Screening for Heart Disease in the Age of Digital Health Technologies: Who, When, and How? - European Medical Journal

Screening for Heart Disease in the Age of Digital Health Technologies: Who, When, and How?

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Cardiology CP
Edward Richardson, Angela Hall, Chris Hare, Pierre Le Page, *Andrew RJ Mitchell

The authors have declared no conflicts on interest

EMJ Cardiol. 2022. ; DOI/10.33590/emjcardiol/22-00093.

Each article is made available under the terms of the Creative Commons Attribution-Non Commercial 4.0 License.


Heart disease affects much of the world’s population, yet many people have no idea that they could have something wrong with them. An opportunity therefore exists for targeted screening for conditions such as cardiovascular disease, heart rhythm changes, valvular heart disease, structural abnormalities, and more subtle, rarer inherited heart conditions. At the same time, the rapid development of digital health technologies and clinical support systems is providing patients and their doctors access to augmented intelligence solutions to diagnose these conditions. This article will focus on how the emerging field of digital health technology can aid screening for heart disease and explore its usefulness in disease specific and population specific groups.


Cardiovascular disease (CVD) is one of the leading causes of mortality and morbidity worldwide, and was the leading cause of death globally in 2019.1 This is despite continued improvement in its management and the reduction of coronary artery disease (CAD).2 One reason for this is the presence of undiscovered conditions in certain individuals. For instance, there are an estimated 260,000 people with familial hypercholesterolaemia in the UK, but only 6–7% of them are diagnosed. Furthermore, over 600 people under 35 years old die a year in the UK from an unrecognised heart condition.1,3 There are particularly prominent examples of younger victims with sudden cardiac death (SCD) in professional athletes. These draw the public’s attention and, whilst uncommon, are dramatic and have a high burden in terms of life-years lost.4

CVD also has significant economic costs, with an estimated 19 billion GBP per year impact on the UK’s economy and around 106 billion EUR across the European Union (EU).1,2 The National Institute for Health and Care Excellence (NICE) in the UK estimated that a 1% drop in cardiovascular risk would prevent 25,000 CVD cases, producing 40 million EUR a year in savings.5


The purpose of screening is to detect a disease in its early stages and treat it to reduce morbidity, mortality, and the associated societal and healthcare costs.6 In an increasingly digital age with many innovations, there are new opportunities to utilise technology to screen for pathology. History, physical examination, ECGs, echocardiograms, CT, and genetic screening can all be utilised. The usefulness of these methods varies depending on the condition being screened. This article will focus on how the emerging field of digital health technology affects screening for CVD and explore its utility in disease- and population-specific screening.

Disease-Specific Screening

Sudden cardiac death

There remains significant debate around the optimal methods of cardiac screening for SCD in the young. The European Society of Cardiology (ESC) recommends screening of high-risk individuals, including athletes, whereas the American Heart Association (AHA) guidelines recommend just screening athletes. There are other discrepancies; the ESC endorses the use of ECGs in screening, but the AHA do not.7,8 This is particularly interesting in the context of the leading causes of death in young competitive athletes. Additionally, a 12-lead ECG can increase the sensitivity of screening and there is good evidence supporting its cost-effectiveness.9,10 There are papers suggesting the focus on athletes ignores the damaging effects SCD in a non-athlete can have on friends, peers, and family; especially given the number of population-wide SCDs that happen during sleep, which could be as high as 40%, and those that occur in non-athletic groups.4,11,12

The risk of false positives and the harm this causes is often cited as the reason more generalised screening is not recommended.13,14 However, screening may also be cheaper than assumed and could be employed in younger age groups. Some papers suggest relying on improving resuscitation over screening is unsatisfactory due to SCD’s poor survival rate and the increased data gathered can be used to further refine screening.11,15 The technologies mentioned later in this article show promise for collecting such data at a reduced cost and raise the possibility of being able to recover data just preceding events, such as SCD. This is without taking into account the potential for artificial intelligence (AI) to revolutionise risk prediction or early warning systems.16

Atrial fibrillation

Targeted screening can also assist in the detection of atrial fibrillation (AF) due to its often insidious nature and the subsequent damaging thromboembolic event.17 The ESC recommends opportunistic screening for AF in anyone older than 65 years due to the risk of ischaemic stroke and increased mortality associated with asymptomatic AF.18 Several studies suggest the use of a manual pulse check with supplementation from single-lead or 12-lead ECG devices due to the ESC requirements for diagnosis.17-19

