Research on the author collaboration network and institutional influence in the transcatheter aortic valve replacement field based on CiteSpace: analysis of the top one hundred highly cited articles.

Research on the author collaboration network and institutional influence in the transcatheter aortic valve replacement field based on CiteSpace: analysis of the top one hundred highly cited articles - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Cardiothorac Surg . 2026 Apr 17;21:303. doi: 10.1186/s13019-026-04165-y Research on the author collaboration network and institutional influence in the transcatheter aortic valve replacement field based on CiteSpace: analysis of the top one hundred highly cited articles Ling Yuan 1 , Qian Yu 1 , Lili Wang 1 , Jingju Xia 2, ✉ , Bo Ma 2, ✉ Author information Article notes Copyright and License information 1 CCU, The People’s Hospital of Liaoning Province, Shenyang, China 2 Department of Nursing, The People’s Hospital of Liaoning Province, No. 33 Wenyi Road, Shenhe District, Shenyang City, 110000 Liaoning Province China ✉ Corresponding author. Received 2025 Apr 16; Accepted 2026 Apr 11; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13231805  PMID: 41998721 Abstract Background Transcatheter aortic valve replacement (TAVR) has revolutionised the management of severe aortic stenosis, expanding its applications from high-risk to intermediate- and low-risk patients. Understanding the key contributors, collaborative networks and thematic evolutions in TAVR research can inform future investigations. Methods This bibliometric study screened the Web of Science Core Collection for the top 100 highly cited TAVR-related publications between 2010 and 2025. We utilised CiteSpace (version 6.2.R1) to analyse author, country and institutional collaborations, along with references, keywords and citation bursts. Results The USA emerged as the most influential country, with leading institutions including the Cleveland Clinic Foundation and Columbia University. Authors such as Martin B. Leon, Rebecca Hahn and Raj R. Makkar held central collaborative roles. Reference co-citation and keyword analyses showed an early emphasis on procedural feasibility and comparisons with surgical aortic valve replacement, followed by a shift toward clinical outcomes, complications (e.g. permanent pacemaker implantation, infective endocarditis) and long-term management. Landmark trials and guidelines (e.g. PARTNER, 2014 American Heart Association/American College of Cardiology Valvular Heart Disease Guideline) exhibited significant citation bursts, underscoring their foundational impact. Conclusions Research on TAVR has evolved in tandem with technological advances and the expansion of clinical indications. Sustained international collaboration and continued study of long-term outcomes, device durability and complication mitigation will be pivotal for optimising TAVR’s therapeutic impact. Supplementary Information The online version contains supplementary material available at 10.1186/s13019-026-04165-y. Keywords: Transcatheter aortic valve replacement, Aortic stenosis, Key contributors, Collaborative networks, Thematic evolution Introduction Transcatheter aortic valve replacement (TAVR) has rapidly developed since its clinical adoption as a breakthrough minimally invasive procedure for treating severe aortic stenosis (AS) [ 1 ]. With the gradual expansion of procedural indications and ongoing device innovations, TAVR has demonstrated favourable safety and efficacy in high-risk and, more recently, intermediate- to low-risk patients [ 2 ]. Increasing clinical evidence suggests that TAVR not only benefits patients who are ineligible for surgical aortic valve replacement (SAVR) but is also increasingly offered to those with moderate and even lower surgical risk profiles, thereby emerging as a critical research focus in the field of structural heart disease [ 3 ]. Concurrently, in-depth investigations into TAVR’s long-term outcomes, complications and indications have fuelled a rapid expansion of research in this area over the past decade. Several bibliometric studies have been conducted to explore the research landscape of TAVR, laying a foundation for understanding the development of this field. Güzel and Arslan (2022) analysed the most-cited TAVR studies to identify core research themes and influential contributors, yet their analysis lacked in-depth exploration of collaborative network dynamics and temporal evolution of research hotspots [ 4 ]. Fang et al. (2024) adopted a visualisation approach to map TAVR research but had limitations in database coverage and quantitative validation of network clustering results [ 5 ]. Oo et al. (2020) focused on the top 100 cited manuscripts in aortic valve replacement, yet their work did not distinguish TAVR-specific research from general aortic valve disease studies and lacked analysis of emerging research frontiers [ 6 ]. While these studies have provided valuable insights, existing bibliometric investigations of TAVR still have notable methodological and conceptual limitations, including narrow database selection, insufficient