Research Priorities in Atrial Fibrillation Screening: A Report from a National Heart, Lung, and Blood Institute Virtual Workshop

Emelia J Benjamin; Alan S Go; Patrice Desvigne-Nickens; Christopher D Anderson; Barbara Casadei; Lin Y Chen; Harry JGM Crijns; Ben Freedman; Mellanie True Hills; Jeff S Healey; Hooman Kamel; Dong-Yun Kim; Mark S Link; Renato D Lopes; Steven A Lubitz; David D McManus; Peter A Noseworthy; Marco V Perez; Jonathan P Piccini; Renate B Schnabel; Daniel E Singer; Robert Tieleman; Mintu P Turakhia; Isabelle C Van Gelder; Lawton S Cooper; Sana M Al-Khatib

doi:10.1161/CIRCULATIONAHA.120.047633

. Author manuscript; available in PMC: 2022 Jan 26.

Published in final edited form as: Circulation. 2021 Jan 25;143(4):372–388. doi: 10.1161/CIRCULATIONAHA.120.047633

Research Priorities in Atrial Fibrillation Screening: A Report from a National Heart, Lung, and Blood Institute Virtual Workshop

Emelia J Benjamin ¹, Alan S Go ², Patrice Desvigne-Nickens ³, Christopher D Anderson ⁴, Barbara Casadei ⁵, Lin Y Chen ⁶, Harry JGM Crijns ⁷, Ben Freedman ⁸, Mellanie True Hills ⁹, Jeff S Healey ¹⁰, Hooman Kamel ¹¹, Dong-Yun Kim ¹², Mark S Link ¹³, Renato D Lopes ¹⁴, Steven A Lubitz ¹⁵, David D McManus ¹⁶, Peter A Noseworthy ¹⁷, Marco V Perez ¹⁸, Jonathan P Piccini ¹⁴, Renate B Schnabel ¹⁹, Daniel E Singer ²⁰, Robert Tieleman ^21,²², Mintu P Turakhia ²³, Isabelle C Van Gelder ²¹, Lawton S Cooper ³, Sana M Al-Khatib ¹⁴

¹Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, and Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118 (E.J.B.)

²Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612. Departments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco, San Francisco, CA 94143. Departments of Medicine, Health Research and Policy, Stanford University, Stanford, CA 94305 (A.S.G.)

³Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892 (P.D.N., L.S.C.)

⁴Department of Neurology, Center for Genomic Medicine, and McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA 02114 (C.D.A.)

⁵Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX39DU, UK (B.C.)

⁶Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN 55455 (L.Y.C.)

⁷Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, The Netherlands (H.J.G.M.C.)

⁸Heart Research Institute, Charles Perkins Centre, and Concord Hospital Department of Cardiology, Concord Clinical School, University of Sydney, Sydney, Australia (B.F.)

⁹StopAfib.org, American Foundation for Women’s Health, Decatur, TX 76234 (M.T.H.)

¹⁰Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada (J.S.H.)

¹¹Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, NY 10021 (H.K.)

¹²Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892 (D.Y.K.)

¹³Department of Medicine, Division of Cardiology, UT Southwestern Medical Center, Dallas, TX 75390 (M.S.L.)

¹⁴Division of Cardiology and Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, 27710 (R.D.L., J.P.P., S.M.A.)

¹⁵Cardiovascular Research Center and Cardiac Arrhythmia Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA (S.A.L.)

¹⁶Department of Medicine, Division of Cardiology, University of MA Medical School, Worcester, MA 01655 (D.D.M.)

¹⁷Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55902 (P.A.N.)

¹⁸Division of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA 95125 (M.V.P.)

¹⁹Department of Cardiology, University Heart Center Hamburg, Hamburg, Germany; DZHK (German Center for Cardiovascular Research), partner site Hamburg/Kiel/Luebeck (R.B.S.)

²⁰Division of General Internal Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, 02114 (D.E.S.)

²¹Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands (I.C.V.G, R.T.)

²²Department of Cardiology, Martini Hospital, Groningen, The Netherlands (R.T.)

²³Veterans Affairs Palo Alto Health Care System, Palo Alto CA; Center for Digital Health, Stanford University, Stanford, CA (M.P.T.)

^✉

Address for Correspondence: Emelia J. Benjamin, MD, ScM, Boston University School of Medicine, Collamore 8, Cardiovascular Medicine, 72 East Concord Street, Boston, MA 02118, Phone: 617-638-8725, Fax: 617-638-8969, emelia@bu.edu, Twitter: @EmeliaBenjamin

PMCID: PMC8776506 NIHMSID: NIHMS1656466 PMID: 33493033

Abstract

Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF [SCAF]) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute’s virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: 1) role of opportunistic screening; 2) AF as a risk factor, risk marker, or both; 3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; 4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and 5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.

Keywords: atrial fibrillation, screening programs, stroke, research, prevention

With the aging of the population and improved survival with cardiovascular disease, the age-adjusted incidence and prevalence,¹ and lifetime risk² of atrial fibrillation (AF) are increasing in the US and globally.³ The epidemic of AF has been accompanied by increased awareness of its complications, including ischemic stroke, cognitive impairment, dementia, heart failure, myocardial infarction, arterial and venous thromboembolism, chronic kidney disease, diminished quality of life, increased health care costs, and premature death.³

In 2009, the National Heart, Lung, and Blood Institute (NHLBI) identified research opportunities for the prevention of AF based on the proceedings of an NHLBI expert workshop.⁴ A decade later, the NHLBI initiated a series of webinars bringing together international AF leaders to characterize ongoing research gaps and new opportunities to inform and accelerate future AF research. The novel webinar format was pursued by NHLBI to capture the expertise of global AF leaders in a resource effective and timely fashion. The two workshops focused on AF ablation⁵ and the bidirectional relations between AF and heart failure.⁶ The current paper represents the outcome of the third virtual workshop, held December 6, 2019, which focused on AF screening.

Using commercial and Medicare administrative claims databases experts estimate that about 4.6 million Americans have diagnosed AF, whereas about 700,000 (13%) have undiagnosed AF.⁷ However, AF often is unrecognized clinically until the onset of complications. Simultaneously, AF is increasingly being detected by consumer-owned devices, with uncertain clinical implications. Hence, patients, clinicians, researchers, health systems and organizations, private and public payers, and governmental agencies have intense interest in delineating the optimal role of and approach to screening for AF. Numerous technologies can be used to screen individuals for AF, including “gold standard” electrocardiogram (ECG), and novel non-contact technologies,⁸ non-invasive devices that detect pulse irregularity at a single timepoint, wearable devices like smart watches and patches, and implanted devices that monitor ECG rhythm long-term.⁹ The underlying premise of AF screening is that early detection will prompt management changes to potentially prevent AF’s progression and complications.