In the 2020 ESC guidance, the sensitivity and specificity of the various AF screening tools were compared with the gold standard of a 12-lead ECG (Table 1).18 There are two large studies, the Apple Heart study, with over 400,000 self-enrolled participants, and the Huawei Heart study, with 200,000. Both studies showed promise for the use of smartwatches and photoplethysmography (PPG) in screening for arrhythmia.18,20,21

Table 1: The sensitivity and specificity of the various atrial fibrillation screening tools.18

A recent meta-analysis supported the systematic and opportunistic screening for AF.22 While it did not examine smartwatches or PPG, it did find that systematic, rather than opportunistic screening, was more effective at identifying patients with AF; however, it was noted that this may not be cost-effective. Others suggest this may be achieved by lowering the age cut off to 40 years old.22 This, combined with the Apple Heart and Huawei Heart studies not requiring in-person appointments, may increase cost-effectiveness further.20,21 The portable single-lead ECG devices have shown promise by increasing the ease and reducing the cost of screening,17,23,24 and could increase the potential for including wider populations.17,22

Coronary artery disease

CAD is one of the leading causes of death worldwide.25 Due to its potentially silent nature there has been great interest in screening, but the optimal approach is debated. Initial screening can involve history and examination to establish risk factors for CAD, alongside blood tests and scoring systems such as the Systemic Coronary Risk Estimation 2 (SCORE2) score.26 These take information such as blood pressure, lifestyle factors, family history, and sex into account alongside co-morbidities. The ESC recommends assessing risk in males over 40 years old and females over 50 years, unless there are known CVD risk factors. Most of the ESC recommendations discuss prevention using lifestyle and medications to adjust modifiable risk factors rather than exploring screening as an option. This makes it difficult to assess the ESC position on more generalised screening.26

In recent years, coronary artery calcium scoring and CT coronary angiography (CTCA) have come to the fore in assessing patients’ CAD risk in a non-invasive manner.27 There are also new ways to assess CAD risk using CTCA in combination with AI. One example is the CaRi-Heart® (Caristo, Oxford, UK), which uses images captured during a standard CTCA alongside traditional risk factors. By calculating the Fat Attenuation Index (FAI), which has been previously validated,28,29 in combination with traditional risk factors, it determines an 8-year absolute risk of a fatal cardiac event, or the CaRi-Heart risk. Inflammation has long been suspected as having a key role in CAD,30 but until FAI was established there was no straightforward way to measure this.28 Caristo takes FAI a step further and has been demonstrated to show a significant clinical benefit over traditional CVD risk factors and has the potential to enhance the utility of CTCA in the risk stratification of CAD.31

Fractional flow reserve also uses CTCA and AI-powered algorithms to establish vessel-specific ischaemia and flow obstruction. It is recommended in British32 and European27 guidance for the risk stratification of those with stable chest pain. Not least in part due to its prognostic value and potential to increase the accuracy of assessing risk in an individual as well as helping select who undergoes invasive strategies such as direct invasive coronary angiography.33

In December 2021, the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE) published their recommendations for non-invasive imaging in coronary syndromes.34 This not only supported the use of fractional flow reserve, but also recommended the use of echocardiograms, especially stress echocardiography, which can also be combined with the power of AI. The EchoGo Pro (Ultromics Dallas, Texas, USA), for instance, uses AI to automatically analyse stress echocardiograms, which it can then use to risk stratify the likelihood of severe CAD in an individual.35 This was shown to be 10% more sensitive than a manual assessment.35


Due to increasing life expectancy, the prevalence of valvular heart disease (VHD) is rising. In Europe, approximately one million people over the age of 75 years suffer from severe aortic stenosis.36 A study in 2016 screened for VHD in primary care patients over the age of 64 years and discovered just over half had previously undetected VHD. Fortunately, the majority had mild disease; however, 6.4% had clinically significant disease.37 They also established a strong association with AF, which suggests this could be a targeted area for screening. Currently, there are no population screening programmes for VHD in adults.

