Europace (2023) 25, 6–27 AFNET/EHRA CONSENSUS DOCUMENT https://doi.org/10.1093/europace/euac062 Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference Renate B. Schnabel 1,2,3, Elena Andreassi Marinelli4, Elena Arbelo5,6,7, Giuseppe Boriani8, Serge Boveda9,10, Claire M. Buckley11, A. John Camm12, Barbara Casadei13, Winnie Chua14, Nikolaos Dagres15, Mirko de Melis16, Lien Desteghe17,18,19,20, Søren Zöga Diederichsen21, David Duncker22, Lars Eckardt1,23, Christoph Eisert24, Daniel Engler2,3, Larissa Fabritz1,2,3,14,25, Ben Freedman26, Ludovic Gillet27, Andreas Goette1,28, Eduard Guasch5,6,7, Jesper Hastrup Svendsen21,29, Stéphane N. Hatem30, Karl Georg Haeusler1,31, Jeff S. Healey32, Hein Heidbuchel17,18, Gerhard Hindricks1,15, F.D. Richard Hobbs33, Thomas Hübner24, Dipak Kotecha34, Michael Krekler35, Christophe Leclercq36, Thorsten Lewalter1,37,38, Honghuang Lin39, Dominik Linz40,41, Gregory Y.H. Lip42,43, Maja-Lisa Løchen44, Wim Lucassen45, Katarzyna Malaczynska-Rajpold46, Steffen Massberg47,48, Jose L. Merino 49, Ralf Meyer50, Lluıs Mont5,6,7, Michael C. Myers36, Lis Neubeck51, Teemu Niiranen52,53, Michael Oeff1, Jonas Oldgren54, Tatjana S. Potpara55, George Psaroudakis56, Helmut Pürerfellner57, Ursula Ravens1,58, Michiel Rienstra59, Lena Rivard60, Daniel Scherr61, Ulrich Schotten1,62, Dipen Shah63, Moritz F. Sinner1,47,48, Rüdiger Smolnik4, Gerhard Steinbeck1,64, Daniel Steven1,65, Emma Svennberg66, Dierk Thomas1,67,68,69, Mellanie True Hills70, Isabelle C. van Gelder71, Burcu Vardar56, Elena Palà72, Reza Wakili1,73, Karl Wegscheider1,3,74, Mattias Wieloch75,76, Stephan Willems1,3,77, Henning Witt78, André Ziegler28, Matthias Daniel Zink79, and Paulus Kirchhof1,2,3,14* 1 Atrial Fibrillation Network (AFNET), Muenster, Germany; 2Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 3German Centre for Cardiovascular Research (DZHK) partner site Hamburg/Kiel/Lübeck, Hamburg, Germany; 4Daiichi Sankyo Europe, Munich, Germany; 5 Arrhythmia Section, Cardiology Department, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain; 6IDIBAPS, Institut d’Investigació August Pi i Sunyer, Barcelona, Spain; 7CIBERCV, Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain; 8Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Polyclinic of Modena, Modena, Italy; 9Cardiology—Heart Rhythm Management Department, Clinique Pasteur, 45 Avenue de Lombez, 31076 Toulouse, France; 10Universiteit Ziekenhuis, Vrije Universiteit Brussel (VUB), Brussels, Belgium; 11School of Public Health, University College Cork, Cork, Ireland; 12Cardiology Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George’s University of London, London, UK; 13RDM, Division of Cardiovascular Medicine, British Heart Foundation Centre of Research Excellence, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; 14Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK; 15 Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; 16Medtronic Bakken Research Center, Maastricht, The Netherlands; 17Research Group Cardiovascular Diseases, University of Antwerp, Antwerp, Belgium; 18Department of Cardiology, Antwerp University Hospital, Antwerp, Belgium; 19Faculty of Medicine and Life * Corresponding author. Tel: +49 40 7410 52438; Fax: +49 40 7410 55862. E-mail address: p.kirchhof@uke.de © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com The 8th AFNET/EHRA consensus conference 7 Sciences, Hasselt University, Hasselt, Belgium; 20Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; 21Department of Cardiology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark; 22Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany; 23Division of Electrophysiology, Department of Cardiology and Angiology, Münster, Germany; 24Preventicus, Jena, Germany; 25University Center of Cardiovascular Science Hamburg, Hamburg, Germany; 26Heart Research Institute, The University of Sydney, Sydney, Australia; 27Roche Diagnostics, Basel, Switzerland; 28St Vincenz Hospital, Paderborn, Germany; 29Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 30IHU ICAN, Sorbonne-Universite, Paris, France; 31Department of Neurology, Universitätsklinikum Würzburg, Würzburg, Germany; 32Population Health Research Institute, McMaster University Hamilton, ON, Canada; 33Oxford Primary Care, University of Oxford, Oxford, UK; 34University of Birmingham & University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; 35Bristol Myers Squibb, New York, NY, USA; 36 Department of Cardiology, University Rennes, Rennes, France; 37Hospital Munich South, Department of Cardiology, Munich, Germany; 38Department of Cardiology, University of Bonn, Bonn, Germany; 39Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA; 40Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands; 41Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 42Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK; 43Department of Clinical Medicine, Aalborg University, Aalborg, Denmark; 44Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway; 45Amsterdam UMC (location AMC), Department General Practice, Amsterdam, The Netherlands; 46Royal Brompton Hospital, London, UK; 47Department of Cardiology, University Hospital, LMU Munich, Munich, Germany; 48German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany; 49Arrhythmia & Robotic EP Unit, La Paz University Hospital, IDIPAZ, Madrid, Spain; 50Medtronic, Dublin, Ireland; 51Centre for Cardiovascular Health Edinburgh Napier University, Edinburgh, UK; 52University of Turku and Turku University Hospital, Turku, Finland; 53National Institute for Health and Welfare, Helsinki, Finland; 54Uppsala Clinical Research Center and Department of Medical Sciences, Uppsala University, Uppsala, Sweden; 55School of Medicine, Belgrade University, Cardiology Clinic, University Clinical Centre of Serbia, Belgrade, Serbia; 56Bayer AG, Leverkusen, Germany; 57 Ordensklinikum Linz, Elisabethinen, Cardiological Department, Linz, Austria; 58Institute of Experimental Cardiovascular Medicine, University Hospital Freiburg, Freiburg, Germany; 59 Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; 60Montreal Heart Institute, University of Montreal, Montreal, Canada; 61Division of Cardiology, Medical University of Graz, Graz, Austria; 62MUMC+, Maastricht, The Netherlands; 63University Hospital of Geneva, Cardiac Electrophysiology Unit, Geneva, Switzerland; 64Center for Cardiology at Clinic Starnberg, Starnberg, Germany; 65University Hospital Cologne, Heart Center, Department of Electrophysiology, Cologne, Germany; 66Karolinska Institutet, Department of Medicine Huddinge, Karolinska University Hospital, Stockholm, Sweden; 67Department of Cardiology, Medical University Hospital, Heidelberg, Germany; 68HCR (Heidelberg Center for Heart Rhythm Disorders), Medical University Hospital Heidelberg, Heidelberg, Germany; 69DZHK (German Center for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany; 70StopAfib.org, American Foundation for Women’s Health, Decatur, TX, USA; 71Department of Cardiology, University Medical Center Groningen, University of Groningen, The Netherlands; 72Neurovascular Research Laboratory, Vall d’Hebron Institute of Research (VHIR), Autonomous University of Barcelona, Barcelona, Spain; 73Department of Cardiology and Vascular Medicine, Westgerman Heart and Vascular Center, University of Duisburg-Essen, Essen, Germany; 74Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Germany; 75Department of Coagulation Disorders, Skane University Hospital, Lund University, Malmö, Sweden; 76Sanofi, Paris, France; 77Asklepios Hospital St Georg, Department of Cardiology and Internal Intensive Care Medicine, Faculty of Medicine, Semmelweis University Campus Hamburg, Hamburg, Germany; 78Pfizer Deutschland GmbH, Berlin, Germany; and 79Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, Aachen, Germany Received 7 March 2022; accepted after revision 11 April 2022; online publish-ahead-of-print 27 July 2022 Abstract Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related com­ plications cause unacceptable morbidity and mortality even on optimal current therapy. This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evi­ dence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impair­ ment, which can be facilitated by AI. ------------------------------------------------------------------------------------------------------------------------------------------------------------ Keywords Atrial fibrillation • Artificial intelligence • Heart failure • Atrial cardiomyopathy • Cognitive function • Dementia • Outcomes • Quality of care • Cost • Research • Rhythm management • Catheter ablation • Anticoagulation • Bleeding • Research priorities • Technology • Stroke • Integrated care • Screening • AFNET • EHRA • Guidelines • Consensus statement Introduction patients and their families, and incurring high costs to healthcare systems.1,2 Despite marked progress in the detection and management of pa­ Implanted devices, and more recently, wearables, as well as other tients with atrial fibrillation (AF), the arrhythmia remains un­ consumer electronics, enable long-term continuous monitoring of detected in a large proportion of patients, particularly in the biosignals, providing the ability to detect rare arrhythmias including elderly. Atrial fibrillation confers an important public health bur­ AF. Initial results from continuous rhythm recording over years den, causing high mortality and morbidity, impacting affected have provided insights into the natural history of AF and generated 8 R.B. Schnabel et al. evidence on AF burden-related outcomes. It appears intuitive that systematic screening approaches, but also through interrogation of more and longer atrial arrhythmias should be associated with higher cardiac implantable devices and consumer devices, e.g. wearables. complication rates, but a clear biological gradient supporting this as­ The recommendation for opportunistic screening during medical vis­ sumption with data is missing.3 Defined screening populations as well its for purposes other than screening was based on a study using pulse as modes of screening and analysis are required to render AF screen­ palpation carried out in the UK before 2005.7 New evidence emerged ing beneficial and cost-effective.4,5 after the publication of the guidelines. The D2AF (Detecting and New data show that early rhythm control therapy, initiated after a re­ Diagnosing Atrial Fibrillation) study showed that opportunistic screening cent clinical diagnosis of AF, can improve cardiovascular outcomes and in primary care did not increase the detection of AF in individuals mortality in patients with AF and cardiovascular risk factors.4 These find­ aged .65 years.8 In addition, no difference in AF detection was demon­ ings will change the concept and practice of rhythm management. strated when using a single-lead handheld ECG during regularly sched­ Rhythm management should preferably include a resolute attempt at uled office visits in the larger VITAL-AF (Screening for Atrial Fibrillation rhythm control in patients with new AF, but also comprise rate control Among Older Patients in Primary Care Clinics) study, except for a signal to prepare patients for recurrences of AF. There has also been increas­ for increased detection in patients aged ≥85.9 Similar observations ing focus on cognitive function assessment in patients with AF for early were made in the randomized MonDAFIS (Systematic monitoring for de­ detection and prevention of cognitive impairment and its