Analysis of 12-lead electrocardiograms shared on Twitter

Abstract

Introduction: A large number of electrocardiograms (ECG) are shared on Twitter every day. Some of them aim to provide information to the readers, and some of them aim to provide training with a mini quiz. This study aimed to discuss the evaluability of ECG images shared on Twitter. Methods: The study sample consisted of 12-lead ECG images shared on Twitter. ECG images shared on 08/01/2020 - 01/31/2021 were manually scanned. Results: A total of 286 tweets matching the criteria were included in the study on the specified dates. The majority of them (n = 231. 80.5%) asked the reader about the ECG. The average number of the tweets' interactions was 70.42 ± 112.17, and the interaction was mainly in the form of "likes" (50.49 ± 80.64). 83.5% of ECGs had a rhythm strip. Total interaction numbers and other parameters were compared. ECGs from which small squares could be selected collected more interactions (p = 0.015). ECGs explained the case or whose diagnosis was clearly stated collected more interactions (p <0.001). Also, it was observed that ECGs without a rhythm strip contained more interaction (p <0.001). Conclusions: We concluded that 12-derivation ECGs shared on Twitter are highly evaluable. There was also a moderate correlation between the number of followers and the number of interactions. For this reason, it is important for accounts with a high number of followers to following that are experts in their field to prevent information pollution.

Keywords

• Electrocardiography
• Social Media
• Twitter messaging

Twitter'da Paylaşılan 12 Derivasyonlu Elektrokardiyogramların Analizi

Abstract

Giriş: Twitter'da her gün çok sayıda elektrokardiyogram (EKG) paylaşılmaktadır. Bazıları okuyuculara bilgi vermeyi, bazıları ise mini bir quiz ile eğitim vermeyi amaçlıyor. Bu çalışma Twitter'da paylaşılan EKG görüntülerinin değerlendirilebilirliğini tartışmayı amaçlamıştır. Yöntemler: Çalışma örneklemi Twitter'da paylaşılan 12 derivasyonlu EKG görüntülerinden oluşturuldu. 08/01/2020 - 01/31/2021 tarihinde paylaşılan EKG görüntüleri manuel olarak taranmıştır. Bulgular: Belirlenen tarihlerde kriterlere uyan toplam 286 tweet çalışmaya dahil edildi. Çoğunluğu (n = 231, %80,5) okuyucuya EKG'yi sordu. Tweetlerin ortalama etkileşim sayısı 70.42 ± 112.17 idi ve etkileşim ağırlıklı olarak "beğeni" (50.49 ± 80.64) şeklindeydi. EKG'lerin %83,5'inde ritim şeridi vardı. Toplam etkileşim sayıları ve diğer parametreler karşılaştırıldı. Küçük karelerin seçilebildiği EKG'ler daha fazla etkileşim topladı (p = 0.015). EKG'ler, vakayı açıklayan veya tanısı açıkça belirtilen daha fazla etkileşim topladı (p <0,001). Ayrıca ritim şeridi olmayan EKG'lerin daha fazla etkileşim içerdiği gözlendi (p<0,001). Sonuç: Twitter'da paylaşılan 12 derivasyonlu EKG'lerin yüksek derecede değerlendirilebilir olduğu sonucuna vardık. Takipçi sayısı ile etkileşim sayısı arasında da orta düzeyde bir ilişki vardı. Bu nedenle takipçi sayısı yüksek, alanında uzman hesapların bilgi kirliliğini önlemesi önemlidir.

