concomitant AF. The overall age-adjusted mortality rates increased (AAPC: +4.1% [95% CI, 3.8-4.4]; P<0.001), especially after 2011 (annual percent change, +6.8% [95% CI, 6.2-7.4]; P<0.001) in men (AAPC, +4.8% [95% CI, 4.4-5.1]; P<0.001), in White subjects (AAPC: +4.2% [95% CI, 3.9 to 4.6]; P<0.001) and in subjects aged <65 years (AAPC: +7.5% [95% CI, 6.7-8.4]; P<0.001). The higher percentage of deaths were registered in the South (32.8%). During the first year of the COVID-19 pandemic, a significant excess in HF-related deaths among patients with AF aged >65 years was observed.

CONCLUSIONS

A worrying increase in the HF-related mortality rate among patients with AF has been observed in the United States over the past 2 decades.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

Improving therapeutic outcomes in heart failure with reduced nonvalvular ejection fraction: A clinical study of heart failure education intervention

OBJECTIVE

The current study delves into the impact of heart failure education intervention on improving therapeutic outcomes for heart failure (HF) patients with reduced nonvalvular ejection fraction.

METHODS

There involved a total of 60 HF patients with non-valvular ejection fraction reduction who met the inclusion requirements. Patients enrolled were randomly distributed into an observation group and a control group. The observation group received heart failure education intervention, while the control group received conventional intervention. The therapeutic effect, changes in physical indicators, cardiac function indicators, coagulation function, self-management scale scores, and the incidence of adverse cardiovascular events were meticulously evaluated.

RESULTS

The total effective proportion in the observation group was 96.67%, which was significantly higher than the control group's proportion of 76.67% (p < .05). After treatment, several parameters in the observation group showed significant improvements compared to the control group: hs-CRP, IL-6, LVEDV value, LVESV value, PT value, APTT value, and TT value were all evidently lower in the observation group. Additionally, the cardiac index, LVEF value, and heart failure self-management scale fraction were significantly higher in the observation group compared to the control group (p < .05). Furthermore, the incidence of adverse cardiovascular events in the observation group was only 6.67%, which was significantly lower than the control group's incidence of 20.00% (p < .05).

CONCLUSION

Heart failure education intervention demonstrates effectiveness in improving the therapeutic outcomes for HF patients and reduced nonvalvular ejection fraction. Additionally, it enhances patients' self-management abilities. Given these positive results, it is highly recommended to promote and implement HF education intervention in clinical practice.

Device innovation in cardiovascular medicine: a report from the European Society of Cardiology Cardiovascular Round Table

Research performed in Europe has driven cardiovascular device innovation. This includes, but is not limited to, percutaneous coronary intervention, cardiac imaging, transcatheter heart valve implantation, and device therapy of cardiac arrhythmias and heart failure. An important part of future medical progress involves the evolution of medical technology and the ongoing development of artificial intelligence and machine learning. There is a need to foster an environment conducive to medical technology development and validation so that Europe can continue to play a major role in device innovation while providing high standards of safety. This paper summarizes viewpoints on the topic of device innovation in cardiovascular medicine at the European Society of Cardiology Cardiovascular Round Table, a strategic forum for high-level dialogue to discuss issues related to the future of cardiovascular health in Europe. Devices are developed and improved through an iterative process throughout their lifecycle. Early feasibility studies demonstrate proof of concept and help to optimize the design of a device. If successful, this should ideally be followed by randomized clinical trials comparing novel devices vs. accepted standards of care when available and the collection of post-market real-world evidence through registries. Unfortunately, standardized procedures for feasibility studies across various device categories have not yet been implemented in Europe. Cardiovascular imaging can be used to diagnose and characterize patients for interventions to improve procedural results and to monitor devices long term after implantation. Randomized clinical trials often use cardiac imaging-based inclusion criteria, while less frequently trials randomize patients to compare the diagnostic or prognostic value of different modalities. Applications using machine learning are increasingly important, but specific regulatory standards and pathways remain in development in both Europe and the USA. Standards are also needed for smart devices and digital technologies that support device-driven biomonitoring. Changes in device regulation introduced by the European Union aim to improve clinical evidence, transparency, and safety, but they may impact the speed of innovation, access, and availability. Device development programmes including dialogue on unmet needs and advice on study designs must be driven by a community of physicians, trialists, patients, regulators, payers, and industry to ensure that patients have access to innovative care.