negative im­ tection of atrial fibrillation in patients with acute ischaemic stroke) study pact on treatment compliance.6 These new data call for better integra­ comparing systematic ECG monitoring with additional Holter-ECG re­ tion of AF screening and early rhythm control in clinical care. cording for up to 7 days in hospital and usual care in patients hospitalized Translational research suggests a complex concept of atrial cardio­ with acute ischaemic stroke.10 These studies illustrate the improvement myopathy as a major driver for AF incidence and progression, as well in AF detection in routine care, i.e. the control arms, during the last dec­ as AF-related complications. Advances have been made in the char­ ade, possibly because of increased awareness by health professionals and acterization and quantification of atrial cardiomyopathy from cellular the public attributable to dissemination of the importance of stroke pre­ to clinical levels. Atrial dysfunction and the broad spectrum of clinical vention by AF detection and treatment. At the same time, these studies and risk factors have been used to define populations at risk of AF in underline the well-known shortcomings of opportunistic AF screening primary stroke prevention as well as post-stroke cohorts. Artificial that may miss high-risk parts of the population. intelligence (AI)-based methods integrating multimodal information Systematic ECG screening could improve AF detection by including and extracting central features have shown promising first results, patients who do not seek medical help, as demonstrated in two large but legal barriers for its implementation have to be overcome while recent randomized clinical trials.11,12 In the population-based, rando­ ensuring fair and secure use of data. To improve estimation of mized STROKESTOP study, AF screening using ECG recordings AF-related risk and to guide therapy of the arrhythmia, further trans­ from a portable device, taken twice daily for 2 weeks, was used in in­ lational science and clinical research is needed to better understand dividuals aged 75–76 years. This intervention resulted in a reduction of the different underlying mechanisms that are reflected by the elec­ the combined outcome of mortality, stroke, and severe bleeding com­ trocardiographic (ECG) pattern of AF. pared with a control group.11 In the LOOP study, AF screening in the intervention group was conducted using an implantable cardiac device, which continuously monitored heart rhythm for 3 years. In case of AF Methods detection for .6 min, oral anticoagulant (OAC) therapy was initiated. Despite a three-fold increase in AF detection and OAC initiation, the The 8th AFNET/European Heart Rhythm Association (EHRA) Consensus LOOP study did not show a significant decrease in the outcome of Conference brought together 83 international interdisciplinary experts in­ cluding arrhythmia and heart failure (HF) specialists, patients with AF, an stroke and systemic embolism compared with the control group.12 AF patient organization, pharmacologists, translational scientists, general With many consumer devices integrating algorithms for detecting AF practitioners, neurologists, nurse practitioners, epidemiologists, consul­ readily available, consumer-led screening is a growing trend that health­ tants in public health medicine, clinical trialists, and health economists in care systems need to respond to. While the conceptual ability of AF de­ Hamburg, Germany, for 2 days of intense discussion in the plenary and tection using consumer electronics, e.g. smartwatches, has been in breakout groups. Participants who could not travel for pandemic-related shown,13–15 substantial challenges remain, including access to the tech­ reasons participated remotely. The results of the expert discussion and nology, usability in elderly populations and in populations with relevant their potential clinical impact are summarized here. risk factors, data privacy, legal, data transfer issues, AF diagnosis valid­ ation, and, importantly, sound information on when to initiate treatment in the light of the potential for over-diagnosis of arrhythmias without Atrial fibrillation screening clinical implications. Once the therapeutic implications of detecting implementation short, rare atrial arrhythmias are understood16,17 and the usability and reliability in elderly populations has been improved, consumer electron­ Approaches to screening ics may be a suitable way to enhance AF screening. The 2020 European Society of Cardiology (ESC) guidelines on AF management recommend opportunistic screening for AF in persons aged ≥65 years (Class I, Level B) as well as systematic screening for Evidence-based implementation of AF in individuals aged ≥75 years, or at high risk of stroke (Class IIa, systematic screening for atrial fibrillation Level B).1 Efforts to reduce the burden of AF complications by an To enable the implementation of systematic screening for AF, the early diagnosis have been made through opportunistic and participants of the 8th AFNET EHRA consensus conference propose The 8th AFNET/EHRA consensus conference 9 AF not detected SYSTEMATIC CONSUMER-LED Reassurance SCREENING Evidence gap SCREENING Handheld Repeat a"er 3-5 years ECG Age 65 and (twice daily two weeks) prolonged screening Le!er If SR connue with ≥75 years OR addional risk Entry point for systemac screening programme Email factors, Text message Single-me digital device ≥ 65 years AND point ECG Paent PPG or unsure • HF, DM, HTN, Invitaon informaon validaon stroke/TIA, MI, LEAD, OR or 5-7day connuous ECG • NT-proBNP ≥125 ng/L Patient monitoring (patch/Holter) or information • Alert from wearable /PPG AF detected ECG device indicave of Patient information Physician AF confirmaon of AF ABC Figure 1 Suggested systematic screening pathway and entry of consumer-led screening into the systematic screening pathway. The scores com­ prise congestive HF, hypertension, age ≥75 years, DM, stroke, vascular disease, age 65–74 years, sex category (female) (CHA2DS2-VASc score) and hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile INR (international normalized ratio), elderly (.65 years), drugs/alcohol concomitantly (HAS-BLED). Consider HAS-BLED to minimize bleeding risk. DM, diabetes mellitus; ECG, electrocardiogram; HF, heart failure; LEAD, lower extremity arterial disease; MI, myocardial infarction; NT-proBNP, N-terminal pro-B-type natriuretic peptide; PPG, photoplethysmography; TIA, transient ischaemic attack; SR, sinus rhythm. a simple, scalable, and pragmatic AF screening pathway suitable for Holter recording could be performed.18 In continuous monitoring, different healthcare systems. Such a pathway may improve timely an AF episode lasting ≥30 s is diagnostic according to current guide­ diagnosis of AF and subsequently prevent AF-related complications. lines.1 As evidence showing the benefits of screening grows At the same time, such efforts can improve research on AF screening (affect-eu.eu),19 discussions with regulatory authorities should be in­ by providing systematic evaluation of screening strategies and de­ itiated to ensure reimbursement for AF screening and define the en­ vices. The consensus conference experts recommend systematic suing diagnostic and therapeutic work-up. screening for all individuals aged ≥75 years, and that systematic Following AF detection, a medical assessment by a medical team screening may be considered for individuals aged 65–74 years with experienced in AF management, in an in-person or remote setting, additional risk factors [e.g. HF, hypertension, diabetes, previous is required to confirm the diagnosis of AF and to evaluate prognosis. stroke/TIA, myocardial infarction (MI), lower extremity artery dis­ The assessment will evaluate patient information, confirmation of ease], elevation of natriuretic peptides [N-terminal pro B-type natri­ the CHA2DS2-VASC [congestive HF, hypertension, age ≥75 years, uretic peptide (NT-proBNP) ≥125 ng/L], or a positive alert by a diabetes mellitus (DM), stroke, vascular disease, age 65–74 years, digital device using photoplethysmography (PPG, Figure 1). sex category (female)] stroke risk score, and detection of additional Individuals considered for screening could be invited by the health­ concomitant cardiovascular conditions and risk factors. care institution responsible for the screening initiative by letter, Subsequently, a comprehensive AF management incorporating all email, phone call, or text message based on local availability and prac­ elements of the guideline-recommended approach (avoiding stroke, tice. Ideally, the invitation should include information on the potential better rhythm and rate management, treatment of concomitant car­ benefits and harms of AF screening in the participants’ own language diovascular conditions), considering diagnosis and therapy of con­ using simple wording. comitant cardiovascular diseases as well as risk factors, can be The ECG screening procedure should be adapted to available initiated.1 Individuals without detected arrhythmias should be reas­ healthcare resources. In case of in-person screening, a single time sured. In these individuals, a timeframe for repeated screening/mon­ point screen using, for example, a rhythm strip or 12-lead ECG, itoring need to be established in different categories of pre-detection could be used to diagnose AF.1 If this does not show AF, 2-week risk. The value of repeated screening and the ideal time point for twice-daily handheld ECG monitoring should be performed. follow-up (FU) using single time point ECGs are still unclear and Alternatively, a 5–7 day (up to 14 days) continuous ECG patch or should be investigated in future trials.20–22 10 R.B. Schnabel et al. Digital approaches to AF screening were rapidly developed in re­ The clinical implications of AF detected by consumer-led screening sponse to the COVID-19 pandemic and may become the preferred are not known and dedicated studies, including outcome studies of pathway. Systematic AF screening can be achieved using entirely consumer-screening led interventions, are needed. In consumer-led digital technology: Electronic medical records or the population screening, a diagnosis of AF can be made if the individual used a de­ registry (if available) can be used to identify participants eligible for vice with ECG recording, and confirmation was provided by a phys­ screening based on age. A letter with a link (e.g. a QR code) to an ician after inspection of the rhythm strip.1 Digital devices can online questionnaire can be sent to confirm whether individuals monitor the rhythm for variable durations depending on the tech­ are willing to participate in the screening programme. Individuals nology used and patient adherence. Therefore, calculated AF bur­ aged 65–74 years would also be invited to answer questions about den will differ depending on the device used.13–15 A structured AF comorbidities (with the help of their general practitioner if needed, management and initiation of therapy should be considered on an Figure 1). Thereafter, a screening device, e.g. an ECG patch or a individual basis. Recent data indicate that untreated opportunistic PPG-based pulse device, can be posted to participants for a record­ screening-detected AF has a similar stroke risk to clinically detected ing and returned to the healthcare provider to complete the digital AF.21,25 Reassuringly, anticoagulation used in the LOOP study did pathway. Alternatively, the