Analyzing fulminant myocarditis research trends and characteristics using the follower-leading clustering algorithm (FLCA): A bibliometric study.

Background: Myocarditis can be classified into 2 categories: fulminant myocarditis (FM) and nonfulminant myocarditis. FM is the most severe type, characterized by its acute and explosive nature, posing a sudden and life-threatening risk with a high fatality rate. Limited research has been conducted on FM characteristics using cluster analysis. This study introduces the following-leading clustering algorithm (`) as a unique method and utilizes it to generate a dual map and timeline view of FM themes, aiming to gain a better understanding of FM.

Methods: The metadata were obtained from the Web of Science (WoS) database using an advanced search strategy based on the topic (TS= (("Fulminant") AND ("Myocarditis"))). The analysis comprised 3 main components: descriptive analytics, which involved identifying the most influential entities using CJAL scores and analyzing publication trends, author collaborations using the FLCA algorithm, and generating a dual map and timeline view of FM themes using the FLCA algorithm. The visualizations included radar plots divided into 4 quadrants, stacked bar and line charts, network charts, chord diagrams, a dual map overlay, and a timeline view.

Results: The findings reveal that the prominent entities in terms of countries, institutes, departments, and authors were the United States, Huazhong University of Science and Technology (China), Cardiology, and Enrico Ammirati from Italy. A dual map, based on the research category, was created to analyze the relationship between citing and cited articles. It showed that articles related to cells and clinical medicine/surgery were frequently cited by articles in the fields of general health/public/nursing and clinical medicine/surgery. Additionally, a visual timeline view was presented on Google Maps, showcasing the themes extracted from the top 100 cited articles. These visualizations were successfully and reliably generated using the FLCA algorithm, offering insights from various perspectives.

Conclusion: A new FLCA algorithm was utilized to examine bibliometric data from 1989 to 2022, specifically focusing on FM. The results of this analysis can serve as a valuable guide for researchers, offering insights into the thematic trends and characteristics of FM research development. This, in turn, can facilitate and promote future research endeavors in this field.