Bibliometric Insights Into Thromboelastography Research Using PubMed and VOSviewer.

Thromboelastography (TEG) has evolved from a primarily surgical tool to a key instrument in broader medical fields, including personalized medicine for coagulopathies. The rationale for conducting this bibliometric analysis of TEG is to understand the evolution and current state of research in this critical field. By identifying publication trends, key contributors, and major developments, this study aims to provide valuable insights to guide future research and clinical practices in TEG. This bibliometric study, utilizing the PubMed database, delineates the research landscape of TEG by analyzing publication patterns, the frequency of keywords, and author collaborations to understand the growth and direction of the discipline. Bibliographic data were extracted from PubMed, encompassing publications up to the year 2024. Using VOSviewer software, network maps were generated to visualize relationships among authors, institutions, and thematic keywords. Rigorous selection criteria were applied, focusing on peer-reviewed articles relevant to TEG's medical and surgical applications, while excluding non-English papers, non-peer-reviewed materials, and non-clinical research. The analysis identified a significant increase in TEG-related publications in recent years: 461 in 2021, 427 in 2022, and 315 in 2023, with 6 publications early in 2024. Key figures such as Moore (111 publications), Nielsen (77 publications), and Moore (71 publications) emerged as prolific contributors. The research also highlighted a heightened focus on demographic-specific characteristics in TEG studies, reflecting the shift toward personalized medicine. This bibliometric analysis provides a comprehensive overview of TEG research, signifying its growing clinical relevance and potential for future applications. Despite limitations, such as potential database selection bias, the study outlines a clear expansion of TEG into various clinical environments and underscores the importance of inter-author collaboration. It suggests that TEG research is progressing towards interdisciplinary applications, including genetic profiling and machine learning, to further enhance patient-specific treatment modalities.