The multidimensional value of natriuretic peptides in heart failure, integrating laboratory and clinical aspects

Natriuretic peptides (NP) play an essential role in heart failure (HF) regulation, and their measurement has improved diagnostic and prognostic accuracy. Clinical symptoms and objective measurements, such as NP levels, should be included in the HF definition to render it more reliable and consistent among observers, hospitals, and healthcare systems. BNP and NT-proBNP are reasonable surrogates for cardiac disease, and their measurement is critical to early diagnosis and risk stratification of HF patients. NPs should be measured in all patients presenting with dyspnea or other symptoms suggestive of HF to facilitate early diagnosis and risk stratification. Both BNP and NT-proBNP are currently used for guided HF management and display comparable diagnostic and prognostic accuracy. Standardized cutoffs for each NP assay are essential for data comparison. The value of NP testing is recognized at various levels, including patient empowerment and education, analytical and operational issues, clinical HF management, and cost-effectiveness.

Comparison of Novel Telemonitoring System Using the Single-lead Electrocardiogram Patch With Conventional Telemetry System

BACKGROUND AND OBJECTIVES

Although a single-lead electrocardiogram (ECG) patch may provide advantages for detecting arrhythmias in outpatient settings owing to user convenience, its comparative effectiveness for real-time telemonitoring in inpatient settings remains unclear. We aimed to compare a novel telemonitoring system using a single-lead ECG patch with a conventional telemonitoring system in an inpatient setting.

METHODS

This was a single-center, prospective cohort study. Patients admitted to the cardiology unit for arrhythmia treatment who required a wireless ECG telemonitoring system were enrolled. A single-lead ECG patch and conventional telemetry were applied simultaneously in hospitalized patients for over 24 hours for real-time telemonitoring. The basic ECG parameters, arrhythmia episodes, and signal loss or noise were compared between the 2 systems.

RESULTS

Eighty participants (mean age 62±10 years, 76.3% male) were enrolled. The three most common indications for ECG telemonitoring were atrial fibrillation (66.3%), sick sinus syndrome (12.5%), and atrioventricular block (10.0%). The intra-class correlation coefficients for detecting the number of total beats, atrial and ventricular premature complexes, maximal, average, and minimal heart rates, and pauses were all over 0.9 with p values for reliability <0.001. Compared to a conventional system, a novel system demonstrated significantly lower signal noise (median 0.3% [0.1-1.6%] vs. 2.4% [1.4-3.7%], p<0.001) and fewer episodes of signal loss (median 22 [2-53] vs. 64 [22-112] episodes, p=0.002).

CONCLUSIONS

The novel telemonitoring system using a single-lead ECG patch offers performance comparable to that of a conventional system while significantly reducing signal loss and noise.

TRIAL REGISTRATION

Clinical Research Information Service Identifier: KCT0008176.

Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm

AIMS

The increasing use of insertable cardiac monitors (ICM) produces a high rate of false positive (FP) diagnoses. Their verification results in a high workload for caregivers. We evaluated the performance of an artificial intelligence (AI)-based ILR-ECG Analyzer™ (ILR-ECG-A). This machine-learning algorithm reclassifies ICM-transmitted events to minimize the rate of FP diagnoses, while preserving device sensitivity.

