Abstract There are major challenges ahead for clinicians treating patients with atrial fibrillation (AF). The population with AF is expected to expand considerably and yet, apart from anticoagulation, therapies used in AF have not been shown to consistently impact on mortality or reduce adverse cardiovascular events. New approaches to AF management, including the use of novel technologies and structured, integrated care, have the potential to enhance clinical phenotyping or result in better treatment selection and stratified therapy. Here, we report the outcomes of the 6th Consensus Conference of the Atrial Fibrillation Network (AFNET) and the European Heart Rhythm Association (EHRA), held at the European Society of Cardiology Heart House in Sophia Antipolis, France, 17–19 January 2017. Sixty-two global specialists in AF and 13 industry partners met to develop innovative solutions based on new approaches to screening and diagnosis, enhancing integration of AF care, developing clinical pathways for treating complex patients, improving stroke prevention strategies, and better patient selection for heart rate and rhythm control. Ultimately, these approaches can lead to better outcomes for patients with AF. Atrial fibrillation, Outcomes, Quality of care, Research, Rate control, Rhythm control, Catheter ablation, Anticoagulation, Bleeding, Research priorities, Technology, Stroke, Integrated care, Screening Introduction The predicted rise in both incidence and prevalence of atrial fibrillation (AF) presents an important health care challenge for cardiovascular and general clinicians.1–4 However, it also offers an opportunity to integrate novel approaches and new technologies to improve patient outcomes. The diagnosis of AF encompasses a broad and heterogeneous group of pathologies,5 and further classification of this condition based on underlying cause or the extent of atrial disease is likely to provide more personalized and effective treatments in the future.1 The care of patients with AF can also be improved by applying structured and patient-centred management that integrates the expertise of different health care professionals.6 Beyond better classification and quality of care, the most immediate advancement in AF management is to incorporate practical ideas, tools, and technologies into routine clinical practice. In particular, these approaches have the potential to (i) provide cost-efficient methods of detection and diagnosis, (ii) allow a platform for local integration of AF care, (iii) develop streamlined clinical pathways for treating complex patients, (iv) improve the benefit-to-risk ratio of stroke prevention strategies, (v) apply more personalized control of heart rate to increase patient well-being and function, and (vi) stratify the choice of rhythm control therapy to enhance treatment success in AF patients. These issues were raised and discussed during the 6th Consensus Conference of the Atrial Fibrillation Network (AFNET) and the European Heart Rhythm Association (EHRA) in Sophia Antipolis, France (17–19 January 2017). Sixty-two specialists in AF attended from 15 member countries of the European Society of Cardiology (ESC), as well as from Australia, Canada, and the USA, in addition to 13 representatives from industry partners. The conference included multidisciplinary workshops on the key themes of the conference, with delegates obtaining consensus opinion within and across workshops with plenary feedback sessions and wide-ranging discussion. In this article, we report on the major outcomes of this conference and present consensus statements on the integration of new approaches to provide maximal benefit to AF patients and their healthcare teams. Diagnosis and screening Atrial fibrillation detection in an era of digital evolution In this workshop, delegates considered the question of what should constitutes a diagnosis of AF, and whether AF detected by screening has the same therapeutic implication as randomized controlled trials where AF has presented clinically. Electrocardiographic (ECG) demonstration of AF is a prerequisite before treatment for AF is initiated.1 The easiest ECG methods to diagnose AF are the 12-lead and ambulatory ECG, but different types of medical technology to diagnose AF are now commonplace, including event recorders, real-time telemetry, and implantable loop recorders. The public also have a variety of options to measure heart rate and identify arrhythmias, including sphygmomanometers, hand-held devices, smartphones, wearables, and health-related apps. As a consequence of the lower detection threshold, it is of great importance to evaluate the quality of rhythm monitors, their sensitivity, specificity, and cost-effectiveness and to develop strategies to interpret the findings. Furthermore, long-term monitoring of atrial rhythm may identify patients with very rare episodes of AF who have a different risk profile than patients who present with clinical AF, for whom currently available treatments have been evaluated. We defined the terminology of ‘self-initiated rhythm monitoring’ for those apparently healthy individuals who decide (for whatever reason) to use commercially available rhythm monitors, whereas ‘AF detection, diagnosis, or screening’ is usually used in patients at risk of AF and its complications. Self-initiated heart rhythm monitoring Medical practice is already transitioning from a profession that remedies acute illnesses to one that prevents disease, often in patients who do not feel acutely unwell. In AF, new technologies are now available that direct populations to seek medical advice based on detection from consumer electronics (Table 1). The hardware and algorithms used in these devices are highly variable,17 validation may be less than for medical devices, and reproducibility could be reduced in the hands of the consumer. This raises the problem of false positives, which may lead to anxiety for patients and costly additional testing with ECG recorders and echocardiography. Conversely, consumer devices can be helpful to highlight the possibility of paroxysmal AF and the need for further investigation (Figure 1). Table 1 Overview of cardiac rhythm assessment available to the public for detecting AF Method Usage Sensitivity/ specificitya (%) Examples References Pulse wave-based methods to detect irregularity Pulse palpation (or heart auscultation) Physical measure to detect pulse irregularity that can be used by medical professionals and the public 94/72 AF awareness campaign by British Heart Foundation 7 Photoplethysmography Devices that use a light shining on skin and a photographic sensor. As well as pulse irregularity, can also detect pulse volume and include advanced algorithms to exclude ectopic beats. Sensitive to motion and may require more than one recording 97–100/92–94 Finger probe, smartphones, smart watches, and fitness bands. 8,9 Oscillometry Devices that measure blood pressure and define the pulse waveform. Principally use irregularity and advanced algorithms to detect AF 92–100/90–97 Microlife BPA 200 (Plus) 10–13 Omron M6 (comfort) Microlife WatchBP ECG hand-held devices usually providing a single lead ECG On-device diagnostic algorithm Devices that collect and some display a single-lead ECG rhythm strip in real time, can make a rhythm diagnosis on the device, and/or transmit to a website for physician or technologist reading. Use both rhythm irregularity and P-wave recognition (variable algorithms) 94–98/76–97 AliveCor (Kardia) heart monitor 12–15 MyDiagnostick Omron HCG-801 Transmitted data devices Devices that have no inbuilt diagnostics but transmit data to a website for either physician or technologist reading or a diagnostic algorithm 94–96/90–95 Merlin ECG event recorder 12,16 Omron HCG-801 Zenicor EKG Method Usage Sensitivity/ specificitya (%) Examples References Pulse wave-based methods to detect irregularity Pulse palpation (or heart auscultation) Physical measure to detect pulse irregularity that can be used by medical professionals and the public 94/72 AF awareness campaign by British Heart Foundation 7 Photoplethysmography Devices that use a light shining on skin and a photographic sensor. As well as pulse irregularity, can also detect pulse volume and include advanced algorithms to exclude ectopic beats. Sensitive to motion and may require more than one recording 97–100/92–94 Finger probe, smartphones, smart watches, and fitness bands. 