Keywords

• Sosyal Medya
• Twitter mesajlaşma
• Elektrokardiyografi

References

1. 1. Alnemer KA, Alhuzaim WM, Alnemer AA, Alharbi BB, Bawazir AS, Barayyan OR, et al. Are Health-Related Tweets Evidence Based? Review and Analysis of Health-Related Tweets on Twitter. J Med Internet Res. 2015;17(10):e246. https://doi.org/10.2196/jmir.4898
2. 2. Williams SA, Terras M, Warwick C. How Twitter Is Studied in the Medical Professions: A Classification of Twitter Papers Indexed in PubMed. Medicine. 2013;2(2):e2. https://doi.org/10.2196/med20.2269
3. 3. Neiger BL, Thackeray R, Burton SH, Thackeray CR, Reese JH. Use of twitter among local health departments: an analysis of information sharing, engagement, and action. J Med Internet Res. 2013;15(8):e177. https://doi.org/10.2196/jmir.2775
4. 4. Edwards S, Roland D. Learning from mistakes on social media. Emerg Med J. 2019;36(8):453-5. https://doi.org/10.1136/emermed-2019-208501
5. 5. Khunti K. Accurate interpretation of the 12-lead ECG electrode placement: A systematic review. Health Education Journal. 2014;73(5):610-23. https://doi.org/10.1177/0017896912472328
6. 6. Liu SS, Zakaria S, Vaidya D, Srivastava MC. Electrocardiogram training for residents: A curriculum based on Facebook and Twitter. J Electrocardiol. 2017;50(5):646-51. https://doi.org/10.1016/j.jelectrocard.2017.04.010
7. 7. Barthelemy FX, Segard J, Fradin P, Nicolas H, Batard E, Pierre P,et al. ECG interpretation in Emergency Department residents: an update and e-learning as a resource to improve skills. Eur J Emerg Med. 2017;24(2):149-56. https://doi.org/10.1097/MEJ.0000000000000312
8. 8. Goost H, Witten J, Heck A, Hadizadeh DR, Weber O, Graff I, et al. Image and diagnosis quality of X-ray image transmission via cell phone camera: a project study evaluating quality and reliability. PLoS One. 2012;7(10):e43402. https://doi.org/10.1371/journal.pone.0043402

9. 9. Khan MG. Rapid ECG interpretation: Springer Science & Business Media; 2008. https://doi.org/10.1007/978-1-59745-408-7
10. 10. Barquera S, Pedroza-Tobías A, Medina C, Hernández-Barrera L, Bibbins-Domingo K, Lozano R, et al. Global Overview of the Epidemiology of Atherosclerotic Cardiovascular Disease. Arch Med Res. 2015;46(5):328-38. https://doi.org/10.1016/j.arcmed.2015.06.006
11. 11. Kusumoto F. ECG interpretation: from pathophysiology to clinical application: Springer Nature; 2020. https://doi.org/10.1007/978-3-030-40341-6
12. 12. Ristagno G, Tang W, Weil MH. Cardiopulmonary resuscitation: from the beginning to the present day. Crit Care Clin. 2009;25(1):133-51, ix. https://doi.org/10.1016/j.ccc.2008.10.004
13. 13. Einthoven W. Weiteres über das Elektrokardiogramm. Archiv für die gesamte Physiologie des Menschen und der Tiere. 1908;122(12):517-84. https://doi.org/10.1007/BF01677829
14. 14. Francis J. ECG monitoring leads and special leads. Indian Pacing Electrophysiol J. 2016;16(3):92-5. https://doi.org/10.1016/j.ipej.2016.07.003
15. 15. Nilsson M, Bolinder G, Held C, Johansson B-L, Fors U, Östergren J. Evaluation of a web-based ECG-interpretation programme for undergraduate medical students. BMC medical education. 2008;8(1):1-7. https://doi.org/10.1186/1472-6920-8-25
16. 16. Harris JM, Salasche SJ, Harris RB. The internet and the globalisation of medical education. Bmj. 2001;323(7321):1106. https://doi.org/10.1136/bmj.323.7321.1106
17. 17. Chretien KC, Kind T. Social media and clinical care: ethical, professional, and social implications. Circulation. 2013;127(13):1413-21. https://doi.org/10.1161/CIRCULATIONAHA.112.128017
18. 18. Salerno SM, Alguire PC, Waxman HS. Training and competency evaluation for interpretation of 12-lead electrocardiograms: recommendations from the American College of Physicians. Annals of internal medicine. 2003;138(9):747-50. https://doi.org/10.7326/0003-4819-138-9-200305060-00012
19. 19. Martin ML, Lewis RJ, Yealy DM. ECG competency-by whom, for whom? Academic Emergency Medicine. 2002;9(4):348. https://doi.org/10.1197/aemj.9.4.348
20. 20. Boğan M, Karadağ M, Boğan F. Examination of emergency medicine physicians’ and residents’ Twitter activities during the first days of the COVID-19 outbreak. International Journal of Travel Medicine and Global Health. 2020;8(2):46-50. https://doi.org/10.34172/IJTMGH.2020.08
21. 21. Alpert JM, Womble FE. Just what the doctor tweeted: physicians’ challenges and rewards of using Twitter. Health Communication. 2016;31(7):824-32. https://doi.org/10.1080/10410236.2015.1007551
22. 22. Choo EK, Ranney ML, Chan TM, Trueger NS, Walsh AE, Tegtmeyer K, et al. Twitter as a tool for communication and knowledge exchange in academic medicine: a guide for skeptics and novices. Medical teacher. 2015;37(5):411-6. https://doi.org/10.3109/0142159X.2014.993371