1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Medicine
  4. Volume 73, 2022
  5. Article

Annual Review of Medicine

Volume 73, 2022

Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications

Abstract

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes—termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence–enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

Keyword(s): artificial intelligence, atrial fibrillation, convolutional neural network, deep neural network, ECG, electrocardiogram, machine learning, sinus rhythm
Loading

Article metrics loading...

/content/journals/10.1146/annurev-med-042420-105906
2022-01-27
2024-05-29
Loading full text...

Full text loading...

/deliver/fulltext/med/73/1/annurev-med-042420-105906.html?itemId=/content/journals/10.1146/annurev-med-042420-105906&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Chugh SS, Havmoeller R, Narayanan K et al. 2014. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 study. Circulation 129:837–47
     [Google Scholar]
  2. 2. 
    Gillis AM, Morck M. 2002. Atrial fibrillation after DDDR pacemaker implantation. J. Cardiovasc. Electrophysiol. 13:6542–47
     [Google Scholar]
  3. 3. 
    Mittal S, Stein K, Gilliam FR3rd et al. 2008. Frequency, duration, and predictors of newly-diagnosed atrial fibrillation following dual-chamber pacemaker implantation in patients without a previous history of atrial fibrillation. Am. J. Cardiol. 102:4450–53
     [Google Scholar]
  4. 4. 
    Tse HF, Lau CP. 2005. Prevalence and clinical implications of atrial fibrillation episodes detected by pacemaker in patients with sick sinus syndrome. Heart 91:3362–64
     [Google Scholar]
  5. 5. 
    Healey JS, Martin JL, Duncan A et al. 2013. Pacemaker-detected atrial fibrillation in patients with pacemakers: prevalence, predictors, and current use of oral anticoagulation. Can. J. Cardiol. 29:2224–28
     [Google Scholar]
  6. 6. 
    Siontis KC, Gersh BJ, Killian JM et al. 2016. Typical, atypical, and asymptomatic presentations of new-onset atrial fibrillation in the community: characteristics and prognostic implications. Heart Rhythm 13:71418–24
     [Google Scholar]
  7. 7. 
    Gladstone DJ, Spring M, Dorian P et al. 2014. Atrial fibrillation in patients with cryptogenic stroke. N. Engl. J. Med. 370:2467–77
     [Google Scholar]
  8. 8. 
    Saposnik G, Gladstone D, Raptis R et al. 2013. Atrial fibrillation in ischemic stroke: predicting response to thrombolysis and clinical outcomes. Stroke 44:99–104
     [Google Scholar]
  9. 9. 
    Attia ZI, Noseworthy PA, Lopez-Jimenez F et al. 2019. An artificial intelligence–enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 394:10201861–67
     [Google Scholar]
  10. 10. 
    Raghunath S, Pfeifer JM, Ulloa-Cerna AE et al. 2021. Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke. Circulation 143:131287–98
     [Google Scholar]
  11. 11. 
    Noseworthy PA, Kaufman ES, Chen LY et al. 2019. Subclinical and device-detected atrial fibrillation: pondering the knowledge gap: a scientific statement from the American Heart Association. Circulation 140:25e944–63
     [Google Scholar]
  12. 12. 
    Hindricks G, Potpara T, Dagres N et al. 2021. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). Eur. Heart J. 42:5373–498
     [Google Scholar]
  13. 13. 
    Colilla S, Crow A, Petkun W et al. 2013. Estimates of current and future incidence and prevalence of atrial fibrillation in the US adult population. Am. J. Cardiol. 112:81142–47
     [Google Scholar]
  14. 14. 
