Screening for Atrial Fibrillation in Community and Primary CareSettings: A Scoping Review

Emma Canty1, Claire MacGilchrist12, Wael Tawfick234, Caroline McIntosh12
1Discipline of Podiatric Medicine, School of Health Sciences, NUI Galway..2Alliance for Research and Innovation in Wounds, NUI Galway..3Vascular Department, University Hospital Galway, Saolta University Health Care Group..4School of Medicine, NUI Galway..

Abstract

Background

Atrial Fibrillation (AF) is the most common tachyarrhythmia and is associated with increased risk of stroke, morbidity and mortality. AF is responsible for up to a quarter of all strokes and is often asymptomatic until a stroke occurs.Screening for AF is a valuable approach to reduce the burden of stroke in the population.

Objectives

The motivation for this review was to synthesise and appraise the evidence for screening for AF in the community. The aims of this scoping review are 1). To describe the prevalence of newly diagnosed AF in screening programmes 2). Identify which techniques/ tools are employed for AF screening 3). To describe the setting and personnel involved in screening for AF.

Eligibility Criteria

All forms of AF screening in adults (≥18 years) in primary and community care settings.

Methods

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping reviews (PRISMA-ScR).

Results

Fifty-nine papers were included; most were cross-sectional studies (n=41) and RCTs (n=7). Prevalence of AF ranged from 0-34.5%. Screening tools and techniquesincluded the 12-lead ECG (n=33), the 1-lead ECG smartphone based Alivecor® (n=14) and pulse palpation (n=12). Studies were undertaken in community settings (n=30) or in urban/rural primary care (n=28). Personnel collecting research data were in the main members of the research team (n=31), GPs (n=16), practice nurses (n=10), participants (n=8) and pharmacists (n=4).

Conclusion

Prevalence of AF increased with advancing age. AF screening should target individuals at greatest risk of the condition including older adults≥65 years of age. Emerging novel technologies may increase the accessibility of AF screening in community and home settings. There is a need for high quality research to investigate AF prevalence and establish accuracy and validity for traditional versus novel screening tools used to screen for AF.

Key Words : Atrial fibrillation, Opportunistic screening, Systematic screening, Arrhythmia, Community, Primary Care.
Professor Caroline McIntosh, Discipline of Podiatric Medicine, School of Health Sciences, NUI Galway, Áras Moyola, Newcastle Road, Galway, Ireland.

Introduction

Atrial fibrillation (AF) is the most common, sustained, progressive tachyarrhythmia worldwide and is associated with increased risk of stroke, systemic embolism and increased morbidity and mortality1, 2. AF is associated with higher morbidity and mortality rates than other cardiac arrhythmias3. AF represents asignificant public health problem that places a burden on health resources and constitutes a public health challenge with high comorbidity 5. The most frequent co-morbidities associated with AF are hypertension, diabetes mellitus, congestive heart failure, ischaemic heart disease and valvular heart disease4. Male gender is an established risk factor for AF however due to greater longevity in females the prevalence across both genders is equivalent 4. The clinical presentation of AF varies significantly in severity and type4. Symptoms are often related to tachycardia and can include palpitations, dizziness, chest pain and dyspnoea5. However, symptoms can be non-specific or absent. Thus, up to one third of AF cases are not recognised because they are asymptomatic and have silent or subclinical AF 4.