pathway could make use of a device al­ not show an increased risk of bleeding in patients with atrial ar­ ready accessible to the participant. If AF is detected and confirmed rhythmias detected by an implantable loop recorder, but the in the recording, participants are invited for an appointment with a stroke rate was lower than expected based on AF diagnosis and healthcare professional to implement therapy as recommended ac­ stroke risk.12 cording to the A–B–C {Atrial fibrillation Better Care [includes A (avoid stroke), B (better symptom control/better rhythm manage­ Knowledge gaps and hurdles ment), and C (cardiovascular risk factors and comorbid conditions There are still important knowledge gaps, e.g. the ideal timing of re­ management)]} pathway,1 which could also potentially take place peat screening and the threshold of AF burden related to an in­ as a digital appointment. creased stroke risk which should lead to the initiation of oral anticoagulation (and other AF therapies) in patients at risk if AF is picked up by long-term monitoring, e.g. using implanted devices. It Consumer-led atrial fibrillation is also unclear whether picking up irregular pulses by PPG signals screening could lead to a more direct recommendation of anticoagulation in For individuals who have screened themselves for AF using their own patients at risk, in the future. For digital AF screening, digital health devices, i.e. consumer-led screening, false positive findings by the de­ literacy, inclusiveness of minorities, and equality in access,26–28 digital vice may be more frequent than in systematic screening. The rate of pathways to enable participation, outreach initiatives, overcoming false positives depends on the specifications of the device, its algo­ geographic distances,29 awareness of AF, and education are key. rithm, and the pre-test probability in the individual. Therefore, con­ Financial coverage needs to be secured, with regard to screening, firmation needs to be done, taking into account individual risk and the ensuing additional visits and investigations, and the subsequent unrecognized symptoms. The large-scale Apple,13 Huawei,23 and treatment. Today, it remains a privilege to advance digital AF screen­ Fitbit (NCT04380415) Heart Studies have illustrated the potential ing when in most regions of the world, even an ECG is not broadly of fast, nationwide recruitment of screening participants and demon­ available and pulse palpation has to be considered as an alternative. strated low rates of false positives among participants who com­ pleted the screening. The vast majority of participants were younger than 50 years old and drop-out rates were high. The studies The growing role of rhythm also showed that consumer-led AF screening leads to increased use management of healthcare resources in the short term. First, arrhythmias require review and verification by a health professional. Secondly, other ar­ The paradigm of rhythm management rhythmia findings will trigger additional contacts with the healthcare Recent controlled trials and observational analyses demonstrated safety system for verification. Thirdly, consumer-based AF screening will of rhythm control therapy.4,30–35 Combined with the effectiveness of also be conducted by individuals without stroke risk factors. There early rhythm control,4 these findings suggest a wider use of rhythm con­ is a lack of evidence on the implications and the benefits of screening trol therapy to improve symptoms and quality of life, and provide an in patients who are not at risk of stroke, and in young populations. additional avenue to prevent outcomes such as stroke and cardiovascu­ The magnitude of the resources involved is not known and should lar death.4,36,37 While other mechanisms are still possible,32,36–38 recent be estimated during the evaluation of consumer-led ECG AF screen­ data clearly support the hypothesis that these clinical effects are ing. Although the resources may be substantial, it can also be advan­ mediated by the lower arrhythmia burden achieved by systematic tageous as consumer-led AF screening could be an entry point into a rhythm management.4,36,37 Therefore, it is timely to consider a new ap­ more systematic screening programme for patients at cardiovascular proach in the management of patients with clinically detected AF. This risk. Consumer-led screening engages people and gets them involved group proposes that this approach should encompass initiation of oral in managing and learning about AF and their health, being more ad­ anticoagulation based on stroke risk, diagnosis and treatment of con­ herent, focusing on prevention, and thereby has the potential to re­ comitant cardiovascular conditions and risk factors, as well as rhythm inforce the concept of shared decision-making. Further evidence to management. Goals of rhythm management include reduction of ad­ determine the effectiveness of consumer-led AF screening would verse outcomes (stroke, HF, hospitalization, cardiovascular mortal­ be of benefit.24 ity),4,35 symptom improvement, and improvement in quality of life. The 8th AFNET/EHRA consensus conference 11 It prioritizes sinus rhythm maintenance, but retains rate control and Attempt at restoration of sinus rhythm AV-nodal therapy (ablate and pace) plus possibly cardiac resynchroniza­ Many patients are suitable for a trial of sinus rhythm restoration to tion as an option when rhythm control is difficult to achieve.39 reduce risk of cardiovascular events.4,35 Although the results of the Rhythm management includes (i) rhythm and rate-controlling EAST-AFNET 4 trial were not yet available, the potential of widely drugs, (ii) AF ablation, (iii) cardioversion, and/or (iv) AV-nodal ther­ used rhythm management was already recognized in the 2020 ESC apy. Successful management requires determination of therapy AF guidelines:1 The main recommendation on rhythm focuses on safety for rhythm or rate control. Several randomized controlled AF-related symptoms, but according to the 2020 ESC AF guidelines, trials underpin the safety of rhythm control therapy in elderly AF pa­ rhythm management using antiarrhythmic drugs (AAD) and AF abla­ tients with concomitant cardiovascular diseases, including AF-CHF tion are reasonable. Both usual care and systematic, early rhythm (Atrial Fibrillation and Congestive Heart Failure),33 CASTLE-AF control equally improve symptoms,4,35 but cardiovascular complica­ (Catheter Ablation versus Standard Conventional Therapy in Patients tions are lower in patients randomized to early rhythm control,4,35 with Left Ventricular Dysfunction and Atrial Fibrillation),34 ATHENA including in asymptomatic patients.36 In addition, the improved (Trial to Assess the Efficacy of Dronedarone 400 mg bid for the AF-related symptoms lead to better quality of life. However, it is im­ Prevention of Cardiovascular Hospitalization or Death from any Cause in Patients with Atrial Fibrillation/Atrial Flutter),35 and EAST-AFNET 4 portant to manage expectations when starting rhythm management: (Early Rhythm-Control Therapy in Patients with Atrial Fibrillation).4 While it is very likely that a reduced AF burden mediates the bene­ This is substantiated by analyses of electronic health records and ob­ ficial effects of rhythm management, successful rhythm management servational data sets that do not show adverse safety signals.31,32 does not require complete freedom from recurrent AF and will of­ Thus, the safety of rhythm management has improved compared ten call for repeated intervention or adaptation of therapy. with the strategy tested in AFFIRM.40 Additional analyses of large A few patients will not be suitable for continued efforts to maintain health data sets are underway and may provide further information sinus rhythm, and occasionally not even for an initial trial of restoration on the safety of early rhythm control therapy. of sinus rhythm. Clinical examples are ‘legacy’ patients who have a very Successful rhythm management in selected patients should be em­ long history of AF without symptoms and in whom there is less evi­ bedded in an overall AF management strategy comprising proper an­ dence for clinical benefit of rhythm management, patients with severe ticoagulation (‘A’) and treatment of concomitant cardiovascular atrial cardiomyopathy and atrial enlargement, patients with a recent conditions and risk factors (‘C’). Reflecting on the new evidence avail­ stroke, patients who decide against rhythm management, end-of-life able today, the meaning of ‘B’ in the A–B–C acronym for AF manage­ palliative patients, and very old patients in whom limited data are avail­ ment could potentially be adapted to Better rhythm management in able. The decision to initiate rhythm management in such patients will selected patients (Figure 2). need clinical acumen and shared decision-making. Management of pa!ents with recently diagnosed AF 1. An!coagula!on / stroke preven!on based on stroke risk NOAC VKA LAAO, selected pa!ents* + 2. Be er rhythm management An!arrhythmic drugs AF abla!on Cardioversion if needed Atrioventricular nodal therapy (drugs or ablate and pace) if needed + 3. Cardiovascular risk factor management Moderate physical exercise, weight management, smoking cessa!on, etc. Therapy for hypertension, heart failure, diabetes, arteriosclerosis, etc. Figure 2 Suggested A–B–C pathway for recently diagnosed AF. *LAAO is a potential option for selected patients with absolute contraindication for oral anticoagulation. LAAO, left atrial appendage occlusion; NOAC, non-vitamin K antagonist oral anticoagulant; VKA, vitamin K antagonist. 12 R.B. Schnabel et al. Lifelong rhythm management major implications for training and resource provision. Rhythm man­ When rhythm management is initiated, the concept of chronic dis­ agement will need to be led not only by electrophysiologists, but also ease management needs to be communicated and the treatment op­ general cardiologists, HF specialists, and other healthcare profes­ tions presented (Figure 3). It is important to set out realistic sionals whose patients have AF. Nurse-led AF clinics may also play treatment goals from the start. a role in improving access to and FU for rhythm management. Rhythm control starting with AAD can prevent AF-related out­ Educating patients on the importance of rhythm management will comes as shown in ATHENA and EAST-AFNET 4.4,35 Rhythm man­ be crucial. Practical next steps should include the following: agement is a lifelong part of AF management and will typically require different treatment choices at different time points. These can in­ (1) Healthcare professionals’ knowledge on prescribing and managing clude referral to AF ablation when AAD do not work,41 repeat AF AAD will need to be updated and learned based on the principles ablation,30,42 or treatment with AAD after AF ablation.43 Atrial fib­ laid out in AF guidelines. European Heart Rhythm Association and rillation ablation and antiarrhythmic drugs appear to have synergistic similar professional organizations are well placed to create new effects.43–45 Cardioversion is an important component of rhythm rhythm management training programmes. management although not a rhythm maintenance strategy per se, (2) Access to catheter ablation will need to be improved. This calls for simple, standardized procedures that can be delivered more widely but typically a bridge to either AAD or AF ablation to achieve longer- and locally to the patient by well-trained teams. More