exploration of cross-country and cross-institutional collaboration mechanisms, lack of quantitative validation for network analysis results, and inadequate capture of the latest research evolution trends after 2020. A comprehensive scientometric analysis that integrates multi-dimensional collaborative network analysis, rigorous temporal evolution exploration and quantitative validation of clustering results remains insufficiently addressed. Bibliometric analysis enables the identification of academic frontiers, evaluation of research hotspots and trends and elucidation of collaborative networks among the most influential authors, institutions and countries. This, in turn, provides strategic insights and data support for both academic studies and clinical practice. Therefore, the present study aims to perform a rigorous and comprehensive bibliometric analysis of highly-cited TAVR-related publications retrieved from the Web of Science (WoS) Core Collection using the citation visualisation tool CiteSpace, thereby mapping the developmental trajectory and emerging directions of TAVR research in terms of authors, institutions, countries, keywords and references. Focusing on TAVR-related literature published between 2010 and 2025, we select the top 100 most frequently cited publications and conduct multi-dimensional and quantitative analyses, such as collaborative network analysis with clustering validation, keyword co-occurrence, reference co-citation and citation burst detection, using CiteSpace. By interpreting the resulting visualisations and quantitative metrics, this study aims to illuminate how TAVR research hotspots have evolved over time, to discern how core research teams and institutions have formed collaborative networks and to identify the key nodes and milestone publications shaping the TAVR field. Distinct from prior studies, our work standardises network analysis metrics, validates clustering results with quantitative indicators, and captures the latest emerging research frontiers in TAVR up to 2024. Finally, based on these findings, we discuss existing gaps in TAVR research and possible directions for future exploration, providing a reference point for researchers and clinicians. By doing so, we hope to further advance the safe application and clinical benefits of TAVR to a broader patient population and to promote continuous innovation in this dynamic domain. Methods Data sources and study selection Database selection rationale We selected the Web of Science (WoS) Core Collection as the primary data source, rather than MEDLINE, based on established bibliometric best practices and prior validation in cardiovascular research bibliometrics. WoS was chosen for its comprehensive coverage of peer-reviewed journals, standardized citation indexing, and compatibility with CiteSpace software—attributes that are critical for ensuring the reliability of co-citation and collaboration network analyses [ 4 , 6 ]. MEDLINE, while valuable for clinical literature retrieval, has limitations in capturing cross-disciplinary citations and standardized author/institution metadata, which are central to our bibliometric framework. Eligibility criteria and study period A detailed list of inclusion and exclusion criteria is provided in Supplementary Table 1. Briefly, we included: (1) original research articles, systematic reviews, and clinical guidelines focused on TAVR; (2) publications with full-text availability; (3) publications in the top 100 most-cited list in the WoS Core Collection for the search term “transcatheter aortic valve replacement” OR “TAVR” OR “transcatheter aortic valve implantation” OR “TAVI“ [ 6 ]. We excluded: (1) conference abstracts, letters to the editor, case reports, and editorial commentaries; (2) duplicate publications (identified via WoS’s deduplication tool and manual cross-verification of titles, authors, and publication dates); (3) publications not focused on TAVR (e.g., general aortic valve disease research without specific TAVR content). No language restrictions were applied to ensure the inclusion of global TAVR research. The selection of the top 100 most-cited publications was justified based on bibliometric precedent in high-impact cardiovascular research and a power analysis of citation distribution in TAVR literature. A sample of 50 publications was deemed insufficient to capture the full breadth of foundational collaboration networks and thematic clusters, while 200 publications would introduce excessive noise and redundancy in network visualizations, compromising interpretability. The final top 100 sample balances comprehensiveness and analytical clarity, ensuring representation of the most impactful work in the field. The primary study period for network analyses was standardized as 2006–2021. This time frame was selected to align with the widespread adoption of TAVR following the publication of pivotal early trials (e.g., PARTNER I) and to ensure sufficient temporal depth for analyzing research evolution. For contextualization, “landmark studies” (defined as practice-changing RCTs, registries, or guidelines published prior to 2010 that established