Research trends and hotspot analysis of sudden cardiac death in hypertrophic cardiomyopathy
Zhen Chen, Fen Ai, Jinlei Li, Yu LiBackground:
Hypertrophic cardiomyopathy is the most common genetic heart disease and a leading cause of sudden cardiac death. This study aims to map the research landscape of Hypertrophic cardiomyopathy -related sudden cardiac death over the past 2 decades using bibliometric methods.
Methods:
We performed a bibliometric analysis of 712 publications retrieved from the Web of Science Core Collection (2005–2025). Using Cite Space, we examined publication trends, collaboration networks, co-citation patterns, and keyword dynamics.
Results:
Annual publication output rose markedly, with pronounced accelerations after the 2014 European Society of Cardiology and 2020 American heart association/American College of Cardiology guidelines. The United States led in productivity (203 publications) and network centrality (0.40), while China ranked second in output (123 publications) but exhibited limited international collaboration (centrality = 0.01). Co‑citation analysis indicated that clinical guidelines and risk‑prediction models form the field’ score knowledge base, and five of the ten most cited references were international guidelines. Keyword analysis defined 4 principal thematic clusters disease entity, clinical outcomes, imaging biomarkers, and risk stratification and documented a shift from early mechanistic studies to multimodal risk prediction, followed more recently by artificial intelligence–driven models and targeted pharmacological therapies (e.g., mavacamten).
Conclusion:
This study provides a systematic, data-driven overview of the knowledge structure and evolutionary trends in Hypertrophic cardiomyopathy-related sudden Cardiac death research. The findings underscore the field’ s guideline-driven character, document the rise of AI-based risk stratification, and describe a paradigm shift toward disease-modifying therapies, offering actionable insights for prioritizing future research and informing clinical decision-making.