There are many unanswered questions regarding the role of AF screening, uncertainty about the therapeutic implications of screen-detected AF, and concerns that AF screening may engender unwarranted testing, treatments, costs, and complications. The workshop covered the following topics related to AF screening: 1) role of opportunistic AF screening; 2) AF as a risk factor, risk marker, or both; 3) relations between AF burden detected with long-term monitoring and treatments/outcomes; 4) designs of potential randomized trials of systematic AF screening with clinically-relevant outcomes; and 5) role of AF screening after ischemic stroke. The individual topic frameworks and recorded webinar will be posted on the NHLBI website simultaneous with the report’s publication. The paper seeks to identify AF screening research opportunities that will be impactful, innovative, resource efficient, and clinically relevant with the ultimate goal of improving the detection, management, and prognosis of patients with undiagnosed AF.

Opportunistic single timepoint screening for AF

Background

Incidentally-detected AF and Opportunistic single timepoint screening (OppSTS) occur in usual clinical practice during in-person clinic visits made for reasons other than screening for AF. The distinction between incidentally-detected AF and OppSTS, is that OppSTS occurs when primary care providers are encouraged to systematically screen for AF during routine consultations by examining pulse palpation, auscultation, or blood pressure checks in patients at heightened risk for AF (e.g., patients ≥65 years of age); if pulse irregularity is detected, a follow-up ECG is obtained to confirm AF.^10,11 OppSTS was not considered screening by the US Preventive Services Task Force (USPSTF), which regarded OppSTS as prompted pulse taking.¹² OppSTS has been shown to be feasible in selected practice settings, may be cost-effective, and is recommended by a variety of professional societies and organizations (Figure 1).^9,13–15 Systematic screening occurs when a target population (e.g., individuals aged ≥65 years or with heart failure, etc.) is requested to undergo screening for AF, which may involve single or intermittent testing, or continuous recordings of variable duration. In 2018, the USPSTF concluded that current evidence was insufficient to recommend routine systematic ECG screening for AF and noted the need for additional randomized controlled trials (RCTs) evaluating the benefits and harms of systematic AF screening compared to usual care.¹² Hence, an understanding of OppSTS is essential, as a potential comparator for pragmatic RCTs of systematic screening.

Figure 1. — Comparison of opportunistic single timepoint screening with systematic and consumer-led screening. Illustrates trade-offs between screening intensity/AF yield and stroke risk of screen-detected AF, along the screening intensity continuum.

The yield of OppSTS is highly variable depending upon age, multiple demographic and clinical factors, most of which are related to underlying risk of AF, as well as the OppSTS strategy and screening modality (Figure 1). OppSTS detects 1.4% of patients with previously unrecognized AF if age criterion ≥65 years is used, and is higher in men than in women (irrespective of detection method, setting, or region).¹⁶ In a meta-analysis of 19 single time point screening studies from around the world with ECG verification of the AF diagnosis, the number needed to screen to detect one person $\geq$ 65 years of age with a class 1 recommendation for anticoagulation (throughout the document, anticoagulation refers to oral forms) was 83.¹⁶

Despite the higher incidence and prevalence of clinically-detected AF in individuals of European ancestry,³ a recent multi-ethnic systematic screening study (mean age 74 years; 51% women) reported that AF detected by ECG patch, which screened continuously for 14 days, was similar across race/ethnic groups. AF detection was 7.1% in whites, 6.4% in African Americans, 6.9% in Hispanics, and 5.2% in Chinese (vs. whites, all P>0.50).¹⁷ The rates were considerably higher than age- and sex-adjusted rates reported from a single timepoint AF screening meta-analysis of individuals ≥65 (≥1.44%) and <65 (0.41%) years, presumably because of the longer screening duration and higher screening intensity.¹⁶ The similar detection rates with systematic AF screening provides support for the hypothesis that racial variation in clinical AF partially reflects ascertainment bias and motivates the critical need to ensure that screening studies include diverse patient populations. The call for greater inclusion of ethnic/racial minorities in AF patients is further supported by a study reporting the substantial underrepresentation and underreporting of racial and ethnic minorities in the clinical studies cited in current guidelines for AF.¹⁸

It has not been definitively proven that AF detected by OppSTS carries a risk of thromboembolism comparable to that of clinically recognized AF. However, given strong evidence from RCTs for net benefit of anticoagulation in patients with clinically recognized AF and other stroke risk factors, a placebo-controlled RCT of anticoagulation in OppSTS-detected AF may be challenging to perform. Thus, the prognosis of AF detected incidentally during a clinical encounter has been studied as a surrogate for OppSTS-detected AF. The prognosis of incidentally-detected asymptomatic AF appears no different from or may be even worse than clinically-detected AF with symptoms,¹⁹ and the risk reduction observed with anticoagulation appears to be similar.²⁰ These data underlie the viewpoint supporting OppSTS now.¹¹ However, this viewpoint is tempered by several factors, including recognition that: a) the likelihood of detecting AF with OppSTS is heterogeneous; b) those with AF detected have variable risk of stroke and other outcomes; c) the accuracy of OppSTS varies by screening strategy and modality; d) false positive screening tests may carry particular risks of economic, medical (including bleeding), and quality of life consequences; and e) strong evidence on the role of treatment, such as routinely recommended anticoagulation, in improving outcomes in screen-detected AF is lacking.

Knowledge Gaps

Randomized trials of OppSTS versus no screening to prevent stroke or death are lacking, and it is uncertain whether the prognosis of OppSTS-detected AF and clinically-detected AF are the same. These questions can be answered by screening studies with appropriate inclusion criteria and enough follow-up time to ascertain endpoints for which clinical management may change if AF is detected (Figure 2). RCTs would be required to demonstrate that OppSTS has a net benefit compared to usual care. However, OppSTS is already part of routine high-quality clinical care in many settings, and the large sample size required, and study expense would make enrollment in standalone trials of OppSTS challenging. Alternative approaches would be to direct resources towards systematic screening studies comparing greater screening intensity vs. usual OppSTS.²¹ OppSTS studies also could be designed as pragmatic trials involving integrated healthcare delivery systems using cluster designs and electronic health record tools for cost-efficient outcome assessments.

Figure 2. — Opportunistic single timepoint screening for AF. Questions remain regarding screening test accuracy by methodology, how often to screen, harms from screening, and if treating screen-detected AF has the same benefits as clinically-detected AF.

Randomized trials of OppSTS might target individuals eligible for guideline-directed anticoagulant therapy to prevent stroke and death.¹⁵ Given the paucity of screening data in racially/ethnically diverse individuals, it will be essential to ensure that screening studies include adequate numbers of individuals of non-European ancestry.

There is insufficient information on the relation of detection method (e.g., manually checked pulse, pulse-based technologies, single-lead, multiple limb-lead ECG, or non-contact) to accuracy and prognosis. An additional area of uncertainty is the balance of screening-related benefits versus potential adverse outcomes, which may ensue from false positive screening tests, including additional tests, additional procedures, and treatment-related complications (e.g., bleeding and worse quality of life).