the clinical basis for TAVR) were included in a dedicated timeline analysis spanning 2006–2019. The truncation of network analyses at 2021, despite the initial mention of 2025, was due to the lag in citation accumulation for post-2021 publications—including these recent works would bias citation density calculations and reduce the robustness of co-citation networks. All temporal inconsistencies across the manuscript have been harmonized with this rationale. Data extraction and preprocessing Data extraction was performed independently by two investigators (X.X. and Y.Y.) in December 2024. The following metadata were extracted for each included publication: title, authors, affiliated institutions, country/region of origin, publication year, journal, citation count, study type, and keywords. Discrepancies in data extraction were resolved via consensus with a third investigator (Z.Z.). Preprocessing steps were conducted to standardize metadata for bibliometric analysis: (1) overlapping terminology (e.g., “TAVR” vs. “TAVI”) was unified as “TAVR” using CiteSpace’s keyword merging function; (2) institutional names were standardized (e.g., “Mayo Clinic Rochester” vs. “Mayo Clinic, Rochester, MN”) via manual verification and cross-referencing with institutional official websites; (3) author names with initial variations were merged to avoid duplicate nodes (e.g., “Smith J” vs. “Smith JA”). All preprocessing steps are detailed in Supplementary Tables 1 to ensure reproducibility. Bibliometric analysis using CiteSpace Software validation and version Bibliometric analysis was performed using CiteSpace (version 6.2.R4), a widely validated tool for scientific knowledge mapping. The validity of CiteSpace in cardiovascular and valvular heart disease research has been established in numerous peer-reviewed studies [ 7 , 8 ]. Core parameters and network definition To ensure reproducibility, all CiteSpace parameters are explicitly reported below, with pruning and threshold choices justified based on preliminary robustness testing: Time slicing: 2010–2021, 1-year slices. Node types: Authors, institutions, countries, keywords, and cited references (analyzed separately for each network). Threshold settings: Top N = 50 nodes per time slice; UN (union of networks) = 10, LBY (look-back years) = 5, e (pruning coefficient) = 1.0. These parameters were selected to retain the most influential nodes and edges while minimizing network clutter—preliminary tests with alternative thresholds (e.g., UN = 5, LBY = 3) yielded less interpretable clusters, while higher thresholds introduced redundant connections. Pruning methods: Pathfinder pruning and pruning sliced networks were applied to refine network structure. After pruning, the final author collaboration network retained 82 nodes and 147 edges (89% of the original network), the institutional network retained 65 nodes and 112 edges (87% of the original network), and the keyword co-occurrence network retained 78 nodes and 163 edges (91% of the original network). Clustering algorithm: Log-Likelihood Ratio (LLR) algorithm for cluster labeling, chosen for its superiority in identifying clinically meaningful thematic clusters in medical research. Validation metrics: Modularity Q (Q) and mean silhouette score (S) were calculated for all clustered networks. A Q-value > 0.3 indicates a robust modular structure, and an S > 0.7 indicates well-separated, meaningful clusters—these thresholds were used to validate our clustering results. Critical to network interpretability, metrics for node size were standardized across all figures and text: (1) For co-authorship networks (authors, institutions, countries), node size represents the total number of publications by that entity; (2) For co-citation and keyword co-occurrence networks, node size represents the total citation count (for cited references) or occurrence frequency (for keywords). The term “influence” is explicitly defined throughout the manuscript as a composite measure of betweenness centrality (BC) and citation count: BC quantifies a node’s role as a “bridge” in the network (higher BC = greater structural influence), while citation count quantifies academic impact. All references to “most influential country/institution/author” are accompanied by reported BC values and citation counts in the Results section. Complementary metrics and study type stratification To address citation bias and capture emerging research areas, we calculated citation density for each included publication as: Citation Density = Total Citations / (2024 – Publication Year + 1). We conducted a focused analysis of citation density over the 2019–2024 period (past 5 years) to identify rapidly evolving hot topics that may have low absolute citations but high recent impact. In addition, we stratified the top 100 most-cited publications by study type. The stratification includes: (1) Top 5 randomized controlled trials (RCTs); (2) Top 5 registry-based studies; (3) Top 5 systematic reviews/consensus statements. Figure preparation and legend standardization All figures were revised to improve visual quality: line thickness between nodes was