Self-palpation for detection of paroxysmal atrial fibrillation: Much noise with little signal

English version

Autoři: Kazem Rahimi ^aff001; Paulus Kirchhof ^aff003
Působiště autorů: Deep Medicine, Big Data Insitute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom ^aff001; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom ^aff002; Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, United Kingdom ^aff003; Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany ^aff004; SWBH and UHB NHS Trusts, Birmingham, United Kingdom ^aff005; AFNET, Münster, Germany ^aff006
Vyšlo v časopise: Self-palpation for detection of paroxysmal atrial fibrillation: Much noise with little signal. PLoS Med 17(3): e32767. doi:10.1371/journal.pmed.1003098
Kategorie: Perspective
doi: https://doi.org/10.1371/journal.pmed.1003098

Souhrn

Kazem Rahimi and Paulus Kirchhof discuss the latest work from Faris Ghazal et al and how it may impact the way we currently detect Atrial Fibrillation in the general population.

Klíčová slova:

Atrial fibrillation – Cancer detection and diagnosis – Diagnostic medicine – Electrocardiography – Health screening – Heart – Medical devices and equipment – Patients

Zdroje

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5. Ghazal, F, Theobald H, Rosenqvist M, Al-Khalili F. Validity of daily self-pulse palpation for atrial fibrillation screening in patients 65 years and older: A cross-sectional study.

6. Freedman B, Camm J, Calkins H, Healey JS, Rosenqvist M, Wang J, et al. Screening for Atrial Fibrillation. Circulation; 2017;135(19): 1851–1867. doi: 10.1161/CIRCULATIONAHA.116.026693 28483832

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8. Yan BP, Lai HSL, Chan CKY, Au ACK, Freedman B, Poh YC, et al. High-throughput, contact-free detection of atrial fibrillation from video with deep learning: a proof-of-concept study. JAMA Cardiol. 2019;5(1): 1–7. doi: 10.1001/jamacardio.2019.4004 31774461

9. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201): 861–867. doi: 10.1016/S0140-6736(19)31721-0 31378392

10. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019;381: 1909–1917. doi: 10.1056/NEJMoa1901183 31722151

11. Healey JS, Alings M, Ha A, Leong-Sit P, Birnie DH, de Graaf JJ, et al. Subclinical Atrial Fibrillation in Older Patients. Circulation. 2017;136(14): 1276–1283. doi: 10.1161/CIRCULATIONAHA.117.028845 28778946

12. Bertaglia E, Blank B, Blomström-Lundqvist C, Brandes A, Cabanelas N, Dan GA, et al. Atrial high-rate episodes: Prevalence, stroke risk, implications for management, and clinical gaps in evidence. Europace. 2019;21(10): 1459–1467. doi: 10.1093/europace/euz172 31377792

13. Ilic D, Djulbegovic M, Jung JH, Hwang EC, Zhou Q, Cleves A, et al. Prostate cancer screening with prostate-specific antigen (PSA) test: a systematic review and meta-analysis. BMJ. 2018;362: k3519. doi: 10.1136/bmj.k3519 30185521

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15. Rahimi K. Subclinical atrial fibrillation in need of more assertive evidence. Eur Heart J. 2017;38(17): 1345–1347. doi: 10.1093/eurheartj/ehx122 28379323

16. Kirchhof P, Blank BF, Calvert M, Camm AJ, Chlouverakis G, Diener H-C, et al. 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. 2017;190: 12–18. doi: 10.1016/j.ahj.2017.04.015 28760205

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18. Engdahl J, Svennberg E, Friberg L, Al-Khalili F, Frykman V, Gudmundsdottir KK, et al. Stepwise mass screening for atrial fibrillation using N-terminal pro b-type natriuretic peptide: The STROKESTOP II study design. Europace. 2017;19(2): 297–302. doi: 10.1093/europace/euw319 28011798

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