8,9 Oscillometry Devices that measure blood pressure and define the pulse waveform. Principally use irregularity and advanced algorithms to detect AF 92–100/90–97 Microlife BPA 200 (Plus) 10–13 Omron M6 (comfort) Microlife WatchBP ECG hand-held devices usually providing a single lead ECG On-device diagnostic algorithm Devices that collect and some display a single-lead ECG rhythm strip in real time, can make a rhythm diagnosis on the device, and/or transmit to a website for physician or technologist reading. Use both rhythm irregularity and P-wave recognition (variable algorithms) 94–98/76–97 AliveCor (Kardia) heart monitor 12–15 MyDiagnostick Omron HCG-801 Transmitted data devices Devices that have no inbuilt diagnostics but transmit data to a website for either physician or technologist reading or a diagnostic algorithm 94–96/90–95 Merlin ECG event recorder 12,16 Omron HCG-801 Zenicor EKG a Mostly compared with 12-lead ECG interpretation by a cardiologist, based on published research studies in ideal situations. Note that some algorithms for AF detection are not publicly available and some commercially available devices have modified the algorithms that were tested in these studies. Figure 1 View largeDownload slide Clinical methods to deal with self-initiated rhythm monitoring by the general public. Figure 1 View largeDownload slide Clinical methods to deal with self-initiated rhythm monitoring by the general public. As ECG-diagnosed AF is the preferred method to decide on treatment1 (an enrolment criterion for all controlled trials of AF interventions), patients with potential AF detected on devices that do not provide an interpretable ECG rhythm strip should undergo further assessment of cardiovascular and stroke risk, with additional rhythm monitoring as clinically required. This can also apply to technology in use by medical professionals, for example atrial high-rate episodes (AHREs) detected on pacemakers.18 The physician should decide the stringency of ECG-based rhythm diagnosis based on these factors. Although there are no data available to assist this decision as yet, it seems reasonable to only initiate further rhythm monitoring if the finding of AF would alter management, such as preventing thrombo-embolism or reducing the risk of other adverse outcomes. Criteria for atrial fibrillation diagnosis and the impact of screening Inclusion criteria regarding documentation of AF have varied in recent clinical trials. Although most trials required two ECGs with documentation of AF on separate days,19–22 one recent trial required a history of AF of any duration recorded by any electrical tracing within the last 12 months23 and another required symptomatic episodes with resting, ambulatory or trans-telephonic ECG within the last 4 weeks.24 Such differences in the recording and documentation of AF may have an influence on the composition and generalizability of patient cohorts. The setting in which patients are selected for inclusion may also determine characteristics, including age and the risk of stroke or adverse cardiovascular events.25,26 The yield from screening for AF will also depend on the underlying risk of incident AF27 and the type of AF. For example, the probability that AF is detected by a short recording in patients with paroxysmal AF depends on the underlying burden. Thus, a screening programme that uses a single time point of detection will favour identification of persistent AF, while intermittent short recordings will identify additional patients with paroxysmal AF and a relatively high AF burden.28,29 Conversely, devices capable of continuous atrial rhythm monitoring will identify many more people with short-duration AF episodes and low AF burden of relatively unknown clinical significance. The impact of atrial fibrillation detection and stroke risk A major question, as yet unanswered, is whether different modes of detection of AF and the resulting AF pattern and burden identified have an implication on stroke risk and the need for anticoagulation. Screening programmes initiated within the health care system will tend to target those patients who have previously unidentified, mostly asymptomatic AF. On the contrary, AF detected by consumer devices will more often identify paroxysmal symptomatic AF which may, when detected or treated early during the course of disease, have a different risk of stroke and systemic embolism.17,30–32 The risk of stroke in a screened population can be enriched by requiring additional risk factors such as age and others, and the decision to anticoagulate will require consideration of the net clinical benefit of anticoagulation, taking into account the risk of stroke, major bleeding, and residual cardiovascular risk. Subclinical AF detected on implanted devices is also associated with elevated stroke risk.32,33 In contrast, AHRE detected by implanted devices may not have the same prognostic impact for stroke as AF detected by ECG recordings, due to the low frequency, short duration of AHRE episodes, and the uncertainty with respect to their nature (AF or other arrhythmias). The stroke risk associated with AHREs is usually lower than for clinically detected AF, and absolute stroke rates with AHRE are often close to 1%, despite the presence of stroke risk factors.18,34,35 In summary, medical technologies provide exciting new options to diagnose and screen for AF (from the patient, health care, and societal perspectives). However, it is uncertain whether detection of atrial arrhythmias using these methods has the same implication as using conventional 12-lead ECG, or indeed those who present clinically with AF, particularly with regard to stroke risk and prevention. More studies are needed to investigate the potentially different AF disease states that are uncovered by the use of more advanced and continuous rhythm monitors. Integrated care of atrial fibrillation patients In this workshop, delegates were asked to consider how to develop integrated care models in AF, the challenges limiting dissemination, and how novel approaches could support further integration. Definition of integrated care Based on the World Health Organization definition of integrated care,36 we defined this approach in AF as ‘a coordinated patient-centred approach by interdisciplinary specialists to improve AF outcomes’. Integrated care enables treatment of AF patients in all five domains of management: acute stabilization, detection and management of underlying cardiovascular co-morbidities and risk factors, appropriate oral anticoagulation for stroke prevention, and treatment with rate and/or rhythm control therapy.37,38 Electronic decision aids can be helpful for both the patient and the care provider, guiding the management team in clinical decision support, offering education, and measuring the effectiveness of treatment. Figure 2 illustrates the concept of integrated, patient-centred AF care. A core team, for example an AF nurse and a cardiologist (or other physician who specializes in AF care), is supported by appropriate technology, and this team communicates with the patient and forms an intermediary with other health care professionals to co-ordinate optimal AF management. Figure 2 View largeDownload slide Integrated AF care and the AF Heart Team. An example of some of the members contributing to comprehensive and integrated AF care, and a rhythm control AF Heart Team. Figure 2 View largeDownload slide Integrated AF care and the AF Heart Team. An example of some of the members contributing to comprehensive and integrated AF care, and a rhythm control AF Heart Team. Eligible patients and entry/exit criteria for integrated care Ideally patients with newly diagnosed AF should have at least one appointment with the core team in an integrated care service, based in either the primary or secondary care setting. This will include a diagnostic assessment, discussion of treatment options, initiation of appropriate therapy according to guidelines, and tailored education and empowerment of the patient and caregiver.1 Thereafter, stable and adequately managed patients can be followed up by supported self-management in the community. Criteria for another visit with the integrated AF team might include worsening of symptoms, hospitalization, stroke or bleeding complications, unstable situations (e.g. haemodynamic compromise or acute symptomatic arrhythmia recurrence), or suboptimal management. Conversely, empowered and educated patients who are clinically stable on fully established, guideline-based treatment with appropriate general practice support could take on their own management without further routine visits. Integrated AF care will take different shapes in different health care environments and will have to answer to challenges, including the extra time for patient interaction, the availability of treatment options to all patients, and the provision of technology support. Funding and reimbursement of integrated care is also dependent on local factors (particularly the provision of hospital and out-of-hospital specialist care), although integrated AF care may be a cost-effective solution to implement good AF management.39 Technology tools to ensure the success of integrated care Although electronic health portals are now available in many countries, the availability of tools that apply to AF specifically is limited. Furthermore, electronic patient records are often owned by health care providers (e.g. hospitals or general practices) and not by patients. As part of the 2016 AF Guidelines, the ESC in collaboration with the CATCH ME consortium have developed smartphone and tablet apps for patients and health care professionals (freely available from Google Play, Amazon, and Apple Appstores).40 The patient app offers information and education about AF, encourages active self-management, and also allows transfer of information to health care professionals. The health care professional app includes a patient register, in which risk factors, co-morbidities, and treatments can be prefilled by patients, and is designed as an interactive management tool incorporating the new ESC AF Guidelines. Other apps and websites are also available, for example educational aids available in numerous languages by cardiovascular and AF-specific charities such as the British Heart Foundation (https://www.bhf.org.uk/heart-health/conditions/atrial-fibrillation), the Atrial Fibrillation Association (www.heartrhythmalliance.org), and ‘AFib Matters’ by EHRA (http://www.afibmatters.org). The Atrial Fibrillation Heart Team for complex management decisions In this workshop, delegates were asked to propose practical measures and requirements for setting up local AF Heart Teams to support advanced AF management. Patient selection The AF Heart Team is proposed as a means to improve the care of selected and complex cases by providing specialist multidisciplinary input.1 It is an adjunct to integrated AF care that provides a comprehensive consideration of therapeutic options to patients who would benefit from such an approach (Figure 2). It is important to highlight that the AF Heart Team, while having an important role to support complex decision-making processes for difficult to manage patients with AF, is not required for the vast majority of AF management decisions. Complex AF patients are characterized by failure of