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

Abstract

Background: Cardiac 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. Method: We 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. Results: (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. Conclusion: This 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.