METHODS AND RESULTS

We selected 546 recipients of ICM followed by the Implicity™ monitoring platform. To avoid clusterization, a single episode per ICM abnormal diagnosis (e.g. asystole, bradycardia, atrial tachycardia (AT)/atrial fibrillation (AF), ventricular tachycardia, artefact) was selected per patient, and analyzed by the ILR-ECG-A, applying the same diagnoses as the ICM. All episodes were reviewed by an adjudication committee (AC) and the results were compared. Among 879 episodes classified as abnormal by the ICM, 80 (9.1%) were adjudicated as 'Artefacts', 283 (32.2%) as FP, and 516 (58.7%) as 'abnormal' by the AC. The algorithm reclassified 215 of the 283 FP as normal (76.0%), and confirmed 509 of the 516 episodes as abnormal (98.6%). Seven undiagnosed false negatives were adjudicated as AT or non-specific abnormality. The overall diagnostic specificity was 76.0% and the sensitivity was 98.6%.

CONCLUSION

The new AI-based ILR-ECG-A lowered the rate of FP ICM diagnoses significantly while retaining a > 98% sensitivity. This will likely alleviate considerably the clinical burden represented by the review of ICM events.

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.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach

AIMS

Brugada syndrome (BrS) is an inherited disease associated with an increased risk of ventricular arrhythmias. Recent studies have reported the presence of an altered atrial phenotype characterized by abnormal P-wave parameters. The aim of this study was to identify BrS based exclusively on P-wave features through an artificial intelligence (AI)-based model.

METHODS AND RESULTS

Continuous 5 min 12-lead ECG recordings were obtained in sinus rhythm from (i) patients with spontaneous or ajmaline-induced BrS and no history of AF and (ii) subjects with suspected BrS and negative ajmaline challenge. The recorded ECG signals were processed and divided into epochs of 15 s each. Within these epochs, P-waves were first identified and then averaged. From the averaged P-waves, a total of 67 different features considered relevant to the classification task were extracted. These features were then used to train nine different AI-based supervised classifiers. A total of 2228 averaged P-wave observations, resulting from the analysis of 33 420 P-waves, were obtained from 123 patients (79 BrS+ and 44 BrS-). Averaged P-waves were divided using a patient-wise split, allocating 80% for training and 20% for testing, ensuring data integrity and reducing biases in AI-based model training. The BrS+ patients presented with longer P-wave duration (136 ms vs. 124 ms, P < 0.001) and higher terminal force in lead V1 (2.5 au vs. 1.7 au, P < 0.01) compared with BrS- subjects. Among classifiers, AdaBoost model had the highest values of performance for all the considered metrics, reaching an accuracy of over 81% (sensitivity 86%, specificity 73%).

CONCLUSION

An AI machine-learning model is able to identify patients with BrS based only on P-wave characteristics. These findings confirm the presence of an atrial hallmark and open new horizons for AI-guided BrS diagnosis.

Telemedicine: an Effective and Low-Cost Lesson From the COVID-19 Pandemic for the Management of Heart Failure Patients

PURPOSE

The purpose of this review is to explore the benefits and controversies that telemedicine (TM), applied to patients with heart failure (HF), can provide in terms of diagnosis, therapeutic management, and prognosis improvement. During the coronavirus disease 19 (COVID-19) outbreak, TM emerged as the most effective and feasible method available to ensure continuous care for chronic diseases. Among these, HF, characterized by high mortality, morbidity, and the need for frequent visits, may benefit of the TM role. HF patients are affected by frequent exacerbations undergoing a progressive prognosis impoverishment, strongly depending on the disease's management. A precise clinical handling is always required, with a constant optimization of the therapy, a continuous control of risk factors, and a sensitive attention to any change in symptoms, clinical signs, and laboratory tests. In this context, TM has shown to improve therapy adherence and HF: patients' self-care, impacting the prognosis even if specific results are controversial. Major evidence shows that TM may allow an adequate primary prevention, reducing the impact of the main cardiovascular risk factors. TM can also be useful for the secondary prevention, early detecting a likely HF exacerbation before it becomes clinically manifest, thereby lowering the need for hospitalization. Moreover, an optimal up-titration of the therapy and an increase in treatment adherence are feasible by using TM. However, some studies did not show unambiguous results, and uncertainties still remain.