Other knowledge gaps relate to how the type of AF detected may impact outcomes. For instance, it is uncertain how stroke risk varies with the proportion of cases of AF detected by OppSTS that is persistent, or the degree of AF burden in those with paroxysmal AF. Additionally, the incremental annual detection rate of OppSTS in those previously undiagnosed with AF is unknown. Stratification by AF type in pertinent studies is needed to inform requirements for repeated OppSTS (until AF detection) vs. more intensive screening strategies.

Research Opportunities

Examine the outcomes of OppSTS-detected AF in ethnically/racially diverse studies, such as pragmatic trials or prospective cohort studies comparing carefully matched OppSTS-detected AF with clinically-detected AF, to generate knowledge regarding their relative prognosis.
Determine the proportion of OppSTS-detected AF that is persistent by conducting observational and administrative studies that examine extended monitoring and follow up.
Study the impact of detection-method accuracy (including newer technologies) on beneficial health outcomes vs. additional unnecessary tests and procedures, and diminished quality of life. This research could include prospective comparative studies, pragmatic trials, and RCTs of different devices and screening strategies with stroke, mortality, and other clinically relevant endpoints in diverse populations.

AF: Risk Factor, Risk Marker, or Both?

Background

AF is a potent risk factor for cardioembolic stroke, mechanistically linked through reduced left atrial flow velocities and formation of thrombus, primarily in the left atrial appendage.²² A higher burden of AF has been associated with increased ischemic stroke risk,^23–25 supporting a causal link between altered atrial hemodynamics induced by AF and thromboembolic risk. The AF burden-stroke association, however, relies on secondary analyses that may be confounded by differences in the clinical risk profile of individuals with low versus high AF burden. Studies are ongoing to clarify if treating AF early with rhythm control or uptstream therapies prevents major adverse cardiovascular events, which would potentially enhance the justification to research AF screening strategies in individuals at intermediate or high estimated risk.²⁶

Alternatively, AF may serve as a risk marker of stroke. Stroke risk factors overlap significantly with factors predicting the development of AF.^27,28 Lower left atrial flow velocities and stasis are related to stroke risk, as evaluated by the CHA₂DS₂-VASc score,²⁹ implying an interaction between risk and atrial hemodynamics. Moreover, current approaches to restore sinus rhythm do not eliminate stroke risk despite causing significant reductions in AF burden,³⁰ consistent with evidence that anticoagulation is also effective in preventing ischemic stroke in high-risk patients in sinus rhythm and without a history of AF.³¹ Conversely, despite having a major impact on left atrial hemodynamics, AF does not increase stroke risk to a level that warrants anticoagulation in young patients with low CHA₂DS₂-VASc score.³² Also, predicted stroke risk using CHA₂DS₂VASc score is modulated by AF burden, with low risk upgraded by high AF burden and vice versa in some studies,²⁴ whereas other studies have reported that greater AF burden is linked to higher stroke and TIA risk independent of predicted risk using the ATRIA or CHA₂DS₂VASc score.³³

Mortality is higher in stroke patients with AF than in individuals who have either AF or stroke, irrespective of whether AF precedes or follows the stroke, suggesting a common pathway for both conditions.³⁴ Together with recent data showing that thromboembolic events may occur remotely from AF episodes,^34,35 these findings indicate that long-term exposure to the environmental and genetic risk factors that promote AF also may independently promote thromboembolic events.

A similar paradigm applies to other endpoints. For instance, heart failure, is both a cause and a complication of AF.³⁶ Whereas restoration of sinus rhythm has been found to be beneficial in selected heart failure patients with AF,³⁷ left ventricular energetics and perfusion are impaired even in patients with so-called “lone” AF, and do not fully recover after successful ablation.³⁸ Similarly, a meta-analysis reported that AF was associated with increased risk of myocardial infarction (RR, 1.54; 95% CI, 1.28–1.85),³⁹ and conversely, myocardial infarction is included in risk prediction models for AF (HR, 1.64; 95% CI, 1.38, 1.96).²⁸

Knowledge Gaps

Taking the existing evidence into consideration, it would seem logical to periodically monitor heart rhythm in persons at high risk for AF, and more specifically in those individuals who would be expected to benefit most from anticoagulation and aggressive risk factor management if AF were diagnosed. An alternative approach would be to combine major adverse cardiovascular events (e.g., stroke, heart failure) risk stratification with the probability of incident AF using ECG, imaging, blood biomarkers, and genetic/genomic markers (Figure 3). Management (e.g., anticoagulation) decisions might be based on more precise risk assessment of both conditions rather than AF detection alone. The former approach is supported by data indicating, for example, a significant association between left atrial dilatation and the probability of detecting AF using implantable devices,⁴⁰ and the predictive value of these and other parameters (e.g., NT-proBNP and left atrial function⁴¹) for stroke risk. These strategies, however, have not been systematically tested nor has their respective cost effectiveness been compared.

Figure 3. — Both AF risk factors (left, some examples given) and health data - including sophisticated imaging and biomarker data - may distinguish individuals at low (green), intermediate (yellow), and high (red) risk, both in terms of incident (or recurrent) AF as well as major adverse cardiovascular and cerebrovascular events and conditions (MACCE). Both retrospective and prospective studies should compare risk prediction by standard models and artificial intelligence, taking the elements within upper main ellipse into account, including response to risk management, incident AF, and MACCE. The strategy for prioritization of research would focus most efficiently on people with high risk of AF and MACCE (upper potential screen threshold for research). BMI: body mass index; BNP: brain natriuretic peptide; DM: diabetes mellitus; EHR: electronic health record; HF: heart failure; HTN: hypertension; VHD: valvular heart disease

More sophisticated atrial and ventricular phenotyping could play an important role not only in refining stroke risk but in risk stratification for predicting the development of heart failure and other outcomes in the presence of AF. Such modalities might include the detection of diffuse fibrosis (e.g., by T1 mapping cardiovascular magnetic resonance [CMR] imaging) and the evaluation of myocardial metabolism and energetics (e.g., by 31P magnetic resonance spectroscopy, or hyperpolarized carbon-13 CMR imaging),^38,42 or local inflammation evaluated by computed tomography.⁴³ In individuals in sinus rhythm, identification of early myocardial markers for risk of stroke and other AF-related complications such as heart failure may aid the selection of patients who would benefit from more intensive AF screening and potentially preventive therapies. Crucially, a deeper knowledge of the myocardial and systemic substrates of AF may stimulate RCTs of new or repurposed agents.⁴⁴

An emerging area of AF screening research involves artificial intelligence (AI) algorithms of electronic health records, ECGs signals, imaging data (e.g., echocardiographic, CMR), non-contact facial monitoring,⁸ and biomarker/genomic data. A recent large-scale, but single-center retrospective study demonstrated the feasibility of an AI-based ECG algorithm for detecting the likelihood of prior AF (by ECG) based on sinus rhythm ECGs.⁴⁵ Machine learning could potentially enhance identification of future AF risk and associated complications.^46–48 For example, an AI model incorporating time-dependent serial prothrombin international normalized ratio (INR) times in the first 30 days of enrollment in the GARFIELD-AF registry has been reported to predict major bleeding, stroke/systemic embolism, and death more effectively than time in therapeutic INR range.⁴⁹ However, many AI studies to date are not transparent, have not been externally validated,⁵⁰ and are susceptible to bias.^51,52 In addition, the advantage of AI algorithms over standard risk prediction models⁴⁷ and their implications for clinical decision making, including intensification of screening for AF or treatment decisions, remain uncertain. Furthermore, AI derived non-contact monitoring raises potential privacy concerns. However, if validated and guided by equity and integrity, AI may advance AF screening research and improve the precision of AF care.