increased by 200%, node contrast was enhanced, and font sizes were standardized to 10pt for readability. Figure legends were completely redeveloped to be self-explanatory, focusing on methodological description rather than result interpretation (which is reserved for the Results and Discussion sections). All legends explicitly define: (1) Node size metric; (2) Color coding (e.g., year of publication, cluster membership); (3) Edge width/strength (e.g., collaboration frequency, co-citation strength); (4) Any unique symbols or labels. Statistical analysis Descriptive statistics were used to summarize publication characteristics (e.g., citation counts, publication years, study types). Citation density was compared between early (2010–2015) and late (2016–2021) publications using the Mann-Whitney U test, given the non-normal distribution of citation data. All statistical analyses were performed using SPSS version 28.0 (IBM Corp., Armonk, NY, USA). A p-value < 0.05 was considered statistically significant. Results Study characteristics Publications from 2016 to 2021 had a significantly higher median citation density (45.8 vs. 29.1 citations per year, p < 0.001) than those from 2010 to 2015, reflecting the rapid growth of emerging TAVR research topics in recent years. Supplementary Table 2 presents the stratification of top influential publications by study type: the top 5 RCTs include landmark trials such as PARTNER 3 and Evolut Low Risk, the top 5 registry-based studies are led by large international registries (e.g., STS/ACC TVT Registry), and the top 5 reviews/consensus statements include key ESC/EACTS and AHA/ACC TAVR guidelines. Trial-driven research accounted for 42% of the top 100 publications, registry-driven research for 31%, and synthesis-driven research for 27%, demonstrating the central role of clinical trials in shaping TAVR evidence. Author cooperation network analysis The author cooperation network (Fig. 1 ) depicts static structural collaboration patterns of key researchers contributing appreciably to the field of TAVR. Martin B. Leon, Rebecca Hahn and Raj R. Makkar emerged as the most influential authors, reflected by their central positions and large nodes, representing high citation frequencies. This network illustrates clustered collaborative structures at a single analytical scale, rather than sequential temporal evolution of teamwork. Collaborative clusters suggest a concentrated network with distinct teams frequently interacting, indicating that influential publications commonly originate from established collaborative teams rather than isolated researchers. Fig. 1. Open in a new tab Author Cooperation Network Analysis. Node size represents the total citation count of each author’s publications within the dataset (larger nodes = more highly cited authors). Node color gradient from blue to red indicates the publication year of the author’s core works (blue = earlier, red = more recent). Lines between nodes represent co-authorship relationships, with thicker lines indicating stronger/frequent collaboration. Only labels of the most influential authors (e.g., Leon MB, Makkar RR) are displayed for clarity; smaller nodes represent contributing authors in the collaborative network Country cooperation network analysis The analysis of cooperation among countries (Fig. 2 ) reveals static structural hubs and cross-national linkage strengths and highlights the USA as the central hub, showcasing the largest node size, indicative of substantial citation frequency and extensive international collaboration. Other influential countries include Canada, Germany, France, UK, Spain and the Netherlands, indicating robust transatlantic cooperation. The network further illustrates moderate involvement from other European nations, such as Switzerland and Italy, and emerging research contributions from the Asia–Pacific regions, including Australia. No temporal evolutionary inference is drawn from this non-temporal structural network. Fig. 2. Open in a new tab Country Cooperation Network Analysis. Node size corresponds to the number of publications originating from each country (larger nodes = higher publication volume). Node color gradient from blue to red denotes the year of the country’s first high-cited TAVR publication in the dataset (blue = earlier entry into the field). Lines between countries indicate international co-authorship, with thicker lines representing more frequent cross-border collaboration. The United Kingdom (UK) is labeled to reflect collaborative research across England, Scotland, Wales, and Northern Ireland Institutional cooperation network analysis Institutional analysis (Fig. 3 ) indicates prominent research institutions notably contributing to TAVR research. Cleveland Clinic Foundation, Columbia University, Cedars-Sinai Medical Center and Emory University represent highly productive institutions with extensive connections. Other influential institutions include Harvard University, Duke University, Mayo Clinic and St. Paul’s Hospital, highlighting a strong North American dominance in research output and collaboration in this domain. Fig. 3. Open in a new tab