    Jones NR, Taylor CJ, Hobbs FDR et al. 2020. Screening for atrial fibrillation: a call for evidence. Eur. Heart J. 41:101075–85
     [Google Scholar]
  15. 15. 
    Perez MV, Mahaffey KW, Hedlin H et al. 2019. Large-scale assessment of a smartwatch to identify atrial fibrillation. N. Engl. J. Med. 381:201909–17
     [Google Scholar]
  16. 16. 
    Healey JS, Lopes RD, Connolly SJ. 2015. The detection and treatment of subclinical atrial fibrillation: evaluating the IMPACT of a comprehensive strategy based on remote arrhythmia monitoring. Eur. Heart J. 36:261640–42
     [Google Scholar]
  17. 17. 
    Healey JS, Connolly SJ, Gold MR et al. 2012. Subclinical atrial fibrillation and the risk of stroke. N. Engl. J. Med. 366:120–29
     [Google Scholar]
  18. 18. 
    Van Gelder IC, Healey JS, Crijns HJ et al. 2017. Duration of device-detected subclinical atrial fibrillation and occurrence of stroke in ASSERT. Eur. Heart J. 38:171339–44
     [Google Scholar]
  19. 19. 
    Capucci A, Santini M, Padeletti L et al. 2005. Monitored atrial fibrillation duration predicts arterial embolic events in patients suffering bradycardia and atrial fibrillation implanted with antitachycardia pacemakers. J. Am. Coll. Cardiol. 46:101913–20
     [Google Scholar]
  20. 20. 
    Swiryn S, Orlov MV, Benditt DG et al. 2016. Clinical implications of brief device-detected atrial tachyarrhythmias in cardiac rhythm management device population. Circulation 134:161130–40
     [Google Scholar]
  21. 21. 
    Botto GL, Padeletti L, Santini M et al. 2009. Presence and duration of atrial fibrillation detected by continuous monitoring: crucial implications for the risk of thromboembolic events. J. Cardiovasc. Electrophysiol. 20:3241–48
     [Google Scholar]
  22. 22. 
    Sanna T, Diener H-C, Passman RS et al. 2014. Cryptogenic stroke and underlying atrial fibrillation. N. Engl. J. Med. 370:262478–86
     [Google Scholar]
  23. 23. 
    Wachter R, Groeschel K, Gelbrich G et al. 2017. Holter-electrocardiogram-monitoring in patients with acute ischaemic stroke (Find-AFRANDOMISED): an open-label randomised controlled trial. Lancet Neurol 16:4282–90
     [Google Scholar]
  24. 24. 
    Gladstone DJ, Wachter R, Schmalstieg-Bahr K et al. 2021. Screening for atrial fibrillation in the older population: a randomized clinical trial. JAMA Cardiol 6:5558–67
     [Google Scholar]
  25. 25. 
    Kalla M, Fabritz L, Kirchhof P. 2019. SMART about watches: We need technical and biological validation of atrial fibrillation screening. JACC Clin. Electrophysiol. 5:2209–11
     [Google Scholar]
  26. 26. 
    Halcox JP, Wareham K, Cardew A et al. 2017. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation 136:191784–94
     [Google Scholar]
  27. 27. 
    Hannun AY, Rajpurkar P, Haghpanahi M et al. 2019. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25:165–69
     [Google Scholar]
  28. 28. 
    Kashou AH, Rabinstein AA, Attia IZ et al. 2020. Recurrent cryptogenic stroke: a potential role for an artificial intelligence–enabled electrocardiogram?. HeartRhythm Case Rep 6:4202–5
     [Google Scholar]
  29. 29. 
    Christopoulos G, Graff-Radford J, Lopez CL et al. 2020. Artificial intelligence–electrocardiography to predict incident atrial fibrillation: a population-based study. Circ. Arrhythm. Electrophysiol. 13:12e009355
     [Google Scholar]
  30. 30. 
    Glotzer TV, Daoud EG, Wyse DG et al. 2009. The relationship between daily atrial tachyarrhythmia burden from implantable device diagnostics and stroke risk: the TRENDS study. Circ. Arrhythm. Electrophysiol. 2:474–80