complex pro­ term rhythm management. AV-nodal-directed therapies such as cedures required for repeat ablations are probably better concen­ beta-blockers, verapamil/diltiazem, digitalis, or pacemakers have an trated in specialized centres. important role in patients with chronic forms of AF and/or a high ar­ (3) Access to cardioversion will need to be improved. This procedure rhythmia burden. But, based on the new evidence, an attempt at si­ can be done under sedation by general cardiologists, emergency nus rhythm restoration should be considered for many patients. physicians, and internists, as well as in specialized centres. Apart from direct current cardioversion, newer drugs such as vernakalant Potential of atrial fibrillation ablation allow more effective rapid pharmacological cardioversion.51 therapy Early rhythm management is effective in patients with AF and HF.37 Recent clinical trials suggest a possible preference for AF ablation in Knowledge gaps and research patients with HF.34,46 In patients with strong AF-related symptoms opportunities and in those in whom an optimal reduction of AF burden seems clin­ The tipping point at which rigorous rhythm management can im­ ically important,47,48 AF ablation is also preferred, especially if done prove outcomes still needs to be defined. Early rhythm control sup­ early in the course of their disease. Atrial fibrillation ablation is also ports the hypothesis that the observed clinical effects are mediated more effective in improving AF-related quality of life and symptoms by the lower arrhythmia burden achieved by rhythm management, than antiarrhythmic drug therapy.49,50 Atrial fibrillation surgery may but the exact mechanisms remain unclear. Furthermore, the identi­ succeed in selected patients in whom other rhythm management op­ fication of patients suitable for antiarrhythmic medication or direct tions fail. ablation therapy needs to be optimized. Selective, more effective, and safer AAD may allow a tailored treatment of AF patients but re­ Practical implications to improve rhythm quires further research. Left atrial appendage removal should be a management routine part of open-heart surgery in patients with AF52 and the In view of recent data, many newly diagnosed patients with AF should role of concomitant AF ablation at the time of cardiac surgery needs have access to early rhythm management. This recommendation has to be defined. Diagnosis Characterizaon of AF & comorbidies Follow-up A Avoid stroke (oral ancoagulaon) B Be!er rhythm management Atrioventricular nodal therapy (if needed) Screening Adapt rhythm management Be!er Outcomes AAD, cardioversion, If AF recurs: through rhythm Accept unsuccessful rhythm control or AF ablaon AAD, AF ablaon, or both management a!empts, revert to rate control in selected set realisc expectaons cardioversion if needed paents (endstage AV-nodal therapy) C Cardiovascular risk factors & Comorbidity detecon and therapy (life style changes, medical therapy, intervenons) Days - Weeks Months - Years Anyme Figure 3 Lifelong AF management incorporating anticoagulation, rhythm management, and concomitant conditions. AAD, antiarrhythmic drugs; AF, atrial fibrillation. The 8th AFNET/EHRA consensus conference 13 Assessment and treatment of underwent catheter ablation had a reduced risk of stroke and de­ mentia compared with non-ablation patients.61 In a population- cognitive impairment in atrial based nationwide Korean cohort, catheter ablation was associated fibrillation with a lower incidence and risk of dementia compared with AF pa­ tients who were medically managed.62 Despite potential residual Atrial fibrillation may lead to cognitive impairment or dementia as a confounding and bias, this could be attributed to the greater reduc­ result of clinically overt and/or silent stroke. However, several path­ tion of AF burden achieved by AF ablation compared with antiar­ ways that are independent of stroke may also be implicated,53,54 rhythmic drug therapy.30 However, AF ablation also causes including AF-induced systemic inflammation, chronic brain hypoperfu­ clinically silent white matter lesions as detected in studies using sion,55 and side effects of AF-specific medication.56 Thus, AF signifi­ MRI.63–66 Whether or not ablation-related MRI-detected acute cantly contributes directly to the burden of cognitive impairment ischaemic brain lesions worsen cognitive function has not been and dementia for patients, and indirectly to their caregivers and soci­ demonstrated so far.54 A recent analysis of over 300 patients under­ ety.6,57 It is likely that an integrated AF management which considers going AF ablation and high-resolution brain MRI did not find an effect the assessment of cognitive function can reduce the risk of dementia.1 of MRI-detected lesions on cognitive function 3 months after AF ab­ However, the potential protective role of AF treatment options for lation.67 Thus, even though it is clearly desirable that ablation-induced maintenance of cognitive function is not well established because brain lesions are minimized, rhythm management could be consid­ the available data are derived from population-based observational ered for appropriately anticoagulated AF patients with cognitive dys­ studies. Fortunately, several randomized trials are ongoing (Table 1). function, given the possible reductions in stroke risk and progression In the absence of specific recommendations in the 2020 guidelines to dementia (Figure 4).68 of the ESC,1 the lack of awareness regarding the impact of AF on cog­ Identification and treatment of cardiovascular risk factors can re­ nitive function among physicians may be a key hurdle in clinical prac­ duce dementia.69 Increasing evidence points towards the importance tice. Involvement of the patient, their family/caregiver(s), and of lifestyle modification in AF patients,70 although more data on cog­ primary care physician is needed. Conversely, screening for AF seems nitive decline and incident dementia are needed. For example, AF pa­ reasonable in patients presenting with dementia (Figure 4), as AF and tients who start or continue regular exercise after the diagnosis of AF dementia share risk factors like hypertension and diabetes, and there­ were observed to have a lower risk of dementia than persistent fore undetected AF may lead to further cognitive decline. non-exercisers.71 Specific aspects of management in patients with cognitive dysfunction Practical and research implications for Cognitive dysfunction is defined as a reduction in one or more cog­ cognitive dysfunction in patients with nitive abilities, such as memory, awareness, judgement, and mental atrial fibrillation acuity, across the adult lifespan. Multiple significant cognitive impair­ Experts suggest that even a moderate suspicion for cognitive impair­ ments in memory plus one or more other cognitive defects are char­ ment should lead to formal cognitive function assessment in AF pa­ acteristic of dementia. As shown in Figure 4, a Mini-Mental State tients.72 In addition, patients with cognitive dysfunction/dementia Examination (MMSE) or Montreal Cognitive Assessment (MoCA) test should be assessed for the presence of AF, as AF has to be regarded provide robust signals suggesting the presence of cognitive impair­ as an independent risk factor of cognitive impairment. The presence ment. Such a finding should trigger a detailed cognitive assessment of cognitive impairment should trigger a stepwise and interdisciplin­ using an established and validated battery of tests. Furthermore, as­ ary diagnostic pathway, aiming for optimal (non-)medical treatment sessment by a neurologist and brain imaging using magnetic reson­ to avoid the progression of cognitive dysfunction.73–76 ance imaging (MRI) can often identify the (most probable) aetiology for cognitive impairment and guide selection of disease- specific treatment. There is clear evidence showing that anticoagulation therapy pre­ Knowledge gaps and research vents strokes in patients with AF and stroke risk factors.1 Some data opportunities suggest that effective anticoagulation can preserve cognitive function Further prospective studies addressing the interplay of cognitive in patients with AF.58 Unfortunately, cognitive dysfunction and de­ (dys-)function and AF and its treatment are urgently needed, despite mentia are among the factors leading to discontinuation of anticoa­ emerging evidence that anticoagulation is associated with a lower risk gulation, thereby enhancing the risk of stroke and death.59,60 of dementia in AF patients.77,78 On the contrary, it is not known if Moreover, cognitive dysfunction was associated with a shorter rhythm management or therapy of concomitant conditions can re­ time in therapeutic range in patients treated with vitamin K duce cognitive decline. Therefore, randomized controlled trials antagonists.58 evaluating management strategies for AF should also evaluate the Evidence from the EAST-AFNET 4 trial shows that early rhythm course of cognitive function. In addition, the FU duration of such management is associated with a lower risk of stroke,4 but there is interventional trials should also be adapted to adequately address no clear evidence that early rhythm management affects cognitive cognitive decline in patients with AF. Ideally, brain imaging would function 2 years after randomization.36 In a large retrospective co­ be obtained to detect structural brain damage associated with cog­ hort study using propensity score matching, AF patients who nitive decline. 14 Table 1 Current studies on cognitive function and dementia in relation to AF Trial / Study Intervention / Treatment Study Details Cognition-Related Outcome ............................................................................................................................................................................................ Oral anticoagulation ARISTA Apixaban vs. warfarin N = 280; FU = 24 months Primary Change in cognitive function (using standardized NCT03839355 Randomized, single blinded neurocognitive assessment) and new silent cerebral Terminated due to low enrolment infarcts on MRI CAF Dabigatran vs. warfarin N = 120; FU = 24 months Incident dementia or moderate cognitive decline at 24 NCT03061006 Prospective, randomized, open label, months blinded outcome assessment trial Secondary endpoints: Stroke/TIA, intracranial bleed, changes from baseline scores on MMSE and Hachinski ischaemic scale GIRAF Dabigatran vs. warfarin N = 200; FU = 24 months Neuropsychological evaluation (MoCA + NCT01994265 Prospective, randomized, open, blinded NINDS-CSN-Vascular Cognitive Impairment outcome assessment, efficacy study Harmonization) after 1 year and at the end of follow-up BRAIN-AF Rivaroxaban vs. standard N = 2280; FU = 5 years Cognitive decline (MoCA decreased ≥ 3 points from NCT02387229 care (placebo/ASA) baseline) and/or stroke/TIA Prospective, randomized, double blind, efficacy study ARTESIA neurocognitive Apixaban vs. ASA N = 1000 Change of cognitive function on MoCA sub-study Prospective, multicentre, double-blind, NCT01938248 randomized controlled trial DaRe2THINK Non-vitamin K oral N = 3000; FU = 5 years Cognitive outcomes are cognitive decline using the UK NCT04700826 anticoagulant vs. no Nationwide England Biobank cognitive assessment tests yearly and at 10 therapy in AF at low to years, plus a sub-study of N=160 with brain imaging at intermediate stroke risk baseline and 3 years plus Cambridge