Accuracy in detecting atrial fibrillation in single-lead ECGs: an online survey comparing the influence of clinical expertise and smart devices

BACKGROUND

Manual interpretation of single-lead ECGs (SL-ECGs) is often required to confirm a diagnosis of atrial fibrillation. However accuracy in detecting atrial fibrillation via SL-ECGs may vary according to clinical expertise and choice of smart device.

AIMS

To compare the accuracy of cardiologists, internal medicine residents and medical students in detecting atrial fibrillation via SL-ECGs from five different smart devices (Apple Watch, Fitbit Sense, KardiaMobile, Samsung Galaxy Watch, Withings ScanWatch). Participants were also asked to assess the quality and readability of SL-ECGs.

METHODS

In this prospective study (BaselWearableStudy, NCT04809922), electronic invitations to participate in an online survey were sent to physicians at major Swiss hospitals and to medical students at Swiss universities. Participants were asked to classify up to 50 SL-ECGs (from ten patients and five devices) into three categories: sinus rhythm, atrial fibrillation or inconclusive. This classification was compared to the diagnosis via a near-simultaneous 12-lead ECG recording interpreted by two independent cardiologists. In addition, participants were asked their preference of each manufacturer's SL-ECG.

RESULTS

Overall, 450 participants interpreted 10,865 SL-ECGs. Sensitivity and specificity for the detection of atrial fibrillation via SL-ECG were 72% and 92% for cardiologists, 68% and 86% for internal medicine residents, 54% and 65% for medical students in year 4-6 and 44% and 58% for medical students in year 1-3; p <0.001. Participants who stated prior experience in interpreting SL-ECGs demonstrated a sensitivity and specificity of 63% and 81% compared to a sensitivity and specificity of 54% and 67% for participants with no prior experience in interpreting SL-ECGs (p <0.001). Of all participants, 107 interpreted all 50 SL-ECGs. Diagnostic accuracy for the first five interpreted SL-ECGs was 60% (IQR 40-80%) and diagnostic accuracy for the last five interpreted SL-ECGs was 80% (IQR 60-90%); p <0.001. No significant difference in the accuracy of atrial fibrillation detection was seen between the five smart devices; p = 0.33. SL-ECGs from the Apple Watch were considered as having the best quality and readability by 203 (45%) and 226 (50%) participants, respectively.

CONCLUSION

SL-ECGs can be challenging to interpret. Accuracy in correctly identifying atrial fibrillation depends on clinical expertise, while the choice of smart device seems to have no impact.

Artificial intelligence and heart failure: A state-of-the-art review

Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.

Telemedicine and Digital Medicine in the Clinical Management of Hypertension and Hypertension-Related Cardiovascular Diseases: A Position Paper of the Italian Society of Arterial Hypertension (SIIA)

High blood pressure is the leading cause of death and disability globally and an important treatable risk factor for cardiovascular, cerebrovascular and chronic kidney diseases. Digital technology, including mobile health solutions and digital therapy, is expanding rapidly in clinical medicine and has the potential to improve the quality of care and effectiveness of drug treatment by making medical interventions timely, tailored to hypertensive patients' needs and by improving treatment adherence. Thus, the systematic application of digital technologies could support diagnosis and awareness of hypertension and its complications, ultimately leading to improved BP control at the population level. The progressive implementation of digital medicine in the national health systems must be accompanied by the supervision and guidance of health authorities and scientific societies to ensure the correct use of these new technologies with consequent maximization of the potential benefits. The role of scientific societies in relation to the rapid adoption of digital technologies, therefore, should encompass the entire spectrum of activities pertaining to their institutional role: information, training, promotion of research, scientific collaboration and advice, evaluation and validation of technological tools, and collaboration with regulatory and health authorities.