     [Google Scholar]
  31. 31. 
    Gonzalez M, Keating RJ, Markowitz SM et al. 2014. Newly detected atrial high rate episodes predict long-term mortality outcomes in patients with permanent pacemakers. Heart Rhythm 11:2214–21
     [Google Scholar]
  32. 32. 
    Kim BS, Chun KJ, Hwang JK et al. 2016. Predictors and long-term clinical outcomes of newly developed atrial fibrillation in patients with cardiac implantable electronic devices. Medicine 95:e4181
     [Google Scholar]
  33. 33. 
    Cheung JW, Keating RJ, Stein KM et al. 2006. Newly detected atrial fibrillation following dual chamber pacemaker implantation. J. Cardiovasc. Electrophysiol. 17:1323–28
     [Google Scholar]
  34. 34. 
    Belkin MN, Soria CE, Waldo AL et al. 2018. Incidence and clinical significance of new-onset device-detected atrial tachyarrhythmia: a meta-analysis. Circ. Arrhythm. Electrophysiol. 11:e005393
     [Google Scholar]
  35. 35. 
    Nalliah CJ, Sanders P, Kottkamp H, Kalman JM. 2016. The role of obesity in atrial fibrillation. Eur. Heart J. 37:201565–72
     [Google Scholar]
  36. 36. 
    Deng H, Shantsila A, Guo P et al. 2018. A U-shaped relationship of body mass index on atrial fibrillation recurrence post ablation: a report from the Guangzhou Atrial Fibrillation Ablation Registry. EBioMedicine 35:40–45
     [Google Scholar]
  37. 37. 
    Kang S-H, Choi E-K, Han K-D et al. 2016. Underweight is a risk factor for atrial fibrillation: a nationwide population-based study. Int. J. Cardiol. 215:449–56
     [Google Scholar]
  38. 38. 
    Abdulla J, Nielsen JR. 2009. Is the risk of atrial fibrillation higher in athletes than in the general population? A systematic review and meta-analysis. Europace 11:91156–59
     [Google Scholar]
  39. 39. 
    Wilhelm M. 2014. Atrial fibrillation in endurance athletes. Eur. J. Prev. Cardiol. 21:81040–48
     [Google Scholar]
  40. 40. 
    Wilhelm M, Roten L, Tanner H et al. 2011. Gender differences of atrial and ventricular remodeling and autonomic tone in nonelite athletes. Am. J. Cardiol. 108:101489–95
     [Google Scholar]
  41. 41. 
    Elliott AD, Linz D, Mishima R et al. 2020. Association between physical activity and risk of incident arrhythmias in 402 406 individuals: evidence from the UK Biobank cohort. Eur. Heart J. 41:151479–86
     [Google Scholar]
  42. 42. 
    Alonso A, Krijthe BP, Aspelund T et al. 2013. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J. Am. Heart Assoc. 2:2e000102
     [Google Scholar]
  43. 43. 
    Christophersen IE, Yin C, Larson MG et al. 2016. A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study. Am. Heart J. 178:45–54
     [Google Scholar]
  44. 44. 
    Martin DT, Bersohn MM, Waldo AL et al. 2015. Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices. Eur. Heart J. 36:261660–68
     [Google Scholar]
  45. 45. 
    Kirchhof P, Blank BF, Calvert M et al. 2017. Probing oral anticoagulation in patients with atrial high rate episodes: rationale and design of the Non-vitamin K antagonist Oral anticoagulants in patients with Atrial High rate episodes (NOAH-AFNET 6) trial. Am. Heart J. 190:12–18
     [Google Scholar]
  46. 46. 
    Lopes RD, Alings M, Connolly SJ et al. 2017. Rationale and design of the Apixaban for the Reduction of Thrombo-Embolism in Patients With Device-Detected Sub-Clinical Atrial Fibrillation (ARTESiA) trial. Am. Heart J. 189:137–45
     [Google Scholar]
/content/journals/10.1146/annurev-med-042420-105906
Loading
  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error