Knowledge graph visualization and retrospective analysis of genetic research on pediatric cardiomyopathy (2000-2024)

Pediatric cardiomyopathy is a leading cause of heart failure and sudden cardiac death in children, posing a severe threat to their health and lives while imposing a heavy burden on families and society. It has become a significant public health challenge. The aim of this retrospective study is to systematically review global research articles on pediatric cardiomyopathy genetics, revealing its knowledge structure and evolutionary trajectory.

Bibliometric methods and natural language processing techniques were jointly applied to analyze research articles on pediatric cardiomyopathy genetics from the Web of Science Core Collection (WOSCC) and PubMed databases. CiteSpace software was utilized to construct national collaboration networks and co-occurrence/evolution maps of keywords, while BERTopic modeling was employed for topic modeling of article abstracts. The macro-structure and micro-semantics of pediatric cardiomyopathy genetics research were systematically investigated, and finally a multi-dimensional knowledge map was constructed.

Over the past 25 years, research teams from 71 countries and regions have published 1,438 articles, demonstrating fluctuating growth in publishing activity. The United States, China, and the United Kingdom are core publishing nations, with the U.S. occupying a central position in publication volume, total citations, and international collaboration networks. This study identified five core themes in pediatric cardiomyopathy genetics, including diverse disease classification and diagnostic/therapeutic mechanisms, systematically revealing the field's research trajectory toward intelligent and precision-oriented transformation. Based on keyword timeline analysis and emergence analysis, the research evolution progressed through three phases: single-gene screening to genomic sequencing (early 2000s-2010), multi-omics integration (early 2010s-2020), and precision medicine with dynamic monitoring (early 2020s-2024).

This study further demonstrates that genetic research in pediatric cardiomyopathy is increasingly integrated into digital healthcare, rapidly advancing toward intelligent and precise diagnosis and treatment. The integration of multi-omics data and artificial intelligence supports personalized risk assessment, dynamic monitoring, and early warning, thereby driving the transformation toward data-driven pediatric cardiovascular health management.