Uncovering research themes in coronary slow flow predictors: a bibliometric and cluster analysis.
- 1Weifang People's Hospital, Shandong Second Medical University, Weifang, 261000, China.
- 2Weifang People's Hospital, Shandong Second Medical University, Weifang, 261000, China. [email protected].
Abstract
Background
This study aims to conduct a comprehensive bibliometric analysis to elucidate research trends, collaborative networks, and the thematic evolution of predictors for coronary slow flow (CSF).Methods
Relevant literature published between 1977 and 2026 was retrieved from the Web of Science Core Collection database. Bibliometric analysis was conducted utilizing VOSviewer, CiteSpace, and R 4.3.3.Results
A total of 366 articles were included, indicating an upward trend in research output. China (118 articles), Turkey (65), and the USA (35) emerged as the leading countries. Prominent institutions included China Medical University (29 articles), Capital Medical University (20), and Tabriz University of Medical Sciences (14). Influential journals were Circulation (impact factor 41.3, JCR Q1), Circulation Journal (5.8, Q1), and American Journal of Physiology-Heart and Circulatory Physiology (3.9, Q1). The most prolific author was Wang Yong, with 12 publications and an H-index of 10. Keyword clustering identified three research themes: pathogenesis of CSF, prediction for CSF following percutaneous coronary intervention, and its prognostic implications. Emerging keywords highlighted increasing attention to "endothelial function", "prevalence", "CSF phenomenon", "myocardial infarction" and "reserve".Conclusions
This study provides a bibliometric overview of key trends and future directions in the field of CSF predictors, emphasizing the growing focus on this area. Future research is anticipated to concentrate on risk stratification and mechanism-targeted intervention strategies.Clinical trial number
Not applicable.
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BibTeX
@article{liu2026uncovering,
title = {{Uncovering research themes in coronary slow flow predictors: a bibliometric and cluster analysis.}},
author = {Difei Liu and Yan Chen and Tao Zhong and Yawen Zhang},
journal = {Journal of cardiothoracic surgery},
year = {2026},
month = jul,
doi = {10.1186/s13019-026-04638-0},
url = {https://europepmc.org/article/MED/42907913},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Uncovering research themes in coronary slow flow predictors: a bibliometric and cluster analysis.
AU - Difei Liu
AU - Yan Chen
AU - Tao Zhong
AU - Yawen Zhang
PY - 2026
DA - 2026/07/01
JO - Journal of cardiothoracic surgery
DO - 10.1186/s13019-026-04638-0
UR - https://europepmc.org/article/MED/42907913
AB - Background This study aims to conduct a comprehensive bibliometric analysis to elucidate research trends, collaborative networks, and the thematic evolution of predictors for coronary slow flow (CSF).Methods Relevant literature published between 1977 and 2026 was retrieved from the Web of Science Core Collection database. Bibliometric analysis was conducted utilizing VOSviewer, CiteSpace, and R 4.3.3.Results A total of 366 articles were included, indicating an upward trend in research output. China (118 articles), Turkey (65), and the USA (35) emerged as the leading countries. Prominent institutions included China Medical University (29 articles), Capital Medical University (20), and Tabriz University of Medical Sciences (14). Influential journals were Circulation (impact factor 41.3, JCR Q1), Circulation Journal (5.8, Q1), and American Journal of Physiology-Heart and Circulatory Physiology (3.9, Q1). The most prolific author was Wang Yong, with 12 publications and an H-index of 10. Keyword clustering identified three research themes: pathogenesis of CSF, prediction for CSF following percutaneous coronary intervention, and its prognostic implications. Emerging keywords highlighted increasing attention to "endothelial function", "prevalence", "CSF phenomenon", "myocardial infarction" and "reserve".Conclusions This study provides a bibliometric overview of key trends and future directions in the field of CSF predictors, emphasizing the growing focus on this area. Future research is anticipated to concentrate on risk stratification and mechanism-targeted intervention strategies.Clinical trial number Not applicable.
SN - 1749-8090
ER -