first- and second-line therapies in the presence of severe AF-related symptoms, a high event rate, and often several coexisting co-morbidities. The two main areas where AF Heart Teams will be useful are: Complex rhythm control therapy, for example failure of catheter ablation to control symptomatic AF, consideration of AF surgery, or other situations that make rhythm control therapy difficult. Complex stroke prevention, for example patients with a relevant contraindication to anticoagulation or the need for left atrial appendage (LAA) exclusion, ligation, or clipping. As different treatment modalities are evolving rapidly, the AF Heart Team offers such patients expertise from several specialities, with the ultimate goal of optimizing the use of available resources and improving the quality of care.41 Set-up and process The constitution of this team depends on the local infrastructure. An interventional electrophysiologist would preferably be the leader of such a team, which also includes a ‘fixed core’ consisting of a general or referring cardiologist and a cardiac surgeon for a rhythm control AF Heart Team, and anticoagulation and stroke specialists for a stroke prevention AF Heart Team. Other specialists are invited as needed, such as anaesthesiologists and experts in cardiac imaging, among others (Figure 2). Once a patient has been discussed within the AF Heart Team and a strategy has been proposed, one member of the team should take responsibility for the proposed management and interact with the patient and referring physician. An AF Heart Team is preferably implemented by defining membership and responsibilities in advance. The team should meet—at least initially—on a regular basis, and close cooperation with other local heart teams will be useful. It is important to critically review and optimize locally available care pathways and design advanced treatment pathways, with the AF Heart Team defining referral pathways for internal and external caregivers (e.g. general practitioners and other local hospitals). The AF Heart Team should be an important driver of improving the quality and efficiency of care, including review of care pathways, and collation and reporting of data on local outcome and complications rates. Stroke prevention In this workshop, delegates were asked to consider the remaining barriers to stroke prevention, including the use of biomarkers to improve patient selection for anticoagulation, the available evidence for the safety of discontinuing anticoagulation after transient AF or AF ablation, how clinicians should manage anticoagulation after serious bleeding, and the role of LAA occluders in current clinical management. Biomarkers to refine risk scores Current clinical risk scores have only a modest predictive ability to define stroke and bleeding risk in individual patients and do not differentiate the severity of component risk factors. This leads to uncertainties of the benefit of stroke prevention treatment, most obvious when considering initiation of oral anticoagulation in patients at the lower end of the risk spectrum by clinical risk scores or in patients with bleeding complications on oral anticoagulation.1 The digital era facilitates the calculation of risk based on continuous variables and more complex risk calculators on smartphones, computers, or with integration into electronic health records. Several biomarkers are linked with underlying pathophysiology and clinical outcomes, including markers of myocardial injury (troponins), cardiac stress and dysfunction [natriuretic peptides, growth differentiation factor (GDF) 15], myocardial fibrosis (galectin-3 and fibroblast growth factors), renal dysfunction (creatinine and cystatin C), inflammation (C-reactive protein and cytokines), and coagulation activity (D-dimer).42 Risk scores combining clinical characteristics and biomarkers have recently been developed, validated (generally in anticoagulated populations), and compared with established clinical risk scores (such as CHA2DS2-VASc43). These biomarker risk scores include, among others, the ATRIA stroke risk score [The AnTicoagulation and Risk factors In Atrial Fibrillation; includes glomerular filtration rate (GFR)]44,45 and the ABC stroke score (Age, Biomarkers, Clinical history; includes troponin and NT-proBNP).46,47 Biomarker-based risk scores for prediction of major bleeding in AF include ORBIT-AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation; GFR < 60 mL/min and categorical cut-offs for haemoglobin or haematocrit)48 and the ABC bleeding score (Age, Biomarkers, Clinical history; haemoglobin, troponin, and GDF-15 or GFR).49 Two recent scores also include the estimation of composite outcomes using a multi-biomarker approach,50,51 allowing clinicians to refine their assessment of balance between stroke and bleeding risk and thus potentially the net clinical benefit of stroke prevention therapies. This approach can avoid the overestimation of bleeding risk that can lead to inappropriate withholding of anticoagulation from suitable patients but is limited by the delay and practical difficulty of relying on biomarkers. The major evidence gaps for this approach at present are the cost-effectiveness and incremental precision of such scores, and the lack of prospective randomized trials to evaluate the use of risk scores on cardiovascular outcomes in AF patients. Properly validated and well-calibrated risk scores delivered by technology solutions may in the future prove useful to support more personalized approaches to anticoagulant therapy. Safety of discontinuing anticoagulation in specific patient groups Atrial fibrillation ablation is increasingly being used to treat symptomatic AF patients, with 1 year success rates of around 60–80% for paroxysmal AF and 50–70% for persistent AF.52–55 Despite these reductions in recurrent AF, it is unclear whether ablation reduces the associated risk of stroke. Between 2% and 5% of patients per year will experience late recurrences of AF, and this seems to continue up to 5 years post-ablation and beyond.54,56 The minimum amount of AF required to increase the risk of stroke is unknown, and the risk stratification schemes such as CHA2DS2-VASc do not take account of AF burden, implying that even one episode of AF may carry the same stroke risk as recurrent or persistent AF. In the TRENDS study (Temporal Relationship of Atrial Tachyarrhythmias, Cerebrovascular Events, and Systemic Emboli Based on Stored Device Data), the risk of stroke increased two-fold in those patients with an atrial tachycardia/AF burden of >5.5 h in any 30 days window.57 In the ASSERT trial (ASymptomatic atrial fibrillation and Stroke Evaluation in pacemaker patients and the atrial fibrillation Reduction atrial pacing Trial), atrial arrhythmias detected within 90 days of pacemaker implant increased the risk of stroke, although the increase was smaller than for conventionally detected AF.34 Further analysis of ASSERT showed that subclinical AF with a duration >24 h (but not less) was associated with increased risk of subsequent stroke or embolism (hazard ratio 3.24, 95% confidence interval 1.51–6.95).32 However, the absolute risk of stroke may still fall below the perceived threshold for anticoagulant treatment. In the ASSERT trial, the annualized risk of stroke reported for patients with brief occurrences of AF were only 0.28%, 0.70%, and 0.97% for patients with CHADS2 scores of 1, 2 and >2, respectively.38 Observational cohort studies have suggested a reduced risk of stroke after catheter ablation58,59; however, propensity matching cannot entirely account for patient selection bias.60 Current guidelines recommend that even patients with ‘successful’ ablation should be treated with OAC according to underlying stroke risk.1 These recommendations reflect the fact that recurrence is common post-AF ablation, recurrent AF is often asymptomatic, and patients accumulate stroke risk factors as they age. Further trials, such as OCEAN (Optimal Anticoagulation for Higher Risk Patients Post-Catheter Ablation for Atrial Fibrillation Trial; NCT02168829) need to report their outcomes before this ‘safety-first’ practice can change. Similarly, the role of new digital technologies that can obtain frequent (or even continuous) rhythm monitoring needs to be studied in the context of stroke rates, also considering the low risk of major complications from contemporary oral anticoagulation. Another important area where anticoagulation is often discontinued is ‘reversible’ or ‘transient’ AF, terms used to describe bouts of AF related to the postoperative state or an acute illness (e.g. sepsis or metabolic disturbances).61,62 Although some patients may have truly self-limiting AF, many are at longer-term risk of AF recurrence (and therefore stroke).63,64 This uncertainty has led to major variation in practice, with some advocating short-term anticoagulation (e.g. 3–6 months), followed by careful monitoring for recurrent AF and others recommending long-term anticoagulation for those with an elevated CHA2DS2-VASc score. Importantly, such patients were not specifically evaluated in the pivotal anticoagulation trials, and so further research is vital to address this major gap in evidence. Anticoagulation after serious bleeding Anticoagulants increase the risk of bleeding, and after minor bleeding events with a clear precipitating cause, oral anticoagulation should often be reinitiated once bleeding has been controlled.1 More severe or life-threatening bleeding [e.g. intracranial haemorrhage (ICH)] requires cessation or even therapeutic ‘reversal’ of anticoagulation, with careful consideration about the risks and benefits of resumption. There is wide variation in clinical practice for whether or not to restart anticoagulation after ICH,65 and patients who are reinitiated on anticoagulation seem to have better outcomes than those who are not.66,67 