Key result
Bibliometric analysis shows research shifting toward AI risk stratification and targeted cardiovascular therapies.
Why the study?
Hypertrophic cardiomyopathy is the most common genetic heart disease and a leading cause of sudden cardiac death, prompting this study to map the research landscape of hypertrophic cardiomyopathy-related sudden cardiac death over the past 2 decades.
This bibliometric analysis highlights that research on sudden cardiac death in hypertrophic cardiomyopathy is strongly driven by international clinical guidelines and is increasingly shifting toward AI-based risk stratification and targeted disease-modifying therapies like mavacamten.
Answers are grounded in this paper and cite their sources.
Research trends toward AI-based SCD prediction and targeted HCM therapies warrant prospective validation; leaves open effects on guideline-directed care.
Background: 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.
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Chen et al. (2026) studied Hypertrophic cardiomyopathy-related sudden cardiac death. Bibliometric analysis using CiteSpace was evaluated on Publication trends, collaboration networks, co-citation patterns, and keyword dynamics.
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