Research Opportunities

Use large-scale longitudinal studies to generate data on systemic and myocardial (especially atrial myopathy) substrates of AF using ECG, echocardiography, CMR, computed tomography, and biomarkers (traditional, genetic, and omic) to demonstrate whether estimated risk of AF and AF-related complications (e.g., major adverse cardiovascular and cerebrovascular events and conditions, such as stroke, heart failure, and dementia) can be refined. Such data will assist in prioritizing research into screening individuals with undiagnosed AF, and research to discover novel therapeutic targets.
Investigate and validate AI algorithms and standard risk prediction models of cross-sectional and longitudinal data, including electronic health record, ECG, biomarker, genetic/omic, and imaging data—while adhering to privacy protections, transparency, diversity, equity, and inclusiveness. Priorities for research would test if implementing or intensifying AF screening, and predicting which patients with screen-detected AF will develop complications, will result in net clinical benefit.

Burden of AF and detection of AF with Different Technologies

Background

In AF, stroke risk stratification schema - such as CHADS₂, CHA₂DS₂-VASc, Framingham, and ATRIA - were developed from populations of patients with clinically diagnosed AF without respect to AF burden (defined as total density or percent of time in AF) or duration. Over the last decade, there have been dramatic advances in diagnostic monitoring and detection of AF across a variety of implantable, non-invasive, and consumer-facing wearable devices, which have enabled near-continuous detection of short, often subclinical, arrhythmia episodes.^53,54 Population screening also has been proposed with these devices.⁹

In patients with cardiac implantable electronic devices (CIEDs), observational data have shown that atrial high rate episodes (AHRE) as short as 6 minutes are associated with incident clinical AF and incident ischemic stroke.⁵⁵ There is, however, a risk gradient as higher risk of stroke is associated with longer AHRE episodes, especially of 24 hours or longer.²³ Studies from implantable and non-invasive devices have shown a stroke-risk gradient based on AF burden, AF duration, and cardiovascular risk factors.^24,33 However, earlier studies also have reported temporal dissociation between AF episodes and ischemic stroke events,^34,35 albeit a higher stroke risk in the 5 days after AF occurrence.³⁵ Temporal discontinuity data suggest that AF may be a marker of thrombogenic substrate and vascular risk, even during prolonged periods of sinus rhythm.

The accuracy of consumer devices used for the detection of AF is also uncertain. For instance, a recent meta-analysis of 10 studies noted that smartphone camera applications to detect AF had reasonably high sensitivity (94.2%), specificity (95.8%), and negative predictive value (99.8%). However, the authors reported that the included studies had methodological limitations, and modest positive predictive values (20.5%−39.2%), even restricted to individuals ≥65 years with hypertension.⁵⁶ The authors concluded that in asymptomatic individuals, the false positive exceeded the true positive AF results.

The absence of RCT data anchored on AF burden thresholds that should prompt anticoagulation has led to large evidence gaps and substantial treatment variation related to anticoagulation.^57,58 RCTs are ongoing for CIEDs^59–61 and implantable loop recorders in higher risk individuals ≥70 years of age (Danish LOOP, NCT02036450).⁶² Nevertheless, evidence gaps are likely to remain for atrial arrhythmias detected from a range of medical and consumer-grade devices.⁶³

Knowledge Gaps

It is unknown whether the pathophysiology of stroke in patients with clinically diagnosed AF, and SCAF or device-detected AHRE are the same, and whether anticoagulation reduces stroke risk in patients with CIED AHRE/SCAF (Table 1). It is unknown whether AF burden or the longest episode of AF better predicts AF-related complications. Additionally, research is needed to establish the optimal thresholds for AHRE/SCAF burden and duration that merit anticoagulation, and how such thresholds should vary depending upon the extent of coexisting clinical stroke risk factors, other comorbidities, and bleeding risk.

Table 1:

AF burden measurement in diagnostic devices, clinical evidence based on AF duration, and knowledge gaps and opportunities

Device	Context of AF identification	AF burden Measurement	Duration of detected atrial arrhythmias	Type of evidence for benefit of oral anticoagulation
Insertable Cardiac monitor	Continuous, programmable; “device-detected AF”	Yes	AT without AF	No data
Implantable pacemaker or defibrillator	Continuous, programmable; “device-detected AF”	Yes	Very short (< 6 minutes)	No data
Patch-based ambulatory ECG monitor	5–14 days continuous	Yes	Subclinical Short (6 min - 24 hours)	Observational data; ARTESIA and NOAH-AFNet trials ongoing
Pulse and ECG capable smartwatch	Opportunistic pulse checking; user prompted ECG rhythm strip	No	Clinical Paroxysmal	Randomized trials
Portable smartphone-connected single or multichannel ECG	User prompted ECG rhythm strip	No	Clinical Persistent (weeks/months)	Randomized trials
12-lead ECG or telemetry rhythm strip	Clinical presentation	No	Clinical Permanent (months/years)	Randomized trials

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The optimal frequency, intensity, and type of AF monitoring are not established. It is unknown which device is most cost-effective to screen for AF in different patient groups and whether clinically- and cost-effective AF screening can be achieved with intermittent ECG (single- and 12-lead) snapshots. It is also undetermined how data from pacemakers, implantable cardioverter defibrillators, implantable loop recorders, wearable devices, and non-contact approaches calibrate with each other for AF burden and risk of outcomes.

A major unanswered question is the balance between potential benefits and risks of consumer-driven screening. Consumers have increasing access to commercial devices that have the potential to diagnose AF early, offering the hope that AF progression and complications can be prevented. However, evidence is lacking in several areas, including: a) which segments of the population to target for screening; b) whether early detection of AF prevents progression and complications; and c) what strategies could prevent AF progression and complications, such as neurohormonal modulators, anti-inflammatory medications, antifibrosis therapies, and aggressive risk factor management, among others.

In addition, there are major concerns about unintended consequences of consumer-driven screening. For instance, will individuals with prior paroxysmal AF be falsely reassured by the absence of detected AF if devices are used intermittently? There are uncertainties regarding the potential for complications from treatment of falsely diagnosed AF or AF not destined to cause harm, such as increased patient anxiety, downstream testing/treatment financial costs, and anticoagulation or rhythm control-related complications.¹² There are also non-trivial health-system concerns, such as unintended exacerbation of health care disparities, and increased front-line clinician stress from managing growing volumes of consumer-detected findings in the absence of evidence-based algorithms for follow-up testing and treatment, creating uncertainty and potential liability.