Neuropsychological Test Automated Battery (CANTAB) cognitive function OCEAN Rivaroxaban vs. ASA N = 1572; FU = up to 3 years Secondary Changes on neuropsychological testing (MoCA and NCT02168829 Prospective, open label, randomized trial MMSE) NOAH-AFNET6 Edoxaban vs. ASA N = 2686; FU = 24 months Cognitive function (MoCA) at 12 and 24 months NCT02618577 Prospective, parallel-group, double-blind, randomized, multicentre trial The LOOP study Internal loop recorder N = 6.004; FU 65 months Cognition-related outcome monitoring and oral Prospective, randomized, single blinded NCT02036450 anticoagulation if AF detected or standard of care Continued R.B. Schnabel et al. Table 1 Continued Trial / Study Intervention / Treatment Study Details Cognition-Related Outcome ............................................................................................................................................................................................ Rhythm management DIAL-F AAD vs. AF ablation N = 888; FU = 24 months Primary MoCA at baseline and 2 years follow-up NCT01816308 Prospective case–control study AFCOG Standard of care intervention N = 600; FU = n/a Differences in cognitive test performance (CANTAB) NCT04033510 for restoring sinus rhythm Single cohort study while in arrhythmia vs. sinus rhythm Comparison of Brain Rhythm control (propafanone) N = 200; FU = 12 months Cognitive functions (k-MoCA) and brain perfusion (CT) The 8th AFNET/EHRA consensus conference Perfusion vs. control (apixaban) Prospective, randomized control trial in Rhythm Control and Rate Control of Persistent Atrial Fibrillation NCT02633774 EAST-AFNET4 Early, comprehensive, rhythm N = 2789; FU = 60 months Secondary MoCA at 24 months after randomization control therapy vs. usual care Prospective, randomized, open, NCT01288352 blinded outcome assessment trial Other UK SAFER trial AF screening vs. no screening N = 126 000; FU = 5 years Secondary Cognitive function Cluster randomized trial SWISS-AF Cohort study N = 2415; FU = 30 months Annual neurocognitive function tests (not specified) NCT02105844 Prospective, observational, multicentre cohort study PLUG dementia trial Cohort study N = 60; FU = 24 months Primary Incident dementia as determined by a neurologist, change NCT03091855 Terminated due to low enrolment in cognitive test (30% decrease in Alzheimer’s Disease Assessment Scale/score of ,50% or 30% change on the Disability Assessment for Dementia) Estimated patient enrolment and completion date were taken from trial registration on ClinicalTrials.gov. ARISTA, Apixaban Versus Warfarin in Reducing Rate of Cognitive Decline, Silent Cerebral Infarcts and Cerebral Microbleeds in Patients With Atrial Fibrillation; CAF, Impact of Anticoagulation Therapy on the Cognitive Decline and Dementia in Patients With Non-Valvular Atrial Fibrillation. GIRAF, Cognitive Impairment Related to Atrial Fibrillation Prevention Trial; BRAIN-AF, Blinded Randomized Trial of Anticoagulation to Prevent Ischemic Stroke and Neurocognitive Impairment in Atrial Fibrillation; ARTESIA, Apixaban for the Reduction of Thrombo-Embolism in Patients With Device-Detected Sub-Clinical Atrial Fibrillation; OCEAN, Optimal Anticoagulation for Higher Risk Patients Post-Catheter Ablation for Atrial Fibrillation Trial; DIAL-F, Cognitive Impairment in Atrial Fibrillation; AFCOG, Acute Cognitive Changes During Atrial Fibrillation Episodes; SWISS-AF, Swiss Atrial Fibrillation Cohort Study; PLUG, Dementia Trial and MRI PLUG Dementia Sub-Study. CANTAB, Cambridge Neuropsychological Test Automated Battery; FU, follow-up; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; MRI, magnetic resonance imaging. 15 16 R.B. Schnabel et al. Paent Paent presenng with Ask the paent, caregivers, Ask the paent, carer, presenng with relave if cognive Paent relave if AF-related Atrial dysfuncon is present presenng Cognive symptoms are present Fibrillaon with Impairment Yes Always Always Periodic Atrial Periodic If not re-assessment Assess cognive funcon Fibrillaon Assess cardiac rhythm status re-assessment done using MoCA test or MMSE and Consider AF screening Normal score(s) Signs of cognive Cognive impairment Impairment AF No AF Baseline Management Assess hypertension, heart failure, anaemia, thyroid funcon etc. and assist follow-up Cognive ImpairmentManagement Atrial Fibrillaon Management Cognive Impairment Management A (Neurologist) B C (should involve a neurologist) Neurology assessment to rule out/treat Detailed cognive assessment Ancoagulaon/st Consider Manage other lifestyle causes of cognive impairment oke prevenon and comorbidies Assessment by a neurologist to rule out rhythm Detailed management (CHA2DS2-VASc) cognive assessment other causes of cognive impairment and Brain (incl. imaging e.g, hypertension (using MRI) to treat specific types of demena Consider OAC ablaon) sleep apnea Consider ancoagulaon Prefer NOAC (non-CHA2DS2-VASc based)* Brain imaging (using MRI) if not done recently alcohol consumpon Check Consider HAS-BLED adherence to N(OAC) Check adherence to N(OAC) Figure 4 Flow chart on three common presentations of AF and cognitive impairment (or dementia): (i) a patient presenting with known AF whose mental state is not the reason for the presentation, (ii) a patient with known cognitive impairment, and (iii) a patient with both, known AF and cognitive impairment. For the patient with known AF, simple tests of cognitive function may confirm the need for detailed assessment to evaluate the cause of cognitive impairment and initiate therapy for reversible causes. Similarly, patients presenting with cognitive impairment should undergo opportunistic screening for AF (pulse palpation eventually followed by an ECG) and, if AF is discovered, it should be assessed and managed initially by a specialist following the A–B–C (Atrial fibrillation Better Care [includes A (avoid stroke), B (better symptom control), C (cardiovascular risk factors and comorbid conditions management)] scheme recommended by the European Society of Cardiology. AF, atrial fibrillation; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; MRI, magnetic resonance imaging; NOAC, non-vitamin K oral anticoagulant; OAC, oral anticoagulant. Atrial (cardio)myopathies: observed that detection increases with advanced impairment (Figure 5). Specific and sensitive non-invasive measures are not avail­ definition, risk factors, and able to validate the clinical relevance of the concept of atrial cardio­ progression myopathy. Indication-specific characterizations have to be developed, e.g. voltage mapping in invasive electrophysiology, combi­ Defining the concept nations of electrocardiographic, imaging, and blood biomarker-based The earliest systematic characterisation of atrial (cardio)myopathy definitions (also see Table 2). The latter has been used as a provisional dates back to 1972.79 In line with the 2017 EHRA/HRS/APHRS/ surrogate of atrial cardiomyopathy in the ARCADIA (AtRial SOLAECE expert consensus,80 we consider atrial cardiomyopathy Cardiopathy and Antithrombotic Drugs In prevention After cryptogenic as an extension of the pathophysiological concept of AF initiation stroke) trial.116 In addition to clinical characteristics and imaging, cir­ and perpetuation that comprises triggers, substrate, and modifying culating biomolecules reflecting general cardiovascular and atrial dis­ factors together with the genetic architecture. Atrial cardiomyop­ ease processes can help to detect and characterize atrial athy affects all cellular components of the atria and manifests in three cardiomyopathy, e.g. natriuretic peptide, bone morphogenic protein main ways, where each component can be present alone or in com­ 10, or fibroblast growth factor 23.104,117–119 Such definitions require bination with the others: electrical, contractile/functional, and struc­ prospective confirmation in relation to interventions and hard clinical tural alterations. We acknowledge that detecting all potential outcomes. manifestations of atrial cardiomyopathy is not feasible yet, mainly be­ cause our diagnostic abilities of atrial imaging or electrical mapping are limited. Furthermore, the underlying pathophysiological pro­ Atrial cardiomyopathy disease cesses are not completely known.81 For application in clinical care, progression atrial cardiomyopathy needs to be quantifiable and uniform defini­ Although atrial cardiomyopathy is not precisely defined, nor the dif­ tions are a prerequisite. Several ways of measurement are possible ferent stages of diseases are described in detail, the prevailing idea is and refined methods will improve detection. To date, it has been that atrial cardiomyopathy progresses: electrical manifestations are The 8th AFNET/EHRA consensus conference 17 Atrial cardiomyopathy Quan fica on Measures Atrial high rate episodes Biomarkers Atrial fibrilla on (molecular) Imaging ECG, voltage mapping Electrical, contrac le/func onal and structural altera ons Sensi vity of detec on Atrial cardiomyopathy extent Reversibility? Figure 5 Atrial cardiomyopathy and its possible phenotypes: AF and atrial high rate episodes. Different potential quantification measures and their sensitivity for detection of atrial cardiomyopathy depending on the extent of impairment. Table 2 Quantification measures of atrial cardiomyopathy Measurement method Parameters References ........................................................................................................................................... X-ray Dilated atrium 82–86 Surface Prolonged P-waves, abnormal P-waves, P-wave terminal force in ECG lead V1, sinoatrial node dysfunction, atrial electrocardiogramme ectopy 87,88 ECGi, electrocardiographic Abnormal activation, prolonged activation time, slowing of conduction velocity imaging 89–95 Echocardiogramme Dilated atrium, reduced contractility, reservoir function, conduit function, emptying fraction, left atrial strain imaging 96 CT scan Abnormal atrial function, fatty infiltration, morphology: atrial sphericity, left atrial appendage shape 97–103 MRI Dilated atrium, decreased contractility, abnormal morphology: sphericity, fibrosis (late gadolinium enhancement) 104–107 Blood biomarkers Natriuretic peptides—atrial dilatation, myocyte stretch Fibroblast growth factor—fibrosis Bone morphogenic protein-10—atrial remodelling Fatty acid binding protein 3—atrial metabolic disturbance C-reactive protein, Interleukin-6—inflammation Factor VIII, von Willebrand factor, Fibrinopeptide A—pre-thrombotic state, hypercoagulability 108–114 Electrophysiological Low voltage in sinus rhythm, prolonged atrial activation time, decreased conduction velocity 3-D mapping 115 Biopsy Fibrosis, fatty infiltration, collagen accumulation, amyloid depositions, endocardial remodelling, vascular rarefaction, molecular imaging Summary of potential methods used to quantify atrial cardiomyopathy. 