AUTHOR=Qiao Changlong , Zhou Jing , Wei Chuansong , Cao Jing , Zheng Ke , Lv Meng 

TITLE=Cardiac surgery-associated acute kidney injury: a decade of research trends and developments

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1572338

DOI=10.3389/fmed.2025.1572338

ISSN=2296-858X

ABSTRACT=BackgroundCardiac surgery-associated acute kidney injury (CSA-AKI) significantly increases postoperative mortality and healthcare costs. Despite the growing volume of CSA-AKI research, the field remains fragmented, with challenges in identifying high-impact studies, collaborative networks, and emerging trends. Bibliometric analysis addresses these gaps by systematically mapping knowledge structures, revealing research priorities, and guiding resource allocation for both researchers and clinicians.MethodWe analyzed 4,474 CSA-AKI-related publications (2014–2023) from the Web of Science Core Collection (WoSCC) using VOSviewer, CiteSpace, the Bibliometrix Package in R, and the bibliometric online analysis platform.ResultsAnnual publications increased steadily, with the USA and China leading productivity. The Journal of Cardiothoracic and Vascular Anesthesia serves as the foremost preferred journal within this domain. Critical Care (IF = 15.1) has the highest impact factor. Yunjie Li published the most papers. John A Kellum has the highest H-index. The definition, pathogenesis or etiology, diagnosis, prediction, prevention and treatment, which are the research basis in CSA-AKI. Machine learning (ML) and prediction models emerged as dominant frontiers (2021–2023), reflecting a shift toward personalized risk stratification and real-time perioperative decision-making. These advancements align with clinical demands for early AKI detection and precision prevention.ConclusionThis study not only maps the evolution of CSA-AKI research but also identifies priority areas for innovation: multicenter validation of predictive models to strengthen generalizability, preventive nephrology frameworks for long-term AKI survivor monitoring, and randomized controlled trials to confirm efficacy of machine learning-based CSA-AKI prediction tools.