Institutional Cooperation Network Analysis. Node size represents the number of publications contributed by each institution (larger nodes = higher publication output). Node color gradient from blue to red indicates the year of the institution’s first high-cited TAVR publication (blue = earlier research initiation). Lines between institutions denote collaborative relationships, with thicker lines suggesting stronger/frequent co-authorship. Major research hubs (Cleveland Clinic Foundation, Columbia University, Cedars-Sinai Medical Center) are highlighted as core nodes in the TAVR research collaboration network Reference network analysis Reference network visualisation depicts the structure and evolution of a research field. Nodes represent entities, such as publications, with size potentially indicating influence (Supplementary Fig. 1). Colours map to publication years, illustrating the field’s timeline from earlier (blue/green) to more recent works (yellow/red). Key influential authors or papers are highlighted (e.g. Leon MB, Smith CR, Webb JG, Adams DH). Distinct clusters suggest different research sub-topics or collaborative groups, revealing the intellectual landscape and key contributions within the domain shown. The timeline visualisation (Fig. 4 ) demonstrates temporal distribution and the emergence of foundational research over the years. Key publications by Leon MB (2010, 2016), Smith CR (2011), Adams DH (2014) and Webb JG (2007, 2009) are prominently represented, reflecting their pivotal roles in shaping TAVR literature over time. Citation bursts are apparent, particularly around notable clinical trials and influential guidelines emerging prominently between 2010 and 2016. Fig. 4. Open in a new tab Timeline of Landmark TAVR Studies (2007–2019). Timeline visualization of practice-changing landmark TAVR studies (defined as RCTs, registries, or guidelines endorsed in major cardiovascular guidelines and with citation counts exceeding the 95th percentile of pre-2010 TAVR publications). Key trials/guidelines are plotted by author and publication year, with the citation burst metric (S = 0.8983) overlaid to illustrate sustained scientific impact and research momentum. Nodes are sized by total citation count of the landmark study; color gradient from blue to red represents the peak citation burst period (blue = earlier burst, red = later burst) Figure 5 presents the co-citation network of cited references (2010–2021), with modularity Q = 0.82 and mean silhouette score = 0.87, indicating robust and well-separated clustering. The clustered reference analysis (Fig. 5 ) outlines distinct thematic clusters identified within TAVR research literature. Prominent clusters include ‘aortic valve’ (0), ‘five-year outcome’ (1) and ‘ACC guideline’ (2). Other important clusters encompass randomised controlled trials (3), comprehensive reviews (7), intermediate-risk patients (8) and clinical research (10). This indicates a diversified yet focused research emphasis on clinical outcomes, guidelines and evidence synthesis within the field. Fig. 5. Open in a new tab Clustered Reference Co-Citation Network Analysis (2010–2021). CiteSpace-based bibliometric map of the 100 most-cited TAVR articles, with modularity Q = 0.82 and mean silhouette score = 0.87 (indicating robust, well-separated clustering). Node size corresponds to total citation frequency of the referenced publication (larger nodes = more highly cited). Node color gradient from blue to red represents the publication year (blue = 2010, red = 2021); ring width around nodes denotes betweenness centrality (wider rings = greater structural influence as a network bridge). Edge width indicates co-citation strength (wider edges = more frequent joint citation); distance between nodes is the inverse of co-citation strength (closer nodes = more frequently co-cited). Red-highlighted burst terms (#0 aortic valve, #1 five-year outcome, #2 ACC guideline, etc.) label thematic clusters ranked by cluster size; #0 designates the primary cluster focused on aortic valve disease pathophysiology and clinical management as the foundation for TAVR research Keyword and research front analysis Supplementary Fig. 2 (Keyword Co-Occurrence Network, 2010–2021) has a modularity Q = 0.79 and mean silhouette score = 0.84, confirming robust clustering. This is a static co-occurrence structure, not a temporal evolutionary map. The keyword co-occurrence network illustrates major research hotspots within TAVR literature, prominently featuring terms such as ‘transcatheter aortic valve replacement’, ‘aortic stenosis’, ‘implantation’, ‘high-risk patients’ and ‘cardiac surgery’. These keywords represent fundamental concepts and clinical issues central to TAVR studies, reflecting primary clinical applications and the patient populations commonly addressed in this field. The keyword clustering visualisation (Supplementary Fig. 3) identified several key thematic clusters: cluster 0 (‘stenosis’), 1 (‘transcatheter aortic valve implantation’), 2 (‘pacemaker’), 3 (‘aortic stenosis’), 4 (‘infective endocarditis’), 5 (‘clinical trials’), 6 (‘implantation impact’) and 7 (‘antithrombotic’). These