18 R.B. Schnabel et al. ion channel remodelling that affects action potential duration and the Potential practical implications development of low-voltage areas in the left atrium, causing AF to evolve from self-limiting to more persistent AF types. Contractile/ • Atrial cardiomyopathy is a valuable emerging concept to explain the nature of AF and its management to patients. functional manifestations include increased atrial size and loss of con­ tractile function, as well as deterioration of conduit and reservoir • The interactions between atrial cardiomyopathy and AF require quantification in patients, initially in research settings. functions of the atria.120 Progression of structural manifestations is characterized by cardiomyocyte hypertrophy, atrial fibrosis, fatty in­ • Better tools to objectively quantify atrial cardiomyopathy are needed. Electrocardiographic and electroanatomical mapping, modern im­ filtration,121 atrial dilatation, and amyloid depositions.122 It can be as­ aging modalities, circulating biomolecules, and innovative combined sumed that early stages of atrial cardiomyopathy may be largely analytical methods may become useful to provide such quantification. reversible, but with this progression of disease, these manifestations • Further potential clinical applications are provided in Table 3. will become more permanent and less reversible. This idea aligns with the recent findings of EAST-AFNET44 which showed that early Knowledge gaps and research rhythm management in AF, i.e. within the first year of diagnosis, is opportunities beneficial for reducing the risk of common cardiovascular outcomes To investigate the central atrial cardiomyopathy hypothesis, we need and in patients with detectable ventricular dysfunction.37 basic, translational, and clinical research to more precisely define at­ Atrial MRI has been proposed as a tool to quantify atrial cardiomy­ rial cardiomyopathy, its risk factors, and progression. Clinically avail­ opathy and to assess the risk of recurrent AF after catheter ablation. able accurate quantification of atrial cardiomyopathy is required to Initial MRI studies (Delayed-Enhancement MRI Determinant of translate the concept of atrial cardiomyopathy into clinically mean­ Successful Radiofrequency Catheter Ablation of Atrial Fibrillation, ingful management strategies. Furthermore, practical definitions of DECAAF 1) have demonstrated the correlation between the atrial cardiomyopathy are required for clinical trials and need to be amount of fibrosis and AF recurrence.123 The multicentre results tested for meaningful outcomes including hard endpoints. of Delayed-Enhancement MRI Determinant of Successful Radiofrequency Catheter Ablation of Atrial Fibrillation (DECAAF II) and Isolation of Pulmonary Veins With the Aid of Magnetic Structuring high-quality care Resonance Imaging (ALICIA)124 trials could not demonstrate that this increased risk is modifiable by AF ablation. It remains unclear Structured quality of care for AF patients includes both having a to what extent currently available MRI algorithms detect atrial car­ structured pathway for delivering optimal quality of care for patients diomyopathy that is related to low-voltage areas.125–127 In and a structured assessment of quality indicators (Figure 6). Quality DECAAF II, the attempt to show the benefit of a fibrosis-guided in­ of care encompasses (i) diagnosis, (ii) referral where needed, (iii) dividualized ablation strategy failed. management, and (iv) FU. The main areas of quality assessment for In addition, the extent of atrial cardiomyopathy has been related to AF diagnosis and management are: patient assessment (baseline the risk for cerebrovascular events.128,129 Signals for atrial cardiomy­ and FU), anticoagulation therapy, rhythm management, and patient- opathy could potentially be detected in community settings based on reported outcome measures, as recently outlined by an ESC and quantification of circulating cardiovascular biomolecules. Whether EHRA-led international task force.138 In addition to treatment do­ the concept of atrial cardiomyopathy will be adopted in clinical prac­ mains and outcomes, patient-reported outcomes are important tice depends on its impact on patient management and whether the measures to quantify quality of care.139 following central knowledge gaps can be sufficiently addressed. The entire assessment of each patient should be structured, includ­ ing detection and management of comorbidities, risk factors and life­ style changes, anticoagulation and stroke prevention, and rhythm Atrial cardiomyopathy and atrial management.1 The upcoming EU-funded projects EHRA-PATHS140 fibrillation (EHRA-PATHS, No. 847770) and AFFIRMO141 (AFFIRMO, No. Quantifying atrial cardiomyopathy and AF in patients may increase 899871) work to develop a framework for how to improve AF man­ understanding of the temporal disconnect between AF and agement through a holistic, inclusive, and personalized care pathway stroke.130 It seems likely that structural remodelling is influenced centred on patients.142 Continuous FU and regular re-evaluation of by the presence of cardiovascular risk factors and diseases as atrial the process and the quality indicators of care are needed. This struc­ remodelling occurs before the development of clinically detected tured approach should be clearly defined, interprofessional, transpar­ AF.131 The atrial cardiomyopathy is aggravated by AF episodes and ent, and easy to implement. Responsibilities need to be clear, taking by concomitant cardiovascular conditions and risk factors. The con­ into account differences between various countries and local health­ cept of atrial cardiomyopathy may also be of value for patient com­ care organizations. Nurses and general practitioners can have a cer­ munication and education to emphasize the importance of tain autonomy in this structured approach; however, they should continuous risk factor and lifestyle management together with long- work in coordination with an expert in AF, e.g. a cardiologist. term use of medical therapies or FU visits because of the presence of An important quality measure is adherence to defined, structured atrial cardiomyopathy. It helps to avoid the notion that with the tem­ care pathways.143 However, there is a knowledge gap on how to poral freedom of AF, further preventive management may no longer measure and ensure adherence to such pathways, which should be be necessary. addressed by new research. The quality of care should be regularly A favourable clinical outcome currently is most likely achieved by evaluated, e.g. yearly, but again, there is a gap of knowledge on the optimal AF treatment (see earlier sections). optimal time interval for reassessment. The 8th AFNET/EHRA consensus conference 19 Table 3 Potential clinical implications of atrial cardiomyopathy • Correlation between MRI-detected fibrosis and AF recurrence, but not ECG phenotype (DECAAF,132 efficacy of late gadolinium enhancement MRI-guided fibrosis ablation vs. conventional catheter ablation of atrial fibrillation) • Correlation between atrial fibrosis and difficulty of SR restoration (DECAAF), risk for cerebrovascular events–surrogate (echo contrast) and major adverse cardiovascular events128,129) Progression, slowing progression • Recovery of voltage reduction after successful ablation (CAMERA-MRI, Catheter Ablation Versus Medical Rate Control in Atrial Fibrillation and Systolic Dysfunction133) • Recovery of fibrosis—left atrial reverse remodelling after ablation (Echo, MRI) • Ventricular fibrosis as marker ablation response (CAMERA-MRI) with regression after restoration of SR134 • Benefit of early rhythm management of AF (EAST)4 • Benefit of risk factor management, e.g. weight reduction135 (REVERSE-AF, PREVEntion and regReSsive Effect of weight-loss and risk factor modification on Atrial Fibrillation) • Benefit of lifestyle changes, e.g. alcohol abstinence • Benefit of upstream therapy (RACE3, targeted therapy of underlying conditions improves sinus rhythm maintenance in patients with persistent atrial fibrillation)136 Potential future clinical implications • End-organ damage assessment (e.g. in hypertension) • Identification of subgroups of AF patients who benefit from therapies not successful in unselected patients, individualized treatment • Tool for patient communication • Improved stroke risk assessment, stroke prevention, recurrent stroke prevention (ARCADIA trial) • OAC indication in borderline risk patients with AF (BRAIN-AF trial) • Prevention of AF onset and progression • Inform about the preferential procedural strategy in ablation procedures beyond pulmonary vein isolation • Better explain the continuation of OAC after ablation (OCEAN trial137) ARCADIA, AtRial Cardiopathy and Antithrombotic Drugs In prevention After cryptogenic stroke; CAMERA-MRI, Catheter Ablation Versus Medical Rate Control in Atrial Fibrillation and Systolic Dysfunction; DECAAF, Delayed-Enhancement MRI-Guided Ablation versus Conventional Catheter Ablation of Atrial Fibrillation; EAST, Early treatment of atrial fibrillation for stroke prevention trial; MRI, magnetic resonance imaging; OAC, oral anticoagulant; OCEAN, Optimal anti-Coagulation for Enhanced-risk patients post-catheter Ablation for atrial fibrillation; RACE3, Routine Versus Aggressive Upstream Rhythm Control for Prevention of Early Atrial Fibrillation in Heart Failure. Structured pathway for quality of care Determinants of structured quality of care assessment An!coagula!on Referral where AF Diagnosis needed Rhythm Outcome Risk factor management measures management Follow-up Management Pa!ent assessment Framework of structured quality of care for AF pa!ents Figure 6 Framework to deliver quality of care detailing a structured pathway for quality of care (left) and determinants of structured quality of care assessment (right). 20 R.B. Schnabel et al. In addition to components of care and clinical outcomes, patient- designed. Further potential issues and barriers in the reported outcomes are useful to assess the quality of care.144–146 guideline-adherent management of AF at the patient and healthcare These can be measured using scales capturing general health-related provider level will need to be identified and addressed. quality of life.138 Several validated tools such as EQ-5D-5L (European Quality of Life 5 Dimensions 5 Level)147,148 or SF-12 (short form sur­ vey)149 are available to measure it. Atrial fibrillation–specific Artificial intelligence in the quality-of-life questionnaires are validated, available,150,151 and re­ management of atrial fibrillation commended by the International Consortium of Healthcare Outcome Measures (ICHOM) for AF.144 Follow-up measurement of quality Artificial intelligence comprises methods that enable a computer to of life with the same tool is required to compare trends for evalu­ learn from data with two main subcategories: the supervised and the ation and decision-making. Patient-reported experience measures unsupervised learning methods. In supervised learning, the output or such as patient satisfaction, subjective experiences (e.g. symptom target is defined (e.g. recognition of a sinus rhythm or AF on the control) or objective experiences (e.g. waiting time to appointment), ECG). The learning process uses labelled data sets to solve classifica­ and treatment burden are indicators to identify the quality of care.152 tion and regression problems. In unsupervised learning, there is no Education should be provided in an appropriate and understand­ prediction of any output or need for labelled data. Instead, raw able way for patients and their caregivers and can improve out­ data are modelled with the goal of identifying patterns and struc­ comes.148,153–155 Education of patients and continued medical tures, and to cluster similar variables. Nowadays, unsupervised education is needed to maintain care pathways, to achieve quality in­ deep neural networks are the most popular method for identifica­ dicators, and to implement new technical developments.156 tion of hidden signatures of