The digital journey: 25 years of digital development in electrophysiology from an Europace perspective

AIMS

Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal.

RESULTS

In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP.

CONCLUSION

Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.

Feasibility of three-dimensional artificial intelligence algorithm integration with intracardiac echocardiography for left atrial imaging during atrial fibrillation catheter ablation

AIMS

Intracardiac echocardiography (ICE) is a useful but operator-dependent tool for left atrial (LA) anatomical rendering during atrial fibrillation (AF) ablation. The CARTOSOUND FAM Module, a new deep learning (DL) imaging algorithm, has the potential to overcome this limitation. This study aims to evaluate feasibility of the algorithm compared to cardiac computed tomography (CT) in patients undergoing AF ablation.

METHODS AND RESULTS

In 28 patients undergoing AF ablation, baseline patient information was recorded, and three-dimensional (3D) shells of LA body and anatomical structures [LA appendage/left superior pulmonary vein/left inferior pulmonary vein/right superior pulmonary vein/right inferior pulmonary vein (RIPV)] were reconstructed using the DL algorithm. The selected ultrasound frames were gated to end-expiration and max LA volume. Ostial diameters of these structures and carina-to-carina distance between left and right pulmonary veins were measured and compared with CT measurements. Anatomical accuracy of the DL algorithm was evaluated by three independent electrophysiologists using a three-anchor scale for LA anatomical structures and a five-anchor scale for LA body. Ablation-related characteristics were summarized. The algorithm generated 3D reconstruction of LA anatomies, and two-dimensional contours overlaid on ultrasound input frames. Average calculation time for LA reconstruction was 65 s. Mean ostial diameters and carina-to-carina distance were all comparable to CT without statistical significance. Ostial diameters and carina-to-carina distance also showed moderate to high correlation (r = 0.52-0.75) except for RIPV (r = 0.20). Qualitative ratings showed good agreement without between-rater differences. Average procedure time was 143.7 ± 43.7 min, with average radiofrequency time 31.6 ± 10.2 min. All patients achieved ablation success, and no immediate complications were observed.

CONCLUSION

DL algorithm integration with ICE demonstrated considerable accuracy compared to CT and qualitative physician assessment. The feasibility of ICE with this algorithm can potentially further streamline AF ablation workflow.

Performance of a multi-sensor implantable defibrillator algorithm for heart failure monitoring in the presence of atrial fibrillation

AIMS

The HeartLogic Index combines data from multiple implantable cardioverter defibrillators (ICDs) sensors and has been shown to accurately stratify patients at risk of heart failure (HF) events. We evaluated and compared the performance of this algorithm during sinus rhythm and during long-lasting atrial fibrillation (AF).

METHODS AND RESULTS

HeartLogic was activated in 568 ICD patients from 26 centres. We found periods of ≥30 consecutive days with an atrial high-rate episode (AHRE) burden <1 h/day and periods with an AHRE burden ≥20 h/day. We then identified patients who met both criteria during the follow-up (AHRE group, n = 53), to allow pairwise comparison of periods. For control purposes, we identified patients with an AHRE burden <1 h throughout their follow-up and implemented 2:1 propensity score matching vs. the AHRE group (matched non-AHRE group, n = 106). In the AHRE group, the rate of alerts was 1.2 [95% confidence interval (CI): 1.0-1.5]/patient-year during periods with an AHRE burden <1 h/day and 2.0 (95% CI: 1.5-2.6)/patient-year during periods with an AHRE-burden ≥20 h/day (P = 0.004). The rate of HF hospitalizations was 0.34 (95% CI: 0.15-0.69)/patient-year during IN-alert periods and 0.06 (95% CI: 0.02-0.14)/patient-year during OUT-of-alert periods (P < 0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95% CI: 1.67-55.31) during periods with an AHRE burden <1 h/day and 2.70 (95% CI: 1.01-28.33) during periods with an AHRE burden ≥20 h/day. In the matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95% CI: 0.12-0.60)/patient-year during IN-alert periods and 0.04 (95% CI: 0.02-0.08)/patient-year during OUT-of-alert periods (P < 0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44).