clusters reflect concurrent thematic domains in the static network, not staged temporal progression. This clustering highlights the evolution of TAVR-related clinical research, with emerging interests in complications management (infective endocarditis, pacemaker implantation), procedural outcomes and the application of antithrombotic therapy post-procedure. Explicit temporal keyword analysis (Fig. 6 ), rather than static network structure, demonstrates the temporal evolution of research topics. Early stages of TAVR research (2010 onwards) emphasised the procedural feasibility and outcomes for high-risk patients, whereas recent years (post-2018) highlight topics such as permanent pacemaker implantation, antithrombotic treatment and infective endocarditis, indicating the field’s shift toward addressing longer-term management and complications. Fig. 6. Open in a new tab The keyword timeline diagram. CiteSpace keyword co-occurrence overlay of the 100 most-cited TAVR articles (2010–2021; UN = 10, LBY = 5, e = 1.0). Node size indicates frequency; burst terms (#0–#7) highlight evolving research priorities from “stenosis” and “transcatheter aortic valve implantation” to late complications such as “pacemaker need,” “infective endocarditis,” and “antithrombotic” strategies. Color gradient shows temporal progression, with red indicating peak activity in 2018–2019 Temporal keyword analysis (Supplementary Fig. 4) further validates the sustained research focus on terms such as ‘transcatheter aortic valve implantation’, ‘replacement’ and ‘high-risk patients’. Recent emergent keywords, notably ‘permanent pacemaker implantation’ and ‘society’, reflect evolving clinical concerns related to procedural complications and broader societal impacts of TAVR adoption. The top 10 keywords with the strongest recent citation bursts (2019–2024) include: (1) LM obstruction (burst strength = 28.7, 2021–2024); (2) Small-size valve durability (burst strength = 26.3, 2020–2024); (3) Cardiogenic shock (burst strength = 24.9, 2021–2024); (4) Artificial intelligence (burst strength = 23.5, 2022–2024); (5) Redo TAVR (burst strength = 22.1, 2020–2024); (6) Antithrombotic management (burst strength = 20.8, 2021–2024). Citation burst analysis Analysis of reference citation bursts (Fig. 7 ) pinpointed seminal studies markedly impacting the field of TAVR. Notably, pivotal trials and clinical guidelines, such as Webb JG (2009), Smith CR (2011), Leon MB (2016) and Adams DH (2014), exhibited substantial citation bursts. These studies have played foundational roles, guided clinical practice and shaped subsequent research trajectories in TAVR. Fig. 7. Open in a new tab Reference Citation Burst Analysis (2010–2024). Top 25 references with the strongest citation bursts in TAVR research (2010–2024). Horizontal bars indicate burst duration (x-axis = years); bar color gradient from blue to red represents burst strength (red = higher strength). Peak burst strength values are labeled for key references (e.g., Adams DH 2014 = 7.78, Leon MB 2016 = 7.79), identifying pivotal trials and guidelines that redirected clinical practice—from early procedural feasibility (2010–2014) to TAVR indications in low-risk patients (2015–2024). Nodes at the base of bars are sized by the total citation count of the reference; edge lines link consecutive high-impact references to illustrate the evolutionary trajectory of TAVR clinical evidence Keyword burst analysis (Supplementary Fig. 5) identifies keywords experiencing prominent citation bursts, marking them as influential terms within a specific period. Notable bursts include ‘prosthesis’ and ‘replacement’ in early years (2010–2011), reflecting initial technical concerns and procedural validation efforts. Recent bursts in ‘permanent pacemaker implantation’ (2019–2021) and ‘society’ (2019–2021) underline contemporary clinical and socio-economic research priorities. Sankey diagram visualisation To show the relationship between major research institutions, journals and keyword topics more intuitively, a Sankey diagram was constructed, as shown in the figure below (Fig. 8 ). The colour and size of the nodes and lines in the figure illustrate the relationship and weight of the following items: Fig. 8. Open in a new tab Sankey Diagram of TAVR Research Networks (2010–2021). Sankey diagram illustrating the tripartite relationship between leading TAVR research institutions, core journals, and key research keywords/subject terms. Node size corresponds to publication volume (larger nodes = more publications associated with the institution/journal/keyword). Line width denotes the strength of the association (wider lines = more publications linking the paired institution-journal, journal-keyword, or institution-keyword). Nodes are color-coded by category: teal = research institutions (e.g., Mayo Clinic, Columbia University, DZHK Berlin), navy = core journals (e.g., NEJM, JACC: CV Interventions, Circulation, European Heart Journal), orange = key research keywords (e.g., high-risk patients, TAVR, aortic stenosis, implantation). The diagram visualizes the core knowledge dissemination network and thematic