The global prevalence of AF was 191.3 rate per 100,000 in 20134 with approximately 1-3% of the population affected 5. Both the prevalence and incidence of AF increase markedly with advancing age5 with reports of AF prevalence of 4.2% in people aged 60-69 years of age6. Hence, due to an ageing population the prevalence of AF is increasing;it is predicted that AF will affect 6-12 million people in the USA by 2050 and 17.9 million people across Europe by the year 20607.However, it can be argued that the true prevalence of AF is unknown.This may bedue to a lack of, or limited access to screening for AF and the fact that AF is often asymptomatic or silent4. AF often remains undiagnosed and untreated which can lead to devastating outcomes. AF is associated with increased risk of systemic embolism and stroke, in fact AF is found in one third of all ischaemic strokes7.Early identification of AF allows for early antithrombotic treatment which can reduce the incidence of stroke and premature death in patients with AF2.AF is also associated with significant morbidity, as measured by disability-adjusted life years 7. Screening for AF is recommended in European guidelines in all patients >65 years of age8. The main rationale for AF screening is to prevent stroke in the population by identifying those with the condition and allowing for early anticoagulation treatment and thus prevent ischaemic events and reduce morbidity and mortality 4. Opportunistic screening is defined as a screening programme that uses a health care professional to check for AF during routine consultations. Whilst systematic screening is defined as a programme where all people above a certain age or who reach set criteria are invited to attend a location for screening9. Various clinical techniques can be employed to screen for AF including pulse palpation and 12 lead ECG with expert interpretation10. The advent of novel technologiesincluding devices such as portable smartphone ECGs and photoplethysmographyare emergingwhich, will make AF screening more accessible in community and homesettings. However, currently the most effective method of screening for AF remains unclear and given the diverse approaches to AF screening and the tools and techniques employed there is a need to review the current evidence-base10. The scoping review did not aim to assess technical or statistical aspects of existing and novel technologies for AF screening. Rather, the motivation for this review is to explore the breadth and extent of the literature, synthesise, appraise the evidence for screening for AF in community settings and inform future research. Therefore, a scoping review methodology was chosen. The aims of this scoping review are 1). To describe the prevalence of newly diagnosed AF in screening programmes 2). Identify which clinical techniques/ tools are employed for screening for AF 3). To describe the setting and health professionals involved in screening for AF in community and primary care settings.

Methods

Protocol

We performed a scoping review in a structured manner, to synthesise the available evidence. We followed the methodology of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping reviews (PRISMA-ScR)11.

Eligibility Criteria

Inclusion Criteria

Research articles published between the years 2000-2020 and written in the English language. The search timeframe was chosen to ensure currency of the evidence in relation to the tools used in AF screening. All forms of screening for new diagnosis of AF in adults (≥18 years) in primary and community care settings were included.

Exclusion Criteria

Studies not in the English language and those out with the period under investigation. Systematic reviews, meta- analyses, reports, pilot studies or unpublished studies were excluded. Participants must not have had a previous AF diagnosis. Studies that consisted of follow-ups for patients that had obtained treatment for AF, studies where AF screening was conducted in an acute/hospital setting, studies where AF was identified post stroke/surgical intervention, studies where AF was diagnosed after a period of monitoring were all excluded.

Information Sources

We carried out a systematic search of databasesincluding Scopus, Google Scholar, Pubmed, Science Direct, Medline and Embase. A grey literature search of the literature was conducted. The literature searches took place in April 2020.

Search Strategy

We used a population, intervention, and outcomes-based approach to identify our search strategy.The population under investigation were people with AF, the intervention was opportunistic or systemic screening and the outcomes were the prevalence of AF, screening tools used, and the setting and health professionals involved in screening for AF. The search commenced on 2nd of April 2020. The databases included were Pubmed (02.04.2020), Scopus (02.04.2020), Google Scholar (06.04.2020), Science Direct (09.04.2020), Medline (09.04.2020) and Embase (10.04.2020). The last search took place on 28.04.2020. This final search included papers identified through reference lists of included papers. All papers were imported into Covidenceand duplicates were removed.

The search used the mesh terms generated from the PICO question to identify studies ([Table 1]). The Boolean operators used are detailed in [Table 2].

Table 1. Keywords used for the literature search
Population Intervention Outcome
Atrial Fibrillation Or Cardiac abnormality Or Cardiac arrhythmia Or Uncoordinated atria contractions Or Vascular Disease Opportunistic Screening Or Systematic Screening Or Pulse palpation Or ECG Rhythm Strip Or Smartphone ECG 12- ECG Diagnosed AF Or Identifying AF


Selection of Sources of evidence

The systematic review management system Covidence was used for the study selection process (www.covidence.org). The review was carried out in four stages: import references, title and abstract screening, full text screening and extraction. On import into Covidence, duplicate papers were automatically removed. Two authors independently screened all titles and abstracts (EC,  CMcI, CMacG), any disagreement on papers were discussed between authors until consensus was reached. In phase two,potentially eligible articles were reviewed in full text and any disagreements were resolved between co-authors (EC,  CMcI, CMacG).