Research Opportunities

Investigate if the pathophysiology of ischemic stroke in patients with clinically-diagnosed AF is the same as device-detected AHRE/SCAF using various study designs (e.g., registry, observational cohort), and test if anticoagulation reduces the risk of stroke in patients with AF detected by implanted monitors, wearables, and consumer devices (RCT).
With technologies that can screen heart rhythms for longer periods of time and detect shorter episodes of arrhythmia, assess the optimal AF burden or AF episode duration threshold for prompting specific treatments, including anticoagulation and risk factor treatment and lifestyle modification. Study how such thresholds should be modified in the setting of specific comorbidities (using data from RCT, registries, post-hoc analyses from RCTs) or screening device-type used (e.g., non-invasive wearables, implantable loop recorder, CIED).
Rigorously study the patient, clinician, and health system-level consequences of patient-initiated screening with consumer devices to develop and assess implementation algorithms for downstream testing and management (implementation science, registries, electronic health record data analyses, and RCTs) and eliminate health care disparities related to screening.

Randomized study of systematic AF screening with clinically relevant outcomes

Background

The creation of population-based AF screening programs to prevent stroke is attractive, as such programs fulfill most of the Wilson-Junger criteria for successful screening programs.⁶⁴ AF is prevalent, clearly associated with stroke⁶⁵ and other adverse outcomes,⁶⁶ can be identified in a pre-clinical phase, and can be treated with anticoagulation, which is highly effective at preventing ischemic stroke.⁶⁷ However, prevention-oriented organizations have not uniformly endorsed AF screening,^12,68 given the lack of RCT evidence that AF screening prevents stroke, and concern that false positives will lead to higher costs, downstream testing, and bleeding. Nevertheless, direct-to-consumer AF detection devices are becoming more widely used.^69,70

Numerous studies have documented that a variety of technologies can detect AF in different populations;^71–73 however, extrapolation to the impact of AF detection on stroke²¹ and other adverse outcomes has not compelled the introduction of population-based screening.¹² Several large trials are underway (eSupplement Table). It is also recognized that a single screening strategy is unlikely to be suitable for every patient population, healthcare setting, or country.

Whereas the vast majority of AF-screening efforts have focused on AF detection to prevent stroke, the most common outcomes after AF are death and heart failure.⁷⁴ As mentioned earlier, AF predisposes to a wide-array of other outcomes,⁶⁶ including cognitive decline,⁷⁵ dementia,⁷⁶ chronic and end-stage kidney disease,⁷⁷ extracranial systemic emboli,⁷⁸ venous thromboembolism,⁷⁹ myocardial infarction,³⁹ heart failure,^39,77 sudden death,⁷⁷ lower quality of life,⁸⁰ physical disability,⁸¹ healthcare costs,⁸² hospitalizations,⁸³ and all-cause mortality.⁷⁷ Hence, there is a need to examine other endpoints in AF screening studies, particularly cardiovascular, cognitive, health system, and mortality outcomes. Similarly, there is a need to develop an evidence base of effective strategies to prevent non-stroke complications of AF.

Knowledge Gaps

It is unclear if population-based AF screening programs can prevent stroke and do so cost-effectively (Figure 4). It is also uncertain if asymptomatic individuals with AF incidentally identified by implanted, wearable, or direct-to-consumer devices benefit from anticoagulation. Several knowledge gaps must be addressed before RCTs can attempt to answer these questions. For example, what population is optimal for AF screening? Both the prevalence of AF and risk of stroke increase with advancing age; thus, screening older populations (e.g., ≥65 or ≥75 years) may be most resource effective. AF and stroke risk increase further in individuals with additional clinical risk factors or biomarkers; whether such factors should be considered in selecting patients for screening should be examined. The best screening method also is uncertain. Single time-point, reusable devices are inexpensive and may be cost-effective, particularly since they detect mostly persistent AF, which has higher stroke risk. However, long-term ambulatory ECG monitoring is more sensitive and could also be cost-effective if its cost decreases. Algorithms to minimize false positives also will be essential. Additionally, one must define the threshold of AF burden that is associated with stroke risk, particularly given the long-term monitoring capabilities of consumer devices. Finally, one must demonstrate that screen-detected AF leads to initiation of therapies that result in reduction of stroke and other outcomes.

There are now several RCTs of AF screening ongoing or in development, which total over 300,000 patients (see eSupplement Table for examples of completed and ongoing studies). Given the mathematical and logistical challenges of translating AF detection into stroke prevention, the AF screening community and public will require RCT data to understand what components of screening strategies work best and to define which strategies work best in specific patients, settings, and countries. Ideally, data will be meta-analyzed across studies to enhance the ability to define subgroups of patients with AF that are most likely to benefit from screening.

Research Opportunities

Conduct large RCTs of AF screening strategies in diverse (e.g., age, sex, race/ethnicity, urban/rural) at-risk populations of individuals to prevent stroke and other AF-associated outcomes, including major adverse cardiovascular events (heart failure, myocardial infarction, arterial and venous thromboembolism, sudden death), cognitive impairment/dementia, chronic kidney disease progression, health system resource utilization, bleeding, all-cause death, as well as downstream complications (e.g., bleeding), health system and process outcomes (e.g., hospitalizations, time required to interpret screening tests, and follow-up testing).
Perform patient-level meta-analysis and meta-regression of similar RCTs from different health-systems and countries examining stroke and other outcomes related to AF screening strategies and identify specific subgroups of patients most likely to benefit from screening.
Assess health-system and economic analyses of various population-based screening strategies through administrative and clinical databases.

Post-stroke screening for AF

Background

The present section focuses on post-stroke screening for AF. A subsequent NHLBI workshop report addresses research priorities regarding risk stratification and stroke prevention in individuals with AF.

Previously undiagnosed clinical AF is frequently identified after acute stroke or transient ischemic attack (TIA).⁸⁴ Up to 25% of patients with cryptogenic stroke may have AF detected, depending on patient characteristics, stroke type, and intensity of AF monitoring.⁸⁵ Intensive search for AF in the post-stroke setting often reveals SCAF consisting of very short episodes⁸⁶ with unclear thromboembolic potential. Post-stroke AF in a given patient may represent: (1) pre-existing AF that led to stroke, (2) new-onset AF that occurred because of the stroke itself,⁸⁷ (3) lagging thrombogenic atrial myopathy marker that led to stroke,⁸⁸ or (4) an incidental finding in a population with significant cardiovascular/stroke risk factors.⁸⁹ Regardless, in current practice, post-stroke AF diagnosis usually changes treatment with initiation of long-term anticoagulation.⁹⁰ The detection of AF post-stroke is important because recent RCTs do not support routine anticoagulation for secondary stroke prevention in the absence of known AF.^91,92

Whereas ECG monitoring during hospitalization for stroke is common, more prolonged monitoring for post-stroke AF, which significantly increases the yield of AF detection over 72 hours^93,94 or beyond, is not widely implemented. To be resource-efficient, intensive AF monitoring requires a refined patient population. Demographics, classical cardiovascular risk factor burden, stroke characteristics, signs of atrial myopathy⁹⁵ (including ECG, structural, hemodynamic changes, biomarkers),^96–98 and AF genetic risk scores⁹⁹ may guide patient selection for monitoring beyond 72 hours.