focus of highly-cited TAVR research Examples of institutions – Baylor Scott & White Health (Plano, TX, USA), University of Manitoba Health Sciences Library (Winnipeg, MB, Canada), Columbia University Medical Center/NewYork-Presbyterian Hospital (New York, NY, USA), Mayo Clinic Foundation (Rochester, MN, USA). Examples of journals or conferences – Journal of the American Medical Association, Journal of Thoracic and Cardiovascular Surgery, UK Journal of Medicine, Journal of the American College of Cardiology, Circulation. Examples of keywords/subject terms – high-risk patients, implantation, mortality, stenosis, outcomes, TAVR, aortic stenosis, transcatheter aortic valve replacement. The citation network analysis identified the top dominant institutions and core authors in the TAVR field, with their specific academic influence mechanisms and representative contributions detailed in Supplementary Table 3. Discussion Our bibliometric analysis of the top 100 cited TAVR publications reveals several noteworthy patterns. First, static structural network analyses show the field is dominated by contributions from North America and Europe, with the USA leading in both publication volume and centrality (influence). Major heart centres and universities in the USA and Canada (along with a few in Europe) form the core of the collaboration network, indicating tightly-knit research clusters driving innovation. Similarly, many of the most prolific individual authors are based in these regions, notably figures such as John G. Webb, Martin B. Leon and Josep Rodes-Cabau, who not only top the publication counts but are also among the most frequently co-cited authors. This close collaboration among leading authors and institutions has undoubtedly propelled TAVR research forward, as evidenced by the strong co-authorship links predominantly within North America and Europe. The high prevalence of AS and an early start in TAVR programmes in these regions have fostered a rich environment for research and development. In contrast, contributions from Asia have risen more recently; for example, Japan and China now rank among the top publishing countries, with China showing robust growth since its first TAVR reports in 2009. These findings underscore how international collaboration and concentrated expertise have shaped the trajectory of the TAVR field to date. Dedicated temporal Sankey and timeline analyses, rather than static structural networks, objectively map the field’s intellectual trajectory, showing a clear shift from procedural feasibility (2010–2014) to outcomes research (2015–2019) and finally to long-term management and re-intervention (2020–2025). The Main Sankey uncovers an intellectual division of labour – foundational leaders, such as Leon MB and Windecker S, drive core themes such as ‘TAVR’ and ‘outcomes’ – whereas emerging investigators focus on specialised niches, such as ‘atrial fibrillation’ and ‘echocardiography’. Most importantly, the Temporal Sankey identifies emerging frontiers, with 2024–2025 keywords such as ‘valve durability’, ‘redo-TAVR’ and ‘artificial intelligence’ signalling the field’s next wave of inquiry. These visualisations provide a data-validated atlas of TAVR’s evolution, collaboration patterns and future directions, offering unique value for strategic planning and research prioritisation. Another important finding is that temporal keyword and burst analyses (not static co‑occurrence networks) reveal the clear evolution of research hotspots and themes in TAVR over time. In the early stages (mid-2000s), studies focused on establishing the feasibility of TAVR and compared it with surgical valve replacement – terms such as ‘prosthesis’, ‘transcatheter implantation’ and ‘stenosis’ had the strongest citation bursts during approximately 2010–2014. This reflects an intense early interest in the technical aspects of the procedure, device performance and learning curve, as well as the imperative to demonstrate TAVR’s benefits against the standard of SAVR [ 4 – 7 ]. By the mid-2010s, as TAVR became more accepted, the focus shifted toward expanding its clinical application and assessing patient outcomes. Keywords from 2014 to 2018 (e.g. ‘elderly patients’, ‘high-risk patients’, ‘mortality’, ‘implantation’) indicate that researchers concentrated on outcome studies in indicated populations and procedural refinements [ 8 , 9 ]. Most recently, in the late 2010s and early 2020s, the spotlight has turned to optimising TAVR in broader contexts and managing its complications. Emerging keywords, such as ‘TAVR’ (as a term in its own right), ‘conduction disturbance’ and ‘coronary access’ (appearing with strong citation bursts in 2020–2023), suggest growing concern with issues such as conduction system complications (e.g. need for pacemakers) and coronary artery access after TAVR [ 10 , 11 ]. These newer topics coincide with TAVR’s expansion into lower-risk, younger patients, where long-term complications and technical challenges (such as coronary obstruction or valve durability) become especially pertinent. In summary, the thematic progression – from procedural development to patient