diseases. However, it requires large Knowledge gaps and unmet needs should be identified and ad­ data sets, and generates its own logical process, creating a black dressed.157,158 An ESC and EHRA-funded project, STEEER-AF box that requires complex explanatory analysis methods. [STEEER-AF Project Study (escardio.org)] is currently evaluating There has been a massive increase of AI in the field of AF, with the impact of an educational programme for HCPs on outcomes in 5298 articles on AF and AI indexed in Pubmed since 2016.163 This patients with AF.159 trend is driven by two main factors. First, the electrical and imaging Objective information on AF, its consequences, and its manage­ signals, which are the two pillars for AF assessment, are well-suited ment should be available on websites, smartphone apps, or in print data for machine learning approaches. Secondly, the creation of large for patients and relatives, ideally available in all relevant local languages. data sets, such as the Computing in Cardiology challenge 2017/ Unbiased resources and reliable sources endorsed by scientific soci­ PhysioNet,164 facilitated the deployment of studies on AI. eties increase confidence, effectiveness, and trustworthiness.160 Artificial intelligence technologies have been used primarily in Health and digital literacy should be taken into account to ensure op­ studies to improve AF detection, and, to a lesser extent, in studies timal education of patients and relatives and avoid potential adverse to predict incident and recurrent AF in patients undergoing rhythm effects.157,161,162 Professional organizations such as EHRA and the control therapy, or response to drug treatment in multimorbid pa­ ESC have an important role in the generation of such information. tients.165 For example, deep neural networks have been used to de­ tect AF based on ECG and pulse plethysmography signals.166–168 Practical implications They improved the performance of AF identification based on such combinations of signals as compared to traditional AF detection • Measuring the quality of care in patients with AF is required to im­ algorithms. prove it. Recent studies have implemented explanatory elements and al­ • Outcomes measures capturing the efficacy and safety of therapy are lowed understanding of ECG features which can be used to better known and should be measured. detect AF. Both, RR interval irregularity and lack of P-waves contrib­ • The quality of care delivery can be estimated by measuring adherence ute to AF detection equally.169 Artificial intelligence algorithms also to evidence-based recommendations in guidelines, possibly through audits. can predict impeding AF within the next 31 days based on a single standard 12-channel ECG in sinus rhythm using a deep learning con­ • In addition to these measures, patient-reported outcomes and pa­ volutional network with an area under the curve of 0.87 for the de­ tient experience are important domains for a comprehensive estima­ tion of quality of care. tection of AF based on ECGs in sinus rhythm.170 When using multiple ECGs within the same time window, the area under the curve increased to 0.90. The same algorithm was subsequently Knowledge gaps and research used to predict incident AF in an independent cohort of elderly pa­ opportunities tients with a mean FU of 7.4 years.171 The performance was compar­ The development of evidence-based educational interventions to im­ able with the clinical AF risk scores combining clinical information, prove knowledge and skills of healthcare providers needs to be fos­ ECG parameters, and blood pressure measurements. Taken to­ tered. Objective and easily ascertainable measures are required to gether, these results demonstrate the predictive strength contained ensure adherence to care pathways. The frequency of their re- in the ECG. Such algorithms might be used to identify patients at risk evaluation needs to be determined. New care pathways should be for AF and in whom monitoring for AF might be initiated. Table 4 lists developed to detect, address, and FU multimorbidity and polyphar­ the ongoing registered trials using AI for AF care. macy in the often older and multimorbid patients with AF. Although these algorithms perform well, the deep neural network Multidisciplinary and patient-centred approaches need to be studies often have not provided sufficient insights into how exactly The 8th AFNET/EHRA consensus conference 21 Table 4 Prospective trials on AI-based methods listed currently under clinicaltrials.gov Intended use of AI method Study title No. Country participants ........................................................................................................................................... AI-guided patient selection for AF catheter ablation AI-PAFA 340 South Korea NCT04997824 AI-based AF detection referring to heart rhythm monitoring with WB-AF 100 Finland wearable devices NCT04917653 AI-enabled ECG-based screening tool for AF BEAGLE 1000 USA NCT04208971 Predicting patient-level new onset atrial fibrillation Re-use of CPRD-GOLD and CPRD-AURUM172 140 000 UK AI-enhanced performance of Apple watch in ablation/ AI-AW 200 France cardioversion patients NCT05045456 Prevention of stroke and sudden cardiac death by AI-enhanced PRICE 10 000 Germany one-channel ECGs NCT04637230 Home-based AF screening with handheld one-lead ECG recording HUATUO-AF 1740 Hong Kong NCT04523649 Correlation between cardiovascular disease and individualized Artificial intelligence with deep learning and genes on 5000 Taiwan differences cardiovascular disease NCT03877614 Remote monitoring of AF recurrence using mHealth technology REMOTE-AF 35 UK NCT05037136 Accuracy of cardiac arrhythmias and conduction disorders Accuracy of cardiac arrhythmias and conduction 110 Brazil diagnosis using a smartwatch disorders diagnosis using a smartwatch NCT04437914 Arrhythmia classification for shockable cardiac rhythms AI-ECG 25 458 USA NCT03662802 Differentiate stroke subtypes and predict source in acute ischemic Validation of 3D simulations in embolic stroke 100 UK stroke NCT05055960 Multimodality imaging for diagnosis and outcomes evaluation of Diagnosis and OutcoMes evaluAtIoN of Multicenter 1000 + 430 China multicenter patients with heart failure with preserved ejection Patients With HFpEF Using Multimodality Imaging fraction and AF NCT04602338 Estimated patient enrolment and completion date were taken from trial registration on ClinicalTrials.gov. AI-PAFA Trial, Artificial Intelligence Guided Patient Selection for Atrial Fibrillation Catheter Ablation: Randomized Clinical Trial; WB-AF, Portable Measurement Methods Combined With Artificial Intelligence in Detection of Atrial Fibrillation; BEAGLE, Batch Enrollment for AI-Guided Intervention to Lower Neurologic Events in Unrecognized AF; AI-AW, Observational Clinical Investigation of EKG Diagnostic Performance of the Apple Watch Augmented With an AI Algorithm; PRICE, Prevention of Stroke and Sudden Cardiac Death by Recording of 1-Channel Electrocardiograms; HUATUO-AF, Home-Based SolUtion for Remote Atrial Fibrillation Screening to PrevenT RecUrrence StrOke; REMOTE-AF, Remote Monitoring of AF Recurrence Using mHealth Technology; AI-ECG, Development of a Novel Convolution Neural Network for Arrhythmia Classification. the algorithm is working and how a diagnostic decision is taken— and use (e.g. General Data Protection Regulation and Good leaving uncertainties on independent verification and generalizability, Clinical Practice) apply to the use of medical data. For reproducibility, jeopardizing its incorporation into daily clinical practice. data accessibility is crucial for validation of algorithm-generated pre­ dictions. Good data accessibility is also required including scientific transparency in accordance with the International Committee of Knowledge gaps and research Medical Journal Editors (ICMJE). In this context, it should be required opportunities that all prospective clinical trials and registries using AI are listed in Artificial intelligence benefits from large, high-quality data sets, where official trial registries. data constitution and access represent a major challenge for both re­ Unfortunately, the current complexity and disorganization of pro­ search and clinical translation. First, different data sets are governed cesses for data sharing and circulation between academic partners by different data protection rules which limit accessibility, especially within Europe and beyond are a major handicap for progress in AI in Europe. Data sets are owned and controlled by diverse stake­ in medicine. Although the European Union (EU) has been vocal holders (e.g. private companies that might develop and commercial­ about data strategies (Strategy for Data | Shaping Europe’s digital fu­ ize AI tools; academic institutions that might also develop, compare, ture (europa.eu)) and FAIR (Findability, Accessibility, and implement them). Strict legal frameworks for data protection Interoperability, and Reuse) guiding principles for scientific data 22 R.B. Schnabel et al. and digital assets management and stewardship, the principles out­ detection, prognosis, and precision treatment, but its role, circum­ lined in these strategies often conflict with other European and stances of its application, and the optimal methods need to be National regulations, e.g. EU general data protection regulation, at defined. the individual project level. In particular, there is also the fundamental aspect of the medical responsibility when using tools for prediction Supplementary material and aid in medical decisions that needs to be evaluated in context Supplementary material is available at Europace online. with the updated medical device regulation (MDR). Acknowledgements Steps to facilitate clinical implementation We wish to thank all participants of the 8th AFNET/EHRA Consensus of artificial intelligence Conference and the staff of AFNET, EHRA, and ESC for the excellent organization of the conference. The results of AI-based investigations and applications will become increasingly available. Beyond formal regulatory approval of such Funding applications by the respective authorities, their performance and The 8th AFNET/EHRA Consensus Conference was co-financed by use will need to be compared in the context of the current state AFNET and European Heart Rhythm Association (EHRA), and received of art of AF care. Such context is typically generated by the medical additional financial support from the AFFECT-EU (Digital, risk-based community and their professional societies such as the ESC and screening for atrial fibrillation in the European community) consortium EHRA. Understanding the methodology and implications of (affect-eu.eu). AFFECT-EU receives funding from the European AI-based AF detection methods requires specific knowledge, in­ Union’s Horizon 2020 research and innovation Programme under grant volving expertise in the fields of informatics, computational net­ agreement No. 847770. Industry participants paid an attendance fee for works, biostatistics, legal and medico-legal implications, and the conference and provided an industry perspective during the discus­ medicine. Additional questions need to be addressed, such as the sions at the meeting, but had no involvement in the writing process. reimbursement of these machine learning derived medical devices Conflict of interest: RBS has received lecture fees and advisory board which likely requires a health economic analysis