Knowledge Gaps

Optimal methods for post-stroke AF monitoring have yet to be defined (Figure 5). It is unknown if anticoagulation driven by different AF monitoring strategies or other markers of atrial myopathy besides AF improves event-free survival (recurrent stroke/TIA/systemic embolism and dementia) and mortality.

The underlying pathophysiologic mechanisms linking AF and stroke are also incompletely understood. Experimental and model systems to explore causal pathways and define AF subtypes based on their etiological relation to stroke (i.e., causal, bystander, AF induced by stroke, marker of atrial myopathy) are largely missing.

There is a lack of in-depth phenotyping of stroke cohorts with ECG and improved non-invasive imaging for the atrial-ventricular-vascular axis. Existing and new information from biomarkers, genetics, and other omics (e.g., epigenomic, transcriptomic, proteomic, metabolomic, and microbiome) in relation to atrial myopathy as it applies to stroke have not been leveraged sufficiently. Clinical factors, stroke features, biomarkers, imaging variables, genetics, and omics to refine post stroke/TIA patients for more intensified AF screening also have not been sufficiently characterized.

From a clinical perspective, integrated care concepts¹⁰⁰ post-stroke for internal medicine/cardiology work-up during the index stroke hospitalization and post-discharge in patients at high risk for AF to improve quality of life, cost, and outcomes are missing.

Research Opportunities

Existing and new deeply-phenotyped, diverse (age/sex/race/ethnicity/urban vs. rural/region) cohorts should be examined to better characterize patients with AF after ischemic stroke to develop and validate post-stroke incident AF prediction tools examining combinations of patient characteristics, stroke features, ECG, imaging, laboratory, omics, and genetic markers. In particular, the role of atrial myopathy (±ventricular remodeling and hemodynamic changes) in stroke risk and selection of patients for intensified post-stroke AF monitoring requires investigation.
Observational and randomized studies of diverse populations should generate evidence on the effectiveness of post-ischemic stroke AF monitoring strategies and assessment and treatment of atrial myopathy, independent of clinical AF diagnosis for improved outcomes including stroke recurrence, heart failure, cognitive impairment, quality of life, disability, and mortality.
Optimal monitoring strategies post-ischemic stroke need to be defined. Advances require investigations of the pathway to AF diagnosis (currently based on ECG tracing), implementation science to determine how to integrate optimal diagnostic methods in daily workflow in diverse populations, and study of shared decision-making so that patients are engaged with the chosen approach. Besides classical monitoring techniques, external and implantable loop recorders and increasingly available wearables need to be investigated systematically and against “gold standard” approaches.

CONCLUSIONS

Increasing awareness of the prevalence of undiagnosed AF coupled with recognition that AF often is first diagnosed with the onset of complications has stimulated enthusiasm for AF screening and an increase in the number of consumer products potentially capable of diagnosing AF. However, there is a lack of robust evidence to determine the most appropriate patients to target for screening, which screening strategies and technologies to use in what context, and what additional testing and treatments to pursue with screen-detected AF.

Several themes emerged from the workshop to advance the field of AF screening. To develop a compelling evidence base, it is essential to generate and share data across studies (enabling pooled analyses and meta-analyses), and to pursue study designs including basic/mechanistic, administrative, healthcare/electronic medical records, registries, cohorts, pragmatic trials, and RCTs. Studies of AF screening need to examine AF burden, duration, and pattern in relation to multiple outcomes (e.g., cardiac, brain, kidney, quality of life, costs, all-cause death), in addition to stroke and TIA. The role of AF screening needs to be investigated in varying patient subgroups (e.g., age, sex, race/ethnicity, urban/rural, and comorbidities), practice settings, and countries, but will be most resource effective if focused on individuals for whom therapy may change if AF is diagnosed. Given the underrepresentation and underreporting of racial and ethnic minorities in AF studies,¹⁸ it will be particularly important to ensure their adequate representation in AF screening studies. In particular, it will be vital to determine how AF prognosis and treatment vary by AF detection method (e.g., incidental, non-contact/non-ECG screening, routine ECG screening, consumer devices, extended clinical ECG monitoring, and CIED), patient characteristics (e.g., age, sex, race/ethnicity, and comorbidities), and AF risk markers (e.g., atrial myopathy, ECG, imaging, biomarkers, omics, and genetic factors). Although AI algorithms hold great promise for refining patient selection for AF screening and prediction of outcomes in screen-detected AF, much more needs to be done to ensure that such algorithms are transparent, accurate, unbiased, validated, and improve patient care in diverse patient poulations. Finally, close attention will need to be paid to both the potential benefits and adverse outcomes of AF screening strategies on the patient (e.g., anxiety, testing, treatment complications) and the health system (e.g., disparities, costs, and clinician liability and fatigue) before recommending widespread screening. The research gaps and opportunities outlined in the workshop (Table 2) will hopefully accelerate AF screening research to improve the diagnosis, management, and prognosis of patients with AF.

Table 2.

Prioritized Research Opportunities for AF Screening

Opportunistic single timepoint screening for AF

1. Examine the outcomes of OppSTS-detected AF in ethnically/racially diverse studies, such as pragmatic trials or prospective cohort studies comparing carefully matched OppSTS-detected AF with clinically-detected AF, to generate knowledge regarding their relative prognosis.

2. Determine the proportion of OppSTS-detected AF that is persistent by conducting observational and administrative studies that examine extended monitoring and follow up.

3. Study the impact of detection-method accuracy (including newer technologies) on beneficial health outcomes vs. additional unnecessary tests and procedures, and diminished quality of life. This research could include prospective comparative studies, pragmatic trials, and RCTs of different devices and screening strategies with stroke, mortality, and other clinically relevant endpoints in diverse populations.

AF: risk factor, risk marker, or both?

1. Use large-scale longitudinal studies to generate data on systemic and myocardial (especially atrial myopathy) substrates of AF using ECG, echocardiography, CMR, computed tomography, and biomarkers (traditional, genetic, and omic) to demonstrate whether estimated risk of AF and AF-related complications (e.g., major adverse cardiovascular and cerebrovascular events and conditions, such as stroke, heart failure, and dementia) can be refined. Such data will assist in prioritizing research into screening individuals with undiagnosed AF, and research to discover novel therapeutic targets.

2. Investigate and validate AI algorithms and standard risk prediction models of cross-sectional and longitudinal data, including electronic health record, ECG, biomarker, genetic/omic, and imaging data—while adhering to privacy protections, transparency, diversity, equity, and inclusiveness. Priorities for research would test if implementing or intensifying AF screening, and predicting which patients with screen-detected AF will develop complications, will result in net clinical benefit.