Population-Specific Screening

For screening to be relevant to an individual, one must consider the disease(s) to screen for. When screening certain populations, cardiologists can screen for many cardiac diseases with the same test, being mindful that there will be different incidences in different populations.

Athlete screening

There are differences in the recommendations and methods of pre-participation cardiac screening for athletes around the world. In some countries such as Italy, this is compulsory.7,10,14 Many other European countries have followed suit in accordance with the ESC recommendations, and several professional sporting bodies, such as the International Olympic Committee (IOC)38 and Fédération Internationale de Football Association (FIFA),39 recommend cardiac screening.

The incidence of sports related sudden cardiac arrest is low, 6–7/million inhabitants per year in one recent study.40 Interestingly, it showed only 5.3% occurred in young competitive athletes, with the remaining occurring in middle-aged recreational sports participants. Only 12% had a history of heart disease.

The causes in the young, below 35 years, include cardiomyopathy, coronary artery anomalies, ion channelopathies, and acquired cardiac conditions with geographical variation in incidences.41 In individuals who are older, CAD accounts for >80% cases with untrained individuals appearing to be at the highest risk.42 Screening of recreational middle-aged sports participants for underlying coronary artery disease may be more beneficial in changing behaviours and reducing the burden of sudden cardiac arrest.42

Risk profiling

Current NICE recommendations suggest a systematic strategy within primary care to identify individuals at the highest risk of CVD.43 This should be reviewed regularly in those over the age of 40 years. Well-validated risk scores such as QRisk344 in the UK and SCORE in Europe aid this and help determine the need for primary preventative interventions. Patients with chronic kidney disease, albuminuria, Type 1 diabetes, or familial history of hypercholesterolaemia should be assumed to be high-risk and treated accordingly.42

The U.S. Preventive Services Task Force (USPSTF)45 has similar recommendations. They advise against screening asymptomatic individuals with a low-risk of CVD using ECG or exercise ECG and find insufficient evidence for a recommendation in intermediate- or high-risk. Their recent review on screening for AF in asymptomatic adults aged 50 years or older found insufficient evidence to assess the benefits versus harms.46 This is consistent with the recent LOOP study,47 where, despite a nearly three-fold rise in AF detection and subsequent anticoagulation, there was only a non-significant trend to benefit. This was coupled with a non-significant trend to harm such as major bleeding. This suggests that the correct demographic to screen has yet to be found.

Screening of sudden cardiac arrest survivors and victims’ families

A standardised approach would help phenotype and genotype individuals and has the potential to do more for our understanding of rare cardiac conditions capable of causing SCD than any whole population screening programme could ever detect. The ESC’s 2015 guidelines suggest that a diagnosis could be made in 50% of families of sudden arrhythmic death victims.48 There are guidelines from the European Society of Pathology (ESP) for autopsy investigations of victims of SCD.49

For survivors of sudden cardiac arrest who come under the care of cardiology, a thorough evaluation of the cause of arrest should be made before discharge to determine the need for implantation of a cardio-defibrillator and other therapies. However, some survivors never come under the care of a specialist cardiology team and the opportunity to screen family members is lost.49

Serendipitous Screening

With the huge rise in the use of cross-sectional radiological imaging over the last 10 years, there has been increasing recognition by radiologists that as they can see the heart, they should analyse it too. Calcification of the coronary arteries is easily visible on both unenhanced and enhanced studies and there have been several papers in recent years guiding how to best interpret and deal with these findings.50

The degree of coronary calcification increases with age, as does the likelihood of having a CT with at least some of the heart visible. Other signs of coronary heart disease can also be visible, such as left ventricular wall scarring, late enhancement of the myocardium, mural, and intracardiac thrombus. CT scanners are now so fast that these features are often visible on non-gated studies.

Now that Schrödinger’s ‘coronary cat’ has been irreversibly observed, the radiologist must decide how to report it without creating unnecessary demand on cardiology services. Estimating the calcium score is feasible but should be put in the clinical context of the patient. For example, a male in their 50s with three-vessel calcification has a clear risk that might benefit from investigation and treatment. A 95-year-old male with metastatic malignancy may not.