Data Charting Process

One author (EC) extracted data using a standardised data extraction form in Excel and a second author (CMcI, CMacG) then independently verified the extracted data. The data extraction form was based on JBI guidelines on data extraction for scoping reviews12.

The following study characteristics were extracted: year of publication, country, setting, study design, participant recruitment, screening tool, data collectors, screening type, eligibility criteria, sample size, gender, risk factors, number of participants with new AF diagnosis, prevalence of AF.

Critical Appraisal of Individual Sources of Evidence

We undertook a narrative synthesis of the research literature assessing systematically and comprehensively the results of each study, highlighting important characteristics of the included studieswithout quality assessment or extensive data synthesis 13.

Results

We included 59studies. A PRISMA flow chart (see [Figure 1]) displays the flow of papers and reasons for exclusion.

Studies were conducted across 22 different countries. The majority of studies were conducted in the USA (n=10), the UK (n=7), Italy (n=5), Hong Kong (n=5), Spain (n=4) and Sweden (n=4), other countries included Australia (n=3), Ireland (n=3), Germany (2), Norway (n=2), China (n=2), Canada (n=2) Denmark (n=1), New Zealand (n=1), Finland (n=1), Japan (n=1), India (n=1), Tanzania (n=1), Netherlands (n=1), Uganda (n=1), Portugal (n=1),Iran (n=1) ([Table 2]).

Figure 1. PRISMA Flowchart

Setting

The majority of studies were undertaken in community settings (n=30) or in urban/rural primary care (n=28). Only one study used multiple different settings.

Study Design

Of the 59 studies included there were n=41 cross sectional studies, n=7 randomised controlled trials,n=6 longitudinal studies, n=2 observational cohort studies, n=1 pseudo longitudinal study n=1 parallel arm cluster controlled study and n=1 prospective pragmatic study.

Prevalence of newly diagnosed AF

The mean prevalence rate of AF across the 59 studies was 6.2%. The prevalence of newly diagnosed AFwas wide ranging across the studies at0-34.5%. African and Asian countries showed the lowest prevalence; in the African studies the prevalence ranged from 0-0.67%14, 15. A low prevalence of AF was also observed in a UK study that screened minority ethnic groups(0.95%)16. Studies conducted in Asian countries generally showed lower prevalence figures ranging from 1.2-5.3%17-25 with the exception of one study based in Hong Kong where the prevalence of AF was 34.5% 26. Participants in this study were recruited directly from Cardiology clinics. European and American countries showed the highest prevalence rates. In Europe, studies conducted in Sweden reported the highest prevalence rates of AF ranging from 12.3-15.4%27-29([Table 2]).

Table 2. Boolean Operators employed
1 :EXP atrial fibrillation 2 :Cardiac* Abnormality/ 3 :EXP arrhythmia* 4 :Uncoordinated atria contraction adj3 5 :Vascular Disease/ 6 :1 or 2 or 3 or 4 or 5 7 :EXP opportunistic* screen* 8 :EXP systematic* screen* 9 :pulse palpation 10 :ECG rhythm strip 11 :12* lead ecg 12 :smartphone ecg 13 :7 or 8 or 9 or 10 or 11 or 12 14: diagnose* atrial fibrillation adj3 15: identify* atrial fibrillation adj3 16: 14 or 15 17: 6 and 13 and 16


Screening tool

Data Collectors

In the majority of studies the personnel collecting the research data were members of the research team (n=31), this was followed by GPs (n=16), practice nurses (n=10), participants themselves (n=8), pharmacists (n=4), trained non-medical volunteers (n=4), cardiac nurse (=2), health care worker (n=1) and Clinical Events Adjudication Committee (n=1). In some studies, multiple personnel were involved in data collection. Cardiologists reviewed ECG readings in 31 studies.