CONCLUSION

Patients received more alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio in comparison with non-AF periods and non-AF patients.

CLINICAL TRIAL REGISTRATION

http://clinicaltrials.gov/Identifier: NCT02275637.

Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.

Electrocardiographic parameters and the risk of new-onset atrial fibrillation in the general population: the Rotterdam Study

AIMS

We aimed to assess the (shape of the) association and sex differences in the link between electrocardiographic parameters and new-onset atrial fibrillation (AF).

METHODS AND RESULTS

A total of 12 212 participants free of AF at baseline from the population-based Rotterdam Study were included. Up to five repeated measurements of electrocardiographic parameters including PR, QRS, QT, QT corrected for heart rate (QTc), JT, RR interval, and heart rate were assessed at baseline and follow-up examinations. Cox proportional hazards- and joint models, adjusted for cardiovascular risk factors, were used to determine the (shape of the) association between baseline and longitudinal electrocardiographic parameters with new-onset AF. Additionally, we evaluated potential sex differences. During a median follow-up of 9.3 years, 1282 incident AF cases occurred among 12 212 participants (mean age 64.9 years, 58.2% women). Penalized cubic splines revealed that associations between baseline electrocardiographic measures and risk of new-onset AF were generally U- and N-shaped. Sex differences in terms of the shape of the various associations were most apparent for baseline PR, QT, QTc, RR interval, and heart rate in relation to new-onset AF. Longitudinal measures of higher PR interval [fully adjusted hazard ratio (HR), 95% confidence interval (CI), 1.43, 1.02-2.04, P = 0.0393] and higher QTc interval (fully adjusted HR, 95% CI, 5.23, 2.18-12.45, P = 0.0002) were significantly associated with new-onset AF, in particular in men.

CONCLUSION

Associations of baseline electrocardiographic measures and risk of new-onset AF were mostly U- and N-shaped. Longitudinal electrocardiographic measures of PR and QTc interval were significantly associated with new-onset AF, in particular among men.

New insertable cardiac monitors show high diagnostic yield and good safety profile in real-world clinical practice: results from the international prospective observational SMART Registry

AIMS

Insertable cardiac monitors (ICMs) are indicated for long-term monitoring of unexplained syncope or palpitations, and for detection of bradycardia, ventricular tachycardia, and/or atrial fibrillation (AF). The aim of our study was to evaluate the safety and clinical value associated with a new generation ICM (Confirm Rx™, Abbott, Illinois, USA), featuring a new remote monitoring system based on smartphone patient applications.

METHODS AND RESULTS

The SMART Registry is an international prospective observational study. The main endpoints were ICM safety (incidence of serious adverse device and procedure-related events (SADEs) at 1 month), ICM clinical value (incidence of device-detected true arrhythmias and of clinical diagnoses and interventions), and patient-reported experience measurements (PREMs). A total of 1400 subjects were enrolled. ICM indications included syncope (49.1%), AF (18.8%), unexplained palpitations (13.6%), risk of ventricular arrhythmia (6.6%), and cryptogenic stroke (6.0%). Freedom from SADEs at 1 month was 99.4% (95% Confidence Interval: 98.8-99.7%). In the 6-month monitoring period, the ICM detected true cardiac arrhythmias in 45.7% of patients and led to clinical interventions in a relevant proportion of patients; in particular, a pacemaker implant was performed after bradycardia detection in 8.9% of subjects who received an ICM for syncope and oral anticoagulation therapy was indicated after AF detection in 15.7% of subjects with cryptogenic stroke. PREMs showed that 78.2% of subjects were satisfied with the remote monitoring patient app.

CONCLUSION

The evaluated ICM is associated with an excellent safety profile and high diagnostic yield. Patients reported positive experiences associated with the use of their smartphone for the device remote monitoring.