The British Society of Cardiovascular Imaging (BSCI) published a consensus statement on this in 2020, which detailed how best to approach incidental cardiac findings.50 They suggest interpretation should be influenced by additional available clinical information. A similar approach is made to aortic valve calcification and other incidental cardiac findings. Reports will alert the clinician to the presence of disease and having a strategy to deal with this is important. Most of the follow up should be suitable for the family physician, with symptomatic disease most likely to require onward referral. A useful pathway exists in their document referring to NICE guidelines.50

Digital Screening

With the worldwide increase in usage of mobile devices, there exists a basis for a digital health approach in the context of arrhythmia, be it as a diagnostic tool or for surveillance. This can be particularly beneficial in the context of AF, where the incremental costs for its use are relatively low.51

Several digital devices are available to diagnose and record heart rhythm changes. Among these is the MyDiagnostick (MyDiagnostick Medical, Maastricht, the Netherlands), a device equipped to record a single-lead ECG that displays a red or green light if AF or sinus rhythm is detected. AF analysis has shown 80–100% sensitivity and 93–99% specificity and a screening study during flu vaccinations found 1.1% of participants had AF.52 The Zenicor-ECG (Zenicor Medical Systems, Stockholm, Sweden) is another hand-held device with no additional hardware. Two electrodes at each end are held and the central display shows a Lead I ECG. AF was identified in 0.9% of participants in one screening study and 3% in another study, both adopting similar protocols. Validity was high when used twice daily along with recordings during symptoms and adjudication of ECGs by a health professional.53,54

The RhythmPad (Cardiocity, Colchester, UK) is a mousepad-style ECG screening device55 that offers an advantage over the single-lead view, as a third electrode can be added for enhanced image clarity. The titanium-based sensors can be placed around both arms and the right leg then attached via leads to a tablet computer that displays a six-lead ECG. Among the advantages over single-lead systems, is the existence of algorithms for the detection of arrhythmias beyond AF. A study utilising it revealed sensitivities of 97.5% for normal sinus rhythm and 95.4% for AF.55

The Zio Patch (iRhythm Technologies, San Francisco, California, USA) provides continuous ECG recording for 14 days with a high diagnostic yield for total arrhythmia detection when compared with Holter monitoring.56 When 24 hours of monitoring was compared between the two methods, the Holter detected more arrhythmias; however, the time to first recorded arrhythmia often occurred after 48 hours, demonstrating the importance of longer duration monitoring. Comfort is an important consideration and impacts compliance. Both the Zio Patch and the similar S-PATCH (Wellysis, Seoul, Republic of Korea) were found to be superior in this regard when compared with traditional Holter ECG monitors.56-58

Commercial wearable devices measure heart rate and rhythm through ECG or PPG systems. ECG monitors can be built into belts, wristbands, adhesive patches, and mobile smartphones. PPG measures changes in microvascular blood volume that translates into pulse waves and a tachogram recording. This technology is advancing, and more arrhythmias are becoming identifiable.59 Diagnostic clarity can be enhanced by over-reading from a competent practitioner in ECG analysis. As well as issues with sensor contact, challenges include signal correlation and patient comfort. Future developments should focus on overcoming such design barriers.

One wearable device, the Apple Watch (Apple, Cupertino, California, USA), has gone some way towards achieving both comfort and accuracy and was evaluated in the Apple Heart Study.20 This showed that 2,161 people had an irregular rhythm, with 34% confirmed with AF on subsequent patch monitoring. Where AF was not the cause of rhythm irregularity, 40% showed other arrhythmias, mostly ectopic beats.20,60 Adverse events were collated and anxiety was recorded most commonly, supporting findings that such devices can cause health anxiety through overuse and is worthy of consideration when considering such devices with patients.20,61

Perhaps the most widely adopted tool in AF screening research is the Kardia device (AliveCor, Mountain View, California, USA), a device that transmits a single-lead ECG wirelessly to a smart mobile device. The NICE recently published their guidance on the Kardia, outlining the cost-effectiveness and ability to identify significantly more AF than Holter monitoring.62 A systematic review explored the feasibility and validity of the device.63 Feasibility metrics, including process, resource, and management, revealed this as an effective tool. Sensitivity and specificity both reached 98% across included studies, with AF detection ranging from 0.8% to 36%, with correlation to the study demographics and screening approach.63 Kardia has also demonstrated utility across a variety of settings, making it versatile and easy to use. Their recent six-lead version offers advantages over the single-lead view, with the addition of more sophisticated algorithms including corrected QT interval ECG analysis.