Table 3. Country of research, age, sample size and prevalence of newly diagnosed AF in the research studies
Study Location Age No. of Participants AF Prevalence %
Perez et al., 201937 USA Not reported 415787 Not reported
Yan et al., 201826 Hong Kong Not reported 217 34.50%
Lau et al., 201343 Australia >/65 109 27.80%
Soliman et al., 201044 USA 21-74 3257 18%
Heckbert et al., 201834 USA >/57 1415 17.50%
Ghazal et al., 201827 Sweden 70-74 324 15.40%
Engdahk et al., 201326 Sweden 75-76 848 14.30%
Wiesel Abraham and Messineo 201345 USA >/65 139 13.43%
Walker et al., 201427, 46 New Zealand >/65 121 12.40%
Svennberg et al., 201527 Sweden 75-76 7173 12.30%
Cunha et al., 202026 Portugal >/40 205 11.20%
Salvatori et al., 201525 Italy >/65 304 11%
Kearley et al., 201447 UK >75 999 11%
Clua-Espuny et al., 201348 Spain >60 1043 10.90%
Smyth et al., 201649 Ireland >/65 7262 10.90%
Bury et al., 201511 Ireland >/70 566 10.30%
Scalvini et al., 201150 Italy Not reported 1719 9.70%
Scalvini et al., 200551 Italy Not reported 7516 9.60%
Hobbs et al., 200524 UK >/65 14802 8.08%
Gonzalez Blanco et al., 201752 Spain >/65 6990 7.90%
Loehr et al., 201953 USA Not reported 2434 7.15%
Baber et al., 201033 USA >/45 26917 6.77%
Lowres et al., 201454 Australia >/65 1000 6.70%
Morgan and Mant 200235 UK >/65 1538 5.30%
Huang et al., 201832 China >/80 1038 5.30%
Turakhia et al., 201555 USA >/55 75 5.30%
Grubb et al., 201923 UK >/65 1805 5.10%
Jaakkola et al., 201722 Finland >/75 215 4.90%
Wiesel and Salomone 201756 USA >/65 11 4.90%
Berge et al., 20186 Norway 63-65 3706 4.50%
Rhys Azhar and Foster 201357 UK >/65 573 4%
Godin et al., 201923 Canada >/65 7585 4%
Orchard et al., 201658 Australia >/65 972 3.80%
Kaassenbrood et al., 201659 Netherlands >60 9450 3.70%
Bacchini et al., 20192 Italy >/50 3071 3.20%
Ostgren et al., 200460 Sweden >/40 1739 3.20%
Schnabel et al., 201261 Germany 34-74 5000 3.20%
Frewn et al., 2013 21 Ireland >/50 4902 3%
Habizadehet et al., 200431 Iran >50 463 2.80%
Quinn et al., 201862 Canada >/65 2054 2.70%
Steinhubl et al., 201863 USA >/65 2054 2.70%
Chan et al., 201619 Hong Kong >/65 1013 2.60%
Halcox et al., 201764 USA >65 1001 2.50%
Chan et al., 201830 Hong Kong >50 11574 2.40%
Suzuki et al., 201523 Japan 40-90 12410 2.40%
Benito et al., 20155 Spain >/65 928 1.83%
Omboni and Verberk 201536 Italy >/18 220 1.80%
Chan et al., 201729 Hong Kong >/18 1322 1.80%
Fitzmaurice et al., 200710 UK >/64 14802 1.60%
Soni et al., 201822 India >40 2100 1.60%
Yap, Pin and Ong 200721 China >/55 1839 1.50%
Chan et al., 201728 Hong Kong >/65 5969 1.20%
Hald et al., 201620 Denmark >/65 970 1.03%
Gill et al., 201119 UK Not reported 5408 0.95%
Berge et al., 20184 Norway >65 1510 0.90%
Dewhurst et al., 201214 Tanzania >70 2232 0.67%
Brunner et al., 201718 Germany >18 7159 0.66%
Rodriguez-Captain 201765 Spain Not reported 13179 0.40%
Muthalay et al., 201814 Uganda >18 856 0%


Screening Type

The majority of studies employed systematic screening (n=29) and opportunistic screening (n=26), four studies used both opportunistic and systematic screening.

