Yang Yuru, Zhao Shuang, Liu Shuai
Department of Nephrology, Shibei Hospital of Jing'an District, Shanghai, PR China.
Department of Nephrology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
Ren Fail. 2025 Dec;47(1):2494049. doi: 10.1080/0886022X.2025.2494049. Epub 2025 Apr 24.
BACKGROUND: Acute kidney injury (AKI) and sepsis are critical clinical conditions associated with high morbidity and mortality. Despite growing research interest, there remains a need for a comprehensive analysis of global research trends in this field. Bibliometric analysis offers a quantitative approach to assessing the evolution of scientific knowledge, collaborative networks, and emerging research areas over time. OBJECTIVE: This study aims to map the global landscape of research on AKI and sepsis over the last two decades (2004-2024), identify major contributors, collaboration networks, key research trends, and highlight gaps in the literature. METHODS: We conducted a bibliometric analysis of research articles from leading databases. The study utilized network visualization techniques to assess co-authorship, citation patterns, and keyword co-occurrence, focusing on the most influential countries, institutions, and research collaborations. RESULTS: Results reveal China leads in publication volume, yet countries like the United States and Australia show higher international collaboration rates and citation impact. Additionally, thematic analyses highlight critical research areas, including biomarkers, bioenergetics, inflammation, and machine learning, marking significant advancements in the understanding and management of AKI. CONCLUSION: This bibliometric analysis offers valuable insights into the evolving landscape of AKI and sepsis research, emphasizing the importance of collaborative efforts to address knowledge gaps and ensure evidence-based care across diverse healthcare settings. Future research should prioritize the development of biomarkers and the integration of AI-driven technologies to enhance early diagnosis and personalize treatment strategies for AKI patients.
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