Remote monitoring of cardiac implantable electronic devices is now recommended by major cardiology societies.64,65 There has been an increase in use over the last few years and advantages include earlier detection of events and identification of device malfunction, permitting earlier intervention. Enhanced patient safety, reducing hospital admissions, and improving quality of care whilst proactively identifying problems contributes to the cost savings. The increasing need for monitoring patients has come at a time when there is clear evolution and improvement in the accuracy and efficacy of digital health devices.


Overall, there are multiple ways of screening for cardiac disease. In most diseases, the exact population that may benefit from screening has yet to be identified. The rapidly expanding field of smart devices and the use of AI may help identify these groups further and provide information about disease trends. Digital devices have the potential to screen large sections of the population for multiple types of pathology. It opens the possibility of examining previously impossible data, such as a patient’s heart rhythm prior to their first collapse. It has the potential to reduce the cost of screening, especially in more remote areas. Various devices may also help monitor the middle-aged starting to exercise, with their potential increased risk of cardiac disease.

Personal devices such as the Apple Watch empower patients to look after their own health and to control their own data. This can occasionally be associated with increased health anxiety but, conversely, can also enable reduction of a patient’s unease. For example, patients can use devices such as the Kardia whenever they get palpitations. There is also the expanding field of AI, especially in combination with imaging. This shows great promise at increasing diagnostic accuracy and assisting the risk stratification of patients.

There are of course negatives aspects to health screening, which will need to be weighed against the benefits. The wide-reaching screening that some of these digital devices might provide could be used very broadly, perhaps to identify a suitable screening target population. Digital health devices are still in adolescence with multiple unknowns; however, the future looks promising.


In conclusion, the expanding field of digital health devices has the potential to offer multiple new methods for screening for heart disease. There needs to be some caution to ensure that these technologies are properly evaluated to comply with the principles of screening, especially when comparing the potential harms and benefits of their use.