EHRA expert consensus document on the management of arrhythmias in frailty syndrome, endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), Latin America Heart Rhythm Society (LAHRS), and Cardiac Arrhythmia Society of Southern Africa (CASSA)

There is an increasing proportion of the general population surviving to old age with significant chronic disease, multi-morbidity, and disability. The prevalence of pre-frail state and frailty syndrome increases exponentially with advancing age and is associated with greater morbidity, disability, hospitalization, institutionalization, mortality, and health care resource use. Frailty represents a global problem, making early identification, evaluation, and treatment to prevent the cascade of events leading from functional decline to disability and death, one of the challenges of geriatric and general medicine. Cardiac arrhythmias are common in advancing age, chronic illness, and frailty and include a broad spectrum of rhythm and conduction abnormalities. However, no systematic studies or recommendations on the management of arrhythmias are available specifically for the elderly and frail population, and the uptake of many effective antiarrhythmic therapies in these patients remains the slowest. This European Heart Rhythm Association (EHRA) consensus document focuses on the biology of frailty, common comorbidities, and methods of assessing frailty, in respect to a specific issue of arrhythmias and conduction disease, provide evidence base advice on the management of arrhythmias in patients with frailty syndrome, and identifies knowledge gaps and directions for future research.

Wearable Devices in Cardiovascular Medicine

Wearable devices, such as smartwatches and activity trackers, are commonly used by patients in their everyday lives to manage their health and well-being. These devices collect and analyze long-term continuous data on measures of behavioral or physiologic function, which may provide clinicians with a more comprehensive view of a patients' health compared with the traditional sporadic measures captured by office visits and hospitalizations. Wearable devices have a wide range of potential clinical applications ranging from arrhythmia screening of high-risk individuals to remote management of chronic conditions such as heart failure or peripheral artery disease. As the use of wearable devices continues to grow, we must adopt a multifaceted approach with collaboration among all key stakeholders to effectively and safely integrate these technologies into routine clinical practice. In this Review, we summarize the features of wearable devices and associated machine learning techniques. We describe key research studies that illustrate the role of wearable devices in the screening and management of cardiovascular conditions and identify directions for future research. Last, we highlight the challenges that are currently hindering the widespread use of wearable devices in cardiovascular medicine and provide short- and long-term solutions to promote increased use of wearable devices in clinical care.

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.

CONCEPTUAL, FUNCTIONAL-ORGANISATIONAL MODEL OF THE REGIONAL CENTRE FOR CARDIOLOGY AND CARDIAC SURGERY BASED ON PUBLIC-PRIVATE PARTNERSHIP

OBJECTIVE

The aim: To substantiate the model of the Regional Center for Cardiology and Cardiac Surgery (RCCCS) based on the principles of public-private partnership (PPP).

PATIENTS AND METHODS

Materials and methods: A systematic approach and analysis, medical-statistical and sociological method, expert assessment and method of conceptual modelling.

RESULTS

Results: A comprehensive medical and social study of the morbidity rate of the population of the Kyiv region (Ukraine) with circulatory system diseases (CSD), as well as an analysis of the activities of the cardiological service of the Kyiv region in 2010-2019, have been made. Deficiencies in the organisation of medical care were identified, and strategic directions for its improvement were substantiated. The results of a sociological survey of CSD patients, cardiologists and experts - health care organisers have been analysed. According to the results of the expert assessment, health care institutions (HCI) providing cardiac care to the population in the Kyiv region are not ready to work under the conditions of the market economy; there is no appropriate regulatory framework, economic and legal independence of HCI, there is no market strategy for the development of HCI.

CONCLUSION

Conclusions: The model of RCCCS, based on the principles of PPP being a medical institution of a new organisational and legal form, is capable of providing high-quality and affordable highly specialised medical care of the third level to the population with diseases of the circulatory system at the regional level.

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.