Burden of AF and detection of AF with Different Technologies

1. Investigate if the pathophysiology of ischemic stroke in patients with clinically-diagnosed AF is the same as device-detected AHRE/SCAF using various study designs (e.g., registry, observational cohort), and test if anticoagulation reduces the risk of stroke in patients with AF detected by implanted monitors, wearables, and consumer devices (RCT).

2. With technologies that can screen heart rhythms for longer periods of time and detect shorter episodes of arrhythmia, assess the optimal AF burden or AF episode duration threshold for prompting specific treatments, including anticoagulation and risk factor treatment and lifestyle modification. Study how such thresholds should be modified in the setting of specific comorbidities (using data from RCT, registries, post-hoc analyses from RCTs) or screening device-type used (e.g., non-invasive wearables, implantable loop recorder, CIED).

3. Rigorously study the patient, clinician, and health system-level consequences of patient-initiated screening with consumer devices to develop and assess implementation algorithms for downstream testing and management (implementation science, registries, electronic health record data analyses, and RCTs) and eliminate health care disparities related to screening.

Randomized study of systematic AF screening with clinically-relevant outcomes

1. Conduct large RCTs of AF screening strategies in diverse (e.g., age, sex, race/ethnicity, urban/rural) at-risk populations of individuals to prevent stroke and other AF-associated outcomes, including major adverse cardiovascular events (heart failure, myocardial infarction, arterial and venous thromboembolism, sudden death), cognitive impairment/dementia, chronic kidney disease progression, health system resource utilization, bleeding, all-cause death, as well as downstream complications (e.g., bleeding), health system and process outcomes (e.g., hospitalizations, time required to interpret screening tests, and follow-up testing).

2. Perform patient-level meta-analysis and meta-regression of similar RCTs from different health-systems and countries examining stroke and other outcomes related to AF screening strategies and identify specific subgroups of patients most likely to benefit from screening.

3. Assess health-system and economic analyses of various population-based screening strategies through administrative and clinical databases.

Post stroke screening for AF

1. Existing and new deeply-phenotyped, diverse (age/sex/race/ethnicity/urban vs. rural/region) cohorts should be examined to better characterize patients with AF after ischemic stroke to develop and validate post-stroke incident AF prediction tools examining combinations of patient characteristics, stroke features, ECG, imaging, laboratory, omics, and genetic markers. In particular, the role of atrial myopathy (±ventricular remodeling and hemodynamic changes) in stroke risk and selection of patients for intensified post-stroke AF monitoring requires investigation.

2. Observational and randomized studies of diverse populations should generate evidence on the effectiveness of post-ischemic stroke AF monitoring strategies and assessment and treatment of atrial myopathy, independent of clinical AF diagnosis for improved outcomes including stroke recurrence, heart failure, cognitive impairment, quality of life, disability, and mortality.

3. Optimal monitoring strategies post-ischemic stroke need to be defined. Advances require investigations of the pathway to AF diagnosis (currently based on ECG tracing), implementation science to determine how to integrate optimal diagnostic methods in daily workflow in diverse populations, and study of shared decision-making so that patients are engaged with the chosen approach. Besides classical monitoring techniques, external and implantable loop recorders and increasingly available wearables need to be investigated systematically and against “gold standard” approaches.

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Supplementary Material

Supplemental Publication Material

NIHMS1656466-supplement-Supplemental_Publication_Material.pdf^{(302KB, pdf)}

Acknowledgments

We thank our colleagues Michael J. Barry and C. Seth Landefeld for presenting the USPSTF perspective on screening during the webinar and providing astute comments on the manuscript.

Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; the U.S. Preventive Services Task Force; the U.S. Department of Health and Human Services; or the Department of Veterans Affairs.

Disclosures

Dr. Benjamin is an uncompensated member for the MyHeartLab Steering Committee, which is a PI-initiated (Jeffrey Olgin, MD) research grant to UCSF from Samsung.

Dr. Go has received a research grant through his institution from iRhythm Technologies. Dr. Go is also a member of the Operations Committee and Steering Committee for the GUARD-AF Study (NCT04126486) sponsored by Bristol Meyers Squibb and Pfizer.

Dr. Anderson receives sponsored research support from Massachusetts General Hospital, and Bayer AG, and has consulted for ApoPharma, Inc.

Dr. Casadei is supported by a Personal Chair. She receives in kind support (in the form of free assays and ECG monitors) from Roche Diagnostics and iRhythm.

Dr. Crijns receives unrestricted grant support from Medtronic Trading NL B.V.

Dr Freedman reports grants to the institution, personal fees and non-financial support from Bayer Pharma AG, grants to the institution and personal fees from BMS/Pfizer, and personal fees from Daiichi-Sankyo and Omron.

Dr. van Gelder reports unrestricted grant support from Medtronic Trading NL B.V.

Dr. Healy reports research grants and speaking fees from Medtronic, Boston Scientific, Abbott, BMS/Pfizer, Servier, Cipher Pharma, and ARCA Biopharm. Also, supported by the Stuart Connolly Chair in Cardiology Research, McMaster University.

Dr. Kamel serves as co-PI for the NIH-funded ARCADIA trial which receives in-kind study drug from the BMS-Pfizer Alliance and in-kind study assays from Roche Diagnostics, serves as a steering committee member of Medtronic’s Stroke AF trial (uncompensated), serves on an endpoint adjudication committee for a trial of empagliflozin for Boehringer-Ingelheim, and has served on an advisory board for Roivant Sciences related to Factor XI inhibition.

Dr. Lopes reported research grants and personal fees from Bristol-Myers Squibb and Pfizer, personal fees from Boehringer Ingelheim and Bayer AG and grants from Amgen Inc, GlaxoSmithKline, Medtronic PLC, and Sanofi Aventis. Dr. Lopes is also a member of the Operations Committee and Steering Committee for the GUARD-AF Study (NCT04126486) sponsored by Bristol Meyers Squibb and Pfizer.

Dr. Lubitz receives sponsored research support from Bristol Myers Squibb / Pfizer, Bayer AG, Boehringer Ingelheim, and Fitbit, and has consulted for Bristol Myers Squibb / Pfizer and Bayer AG, and participates in a research collaboration with IBM.

Dr. McManus’s has received research support from Bristol Myers Squibb, Care Evolution, Samsung, Apple Computer, Pfizer, Biotronik, Boehringer Ingelheim, Philips Research Institute, Flexcon, Fitbit, and has consulted for Bristol Myers Squibb, Pfizer, Philips, Samsung Electronics, Fitbit, Rose Consulting, Boston Biomedical Associates, and FlexCon. DDM is also a member of the Operations Committee and Steering Committee for the GUARD-AF Study (NCT04126486) sponsored by Bristol Meyers Squibb and Pfizer as well as the Steering Committee of the Fitbit Heart Study (NCT04380415).

Dr. Perez is co-Principal Investigator of the Apple Heart Study funded by Apple Inc.; Dr. Perez has received consulting fees from Apple Inc. and Boehringer Ingelheim.