comparing costs of fees from BMS/Pfizer outside this work and has received funding from current practice with an AI supported pathway. Cardiology soci­ the European Research Council (ERC) under the European Union’s eties like ESC and EHRA are encouraged to establish and promote Horizon 2020 research and innovation programme under the grant working groups equipped with such expertise to generate in­ agreement No 648131, from the European Union’s Horizon 2020 re­ formed guidance on the sharing and use of AI-related data and al­ search and innovation programme under the grant agreement No 847770 (AFFECT-EU) and German Center for Cardiovascular gorithms, devices, and products to their society members and Research (DZHK e.V.) (8121710103); German Ministry of Research the medical community. and Education (BMBF 01ZX1408A) and ERACoSysMed3 (031L0239).EAM is employee of Daiichi Sankyo Europe GmbH produ­ cing and marketing an oral anticoagulant (edoxaban). EA has received Summary consulting / speaker fees for Biosense Webster. GB has received speak­ er’s fees of small amount from Boston, Bayer, Daiichi, Boehringer. SB is During the 8th Joint AFNET/EHRA Consensus Conference, we have consultant for Medtronic, Boston Scientific, Microport, and Zoll. JC has clarified current AF screening and management strategies, especially received consulting fees / honoraria fees from Acesion, Allergan, Alta an update on the role of rhythm management, with clear implications Thera, Arca, lncarda, Menarini, Milestone, Sanofi, Bayer, Daiichi Sankyo, for clinical practice. Research gaps in clinical and translational science Pfizer, Abbott, Biosense Webster, Biotronik, Boston Scientific, Lilly, have been identified and tasks and mandates for the ESC and EHRA Medtronic, Johnson and Johnson. BC has received in kind contribution suggested. for research Support from iRhythm. WC has received advisory board Main outcomes of the 8th Joint AFNET/EHRA Consensus fees for Roche Diagnostics AG. MDM is employee of Medtronic. SZB has received fees as member of Advisory Board in Bristol Myers Conference: Squibb-Pfizer. DD has received fees from Abbott, Astra Zenica, Bayer, • New evidence supports the implementation of systematic screening Bosten Scientific, Bristol Myers Squibb-Pfizer, Medtonic, Zoll. LE has re­ for AF to achieve long-term reduction in a combined outcome of ceived lecture Honoria from Medtronic, Biotronik, Boston Scientific, mortality, stroke, and severe bleeding. Boehringer Ingelheim, Daiichy Sankyo, Bayer, MMS, Pfizer, Sanofi and re­ • Rhythm management, including a consideration of an attempt at ceived research grants from DFG and DGK. CE is employee of rhythm control, should be part of the initial treatment in patients Preventicus GmbH. LF has received institutional research grants and with recently diagnosed AF. non-financial support from European Union, DFG, British Heart • As AF can be regarded as a risk factor for stroke and cognitive decline, Foundation, Medical Research Council (UK), NIHR, and several biomed­ attention should be paid to cognitive function, which is a cornerstone ical companies. The Institute of Cardiovascular Research, University of for compliance with AF treatment. Birmingham, has received an Accelerator Award by the British Heart • The concept of atrial cardiomyopathy requires better and objective Foundation M/18/2/34218. LF is listed as inventor of two patents held tools for its quantification and research to align cardiomyopathy by University of Birmingham (Atrial Fibrillation Therapy WO and AF. 2015140571, Markers for Atrial Fibrillation WO 2016012783). BF re­ • Defining and measuring the quality of care in terms of process, pa­ ceiving fees from Bristol-Myers Squibb and Pfizer Alliance, Bayer, tient experience, and outcomes will be important to improve it. Daiichi Sankyo, Omron. (largely speaker fees and travel support for • Artificial intelligence has the potential to unravel the complex patho­ speaking at session or official satellites of large international/continental physiology leading to atrial cardiomyopathy and AF, early AF society meetings) and investigator-initiated research grants to the The 8th AFNET/EHRA consensus conference 23 institution from Bristol-Myers and Squibb and Pfizer Alliance and Johnson Medical Limited, (United Kingdom), Bayer Healthcare Ownership / Employee of Nil. LG and AZ are employees of Roche (Germany). U.S. is co-founder and shareholder of YourRhythmics BV, Diagnostics International Ltd. AG has received funding from Daiichi a spin-off company of the University Maastricht and Research grant Sankyo, Astra Zenica, Bayer, Bristol Myers Squibb-Pfizer, Viola, from the Dutch Heart Foundation (CVON RACE V, CVON2014–09) Medtonic, Berlin Charitè. JHS has received Advisory board fees in European Union’s Horizon 2020 Research and Innovation Program Medtronic and Speaker fee from Medtronic. KGH has received fees granted to MS under the Marie Sklodowska-Curie grant agreement from Abbott, Alexion, AMARIN, AstraZeneca, Bayer, Biotronik, #813716 (TRAIN-HEART Innovative Training Network), and various Boehringer Ingelheim, Bristol-Myers-Squibb, Daiichi Sankyo, Edwards other programs of the European Union granted to US (ITN Network Lifesciences, Medtronic, Pfizer, Premier Research, SUN Pharma and Personalize AF: Personalized Therapies for Atrial Fibrillation: a translational W. L. Gare & Associates and Research Grants from Bayer Vital, network – grant #860974; CATCH ME: Characterizing Atrial fibrillation by Sanofi-Aventis. JSH received fees from Boston Scientific, Servier, Bayer, Translating its Causes into Health Modifiers in the Elderly – grant #633196; Myokardia, Bristol-Myers-Squibb, Pfizer, and research grants from MAESTRIA: Machine Learning Artificial Intelligence Early Detection Medtronic, Boston Scientific, Bristol-Myers-Squibb, Abbott. HH has re­ Stroke Atrial Fibrillation – grant #965286; REPAIR: Restoring cardiac ceived lecture and consultancy fees from Abbott, Biotronik, Bristol- mechanical function by polymeric artificial muscular tissue – grant Myers-Squibb- Pfizer, Medscape, Daiichi Sankyo, Springer Healthcare #952166). DS has received consultation fees from Biosense Webster, Ltd and receive un conditional research grants through the Univerity Abbott, Boston Scientific, Consultant with stock option: SentiAR, Arga of Antwerp and/or University of Hasselt from Abbott, Bayer, Medtech. RS is employee of Daiichi Sankyo Europe GmbH. DS has re­ Biotronik, Biosense-Webster, Fibrickeck/Qompium, Medtronic, Bristol- ceived consultation fees from Boston Scientific, Abbott and Research Myers-Squibb- Pfizer, Boston Scientific, Daiichi Sankyo and Boehringer grant from Biosense Webster. ES has received lecture fees from Bayer, Ingelheim. RH has received speacker fees from BI, Bayer and Bristol- Bristol-Myers and Squibb- Pfizer, Boehringer Ingelheim, Johnson & Myers-Squibb- Pfizer and AZ. TH is CEO of Preventicus GmbH. DK Johnson, Merck Sharp & Dohme and Sanofi. DT has received lecture has received funding from Bayer, AtriCure, Protherics Medicines fees from Bayer Vital, Bristol-Myers and Squibb- Pfizer, Daiichi Sankyo, Development and Myokardia and Research grant from rants from the Medtonic, Zoll CMS, Sanofi, St. Jude Medical and research grant from National Institute for Health Research (NIHR CDF-2015-08-074 Daiichi Sankyo. MTH is employee/owner of American Foundation of wo­ RATE-AF; NIHR HTA-130280 DaRe2THINK; NIHR EME- 132974 men’s Health /StopAfib.org and employee/owner of True Hills Inc.. BV is D2T-NV), the British Heart Foundation (PG/17/55/33087 and AA/18/ employee of Bayer AG. RW has received consultation fees from Boston 2/34218), EU/EFPIA IMI (BigData@Heart 116074),the European Scientific, Biotronic, Pfizer, Daiichi Sankyo, Bayer, Adagio Medical and Society of Cardiology supported by educationalgrants from Boehringer Research grant from Bristol-Myers and Squibb- Pfizer, Boston Scientific. Ingelheim/BMS-Pfizer Alliance/Bayer/Daiichi Sankyo/Boston Scientific, CW has received consulation fees from Biotronik, Boston Scientific, the NIHR/the University of Ox- ford Biomedical Research Centre, and Novartis and research grant from BMBF, AFNET, DZHK, Biotronik. British Heart Foundation/ the University of Birmingham Accelerator MW is employee and shareholder of Sanofi. SW has received Award (STEEER-AF NCT04396418), Amomed Pharma, and IRCCS Consulting fees from Boston Scientific, Abbott, Bayer, Bristol-Myers San Raffaele/Menarini (beta-blockers in Heart Failure Collaborative and Squibb- Pfizer, Boehringer Ingelheim and research grant from Group NCT0083244). MK is employee of Bristol-Myers and Squibb. Boston Scientific. HW is employee and stockholder of Pfizer CL has received fees from medtonic, Boston Scientific, Biotronik and Germany. MDZ has received advisory and speaker fee from Bristol- Bristol-Myers and Squibb- Pfizer and research grants from Rennes Myers and Squibb- Pfizer. PK reports grants and non-financial support Univerity, Metronik, Biotonik and Boston Scientific. DL has received re­ from BMBF (German Ministry of Education and Research), grants from search grant for EHRA-PATHS, NovoNordisk Young Investigator Sanofi and Abbott, grants and non-financial support from EHRA Award. MLL has received lecture fees from Bristol-Myers and Squibb (European Heart Rhythm Association), and grants from German Heart and research grant from H2020 AFFECT-EU (grant No. 847770). SM Foundation and DZHK (German Center for Cardiovascular Research), has received research grant from Daiichi Sankyo (EPDAURUS IIT) and during the conduct of the study, and grants from European Union, Bristol-Myers and Squibb (APPROACH ACS AF IIT). JLM has received British Heart Foundation, Leducq Foundation, Medical Research Council Abbott, Boston Scientific, Biotronik, Boehringer Ingelheim, Sanofi, (UK), and non-financial support from German Centre for Heart Microport and received research grants from Medtronic, Abbott, Research, outside the submitted work; in addition, P.K. has a patent Microport, Biosense. RM is employee of Medtronic. LM is Stockholder Atrial Fibrillation Therapy WO 2015140571 issued to University of for Galgomedical and Corify and receiving consuting fees from Abbott, Birmingham and a patent Markers for Atrial Fibrillation WO 2016012783 Biosense-Webster, Bosten Scientific, Medtronik and receiving research issued to University of Birmingham. grants from Abbott, Biosense-Webster, Bosten Scientific, Medtronik. LN has received consulting fees from Bristol-Myers and Squibb- Pfizer. GP is employee of Bayer AG. HP has received consulting fees from Data availability Abbott, Biosense-Webster, Bosten Scientific, Medtronik and receiving The data underlying this article will be shared on reasonable request to research grants from Abbott, Bayer, Biosense-Webster, Bosten the corresponding author. Scientific, Medtronik, Bristol-Myers and Squibb- Pfizer. MR has received consulting fees for Medtonic, Arca BiopharmaInc, Roche and received re­ search grants from Dutch Heart Foundation: RACE V, RED-CVD, References 1. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomstrom-Lundqvist C, et al. CVON-AI, DECISION studies; grant from SJM/Abbott to institution: 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation devel­ VIP-HF study; Grant for Medtronic to institution: Cryoballoon AF regis­ oped in collaboration with the European Association for Cardio-Thoracic Surgery try/biobank study. 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