Table 4. Prevalence AF Risk Factors
Risk Factors Range (%)
Hx of Hypertension 4.5-100%
Hx of Diabetes Mellitus 2.3- 45.9%
Hx of Tia/Stroke 1-18.9%
Hx of Heart Disease 1.1-50.7%
Hx of Smoking 2.7-50.9%
Hx of Heart Failure 0.3- 32%


Discussion

We report the findings of a scoping review, a form of structured evidence collation, used to address a broad research question12.The objective of this scoping review was to broadly synthesise and appraise the evidence for screening for AF in community settings. More specifically, we set out to describe the prevalence of newly diagnosed AF in screening programmes, identify which clinical techniques/ tools are employed for screening for AF and to describe the setting and health professionals currently involved in screening for AF in community and primary care settings.

Table 5. Summary of the Data Collection Tool employed in the Research Studies
Data Collection Tool Study Total
12-lead ECG Brunner et al., 2017, Baber et al., 2010, Berge et al., 2018, Chan et al., 2016, Dewhurst et al., 2012, Frewn et al., 2013, Ghazal et al., 2018, Godin et al., 2019, Habibzadehet et al., 2004, Salvatori et al., 2015, Chan et al., 2017, Clua-Espuny et al., 2001, Fiztmaurice et al., 2007, Engdahk et al., 2013, Gill et al., 2011, Blanco et al., 2017, Hald et al., 2016, Hobbs et al., 2005, Huang et al., 2018, Jaakkola et al., 2017, Kearly et al., 2014, Lau e al., 2012, Loehr et al., 2019, Morgan and Mant 2002, Orchard et al., 2016, Ostgren et al., 2004, Quinn et al., 2018 Rhys Azhar & foster 2013, Rodriguez-Captain et al., 2016, Scalvini et al., 2005, Scalvini et al., 2010, Schabel et al., 2012, Smyth et al., 2016, Solimon et al., 2010, Yan et al., 2018 35
7- lead ECG Baber t al., 2010 1
3- lead ECG Bury et al., 2015 1
1 lead ECG – smartphone based alive cor Brunner et al., 2017, Chan et al., 2016, Chan et al., 2017, Godin et al., 2019, Grubb et al., 2019, Chan et al., 2018, Chan et al., 2017, Cunha et al., 2020, Halcox et al., 2017, Jaakkola et al., 2017, Lau et al., 2012, Lowres et al., 2014, Orchard et al., 2016, Soni et al., 2018 14
1 lead handheld portable ECG Zenicor Berge et al., 2017, Chazal et al., 2018, Engdahk et al., 2013, Svennberg et al., 2015 4
1 lead CardioCard Muthalay et al., 213 1
1 lead Cardio-A Palm ECG Omboni and Verberk 2015 1
1 lead MyDiagnostick Kassenbrood et al., 2016 1
1 lead Omron Monitor Kearly et al., 2014 1
1 lead HeartCheck Quinn et al., 2018 1
Pulse Palpation Benito et al., 2015, Cunha et al., 2020, Fitzmaurice et al., 2007, Blanco et al., 2017, Hald et al., 2016, Hobbs et al., 2005, Jaakkola et al., 2017, Lowres et al., 2014, Morgan and Mant 2002, Quinn et al., 2018, Rhys, Azhar and Foster 2013, Smyth et al, 2016 12
Cardiac Examination Berge et al., 2018 1
24-48 hour Holter Monitor Salvatori et al., 2015, Loehr et al., 2019, Quinn et al., 2010 3
Medical Records Clua-Espuny 2013 1
Cardio Rhythm Smartphone 3PG waveforms Chan et al., 2016 Yan et al., 2018 2
MicrolifeAFIB (BP monitor used to detect AF ) Bacchini et al., 2019, Chan et al., 2017, Kearly et al., 2014, Omboni and Verberk 2015, Quinn et al., 2018, Wiesel, Abraham and Messineo 2013, Wiesel and Salomone 2017 7
Zio Patch XT (single channel ECG patch monitor) Heckbert et al., 2018, Steinhubl et al., 2018, Turakhra et al., 2015 3
Applewatch Photoplethysmography Perex et al., 2019 1
Heartrak 2 (ECG event monitor) Wiesel, Abraham and Messineo 2013 1
*Some studies employed more than one methods of screening