Patients at highest risk of recurrent bleeding are often those at highest risk of thrombo-embolic stroke.1 The risk of recurrent ICH can be stratified by ICH location (deep vs. lobar) and markers of small vessel disease. Cerebral amyloid angiopathy is associated with a high annual bleeding risk of around 10%.68 Advances in cerebral imaging,69 biomarkers and technology for AF screening all have the potential to clarify stroke and bleeding risk in individual patients. Left atrial appendage occlusion Exclusion of the LAA is now possible with percutaneous devices, although scientific evidence is mainly based on observational studies and registries, with just two randomized controlled trials of a single device compared with warfarin therapy.70–72 The Watchman® device has been approved by the Food and Drug Administration (FDA) for patients with AF not related to heart valve disease, at an increased risk of stroke and suitable for warfarin but with an appropriate reason to seek a warfarin alternative. In a composite analysis, the device was associated with less haemorrhagic strokes and cardiovascular/unexplained death than warfarin, but there were more ischaemic strokes in the device group.73 Unfortunately, there are no direct comparisons of occluder therapy and non-vitamin K antagonist oral anticoagulants, and no comparisons of occluders in patients deemed ineligible for anticoagulation. Left atrial appendage occluders are often used in AF patients who cannot be anticoagulated, a group with no other realistic treatments. Further information is needed about long-term efficacy, adverse events, and comparison with other stroke prevention strategies, such as thoracoscopic LAA exclusion. It is also unclear whether the results from one device can be extrapolated to the many others in development or what the minimal duration of antithrombotic therapy after LAA exclusion should be. Adequately powered controlled trials are urgently needed to inform the best use of these devices, and several such studies are under way. Rate control therapy In this workshop, delegates were asked to consider novel approaches to heart rate control, to define the gaps in current evidence, and consider the impact of new technologies on rate control in routine clinical practice. When and how to use rate control therapy Rate control is usually the first-line treatment strategy for patients with symptomatic AF1 but has a relatively poor evidence-base.74 There are also two major groups of patients in whom rate control is used even when a rhythm control strategy is attempted.75 First, rate control should be background therapy for nearly all AF patients, because well-controlled heart rates are important during relapses of AF. Secondly, rate control is the therapy of choice to contain symptoms in patients for whom the risks of restoring sinus rhythm outweigh the benefits, or in those in whom advanced rhythm control fails. The choice of rate-controlling drugs, alone or in combination, depends on symptoms, co-morbidities and potential side effects. Following the RACE II trial (RAte Control Efficacy in permanent atrial fibrillation II),76 AF guidelines have adopted a lenient rate control strategy as the first-choice approach, with stricter control reserved for patients with persistent symptoms or deterioration in cardiac function.1 Even in heart failure and reduced ejection fraction, control of heart rate with beta-blockers was not associated with mortality benefit in the subgroup of patients with AF,77 in contrast to a marked benefit in women and men with sinus rhythm of all ages.78 In the case of cardiac resynchronization therapy that necessitates continuous biventricular pacing, effective slowing of intrinsic AF is required to prevent adverse outcomes.79 New approaches to monitoring heart rate control Table 2 lists the major approaches to assessing rate control, including novel methods such as wearable monitors and smartphone applications. Key differences are concerned with cost (to the patient and health care systems), the ability to correlate heart rate with symptoms and patient activity, and the capacity to measure AF burden. There are also multifaceted and contradictory patient effects; the ability to record and transmit an ECG will reassure many and underpin independent patients who ‘own’ their disease management but can also increase anxiety, generate a focus on numerical heart rate, and potentially lead to incorrect self-management. Each approach has specific limitations due to the type of technology (as discussed in the screening section), which need to be taken into account when clinicians appraise the results. Table 2 Tools to assess the effectiveness of rate control Technology Advantages Limitations Applicability to type of AF Pulse palpation Correlation to symptoms Difficult to assess All types Inaccurate (pulse deficit) Standard 12-lead resting ECG (10 s) Gold standard for AF diagnosis No correlation to symptoms Persistent and permanent Exercise test Heart rate dynamics No validation for moderate exercise Persistent and permanent Ambulatory Holter ECG Day and night heart rate dynamics Accurate correlation to symptoms needs patient education All types of AF Correlation with symptoms External event recorder ±telemonitoring Day and night heart rate dynamics Not widely available All types of AF Correlation with symptoms Wearable heart rate monitors (smartphones, watches, and bands) Correlation to symptoms Patient education essential All types of AF Self-management and empowerment Potential anxiety for patient Increased workload for physician Wearable heart rate monitors (pulse detection) Potential for wide use Pulse wave only All types of AF Diagnostic functions available in implanted cardiac devices (pacemakers, implanted monitors, and defibrillators) Day and night heart rate dynamics Cost of remote monitoring or hospital visits All types of AF in patients with implanted devices Correlation to symptoms Technology Advantages Limitations Applicability to type of AF Pulse palpation Correlation to symptoms Difficult to assess All types Inaccurate (pulse deficit) Standard 12-lead resting ECG (10 s) Gold standard for AF diagnosis No correlation to symptoms Persistent and permanent Exercise test Heart rate dynamics No validation for moderate exercise Persistent and permanent Ambulatory Holter ECG Day and night heart rate dynamics Accurate correlation to symptoms needs patient education All types of AF Correlation with symptoms External event recorder ±telemonitoring Day and night heart rate dynamics Not widely available All types of AF Correlation with symptoms Wearable heart rate monitors (smartphones, watches, and bands) Correlation to symptoms Patient education essential All types of AF Self-management and empowerment Potential anxiety for patient Increased workload for physician Wearable heart rate monitors (pulse detection) Potential for wide use Pulse wave only All types of AF Diagnostic functions available in implanted cardiac devices (pacemakers, implanted monitors, and defibrillators) Day and night heart rate dynamics Cost of remote monitoring or hospital visits All types of AF in patients with implanted devices Correlation to symptoms Table 3 Clinical factors associated with atrial damage and a predisposition to AF Coexisting risk factors Drivers for AF Heart failure/disease Monogenic AF Hypertension Polygenic AF risk Age Atrial electrical foci Diabetes mellitus Inflammation (postoperative or inflammatory disease) Stroke or transient ischaemic attack Valvular heart disease Kidney disease Atrial ageing Obesity AF indicators Sleep apnoea Atrial high-rate episodes Sedentary lifestyle, alcohol, smoking, and habitual vigorous exercise Atrial runs or premature atrial complexes AF-related symptoms Atrial cardiomyopathies Dyspnoea, lethargy, and palpitations Biomarkers Coexisting risk factors Drivers for AF Heart failure/disease Monogenic AF Hypertension Polygenic AF risk Age Atrial electrical foci Diabetes mellitus Inflammation (postoperative or inflammatory disease) Stroke or transient ischaemic attack Valvular heart disease Kidney disease Atrial ageing Obesity AF indicators Sleep apnoea Atrial high-rate episodes Sedentary lifestyle, alcohol, smoking, and habitual vigorous exercise Atrial runs or premature atrial complexes AF-related symptoms Atrial cardiomyopathies Dyspnoea, lethargy, and palpitations Biomarkers Gaps in knowledge for rate control Unfortunately, there are many evidence gaps in rate control that affect clinical management of AF. We identified the key areas in need of further study: Optimal heart rate (rest and exercise) with respect to symptoms and outcomes and taking into account other comorbidities such as heart failure.80 Selection of drugs and drug combinations in general, but also in specific patient groups, for example heart failure with preserved81 or reduced systolic function82 and pulmonary disease.83 Parameters to assess the success of rate control and their association with prognosis (heart rate, symptoms, B-type natriuretic peptide, and others). Measurement of patient benefit, including AF-specific quality of life.84 The role of irregularity (RR interval) vs. absolute heart rate and their correlation with symptoms and the effects of specific drugs on outcomes such as cardiac function.85 Potential role for ‘pill-in-the-pocket’ approaches to rate control, similar to that used for flecainide and propafenone in rhythm control. Approaches for rhythm control In this workshop, delegates were asked to consider new paradigms for improving the success of rhythm control strategies, moving beyond the conventional time-based concept of AF classification. Context and success of rhythm control Rhythm control therapy is very effective in some patients, whereas others experience early, frustrating therapy failures despite concerted efforts to restore and maintain sinus rhythm.1,86–89 