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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Simple Clinical Risk Score (C2HEST) for Predicting Incident Atrial Fibrillation in Asian Subjects
Yan‐Guang Li, Daniele Pastori, Alessio Farcomeni, et al.
CHEST Journal (2018) Vol. 155, Iss. 3, pp. 510-518
Open Access | Times Cited: 159

Showing 1-25 of 159 citing articles:

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines
José A. Joglar, Mina K. Chung, Anastasia L. Armbruster, et al.
Circulation (2023) Vol. 149, Iss. 1
Open Access | Times Cited: 991
2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation
José A. Joglar, Mina K. Chung, Anastasia L. Armbruster, et al.
Journal of the American College of Cardiology (2023) Vol. 83, Iss. 1, pp. 109-279
Open Access | Times Cited: 301
Searching for Atrial Fibrillation Poststroke
Renate B. Schnabel, Karl Georg Hæusler, Jeff S. Healey, et al.
Circulation (2019) Vol. 140, Iss. 22, pp. 1834-1850
Open Access | Times Cited: 221
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
Shaan Khurshid, Samuel Friedman, Christopher Reeder, et al.
Circulation (2021) Vol. 145, Iss. 2, pp. 122-133
Open Access | Times Cited: 198
Integrated care for optimizing the management of stroke and associated heart disease: a position paper of the European Society of Cardiology Council on Stroke
Gregory Y.H. Lip, Deirdre A. Lane, Radosław Lenarczyk, et al.
European Heart Journal (2022) Vol. 43, Iss. 26, pp. 2442-2460
Open Access | Times Cited: 87
Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care
Niraj Varma, Janet K. Han, Rod Passman, et al.
Journal of the American College of Cardiology (2024) Vol. 83, Iss. 5, pp. 611-631
Closed Access | Times Cited: 21
2018 Korean Guideline of Atrial Fibrillation Management
Boyoung Joung, Jung‐Myung Lee, Ki Hong Lee, et al.
Korean Circulation Journal (2018) Vol. 48, Iss. 12, pp. 1033-1033
Open Access | Times Cited: 130
C 2 HEST Score and Prediction of Incident Atrial Fibrillation in Poststroke Patients: A French Nationwide Study
Yan‐Guang Li, Arnaud Bisson, Alexandre Bodin, et al.
Journal of the American Heart Association (2019) Vol. 8, Iss. 13
Open Access | Times Cited: 94
European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population
Jens Cosedis Nielsen, Yenn‐Jiang Lin, Márcio Jansen de Oliveira Figueiredo, et al.
Journal of Arrhythmia (2020) Vol. 36, Iss. 4, pp. 553-607
Open Access | Times Cited: 91
European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population
Jens Cosedis Nielsen, Yenn‐Jiang Lin, Márcio Jansen de Oliveira Figueiredo, et al.
EP Europace (2020) Vol. 22, Iss. 8, pp. 1147-1148
Open Access | Times Cited: 87
Development and Validation of a Prediction Model for Atrial Fibrillation Using Electronic Health Records
Olivia L. Hulme, Shaan Khurshid, Lu‐Chen Weng, et al.
JACC. Clinical electrophysiology (2019) Vol. 5, Iss. 11, pp. 1331-1341
Open Access | Times Cited: 81
Effect of Intensive Blood Pressure Lowering on the Risk of Atrial Fibrillation
Elsayed Z. Soliman, Fazlur Rahman, Zhu-Ming Zhang, et al.
Hypertension (2020) Vol. 75, Iss. 6, pp. 1491-1496
Open Access | Times Cited: 81
Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
Jelle C.L. Himmelreich, Lieke Veelers, Wim A M Lucassen, et al.
EP Europace (2020) Vol. 22, Iss. 5, pp. 684-694
Open Access | Times Cited: 73
Searching for atrial fibrillation: looking harder, looking longer, and in increasingly sophisticated ways. An EHRA position paper
Zbigniew Kalarus, Georges H. Mairesse, Adam Sokal, et al.
EP Europace (2022) Vol. 25, Iss. 1, pp. 185-198
Open Access | Times Cited: 66
Comparing Outcomes in Asymptomatic and Symptomatic Atrial Fibrillation: A Systematic Review and Meta-Analysis of 81,462 Patients
Daria Sgreccia, Marcella Manicardi, Vincenzo Livio Malavasi, et al.
Journal of Clinical Medicine (2021) Vol. 10, Iss. 17, pp. 3979-3979
Open Access | Times Cited: 65
Atrial fibrillation and stroke: State-of-the-art and future directions
Sandra Elsheikh, Andrew Hill, Greg Irving, et al.
Current Problems in Cardiology (2023) Vol. 49, Iss. 1, pp. 102181-102181
Open Access | Times Cited: 42
U-shaped association between the triglyceride–glucose index and atrial fibrillation incidence in a general population without known cardiovascular disease
Xiao Liu, Ayiguli Abudukeremu, Yuan Jiang, et al.
Cardiovascular Diabetology (2023) Vol. 22, Iss. 1
Open Access | Times Cited: 35
Artificial-intelligence-based risk prediction and mechanism discovery for atrial fibrillation using heart beat-to-beat intervals
Fan Lin, Peng Zhang, Yuting Chen, et al.
Med (2024) Vol. 5, Iss. 5, pp. 414-431.e5
Open Access | Times Cited: 14
Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?
Adrian Petzl, Gilbert Jabbour, Julia Cadrin‐Tourigny, et al.
EP Europace (2024) Vol. 26, Iss. 8
Open Access | Times Cited: 12
Deep learning of left atrial structure and function provides link to atrial fibrillation risk
James P. Pirruccello, Paolo Di Achille, Seung Hoan Choi, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 11
Long-term changes in frailty and incident atrial fibrillation, heart failure, coronary heart disease, and stroke: A prospective follow-up study
Lingli Cai, Ying Sun, Jingjing Zhu, et al.
Heart Rhythm (2025)
Closed Access | Times Cited: 1
Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy
Paschalis Karakasis, Panagiotis Theofilis, Μarios Sagris, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 8, pp. 2627-2627
Open Access | Times Cited: 1
The inflammation-resolution promoting molecule resolvin-D1 prevents atrial proarrhythmic remodelling in experimental right heart disease
Roddy Hiram, Feng Xiong, Patrice Naud, et al.
Cardiovascular Research (2020) Vol. 117, Iss. 7, pp. 1776-1789
Open Access | Times Cited: 56
Prevalence, management and impact of chronic obstructive pulmonary disease in atrial fibrillation: a systematic review and meta-analysis of 4,200,000 patients
Giulio Francesco Romiti, Bernadette Corica, Eugenia Pipitone, et al.
European Heart Journal (2021) Vol. 42, Iss. 35, pp. 3541-3554
Open Access | Times Cited: 53
How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?
Konstantinos C. Siontis, Xiaoxi Yao, James P. Pirruccello, et al.
Circulation Research (2020) Vol. 127, Iss. 1, pp. 155-169
Open Access | Times Cited: 51
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