Prevalence of AF

The mean prevalence rate of AF across the 59 studies was 6.2%, however the prevalence of newly diagnosed AF was wide ranging from 0-34.5% across the studies and therefore the mean prevalence should be interpreted with caution. The highest prevalence for AF was reported in a Hong Kong based study (34.5%) 27. This study used a novel method of AF screening using an iPhone camera to detect and analyse photoplethysmographic signals from the face by extracting subtle beat to beat variations of skin colour that reflect the cardiac pulsatile signal 27. However, participants in this study were recruited directly from cardiology services, which, is likely to have inflated the prevalence of AF given the population under investigation.There is a high chance of selection bias in this study given the methodological approaches employed. The lowest prevalence of AF was 0%; this low prevalence was reported following a screening programme set in community health fairs, targeting eight villages in rural Uganda 14. Residents of Nyakabare Parish were invited to free community health fairs and 856 (47.2%) adults in the area attended.The patients underwent a 10 second seated ECG recording using a portable ECG machine (CardioCard Digital ECG Box®)14. The authors conclude that AF appears to be less prevalent in rural Uganda than in developed countries and this may be due togenetic and/or environmental factors or related to survivorship bias. However, the profile of the population under investigation was young. The sample consisted of 320 (37.5%) men; the mean age was 42.3 ± 17.5 years. Only 127 (14.8%) participants were aged >65 years old 14. AF prevalence is known to increase significantly with advancing age and therefore the reported 0% prevalenceshould be interpreted with caution.

Prevalence ratesof AF varied across continents, which, could be due to genetic or environmental factors. The prevalence of primary AF risk factors, for instance hypertension and diabetes, are increased in racial and ethnic minorities 30. However, it has been shown consistently in epidemiological studies and clinical trials, that there is a lower incidence and prevalence of AF in ethnic and racial minorities30, 31. In this study, it was apparent that prevalence rates were generally lower in low and lower middle-income countries compared to upper middle income and high income countries. Ethnic and racial minorities are less likely to be insured and have primary care providers andthe limited participation of minorities in trials for AF management and stroke prevention has previously been recognised30, 31.

Only two community-screening studies took place in African countries (Tanzania and Uganda)1516. In both studies,screening took place in rural villages. It is feasible that many older people with comorbidities and at high risk of AF might not have had the means to travel to the centres to partake in the screening programme hence the younger profile of the study participants 14.As AF is often asymptomatic, AF may be viewed as less of a public health concern therefore screening initiatives may not be a priority in lower income countries with limited health resources. Opportunistic screening is often reliant on patientsattending paid appointments, or a government-funded appointment. People in lower income countries are more likely to have limited resources to access healthcare making opportunistic screening challenging in these populations31.Clinicians have also argued that AF might be lower in ethnicity minority groups due to AFpresenting differently in these individuals. There is evidence to suggest that ethnic minority individuals may be more likely to have paroxysmal AF rather than persistent AF 63. Paroxysmal AF screening lacks research across all ethnicities due to its more time constraining screening process. The U.N projects that the average life expectancy in Tanzania is 65.46years and in Uganda is 63.41 years. Therefore, lower life expectancy and survivorship bias could be another factor that links ethnic minorities to lower AF prevalence levels31.

Across all studies, it was evident that the prevalence of AF significantly increased with advancing age.Higher prevalence was observed when targeted screening of older adults occurred, as evidenced in the prevalence studies conducted in Sweden 28-30 which had the highest prevalence rates in Europe. They targeted individuals aged 70-76 years of age and therefore the higher prevalence rates are expected given the population under investigation.As the goal of medical screening is detection of cases with an elevated probability of having the disorder of interest then future studies should target individuals at greatest risk of AF including older adults >65 years of age which is consistent withEuropean guidelines whereby screening is recommended in all patients > years of age 8.

Setting

The majority ofresearchers collected data in either community or urban/rural primary care settings. Primary care mainly consisted of GP practices. Community screening consisted mainly of screening centres, home visits and pharmacies. Only one study took place across multiple different settings. Using multiple different settings showed signs of inconsistencies and higher risk of biasbecause researchers employed different protocols, methods and data collection tools in each of the settings. Furthermore, participant recruitment varied in the multiple settings, with one site using cardiologists who already knew the patients’ medical historyprior to opportunistically screening for AF32.