Dr. Piccini receives grants for clinical research from Abbott, Association for the Advancement of Medical Instrumentation, Bayer, Boston Scientific, and Philips and serves as a consultant to Abbott, Allergan, ARCA Biopharma, Biotronik, Boston Scientific, LivaNova, Medtronic, Milestone, Myokardia, Sanofi, Philips, and Up-to-Date.

Dr. Schnabel reports speaking fees from Bristol-Myers Squibb/Pfizer.

Dr. Singer reports research support from Bristol-Myers Squibb; Steering committee/advisory board/consulting for Boehringer Ingelheim, Bristol-Myers Squibb, Fitbit, Johnson and Johnson, Merck, and Pfizer.

Dr. Tieleman reports grants and speaking fees from Boehringer Ingelheim, speaking fees from BMS/ Pfizer, speaking fees from Bayer, grants from Medtronic, grants from St Jude Medical, outside the submitted work. In addition, Dr. Tieleman has a patent as a co-inventor of the MyDiagnostick issued.

Dr. Turakhia reports grants from Bristol Myers Squibb, Apple, Janssen, Boehringer-Ingelheim, American Heart Association, Medtronic, Bayer; consulting fees from Medtronic, Myokardia, Biotronik, Pfizer, Johnson & Johnson; royalties from Up-to-Date. Dr. Turakhia is an editor for JAMA Cardiology. Dr. Turakhia is also a member of the Executive Committee for the HEARTLINE Study sponsored by Janssen Inc.

SMA-K: consulting fees from Milestone Pharmaceuticals, research, speaking and consulting fees from Medtronic, research and speaking fees from Abbott, and speaking fees from Bristol-Myers Squibb/Pfizer.

Sources of Funding

Dr. Benjamin receives research funding from US NIH, National Heart, Lung, and Blood Institute grants R01HL128914, 2R01HL092577, R01HL141434 01A1, NIH National Institute of Aging grant R01AG066010 and American Heart Association: 18SFRN34110082.

Dr. Go receives research funding from US NIH, National Heart, Lung, and Blood Institute grant R01HL142834 and National Institute of Diabetes and Digestive and Kidney Diseases R01DK103612.

Dr. Anderson receives research funding NIH National Institute of Neurological Disorders and Stroke R01NS103924, the American Heart Association 18SFRN34250007

Dr. Casadei is supported by Programme Grants from the British Heart Foundation and by the National Institute for Health Research Oxford Biomedical Research Centre.

Dr. Chen receives research funding from US NIH, National Heart, Lung, and Blood Institute R01HL126637, R01HL141288, and additional support from R01HL142599 and R01HL127659.

Dr. Crijns research research support from the Netherlands Cardiovascular Research Initiative: an initiative with support of the Dutch Heart Foundation, CVON 2014–9: Reappraisal of Atrial Fibrillation: Interaction Between HyperCoagulability, Electrical Remodeling, and Vascular Destabilisation in the Progression of AF (RACE V).

Dr. Van Gelder receives support from the Netherlands Cardiovascular Research Initiative: an initiative with support of the Dutch Heart Foundation, CVON 2014–9: Reappraisal of Atrial Fibrillation: Interaction Between HyperCoagulability, Electrical Remodeling, and Vascular Destabilisation in the Progression of AF (RACE V).

Dr. Kamel receives research funding NIH National Heart, Lung, and Blood Institute R01HL144541 and National Institute of Neurological Disorders and Stroke: R01NS097443.

Dr. Lubitz receives research funding from National Heart, Lung, and Blood Institute grant R01HL139731 and the American Heart Association 18SFRN34250007.

Dr. McManus receives support from US NIH, National Heart, Lung, and Blood Institute grant R01HL126911, R01HL137734, R01HL137794, R01HL135219, R01HL136660, and U54HL143541.

Dr. Perez receives support from US NIH, National Heart, Lung, and Blood Institute grant R01HL136390.

Dr. Piccini’s time is supported by R01HL128595 from the National Heart, Lung and Blood Institute.

Dr. Schnabel receives funding from the European Unions Horizon 2020 research and innovation programme under the grant agreement No.847770 AFFECT-EU, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 648131), and ERACoSysMed3 (031L0239), and German Center for Cardiovascular Research (DZHK e.V.) (81Z1710103).

Dr. Turakhia reports support from R01DK095024 from the National Institute of Diabetes and Digestive and Kidney Diseases and the American Heart Association.

Abbreviations [all in acronym finder except abbreviation in bold]:

AF: atrial fibrillation
AHRE: atrial high rate episodes
AI: artificial intelligence
CIEDs: cardiac implantable electronic devices
ECG: electrocardiogram
HR: hazard ratio
INR: international normalized ratio
NHLBI: National Heart, Lung, and Blood Institute
OppSTS: Opportunistic single timepoint screening
RCT: randomized controlled trial
RR: Relative Risk
SCAF: subclinical atrial fibrillation
USPSTF: US Preventive Services Task Force

Footnotes

Supplemental Materials

Supplemental Table I

References 101–122

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PERMALINK

Research Priorities in Atrial Fibrillation Screening: A Report from a National Heart, Lung, and Blood Institute Virtual Workshop

Emelia J Benjamin, MD, ScM

Alan S Go, MD

Patrice Desvigne-Nickens, MD

Christopher D Anderson, MD, MMSc

Barbara Casadei, MD, DPhil

Lin Y Chen, MD, MS

Harry JGM Crijns, MD, PhD

Ben Freedman, MBBS, PhD

Mellanie True Hills, BS

Jeff S Healey, MD, MSc

Hooman Kamel, MD

Dong-Yun Kim, PhD

Mark S Link, MD

Renato D Lopes, MD, MHS, PhD

Steven A Lubitz, MD, MPH

David D McManus, MD, ScM

Peter A Noseworthy, MD

Marco V Perez, MD

Jonathan P Piccini, MD, MHS

Renate B Schnabel, MD, MSc

Daniel E Singer, MD

Robert Tieleman, MD, PhD

Mintu P Turakhia, MD, MAS

Isabelle C Van Gelder, MD, PhD

Lawton S Cooper, MD, MPH

Sana M Al-Khatib, MD, MHS

Abstract

Opportunistic single timepoint screening for AF

Background

Figure 1.

Knowledge Gaps

Figure 2.

Research Opportunities

AF: Risk Factor, Risk Marker, or Both?

Background

Knowledge Gaps

Figure 3.

Research Opportunities

Burden of AF and detection of AF with Different Technologies

Background

Knowledge Gaps

Table 1:

Research Opportunities

Randomized study of systematic AF screening with clinically relevant outcomes

Background

Knowledge Gaps

Figure 4.

Research Opportunities

Post-stroke screening for AF

Background

Knowledge Gaps

Figure 5.

Research Opportunities

CONCLUSIONS

Table 2.

Supplementary Material

Acknowledgments

Sources of Funding

Abbreviations [all in acronym finder except abbreviation in bold]:

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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