Technical failure can contribute to recurrent AF (e.g. due to reconnection of isolated pulmonary veins90) or deterioration of associated conditions and should be reduced by structured and high-quality care.6 Combining therapy modalities, making use of all rhythm control treatment options (antiarrhythmic drugs, catheter ablation and AF surgery1,91) and involving patients in the care process37,92 can help to manage expectations and maintain patient satisfaction. In the future, personalized implementation of different rhythm control therapy modalities may improve this situation.5,93 In addition, there is a growing realization that rhythm control interventions only target part of the relevant disease processes driving recurrent AF.94 A variety of clinical conditions such as obesity, lack of exercise, hypertension, heart failure, and sleep apnoea have been associated with recurrent AF as well as with newly diagnosed AF.95–99 Atrial damage caused by such factors can promote recurrent AF (Table 3). Atrial myocardium is affected by several cardiac and non-cardiac diseases or abnormalities.100 Of note, atrial cells (cardiomyocytes, fibroblasts, endothelial cells, and neurons) react extensively to pathological stimuli,100 and therefore atrial cardiomyopathies can contribute to arrhythmia occurrence.101,102 These markers for atrial damage can be found by careful analysis of electrical atrial function103,104 and/or by assessment of atrial structure and function.105,106 Integrated AF care tackling these underlying conditions may have an important role in successful rhythm control therapy.107 Role of imaging to support rhythm control AF development and the recurrence of AF following rhythm control are significantly related to left atrial (LA) substrate, including the extent of dilatation and fibrosis and the severity of dysfunction. These three parameters can be assessed and quantified using non-invasive imaging techniques, in addition to defining the pulmonary vein anatomy to support successful AF ablation (Figure 3). LA size is preferably measured as a volume using 3D imaging techniques, including 3D echocardiography, computed tomography (CT), or cardiac magnetic resonance (CMR) imaging. Due to differences between these imaging techniques and changes during the cardiac cycle, systematic use of the same technique and care with timing of volume assessment are required during follow-up. Figure 3 View largeDownload slide Multi-modality imaging to support rhythm control of AF. Assessment of left atrial volume (A) (real-time 3D transthoracic echocardiography), function (B) (2D speckle-tracking echocardiography, from which the longitudinal strain of the LA can be measured and the reservoir (εs), conduit (εe), and booster pump (εa) functions derived), fibrosis (C) (time interval between the onset of the P-wave and active atrial contraction measured with tissue Doppler imaging, PA-TDI), and pulmonary vein anatomy (D) (computed tomography). LA, left atrium; LIPV, left inferior pulmonary vein; LSPV, left superior pulmonary vein; RIPV, right inferior pulmonary vein; RSPV, right superior pulmonary vein. Figure 3 View largeDownload slide Multi-modality imaging to support rhythm control of AF. Assessment of left atrial volume (A) (real-time 3D transthoracic echocardiography), function (B) (2D speckle-tracking echocardiography, from which the longitudinal strain of the LA can be measured and the reservoir (εs), conduit (εe), and booster pump (εa) functions derived), fibrosis (C) (time interval between the onset of the P-wave and active atrial contraction measured with tissue Doppler imaging, PA-TDI), and pulmonary vein anatomy (D) (computed tomography). LA, left atrium; LIPV, left inferior pulmonary vein; LSPV, left superior pulmonary vein; RIPV, right inferior pulmonary vein; RSPV, right superior pulmonary vein. In general, despite these limitations, LA dilatation has been associated with the development of AF and recurrence of AF after catheter ablation.108 The extent of LA fibrosis is related to LA size, although small atria can still exhibit fibrosis and larger atria may not.109 In a multicentre observational study, extensive LA fibrosis on CMR was associated with an AF recurrence rate of 51% almost 1 year after first catheter ablation compared to 15% in patients with the least fibrosis.110 Echocardiography can also indirectly assess LA fibrosis, including integrated backscatter techniques and the time interval between the onset of the P-wave and atrial contraction measured with tissue Doppler imaging (TDI); both techniques are predictive of AF recurrence.109,111,112 The atria provide an important contribution to the performance of the heart101,113 and serve as a volume reservoir to regulate ventricular filling and a booster pump in late diastole. Left atrial function is typically assessed by echocardiography using transmitral and pulmonary vein Doppler, TDI (active LA contraction reflected by the atrial velocity, a′) and volume-based measures.114 Much of the information on LA function can also be derived from CMR and CT, but for practical reasons, echocardiography is mostly used in the clinical setting. Active deformation of the LA during the cardiac cycle can be assessed with strain imaging from 2D speckle-tracking echocardiography, with LA global strain identified as another important predictor of AF recurrence after catheter ablation.115 Atrial cardiomyopathy A recent expert consensus described the concept of an atrial cardiomyopathy as ‘any complex of structural, architectural, contractile or electrophysiological changes affecting the atria with the potential to produce clinically-relevant manifestations’.105 Histopathological alterations reflecting such atrial cardiomyopathies are often not specific to the damaging factor and may also vary substantially over time.116,117 Importantly, atrial cardiomyopathies with pathological or mechanical atrial alterations may exist in the absence of atrial arrhythmia or AF. Thus, these alterations may contribute to a ‘pre-AF state’ (Figure 4), which could include electrical irritability, structural changes and neurohormonal activation. Characterization of atrial pathology and imaging techniques, in particular, are of utmost importance, consistent with the observation that recurrent AF after catheter ablation seems to be higher in patients with signs of atrial cardiomyopathy.118 Blood biomarkers (natriuretic peptides, galectin-3, and others6) or imaging of subtle cardiac dysfunction (e.g. cardiac strain) may be able to detect drivers or markers of atrial cardiomyopathic damage. The ultimate goal of these markers is to define different types of AF that are characterized by a specific pathophysiology which may warrant early aggressive intervention or will respond favourably to stratified therapy. This group feels that assessing and reversing the major factors damaging the atria in clinical practice would be an important step to underpin a more systematic approach to rhythm control therapy in AF patients. Providing this new approach is shown to be clinically effective, it would support the development of personalized rhythm control therapy and afford a pathway for improvement in clinical outcomes and patient well-being. Figure 4 View largeDownload slide Pre-AF, atrial cardiomyopathy, and the spectrum of AF management. AHRE, atrial high rate episodes; PAC, premature atrial complex. Figure 4 View largeDownload slide Pre-AF, atrial cardiomyopathy, and the spectrum of AF management. AHRE, atrial high rate episodes; PAC, premature atrial complex. Conclusions The 6th Consensus Conference of AFNET and the EHRA outlined a vision for future management that incorporates new approaches and novel technologies to improve outcomes for patients with AF. With large increases in the burden of AF expected in coming decades, better diagnosis, integration of care, patient involvement and stratification of treatment selection by a multidisciplinary AF team could help to offset the impact of AF on health care services. Supplementary material Supplementary material is available at Europace online. Acknowledgements We wish to thank all participants of the 6th AFNET/EHRA consensus conference and especially the staff of AFNET, the EHRA, and the ESC for excellent organization of the conference. Conflict of interest: a detailed list of disclosures of financial relations is provided in the Supplementary material online, Appendix. Funding The 6th AFNET/EHRA consensus conference was co-financed by AFNET and EHRA, and received additional financial support from the CATCH ME consortium (EU Horizon 2020 grant number 633196). Industry participants paid an attendance fee for the conference and provided an industry perspective during the discussions at the meeting but had no involvement in the writing process. References 1 Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B et al. 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Europace 2016; 18: 1609– 78. Google Scholar CrossRef Search ADS PubMed 2 Lane DA, Skjoth F, Lip GYH, Larsen TB, Kotecha D. Temporal trends in incidence, prevalence, and mortality of atrial fibrillation in primary care. J Am Heart Assoc 2017; 6: e005155. Google Scholar CrossRef Search ADS PubMed 3 Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 2014; 129: 837– 47. Google Scholar CrossRef Search ADS PubMed 4 Krijthe BP, Kunst A, Benjamin EJ, Lip GY, Franco OH, Hofman A et al. Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur Heart J 2013; 34: 2746– 51. Google Scholar CrossRef Search ADS PubMed 5 Fabritz L, Guasch E, Antoniades C, Bardinet I, Benninger G, Betts TR et al. Expert consensus document: defining the major health modifiers causing atrial fibrillation: a roadmap to underpin personalized prevention and treatment. Nat Rev Cardiol 2015; 13: 230– 7. Google Scholar CrossRef Search ADS PubMed 6 Kirchhof P, Breithardt G, Bax J, Benninger G, Blomstrom-Lundqvist C, Boriani G et al. A roadmap to improve the quality of atrial fibrillation management: proceedings from the fifth Atrial Fibrillation Network/European Heart Rhythm Association consensus conference. Europace 2016; 18: 37– 50. Google Scholar CrossRef Search ADS PubMed 7 Cooke G, Doust J, Sanders S. Is pulse palpation helpful in detecting atrial fibrillation? A systematic review. J Fam Pract 2006; 55: 130– 4. Google Scholar PubMed 8 Lewis M, Parker D, Weston C, Bowes M. Screening for atrial fibrillation: sensitivity and specificity of a new methodology. Br J Gen Pract 2011; 61: 38– 9. Google Scholar CrossRef Search ADS PubMed 9 McManus DD, Chong JW, Soni A, Saczynski JS, Esa N, Napolitano C et al. PULSESMART: pulse-based arrhythmia discrimination using a novel smartphone application. J Cardiovasc Electrophysiol 2016; 27: 51– 7. Google Scholar CrossRef Search ADS PubMed 10 Marazzi G, Iellamo F, Volterrani M, Lombardo M, Pelliccia F, Righi D et al. Comparison of Microlife BP A200 Plus and Omron M6 blood pressure monitors to detect atrial fibrillation in hypertensive patients. Adv Therapy 2012; 29: 64– 70. Google Scholar CrossRef Search ADS 11 Wiesel J, Arbesfeld B, Schechter D. Comparison of the Microlife blood pressure monitor with the Omron blood pressure monitor for detecting atrial fibrillation. Am J Cardiol 2014; 114: 1046– 8. Google Scholar CrossRef Search ADS PubMed 12 Kearley K, Selwood M, Van den Bruel A, Thompson M, Mant D, Hobbs FR et al. Triage tests for identifying atrial fibrillation in primary care: a diagnostic accuracy study comparing single-lead ECG and modified BP monitors. BMJ Open 2014; 4: e004565. Google Scholar CrossRef Search ADS PubMed 13 Lau J, Lowres N, Neubeck L, Brieger D, Sy R, Galloway C et al. Performance of an automated iPhone ECG algorithm to diagnose atrial fibrillation in a community AF screening program (SEARCH-AF). Heart Lung Circ 2013; 22: S205. Google Scholar CrossRef Search ADS 14 Tieleman RG, Plantinga Y, Rinkes D, Bartels GL, Posma JL, Cator R et al. Validation and clinical use of a novel diagnostic device for screening of atrial fibrillation. Europace 2014; 16: 1291– 5. Google Scholar CrossRef Search ADS PubMed 15 Vaes B, Stalpaert S, Tavernier K, Thaels B, Lapeire D, Mullens W et al. The diagnostic accuracy of the MyDiagnostick to detect atrial fibrillation in primary care. BMC Family Practice 2014; 15: 113. Google Scholar CrossRef Search ADS PubMed 16 Doliwa PS, Frykman V, Rosenqvist M. Short-term ECG for out of hospital detection of silent atrial fibrillation episodes. Scand Cardiovasc J 2009; 43: 163– 8. Google Scholar CrossRef Search ADS PubMed 17 Freedman B, Camm AJ, Calkins H, Healey JS, Rosenqvist M, Wang J et al. Screening for atrial fibrillation: a report of the AF-SCREEN international collaboration. Circulation 2017; 135: 1851– 67. Google Scholar CrossRef Search ADS PubMed 18 Camm AJ, Simantirakis E, Goette A, Lip GY, Vardas P, Calvert M et al. Atrial high-rate episodes and stroke prevention. Europace 2017; 19: 169– 79. Google Scholar CrossRef Search ADS PubMed 19 Connolly SJ, Ezekowitz MD, Yusuf S, Eikelboom J, Oldgren J, Parekh A et al. Dabigatran versus warfarin in patients with atrial fibrillation. N Engl J Med 2009; 361: 1139– 51. Google Scholar CrossRef Search ADS PubMed 20 Fetsch T, Bauer P, Engberding R, Koch HP, Lukl J, Meinertz T et al. Prevention of atrial fibrillation after cardioversion: results of the PAFAC trial. Eur Heart J 2004; 25: 1385– 94. Google Scholar CrossRef Search ADS PubMed 21 Granger CB, Alexander JH, McMurray JJ, Lopes RD, Hylek EM, Hanna M et al. Apixaban versus warfarin in patients with atrial fibrillation. N Engl J Med 2011; 365: 981– 92. Google Scholar CrossRef Search ADS PubMed 22 Patel MR, Mahaffey KW, Garg J, Pan G, Singer DE, Hacke W et al. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med 2011; 365: 883– 91. Google Scholar CrossRef Search ADS PubMed 23 Ruff CT, Giugliano RP, Antman EM, Crugnale SE, Bocanegra T, Mercuri M et al. Evaluation of the novel factor Xa inhibitor edoxaban compared with warfarin in patients with atrial fibrillation: design and rationale for the Effective aNticoaGulation with factor xA next GEneration in Atrial Fibrillation-Thrombolysis In Myocardial Infarction study 48 (ENGAGE AF-TIMI 48). Am Heart J 2010; 160: 635– 41. Google Scholar CrossRef Search ADS PubMed 24 Patten M, Maas R, Bauer P, Luderitz B, Sonntag F, Dluzniewski M et al. Suppression of paroxysmal atrial tachyarrhythmias–results of the SOPAT trial. Eur Heart J 2004; 25: 1395– 404. Google Scholar CrossRef Search ADS PubMed 25 Kirchhof P, Nabauer M, Gerth A, Limbourg T, Lewalter T, Goette A et al. Impact of the type of centre on management of AF patients: surprising evidence for differences in antithrombotic therapy decisions. Thromb Haemost 2011; 105: 1010– 23. Google Scholar CrossRef Search ADS PubMed 26 Turakhia MP, Hoang DD, Xu X, Frayne S, Schmitt S, Yang F et al. Differences and trends in stroke prevention anticoagulation in primary care vs cardiology specialty management of new atrial fibrillation: the retrospective evaluation and assessment of therapies in AF (TREAT-AF) study. Am Heart J 2013; 165: 93– 101.e101. Google Scholar CrossRef Search ADS PubMed 27 Schnabel RB, Yin X, Gona P, Larson MG, Beiser AS, McManus DD et al. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: a cohort study. Lancet 2015; 386: 154– 62. Google Scholar CrossRef Search ADS PubMed 28 Kirchhof P, Bax J, Blomstrom-Lundquist C, Calkins H, Camm AJ, Cappato R et al. Early and comprehensive management of atrial fibrillation: proceedings from the 2nd AFNET/EHRA consensus conference on atrial fibrillation entitled ‘research perspectives in atrial fibrillation’. Europace 2009; 11: 860– 85. Google Scholar CrossRef Search ADS PubMed 29 Svennberg E, Engdahl J, Al-Khalili F, Friberg L, Frykman V, Rosenqvist M. Mass screening for untreated atrial fibrillation: the STROKESTOP study. Circulation 2015; 131: 2176– 84. Google Scholar CrossRef Search ADS PubMed 30 Potpara TS, Polovina MM, Licina MM, Marinkovic JM, Lip GY. Predictors and prognostic implications of incident heart failure following the first diagnosis of atrial fibrillation in patients with structurally normal hearts: the Belgrade atrial fibrillation Study. Eur J Heart Fail 2013; 15: 415– 24. Google Scholar CrossRef Search ADS PubMed 31 Hess PL, Healey JS, Granger CB, Connolly SJ, Ziegler PD, Alexander JH et al. The role of cardiovascular implantable electronic devices in the detection and treatment of subclinical atrial fibrillation: a review. JAMA Cardiol 2017; 2: 324– 31. Google Scholar CrossRef Search ADS PubMed 32 Van Gelder IC, Healey JS, Crijns H, Wang J, Hohnloser SH, Gold MR et al. Duration of device-detected subclinical atrial fibrillation and occurrence of stroke in ASSERT. Eur Heart J 2017; 38: 1339– 44. Google Scholar CrossRef Search ADS PubMed 33 Boriani G, Glotzer TV, Santini M, West TM, De Melis M, Sepsi M et al. Device-detected atrial fibrillation and risk for stroke: an analysis of > 10, 000 patients from the SOS AF project (Stroke preventiOn Strategies based on Atrial Fibrillation information from implanted devices). Eur Heart J 2014; 35: 508– 16. Google Scholar CrossRef Search ADS PubMed 34 Healey JS, Connolly SJ, Gold MR, Israel CW, Van Gelder IC, Capucci A et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med 2012; 366: 120– 9. Google Scholar CrossRef Search ADS PubMed 35 Brambatti M, Connolly SJ, Gold MR, Morillo CA, Capucci A, Muto C et al. Temporal relationship between subclinical atrial fibrillation and embolic events. Circulation 2014; 129: 2094– 9. Google Scholar CrossRef Search ADS PubMed 36 Schrijvers G. Integrated Care Better and Cheaper . Amsterdam: Reed Business Information; 2016. 37 Hendriks JM, de Wit R, Crijns HJ, Vrijhoef HJ, Prins MH, Pisters R et al. Nurse-led care vs. usual care for patients with atrial fibrillation: results of a randomized trial of integrated chronic care vs. routine clinical care in ambulatory patients with atrial fibrillation. Eur Heart J 2012; 33: 2692– 9. Google Scholar CrossRef Search ADS PubMed 38 Carter L, Gardner M, Magee K, Fearon A, Morgulis I, Doucette S et al. An integrated management approach to atrial fibrillation. J Am Heart Assoc 2016; 5: e002950. Google Scholar CrossRef Search ADS PubMed 39 Hendriks J, Tomini F, van Asselt T, Crijns H, Vrijhoef H. Cost-effectiveness of a specialized atrial fibrillation clinic vs. usual care in patients with atrial fibrillation. Europace 2013; 15: 1128– 35. Google Scholar CrossRef Search ADS PubMed 40 Kotecha D, Chua W, Fabritz L, Hendriks J, Casadei B, Schotten U et al. European Society of Cardiology (ESC) smartphone and tablet applications for patients with atrial fibrillation and their healthcare providers. Europace 2017; doi: 10.1093/europace/eux299 41 Feldman AM, Weitz H, Merli G, DeCaro M, Brechbill AL, Adams S et al. The physician-hospital team: a successful approach to improving care in a large academic medical center. Acad Med 2006; 81: 35– 41. Google Scholar CrossRef Search ADS PubMed 42 Hijazi Z, Oldgren J, Siegbahn A, Wallentin L. Application of biomarkers for risk stratification in patients with atrial fibrillation. Clin Chem 2017; 63: 152– 64. Google Scholar CrossRef Search ADS PubMed 43 Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the Euro Heart Survey on atrial fibrillation. Chest 2010; 137: 263– 72. Google Scholar CrossRef Search ADS PubMed 44 Singer DE, Chang Y, Borowsky LH, Fang MC, Pomernacki NK, Udaltsova N et al. A new risk scheme to predict ischemic stroke and other thromboembolism in atrial fibrillation: the ATRIA study stroke risk score. J Am Heart Assoc 2013; 2: e000250. Google Scholar PubMed 45 Aspberg S, Chang Y, Atterman A, Bottai M, Go AS, Singer DE. Comparison of the ATRIA, CHADS2, and CHA2DS2-VASc stroke risk scores in predicting ischaemic stroke in a large Swedish cohort of patients