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

[Antiarrhythmic drugs in the present day]

Cardiac arrhythmias cause a significant proportion of hospitalizations and physician contacts worldwide. By using antiarrhythmic drugs, cardiac arrhythmias can be effectively treated and the frequency of recurrences reduced. Atrial fibrillation and heart failure represent diseases in which antiarrhythmic drugs are more often used on a long-term basis. The aim of this article is to provide an overview of the most common antiarrhythmic drugs and their uses as well as to provide recommendations for adequate handling and use, especially in the outpatient setting. In addition to long-term use, some antiarrhythmic drugs are also administered for the acute management of supraventricular or ventricular tachycardia. Relevant contraindications, side effects and interactions must be considered, meaning that patients should be followed up when using these potent drugs. This article shows in detail what to consider when using antiarrhythmic drugs in order to ensure not only effective but also safe treatment.

[Characterization of atrial fibrillation burden using wearables]

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

[Digital precision medicine in rhythmology : Risk prediction of recurrences, sudden cardiac death, and outcome]

Digital precision medicine is gaining increasing importance in rhythmology, especially in the treatment of cardiac arrhythmias. This trend is driven by the advancing digitization in healthcare and the availability of large amounts of data from various sources such as electrocardiograms (ECGs), implants like pacemakers and implantable cardioverter-defibrillators (ICDs), as well as wearables like smartwatches and fitness trackers. Through the analysis of this data, physicians can develop more precise and individualized diagnoses and treatment strategies for patients with cardiac arrhythmias. For example, subtle changes in ECGs can be identified, indicating potentially dangerous arrhythmias. Genetic analyses and resulting large datasets also play an increasingly significant role, especially in hereditary ion channel disorders such as long QT syndrome (LQTS) and Brugada syndrome (BrS), as well as in lone atrial fibrillation (AF). Precision medicine enables the development of individualized treatment approaches tailored to the specific needs and risk factors of each patient. This can help improve screening strategies, reduce adverse events, and ultimately enhance the quality of life for patients. Technological advancements such as big data, artificial intelligence, machine learning, and predictive analytics play a crucial role in predicting the risk of arrhythmias and sudden cardiac death. These concepts enable more precise and personalized predictions and support physicians in the treatment and monitoring of their patients.

[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.

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.

Validation of a machine learning algorithm to identify pulmonary vein isolation during ablation procedures for the treatment of atrial fibrillation: results of the PVISION study

AIMS

Pulmonary vein isolation (PVI) is the cornerstone of ablation for atrial fibrillation. Confirmation of PVI can be challenging due to the presence of far-field electrograms (EGMs) and sometimes requires additional pacing manoeuvres or mapping. This prospective multicentre study assessed the agreement between a previously trained automated algorithm designed to determine vein isolation status with expert opinion in a real-world clinical setting.

METHODS AND RESULTS

Consecutive patients scheduled for PVI were recruited at four centres. The ECGenius electrophysiology (EP) recording system (CathVision ApS, Copenhagen, Denmark) was connected in parallel with the existing system in the laboratory. Electrograms from a circular mapping catheter were annotated during sinus rhythm at baseline pre-ablation, time of isolation, and post-ablation. The ground truth for isolation status was based on operator opinion. The algorithm was applied to the collected PV signals off-line and compared with expert opinion. The primary endpoint was a sensitivity and specificity exceeding 80%. Overall, 498 EGMs (248 at baseline and 250 at PVI) with 5473 individual PV beats from 89 patients (32 females, 62 ± 12 years) were analysed. The algorithm performance reached an area under the curve (AUC) of 92% and met the primary study endpoint with a sensitivity and specificity of 86 and 87%, respectively (P = 0.005; P = 0.004). The algorithm had an accuracy rate of 87% in classifying the time of isolation.

CONCLUSION

This study validated an automated algorithm using machine learning to assess the isolation status of pulmonary veins in patients undergoing PVI with different ablation modalities. The algorithm reached an AUC of 92%, with both sensitivity and specificity exceeding the primary study endpoints.

Detection of subclinical atrial fibrillation with cardiac implanted electronic devices: What decision making on anticoagulation after the NOAH and ARTESiA trials?