Type of Screening

Four studies used both opportunistic and systematic screening studies 29, 33-35. Overall, no significant difference was evident in the outcomes of studies that used opportunistic versus systemic screening.Therefore, neither approach is considered superior. Both approaches have strengths and limitations but both forms are effective if executed in an appropriate manner. Systematic screening can be conducted over a shorter timeframe than opportunistic screening; however, opportunistic screening can be more cost effective than systematic screening 10. Furthermore, primary care providers, including general practitioners, community health workers and pharmacists, are in a unique position to be proactive with their patients and actively seek patients with AF through opportunistic screening programmes 3, 11.

Data Collectors

The research team, cardiologists and general practitioners most frequently conducted data collection. Approximately half of study teams used at least one cardiologist to review ECG readings and confirm AF diagnoses. Most papers highlighted the importance of using the resources of a cardiologist to review new AF diagnoses. However, the use of a cardiologist was not feasible or attainable in some studies due to limited resources. In the absence of an expert cardiologist in the research team to confirm diagnoses, participants were told to contact a GP/cardiologist for review. In the majority of studies, the data collector(s) were either research personnel or a health professional, however, in four studies, layperson volunteers were trained to use portable ECG devices to screen for AF15, 17, 20, 36. Furthermore, in eight studies, participants were the data collectors, and one project a Clinical Events Adjudication Committee was employed.

Novel technologies

The emergence of various novel technologies has significantly widened the scope for ECG monitoring and detection within the communitybased setting. SMART technologiesfor AF detecting and monitoringinclude the Cardiio Rhythm Smartphone®19, 26, Apple watch photothermography37 and Alivecor® whichwas themost frequently utilised SMART technology in the literature(n=14)17, 38, 39. In a recent systematic review, the Alivecor® was found to be convenient, valid, and a feasible means of monitoring for AF that can be successfully implemented into both opportunistic and systematic screening strategies for AF40. The advent of SMART devices will undoubtedly increase the opportunities for AF screening across a range of settings but especially in the community and home setting.Additional advantages of these technologies over traditional methods include accessibility, low cost and ease of use. The latter is particularly encouraging as this means that a wider range of health and social care professionals and patients, can use these devices and proactively partake in AF screening. It is important however, that high quality research is conductedto establish accuracy and validity for these emerging devices. If being used independently, appropriate support is requiredto ensure patient safety.

Strengths

Scoping reviews have been described as a process of mapping the existing literature or evidence base 41. We followed the methodology of the Preferred Reporting Items for Systematic Reviews and MetaAnalyses extension for Scoping reviews (PRISMAScR)11 and systematically and comprehensively searched, analysed and synthesised the research literatureon screening for AF in community settings and primary care settings.

Limitations

Scoping reviews differ from other types of systematic reviews in that they provide an overview of the existing literature without quality assessment or extensive data synthesis 41. Due to high heterogeneity across studies in terms of prevalence of AF and the different population screened and the diversity of methodological approaches employed in AF screening research it is not possible to conduct a meta-analysis and pool data 42.Instead, we present a narrative synthesis of the findings and an overview of the existing literature without quality assessment.

Conclusions

Despite the significant range in the prevalence of newly diagnosed AF cases across the studies (0-34%), the prevalence of AF was consistently found to increase with advancing age across the studies thus demonstrating the association between higher prevalence of AF and advancing age. Future studies of opportunistic or systematic screening for AF should target individuals at greatest risk of the condition including older adults >65 years of age. In the main, studies took place in community settings primarily in primary care and GP practices. The 12-lead ECG was the most frequently employed clinical technique employed in screening for AF. This was followed by smartphone based AliverCor® (1 lead ECG) and pulse palpation. Emerging novel technologies will undoubtedly increase the opportunities for AF screening across a range of settings, including community and home settings, which will increase the accessibility of AF screening and allow for more health and social professionals to partake in opportunistic screening of high-risk populations. Furthermore, SMART technologies also have the potential for greater self-monitoring in home settings. There is a need for larger scale, high quality studies investigating AF screening, with robust methodologies across a wider demographic, to provide accurate prevalence data for AF and to establish the accuracy and validity of the various traditional approaches versus new and novel technologies for AF screening.

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