with atrial fibrillation. Eur Heart J 2016; 37: 3203– 10. Google Scholar CrossRef Search ADS PubMed 46 Hijazi Z, Lindback J, Alexander JH, Hanna M, Held C, Hylek EM et al. The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation. Eur Heart J 2016; 37: 1582– 90. Google Scholar CrossRef Search ADS PubMed 47 Oldgren J, Hijazi Z, Lindback J, Alexander JH, Connolly SJ, Eikelboom JW et al. Performance and validation of a novel biomarker-based stroke risk score for atrial fibrillation. Circulation 2016; 134: 1697– 707. Google Scholar CrossRef Search ADS PubMed 48 O’Brien EC, Simon DN, Thomas LE, Hylek EM, Gersh BJ, Ansell JE et al. The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation. Eur Heart J 2015; 36: 3258– 64. Google Scholar PubMed 49 Hijazi Z, Oldgren J, Lindback J, Alexander JH, Connolly SJ, Eikelboom JW et al. The novel biomarker-based ABC (age, biomarkers, clinical history)-bleeding risk score for patients with atrial fibrillation: a derivation and validation study. Lancet 2016; 387: 2302– 11. Google Scholar CrossRef Search ADS PubMed 50 Ruff CT, Giugliano RP, Braunwald E, Murphy SA, Brown K, Jarolim P et al. Cardiovascular biomarker score and clinical outcomes in patients with atrial fibrillation: a subanalysis of the ENGAGE AF-TIMI 48 randomized clinical trial. JAMA Cardiol 2016; 1: 999– 1006. Google Scholar CrossRef Search ADS PubMed 51 Fanola CL, Giugliano RP, Ruff CT, Trevisan M, Nordio F, Mercuri MF et al. A novel risk prediction score in atrial fibrillation for a net clinical outcome from the ENGAGE AF-TIMI 48 randomized clinical trial. Eur Heart J 2017; 38: 888– 96. Google Scholar PubMed 52 Calkins H, Reynolds MR, Spector P, Sondhi M, Xu Y, Martin A et al. Treatment of atrial fibrillation with antiarrhythmic drugs or radiofrequency ablation: two systematic literature reviews and meta-analyses. Circ Arrhythm Electrophysiol 2009; 2: 349– 61. Google Scholar CrossRef Search ADS PubMed 53 Cappato R, Calkins H, Chen SA, Davies W, Iesaka Y, Kalman J et al. Updated worldwide survey on the methods, efficacy, and safety of catheter ablation for human atrial fibrillation. Circ Arrhythm Electrophysiol 2010; 3: 32– 8. Google Scholar CrossRef Search ADS PubMed 54 Ganesan AN, Shipp NJ, Brooks AG, Kuklik P, Lau DH, Lim HS et al. Long-term outcomes of catheter ablation of atrial fibrillation: a systematic review and meta-analysis. J Am Heart Assoc 2013; 2: e004549. Google Scholar CrossRef Search ADS PubMed 55 Wilber DJ, Pappone C, Neuzil P, De Paola A, Marchlinski F, Natale A et al. Comparison of antiarrhythmic drug therapy and radiofrequency catheter ablation in patients with paroxysmal atrial fibrillation: a randomized controlled trial. JAMA 2010; 303: 333– 40. Google Scholar CrossRef Search ADS PubMed 56 Steinberg JS, Palekar R, Sichrovsky T, Arshad A, Preminger M, Musat D et al. Very long-term outcome after initially successful catheter ablation of atrial fibrillation. Heart Rhythm 2014; 11: 771– 6. Google Scholar CrossRef Search ADS PubMed 57 Glotzer TV, Daoud EG, Wyse DG, Singer DE, Ezekowitz MD, Hilker C et al. The relationship between daily atrial tachyarrhythmia burden from implantable device diagnostics and stroke risk: the TRENDS study. Circ Arrhythm Electrophysiol 2009; 2: 474– 80. Google Scholar CrossRef Search ADS PubMed 58 Friberg L, Tabrizi F, Englund A. Catheter ablation for atrial fibrillation is associated with lower incidence of stroke and death: data from Swedish health registries. Eur Heart J 2016; 37: 2478– 87. Google Scholar CrossRef Search ADS PubMed 59 Saliba W, Schliamser JE, Lavi I, Barnett-Griness O, Gronich N, Rennert G. Catheter ablation of atrial fibrillation is associated with reduced risk of stroke and mortality: a propensity score-matched analysis. Heart Rhythm 2017; 14: 635– 42. Google Scholar CrossRef Search ADS PubMed 60 Ziff OJ, Lane DA, Samra M, Griffith M, Kirchhof P, Lip GY et al. Safety and efficacy of digoxin: systematic review and meta-analysis of observational and controlled trial data. BMJ 2015; 351: h4451. Google Scholar CrossRef Search ADS PubMed 61 Fonarow GC. Exploring the potential benefits and risks of anticoagulation for atrial fibrillation during hospitalization for sepsis. JAMA Cardiol 2016; 1: 690– 1. Google Scholar CrossRef Search ADS PubMed 62 Klein Klouwenberg PM, Frencken JF, Kuipers S, Ong DS, Peelen LM, van Vught LA et al. Incidence, predictors, and outcomes of new-onset atrial fibrillation in critically ill patients with sepsis. A cohort study. Am J Respir Crit Care Med 2017; 195: 205– 11. Google Scholar CrossRef Search ADS PubMed 63 Lee SH, Kang DR, Uhm JS, Shim J, Sung JH, Kim JY et al. New-onset atrial fibrillation predicts long-term newly developed atrial fibrillation after coronary artery bypass graft. Am Heart J 2014; 167: 593– 600.e591. Google Scholar CrossRef Search ADS PubMed 64 Lubitz SA, Yin X, Rienstra M, Schnabel RB, Walkey AJ, Magnani JW et al. Long-term outcomes of secondary atrial fibrillation in the community: the Framingham Heart Study. Circulation 2015; 131: 1648– 55. Google Scholar CrossRef Search ADS PubMed 65 Pasquini M, Charidimou A, van Asch CJ, Baharoglu MI, Samarasekera N, Werring DJ et al. Variation in restarting antithrombotic drugs at hospital discharge after intracerebral hemorrhage. Stroke 2014; 45: 2643– 8. Google Scholar CrossRef Search ADS PubMed 66 Kuramatsu JB, Gerner ST, Schellinger PD, Glahn J, Endres M, Sobesky J et al. Anticoagulant reversal, blood pressure levels, and anticoagulant resumption in patients with anticoagulation-related intracerebral hemorrhage. JAMA 2015; 313: 824– 36. Google Scholar CrossRef Search ADS PubMed 67 Nielsen PB, Larsen TB, Skjoth F, Gorst-Rasmussen A, Rasmussen LH, Lip GY. Restarting anticoagulant treatment after intracranial hemorrhage in patients with atrial fibrillation and the impact on recurrent stroke, mortality, and bleeding: a nationwide cohort study. Circulation 2015; 132: 517– 25. Google Scholar CrossRef Search ADS PubMed 68 O’Donnell HC, Rosand J, Knudsen KA, Furie KL, Segal AZ, Chiu RI et al. Apolipoprotein E genotype and the risk of recurrent lobar intracerebral hemorrhage. N Engl J Med 2000; 342: 240– 5. Google Scholar CrossRef Search ADS PubMed 69 Haeusler KG, Wilson D, Fiebach JB, Kirchhof P, Werring DJ. Brain MRI to personalise atrial fibrillation therapy: current evidence and perspectives. Heart 2014; 100: 1408– 13. Google Scholar CrossRef Search ADS PubMed 70 Holmes DRJr, Kar S, Price MJ, Whisenant B, Sievert H, Doshi SK et al. Prospective randomized evaluation of the Watchman Left Atrial Appendage Closure device in patients with atrial fibrillation versus long-term warfarin therapy: the PREVAIL trial. J Am Coll Cardiol 2014; 64: 1– 12. Google Scholar CrossRef Search ADS PubMed 71 Holmes DR, Reddy VY, Turi ZG, Doshi SK, Sievert H, Buchbinder M et al. Percutaneous closure of the left atrial appendage versus warfarin therapy for prevention of stroke in patients with atrial fibrillation: a randomised non-inferiority trial. Lancet 2009; 374: 534– 42. Google Scholar CrossRef Search ADS PubMed 72 Reddy VY, Doshi SK, Sievert H, Buchbinder M, Neuzil P, Huber K et al. Percutaneous left atrial appendage closure for stroke prophylaxis in patients with atrial fibrillation: 2.3-year follow-up of the PROTECT AF (Watchman Left Atrial Appendage System for Embolic Protection in Patients with Atrial Fibrillation) Trial. Circulation 2013; 127: 720– 9. Google Scholar CrossRef Search ADS PubMed 73 Holmes DRJr, Doshi SK, Kar S, Price MJ, Sanchez JM, Sievert H et al. Left atrial appendage closure as an alternative to warfarin for stroke prevention in atrial fibrillation: a patient-level meta-analysis. J Am Coll Cardiol 2015; 65: 2614– 23. Google Scholar CrossRef Search ADS PubMed 74 Kotecha D, Calvert M, Deeks JJ, Griffith M, Kirchhof P, Lip GY et al. A review of rate control in atrial fibrillation, and the rationale and protocol for the RATE-AF trial. BMJ Open 2017; 7: e015099. Google Scholar CrossRef Search ADS PubMed 75 Van Gelder IC, Rienstra M, Crijns HJ, Olshansky B. Rate control in atrial fibrillation. Lancet 2016; 388: 818– 28. Google Scholar CrossRef Search ADS PubMed 76 Van Gelder IC, Groenveld HF, Crijns HJGM, Tuininga YS, Tijssen JGP, Alings AM et al. Lenient versus strict rate control in patients with atrial fibrillation. N Engl J Med 2010; 362: 1363– 73. Google Scholar CrossRef Search ADS PubMed 77 Kotecha D, Holmes J, Krum H, Altman DG, Manzano L, Cleland JG et al. Efficacy of beta blockers in patients with heart failure plus atrial fibrillation: an individual-patient data meta-analysis. Lancet 2014; 384: 2235– 43. Google Scholar CrossRef Search ADS PubMed 78 Kotecha D, Manzano L, Krum H, Rosano G, Holmes J, Altman DG et al. Effect of age and sex on efficacy and tolerability of beta blockers in patients with heart failure with reduced ejection fraction: individual patient data meta-analysis. BMJ 2016; 353: i1855. Google Scholar CrossRef Search ADS PubMed 79 Boriani G, Gasparini M, Landolina M, Lunati M, Proclemer A, Lonardi G et al. Incidence and clinical relevance of uncontrolled ventricular rate during atrial fibrillation in heart failure patients treated with cardiac resynchronization therapy. Eur J Heart Fail 2011; 13: 868– 76. Google Scholar CrossRef Search ADS PubMed 80 Kotecha D, Flather MD, Altman DG, Holmes J, Rosano G, Wikstrand J et al. Heart rate and rhythm and the benefit of beta-blockers in patients with heart failure. J Am Coll Cardiol 2017; 69: 2885– 96. Google Scholar CrossRef Search ADS PubMed 81 Kotecha D, Lam CS, Van Veldhuisen DJ, Van Gelder IC, Voors AA, Rienstra M. Heart failure with preserved ejection fraction and atrial fibrillation: vicious twins. J Am Coll Cardiol 2016; 68: 2217– 28. Google Scholar CrossRef Search ADS PubMed 82 Kotecha D, Piccini JP. Atrial fibrillation in heart failure: what should we do? Eur Heart J 2015; 36: 3250– 7. Google Scholar PubMed 83 Goudis CA. Chronic obstructive