Key result
Elderly ACS prediction model research grows at ~6% annually since 2007.
Research on clinical prediction models in elderly acute coronary syndrome is showing steady growth, with a shift from traditional statistical methods to machine learning approaches.
Steady growth in ACS prediction model research signals rising ML interest; leaves open prospective outcome validation before clinical adoption.
Rapid global aging has intensified the cardiovascular disease burden, making clinical prediction models (CPMs) crucial for risk stratification and management of elderly patients with acute coronary syndrome (ACS), yet the knowledge structure of this field remains insufficiently characterized. Literature published up to November 14, 2025, was retrieved from the Web of Science Core Collection, and Bibliometrix, VOSviewer, and CiteSpace were used to analyze annual publication trends, core contributors, collaboration networks, cocited references, and keyword clusters. A total of 3,899 articles were included. From 2007 to 2025, annual publications increased from 98 to 281, with an average annual growth rate of 6.03%, peaking at 303 articles in 2022. The United States was the most productive country (1,419 articles), Harvard Medical School the leading institution (130 articles), and Krumholz HM the most prolific author (52 articles). Research hotspots concentrated on mortality and prognostic risk prediction, models associated with clinical interventions (particularly those related to percutaneous coronary intervention), and machine learning applications; since 2018, the research frontier has evolved toward intelligent technologies. Research on CPMs in elderly ACS thus shows stable and continuous growth, with a shift from traditional statistical methods toward intelligent and individualized approaches. Future priorities include externally validated models incorporating geriatric-specific variables, multicenter validation in real-world settings, and standardized comparisons of modeling approaches. The mapped evidence also informs regenerative cardiology, in which validated prediction models are needed to select elderly patients for emerging tissue engineering and regenerative interventions. Bibliometric mapping characterizes publication patterns and knowledge structures but cannot by itself establish model quality or clinical benefit.
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万瑜 et al. (2026) studied Acute coronary syndrome in elderly patients (n=3,899). Bibliometric analysis was evaluated on Annual publication trends, core contributors, collaboration networks, cocited references, and keyword clusters. A bibliometric analysis of 3,899 articles on clinical prediction models in elderly acute coronary syndrome showed an average annual publication growth rate of 6.03% from 2007 to 2025.
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