Atrial fibrillation (AF) may be asymptomatic and the extensive monitoring capabilities of cardiac implantable electronic devices (CIEDs) revealed asymptomatic atrial tachi-arrhythmias of short duration (minutes-hours) occurring in patients with no prior history of AF and without AF detection at a conventional surface ECG. Both the terms "AHRE" (Atrial High-Rate Episodes) and subclinical AF were used in a series of prior studies, that evidenced the association with an increased risk of stroke. Two randomized controlled studies were planned in order to assess the risk-benefit profile of anticoagulation in patients with AHRE/subclinical AF: the NOAH and ARTESiA trials. The results of these two trials (6548 patients enrolled, overall) show that the risk of stroke/systemic embolism associated with AHRE/subclinical AF is in the range of 1-1.2 % per patient-year, but with an important proportion of severe/fatal strokes occurring in non-anticoagulated patients. The apparent discordance between ARTESiA and NOAH results may be approached by considering the related study-level meta-analysis, which highlights a consistent reduction of ischemic stroke with oral anticoagulants vs. aspirin/placebo (relative risk [RR] 0.68, 95 % CI 0.50-0.92). Oral anticoagulation was found to increase major bleeding (RR 1.62, 95 % CI 1.05-2.5), but no difference was found in fatal bleeding (RR 0.79, 95 % CI 0.37-1.69). Additionally, no difference was found in cardiovascular death or all-cause mortality. Taking into account these results, clinical decision-making for patients with AHRE/subclinical AF at risk of stroke, according to CHA2DS2-VASc, can now be evidence-based, considering the benefits and related risks of oral anticoagulants, to be shared with appropriately informed patients.

Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference

AIMS

Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA).

METHODS AND RESULTS

Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF.

CONCLUSIONS

Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF.

Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation

AIMS

Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions.

METHODS AND RESULTS

Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7-99.9%], specificity (99.9%; CI: 99.8-100.0%), positive predictive value (99.6%; CI: 99.1-100.0%), and negative predictive value (99.6%; CI: 99.0-100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland-Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m.

CONCLUSION

Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.

The feasibility of population screening for paroxysmal atrial fibrillation using hand-held electrocardiogram devices

AIMS

There are few data on the feasibility of population screening for paroxysmal atrial fibrillation (AF) using hand-held electrocardiogram (ECG) devices outside a specialist setting or in people over the age of 75. We investigated the feasibility of screening when conducted without face-to-face contact ('remote') or via in-person appointments in primary care and explored impact of age on screening outcomes.

METHODS AND RESULTS

People aged ≥65 years from 13 general practices in England participated in screening during 2019-20. This involved attending a practice nurse appointment (10 practices) or receiving an ECG device by post (three practices). Participants were asked to use a hand-held ECG for 1-4 weeks. Screening outcomes included uptake, quality of ECGs, AF detection rates, and uptake of anticoagulation if AF was detected. Screening was carried out by 2141 (87.5%) of people invited to practice nurse-led screening and by 288 (90.0%) invited to remote screening. At least 56 interpretable ECGs were provided by 98.0% of participants who participated for 3 weeks, with no significant differences by setting or age, except people aged 85 or over (91.1%). Overall, 2.6% (64/2429) screened participants had AF, with detection rising with age (9.2% in people aged 85 or over). A total of 53/64 (82.8%) people with AF commenced anticoagulation. Uptake of anticoagulation did not vary by age.

CONCLUSION

Population screening for paroxysmal AF is feasible in general practice and without face-to-face contact for all ages over 64 years, including people aged 85 and over.

Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm

AIMS

Guidelines recommend opportunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring. The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF.

METHODS AND RESULTS

This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex.

CONCLUSION

An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model. An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups.

Accuracy and comprehensibility of chat-based artificial intelligence for patient information on atrial fibrillation and cardiac implantable electronic devices

AIMS

Natural language processing chatbots (NLPC) can be used to gather information for medical content. However, these tools contain a potential risk of misinformation. This study aims to evaluate different aspects of responses given by different NLPCs on questions about atrial fibrillation (AF) and clinical implantable electronic devices (CIED).

METHODS AND RESULTS

Questions were entered into three different NLPC interfaces. Responses were evaluated with regard to appropriateness, comprehensibility, appearance of confabulation, absence of relevant content, and recommendations given for clinically relevant decisions. Moreover, readability was assessed by calculating word count and Flesch Reading Ease score. 52, 60, and 84% of responses on AF and 16, 72, and 88% on CIEDs were evaluated to be appropriate for all responses given by Google Bard, (GB) Bing Chat (BC) and ChatGPT Plus (CGP), respectively. Assessment of comprehensibility showed that 96, 88, and 92% of responses on AF and 92 and 88%, and 100% on CIEDs were comprehensible for all responses created by GB, BC, and CGP, respectively. Readability varied between different NLPCs. Relevant aspects were missing in 52% (GB), 60% (BC), and 24% (CGP) for AF, and in 92% (GB), 88% (BC), and 52% (CGP) for CIEDs.

CONCLUSION

Responses generated by an NLPC are mostly easy to understand with varying readability between the different NLPCs. The appropriateness of responses is limited and varies between different NLPCs. Important aspects are often missed to be mentioned. Thus, chatbots should be used with caution to gather medical information about cardiac arrhythmias and devices.

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.