pulmonary disease and atrial fibrillation: an unknown relationship. J Cardiol 2017; 69: 699– 705. Google Scholar CrossRef Search ADS PubMed 84 Kotecha D, Ahmed A, Calvert M, Lencioni M, Terwee CB, Lane DA et al. Patient-reported outcomes for quality of life assessment in atrial fibrillation: a systematic review of measurement properties. PLoS One 2016; 11: e0165790. Google Scholar CrossRef Search ADS PubMed 85 Kotecha D, Mohamed M, Shantsila E, Popescu BA, Steeds RP. Is echocardiography valid and reproducible in patients with atrial fibrillation? A systematic review. Europace 2017; 19: 1427– 38. Google Scholar CrossRef Search ADS PubMed 86 Kirchhof P, Andresen D, Bosch R, Borggrefe M, Meinertz T, Parade U et al. Short-term versus long-term antiarrhythmic drug treatment after cardioversion of atrial fibrillation (Flec-SL): a prospective, randomised, open-label, blinded endpoint assessment trial. Lancet 2012; 380: 238– 46. Google Scholar CrossRef Search ADS PubMed 87 Verma A, Jiang CY, Betts TR, Chen J, Deisenhofer I, Mantovan R et al. Approaches to catheter ablation for persistent atrial fibrillation. N Engl J Med 2015; 372: 1812– 22. Google Scholar CrossRef Search ADS PubMed 88 Kuck KH, Brugada J, Furnkranz A, Metzner A, Ouyang F, Chun KR et al. Cryoballoon or radiofrequency ablation for paroxysmal atrial fibrillation. N Engl J Med 2016; 374: 2235– 45. Google Scholar CrossRef Search ADS PubMed 89 Buiatti A, von Olshausen G, Barthel P, Schneider S, Luik A, Kaess B et al. Cryoballoon vs. radiofrequency ablation for paroxysmal atrial fibrillation: an updated meta-analysis of randomized and observational studies. Europace 2017; 19: 378– 84. Google Scholar CrossRef Search ADS PubMed 90 Kuck KH, Hoffmann BA, Ernst S, Wegscheider K, Treszl A, Metzner A et al. Impact of complete versus incomplete circumferential lines around the pulmonary veins during catheter ablation of paroxysmal atrial fibrillation: results from the gap-atrial fibrillation-german atrial fibrillation competence network 1 trial. Circ Arrhythm Electrophysiol 2016; 9: e003337. Google Scholar CrossRef Search ADS PubMed 91 Darkner S, Chen X, Hansen J, Pehrson S, Johannessen A, Nielsen JB et al. Recurrence of arrhythmia following short-term oral AMIOdarone after CATheter ablation for atrial fibrillation: a double-blind, randomized, placebo-controlled study (AMIO-CAT trial). Eur Heart J 2014; 35: 3356– 64. Google Scholar CrossRef Search ADS PubMed 92 Berti D, Hendriks JM, Brandes A, Deaton C, Crijns HJ, Camm AJ et al. A proposal for interdisciplinary, nurse-coordinated atrial fibrillation expert programmes as a way to structure daily practice. Eur Heart J 2013; 34: 2725– 30. Google Scholar CrossRef Search ADS PubMed 93 Syeda F, Holmes AP, Yu TY, Tull S, Kuhlmann SM, Pavlovic D et al. PITX2 modulates atrial membrane potential and the antiarrhythmic effects of sodium-channel blockers. J Am Coll Cardiol 2016; 68: 1881– 94. Google Scholar CrossRef Search ADS PubMed 94 Wijesurendra RS, Liu A, Eichhorn C, Ariga R, Levelt E, Clarke WT et al. Lone atrial fibrillation is associated with impaired left ventricular energetics that persists despite successful catheter ablation. Circulation 2016; 134: 1068– 81. Google Scholar CrossRef Search ADS PubMed 95 Abed HS, Wittert GA, Leong DP, Shirazi MG, Bahrami B, Middeldorp ME et al. Effect of weight reduction and cardiometabolic risk factor management on symptom burden and severity in patients with atrial fibrillation: a randomized clinical trial. JAMA 2013; 310: 2050– 60. Google Scholar CrossRef Search ADS PubMed 96 Fein AS, Shvilkin A, Shah D, Haffajee CI, Das S, Kumar K et al. Treatment of obstructive sleep apnea reduces the risk of atrial fibrillation recurrence after catheter ablation. J Am Coll Cardiol 2013; 62: 300– 5. Google Scholar CrossRef Search ADS PubMed 97 Kornej J, Hindricks G, Shoemaker MB, Husser D, Arya A, Sommer P et al. The APPLE score: a novel and simple score for the prediction of rhythm outcomes after catheter ablation of atrial fibrillation. Clin Res Cardiol 2015; 104: 871– 6. Google Scholar CrossRef Search ADS PubMed 98 Santhanakrishnan R, Wang N, Larson MG, Magnani JW, McManus DD, Lubitz SA et al. Atrial fibrillation begets heart failure and vice versa: temporal associations and differences in preserved versus reduced ejection fraction. Circulation 2016; 133: 484– 92. Google Scholar CrossRef Search ADS PubMed 99 Gorenek B, Pelliccia A, Benjamin EJ, Boriani G, Crijns HJ, Fogel RI et al. European Heart Rhythm Association (EHRA)/European Association of Cardiovascular Prevention and Rehabilitation (EACPR) position paper on how to prevent atrial fibrillation endorsed by the Heart Rhythm Society (HRS) and Asia Pacific Heart Rhythm Society (APHRS). Europace 2017; 19: 190– 225. Google Scholar PubMed 100 Andrade J, Khairy P, Dobrev D, Nattel S. The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. Circ Res 2014; 114: 1453– 68. Google Scholar CrossRef Search ADS PubMed 101 Hoit BD. Left atrial size and function: role in prognosis. J Am Coll Cardiol 2014; 63: 493– 505. Google Scholar CrossRef Search ADS PubMed 102 Goette A, Bukowska A, Dobrev D, Pfeiffenberger J, Morawietz H, Strugala D et al. Acute atrial tachyarrhythmia induces angiotensin II type 1 receptor-mediated oxidative stress and microvascular flow abnormalities in the ventricles. Eur Heart J 2009; 30: 1411– 20. Google Scholar CrossRef Search ADS PubMed 103 Lankveld T, de Vos CB, Limantoro I, Zeemering S, Dudink E, Crijns HJ et al. Systematic analysis of ECG predictors of sinus rhythm maintenance after electrical cardioversion for persistent atrial fibrillation. Heart Rhythm 2016; 13: 1020– 7. Google Scholar CrossRef Search ADS PubMed 104 Lankveld T, Zeemering S, Scherr D, Kuklik P, Hoffmann BA, Willems S et al. Atrial fibrillation complexity parameters derived from surface ecgs predict procedural outcome and long-term follow-up of stepwise catheter ablation for atrial fibrillation. Circ Arrhythm Electrophysiol 2016; 9: e003354. Google Scholar CrossRef Search ADS PubMed 105 Goette A, Kalman JM, Aguinaga L, Akar J, Cabrera JA, Chen SA et al. EHRA/HRS/APHRS/SOLAECE expert consensus on atrial cardiomyopathies: definition, characterization, and clinical implication. Europace 2016; 18: 1455– 90. Google Scholar CrossRef Search ADS PubMed 106 Donal E, Lip GY, Galderisi M, Goette A, Shah D, Marwan M et al. EACVI/EHRA expert consensus document on the role of multi-modality imaging for the evaluation of patients with atrial fibrillation. Eur Heart J Cardiovasc Imaging 2016; 17: 355– 83. Google Scholar CrossRef Search ADS PubMed 107 Savelieva I, Kakouros N, Kourliouros A, Camm AJ. Upstream therapies for management of atrial fibrillation: review of clinical evidence and implications for European Society of Cardiology guidelines. Part I: primary prevention. Europace 2011; 13: 308– 28. Google Scholar CrossRef Search ADS PubMed 108 Tops LF, Schalij MJ, Bax JJ. Imaging and atrial fibrillation: the role of multimodality imaging in patient evaluation and management of atrial fibrillation. Eur Heart J 2010; 31: 542– 51. Google Scholar CrossRef Search ADS PubMed 109 den Uijl DW, Delgado V, Bertini M, Tops LF, Trines SA, van de Veire NR et al. Impact of left atrial fibrosis and left atrial size on the outcome of catheter ablation for atrial fibrillation. Heart 2011; 97: 1847– 51. Google Scholar CrossRef Search ADS PubMed 110 Marrouche NF, Wilber D, Hindricks G, Jais P, Akoum N, Marchlinski F et al. Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study. JAMA 2014; 311: 498– 506. Google Scholar CrossRef Search ADS PubMed 111 Bertini M, Borleffs CJ, Delgado V, Ng AC, Piers SR, Shanks M et al. Prediction of atrial fibrillation in patients with an implantable cardioverter-defibrillator and heart failure. Eur J Heart Fail 2010; 12: 1101– 10. Google Scholar CrossRef Search ADS PubMed 112 den Uijl DW, Gawrysiak M, Tops LF, Trines SA, Zeppenfeld K, Schalij MJ et al. Prognostic value of total atrial conduction time estimated with tissue Doppler imaging to predict the recurrence of atrial fibrillation after radiofrequency catheter ablation. Europace 2011; 13: 1533– 40. Google Scholar CrossRef Search ADS PubMed 113 Schotten U, Verheule S, Kirchhof P, Goette A. Pathophysiological mechanisms of atrial fibrillation: a translational appraisal. Physiol Rev 2011; 91: 265– 325. Google Scholar CrossRef Search ADS PubMed 114 Marsan NA, Tops LF, Holman ER, Van de Veire NR, Zeppenfeld K, Boersma E et al. Comparison of left atrial volumes and function by real-time three-dimensional echocardiography in patients having catheter ablation for atrial fibrillation with persistence of sinus rhythm versus recurrent atrial fibrillation three months later. Am J Cardiol 2008; 102: 847– 53. Google Scholar CrossRef Search ADS PubMed 115 Motoki H, Negishi K, Kusunose K, Popovic ZB, Bhargava M, Wazni OM et al. Global left atrial strain in the prediction of sinus rhythm maintenance after catheter ablation for atrial fibrillation. J Am Soc Echocardiogr 2014; 27: 1184– 92. Google Scholar CrossRef Search ADS PubMed 116 Corradi D. Atrial fibrillation from the pathologist’s perspective. Cardiovasc Pathol 2014; 23: 71– 84. Google Scholar CrossRef Search ADS PubMed 117 Corradi D, Callegari S, Maestri R, Benussi S, Alfieri O. Structural remodeling in atrial fibrillation. Nat Clin Pract Cardiovasc Med 2008; 5: 782– 96. Google Scholar CrossRef Search ADS PubMed 118 Lemola K, Desjardins B, Sneider M, Case I, Chugh A, Good E et al. Effect of left atrial circumferential ablation for atrial fibrillation on left atrial transport function. Heart Rhythm 2005; 2: 923– 8. Google Scholar CrossRef Search ADS PubMed © The Author 2018. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://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 email@example.com
Europace – Oxford University Press
Published: Mar 1, 2018
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