British Heart Foundation (BHF). UK factsheet. Available at: Last accessed: 28 December 2021. Piepoli MF et al.; ESC Scientific Document Group. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts). Developed with the special contribution of the European Association for Cardiovascular Prevention Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315-81. Gillespie S. Saving lives through screening families: it’s only just beginning. 2019. Available at: Last accessed: 28 December 2021. Ackerman M et al. Sudden cardiac death in the young. Circulation. 2016;133(10):1006-26. Collins M et al. An economic evaluation of salt reduction policies to reduce coronary heart disease in England: a policy modeling study. Value Health. 2014;17(5):517-24. Saquib N et al. Does screening for disease save lives in asymptomatic adults? Systematic review of meta-analyses and randomized trials. Int J Epidemiol. 2015;44(1):264-77. Corrado D et al. Cardiovascular pre-participation screening of young competitive athletes for prevention of sudden death: proposal for a common European protocol. Consensus statement of the Study Group of Sport Cardiology of the Working Group of Cardiac Rehabilitation and Exercise Physiology and the Working Group of Myocardial and Pericardial Diseases of the European Society of Cardiology. Eur Heart J. 2005;26(5):516-24. Maron BJ et al.; American Heart Association Council on Nutrition, Physical Activity, and Metabolism. Recommendations and considerations related to preparticipation screening for cardiovascular abnormalities in competitive athletes: 2007 update. Circulation. 2007;115(12):1643-55. Asif IM, Drezner JA. Sudden cardiac death and preparticipation screening: the debate continues-in support of electrocardiogram-inclusive preparticipation screening. Prog Cardiovasc Dis. 2012;54(5):445-50. Corrado D et al. Risk of sports: do we need a pre-participation screening for competitive and leisure athletes? Eur Heart J. 2011;32(8):934-44. De Lazzari C et al. Screening high school students in Italy for sudden cardiac death prevention by using a telecardiology device: a retrospective observational study. Cardiol Young. 2017;27(1):74-81. Dhutia H, MacLachlan H. Cardiac screening of young athletes: a practical approach to sudden cardiac death prevention. Curr Treat Options Cardiovasc Med. 2018;20(10):85. Van Brabandt H et al. Harms and benefits of screening young people to prevent sudden cardiac death. BMJ. 2016;353:i1156. Le Page P et al. How to screen for inherited cardiac diseases. e-Journal of Cardiology Practice. 2013;11:23. Lampert R, Myerburg RJ. The true incremental cost of ECG screening: the price is not right, but the cost appears effective. J Am Coll Cardiol. 2013;61(14):1553-4. Vardas PE et al. The year in cardiovascular medicine 2021: digital health and innovation. Eur Heart J. 2022;43(4):271-9. Richardson E et al., "Screening for atrial fibrillation and the role of digital health technologies", Cismaru G (ed.), Epidemiology and Treatment of Atrial Fibrillation (2019), London: IntechOpen, pp.1-10. Hindricks G et al.; ESC Scientific Document Group. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J. 2020;42(5):373-498. Lobban T et al. 1357 Identifying the undiagnosed AF patient through “Know Your Pulse” community pharmacy based events held in ten countries during Arrhythmia Alliance World Heart Rhythm Week 2017. Eur Heart J. 2018;39(Suppl 1):ehy565.1357. Turakhia MP et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J. 2019;207:66-75. Guo Y et al. Population-based screening or targeted screening based on initial clinical risk assessment for atrial fibrillation: a report from the Huawei Heart Study. J Clin Med. 2020;9(5):1493. Petryszyn P et al. Effectiveness of screening for atrial fibrillation and its determinants. A meta-analysis. PLoS One. 2019;14(3):e0213198. Mitchell ARJ, Le Page P. Living with the handheld ECG. BMJ Innov. 2015;1:46-8. Proietti M et al.; Belgium Heart Rhythm Week Investigators. Cost-effectiveness analysis of a handheld ECG machine used for a population-wide screening programme: the Belgian Heart Rhythm Week Screening programme. Eur Heart J. 2017;38(Suppl1):ehx504.P4596. Degrell P et al. Screening for coronary artery disease in asymptomatic individuals: why and how? Arch Cardiovasc Dis. 2015;108(12):675-82. Visseren FLJ et al.; ESC Scientific Document Group. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies with the special contribution of the European Association of Preventive Cardiology (EAPC). Eur Heart J. 2021;42(34):3227-337. Knuuti J et al.; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes: The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur Heart J. 2019;41(3):407-77. Antonopoulos AS et al. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med. 2017;9(398):eaal2658. Oikonomou EK et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018;392(10151):929-39. Christodoulidis G et al. Inflammation in coronary artery disease. Cardiol Rev. 2014;22(6):279-88. Oikonomou EK et al. Standardized measurement of coronary inflammation using cardiovascular computed tomography: integration in clinical care as a prognostic medical device. Cardiovasc Res. 2021;117(13):2677-90. National Institute for Health and Care Excellence (NICE). Recent-onset chest pain of suspected cardiac origin: assessment and diagnosis. 2016. Available at: Last accessed: 28 February 2022. Williams MC, Newby DE. Prognostic value of fractional flow reserve from computed tomography. Heart. 2022;108(3):160-1. Edvardsen T et al. Non-invasive imaging in coronary syndromes: recommendations of the European Association of Cardiovascular Imaging and the American Society of Echocardiography, in collaboration with the American Society of Nuclear Cardiology, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance. Eur Heart J Cardiovasc Imaging. 2021;23(2):e6-33. Upton R et al. Automated echocardiographic detection of severe coronary artery disease using artificial intelligence. JACC Cardiovasc Imaging. 2022;15(5):715-27. Thoenes M et al. Patient screening for early detection of aortic stenosis (AS)-review of current practice and future perspectives. J Thorac Dis. 2018;10(9):5584-94. d'Arcy JL et al. Large-scale community echocardiographic screening reveals a major burden of undiagnosed valvular heart disease in older people: the OxVALVE Population Cohort Study. Eur Heart J. 2016;37(47):3515-22. Bille K et al. Sudden cardiac death in athletes: the Lausanne Recommendations. Eur J Cardiovasc Prev Rehabil. 2006;13(6):859-75. Davogustto G, Higgins J. Sudden cardiac death in the soccer field: a retrospective study in young soccer players from 2000 to 2013. Phys Sportsmed. 2014;42(4):20-9. Karam N et al. Evolution of incidence, management, and outcomes over time in sports-related sudden cardiac arrest. J Am Coll Cardiol. 2022;79(3):238-46. Egger F et al. FIFA Sudden Death Registry (FIFA-SDR): a prospective, observational study of sudden death in worldwide football from 2014 to 2018. Br J Sports Med. 2022;56(2):80-7. Pelliccia A et al.; ESC Scientific Document Group. 2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease. Eur Heart J. 2021;42(1):17-96. National Institute for Health and Care Excellence (NICE). Cardiovascular disease: risk assessment and reduction, including lipid modification. 2016. Available at: Last accessed: 1 February 2021. Hippisley-Cox J et al. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357:j2099. Screening for cardiovascular disease risk with electrocardiography: recommendation statement. Am Fam Physician. 2018;98(6):Online. Kahwati LC et al. Screening for atrial fibrillation: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2022;327(4):368-83. Svendsen JH et al. Implantable loop recorder detection of atrial fibrillation to prevent stroke (the LOOP study): a randomised controlled trial. Lancet. 2021;398(10310):1507-16. Priori SG et al.; ESC Scientific Document Group. 2015 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC). Eur Heart J. 2015;36(41):2793-867. Basso C et al. Guidelines for autopsy investigation of sudden cardiac death: 2017 update from the Association for European Cardiovascular Pathology. Virchows Arch. 2017;471(6):691-705. Williams MC et al. Reporting incidental coronary, aortic valve and cardiac calcification on non-gated thoracic computed tomography, a consensus statement from the BSCI/BSCCT and BSTI. Br J Radiol. 2021;94(1117):20200894. Giebel GD, Gissel C. Accuracy of mHealth devices for atrial fibrillation screening: systematic review. JMIR Mhealth Uhealth. 2019;7(6):e13641. Kaasenbrood F et al. Yield of screening for atrial fibrillation in primary care with a hand-held, single-lead electrocardiogram device during influenza vaccination. Europace. 2016;18(10):1514-20. Berge T et al. Systematic screening for atrial fibrillation in a 65-year-old population with risk factors for stroke: data from the Akershus Cardiac Examination 1950 study. Europace. 2018;20(FI_3):f299-305. Svennberg E et al. Mass screening for untreated atrial fibrillation: the STROKESTOP study. Circulation. 2015;131(25):2176-84. Sabar MI et al. A study to assess a novel automated electrocardiogram technology in screening for atrial fibrillation. Pacing Clin Electrophysiol. 2019;42(10):1383-9. Barrett PM et al. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. Am J Med. 2014;127(1):95.e11-7. Kim ED et al. Two-week burden of arrhythmias across CKD severity in a large community-based cohort: the ARIC study. J Am Soc Nephrol. 2021;32(3):629-38. Upadhyayula S, Kasliwal R. Wellysis S-PAtch Cardio versus conventional Holter ambulatory electrocardiographic monitoring (the PACER trial): preliminary results. J Clin Prev Cardiol. 2019;8:173-82. Bayoumy K et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol. 2021;18(8):581-99. Perino AC et al. Arrhythmias other than atrial fibrillation in those with ani rregular pulse detected with a smartwatch: findings from the Apple Heart Study. Circ Arrhythm Electrophysiol. 2021;14(10):e010063. Rosman L et al. When smartwatches contribute to health anxiety in patients with atrial fibrillation. Cardiovasc Digit Health J. 2020;1(1):9-10. National Institute of Health and Care Excellence (NICE). KardiaMobile for the detection of atrial fibrillation. 2022. Available at: Last accessed: 24 January 2022. Hall A et al. Effectiveness of a single lead AliveCor electrocardiogram application for the screening of atrial fibrillation: a systematic review. Medicine (Baltimore). 2020;99(30):e21388. Glikson M et al.; ESC Scientific Document Group. 2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy: developed by the Task Force on cardiac pacing and cardiac resynchronization therapy of the European Society of Cardiology (ESC) with the special contribution of the European Heart Rhythm Association (EHRA). Eur Heart J. 2021;42(35):3427-520. Slotwiner D et al. HRS Expert Consensus Statement on remote interrogation and monitoring for cardiovascular implantable electronic devices. Heart Rhythm. 2015;12(7):e69-100.

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