outcomes to long-term and technical challenges – illustrates how TAVR research has matured alongside the technology’s clinical adoption. Our analysis also highlights a set of highly influential publications that have shaped the TAVR landscape. Notably, several landmark clinical trials and guideline papers emerge as central nodes in the co-citation network. For example, the ‘2014 AHA/ACC Guideline for the Management of Patients with Valvular Heart Disease’ stands out with the highest co-citation count, underlining its integrative influence on the field [ 12 ]. In fact, about half of the top 10 co-cited references are pivotal studies published in The New England Journal of Medicine, reflecting the impact of major TAVR trial results reported in that high-profile venue [ 13 – 22 ]. These include the seminal PARTNER trial series and other randomised trials that compared TAVR with SAVR in various risk cohorts, as well as key follow-up studies. The dominance of such trials and guidelines among top-cited works aligns with the prevalent themes identified; they often centre on comparing surgical and transcatheter approaches and reporting clinical outcomes. This observation is reinforced by the content of the most-cited papers themselves, which frequently discuss perioperative outcomes, procedural refinements and patient selection for TAVR versus surgery [ 18 ]. Many of these classic studies showed notable citation ‘bursts’, indicating that they were rapidly and widely cited soon after publication as the community recognised their importance. For instance, the early TAVR trial by Leon et al. (2010) and subsequent trials in 2011–2012 immediately influenced practice and research directions [ 5 , 13 ], just as later trials in 2019 demonstrating TAVR in low-risk patients spurred a flurry of citations and follow-up studies [ 20 , 23 ]. Likewise, foundational contributions, such as Gilard et al.’s first-in-human TAVR report (2012), and major consensus documents have had enduring relevance [ 15 ]. In short, the bibliometric data confirm that a few key papers – landmark trials and guidelines – have disproportionately driven the discourse, serving as cornerstones upon which much of TAVR’s evidence base and clinical practice have been built. Their high citation frequencies and centrality attest to their role in accelerating the acceptance of TAVR and guiding new investigations. Our study provides insights into future directions for TAVR research. One clear recommendation is to address the remaining knowledge gaps regarding long-term outcomes and durability of transcatheter valves. As TAVR is now being offered to younger, lower-risk patients, there is an urgent need for data on how these bioprosthetic valves perform over 10–20 + years [ 24 ]. Issues such as valve longevity, the feasibility of redo TAVR or surgical valve replacement after an initial TAVR and the optimal strategies for lifelong patient management are still not fully resolved [ 25 ]. Research focusing on these aspects will be crucial to ensure that expanding TAVR indications do not compromise long-term patient outcomes. Another emerging priority is improving the management of TAVR-related complications. For example, persistent challenges, such as conduction disturbances (atrioventricular block requiring pacemaker implantation), paravalvular leak and difficulty in coronary access post-TAVR, are highlighted by recent keyword bursts [ 26 ]. Future studies can explore new valve designs, implantation techniques or adjunct therapies to mitigate these complications. Furthermore, as the field matures, risk prediction and patient selection will benefit from more sophisticated approaches. The development of better predictive models (potentially incorporating machine learning or large-scale registry data) can help tailor the TAVR versus SAVR decision to individual patient characteristics beyond the traditional risk scores. We also see an opportunity for research into expanding TAVR to new populations – for instance, patients with pure aortic regurgitation, bicuspid aortic valves or other scenarios that were not well represented in early trials. Such expansions will require careful study to establish their safety and efficacy [ 27 ]. Our findings suggest that enhanced global collaboration will play a pivotal role in future TAVR advancements. Although North America and Europe have led so far, increased participation from developing countries and emerging cardiac centres could greatly enrich the research landscape. Sharing of expertise and multicentre international trials will be important to validate TAVR outcomes across diverse healthcare settings and patient groups. We encourage efforts to dismantle academic and institutional barriers, fostering a more inclusive research network that accelerates innovation worldwide. This disparity creates a significant evidence gap for underrepresented ethnic groups and LMIC populations, where TAVR adoption is rapidly increasing but clinical data are scarce.We strongly advocate for high-quality, large-scale RCTs and registries from LMICs to address these evidence gaps. Limitation and future directions This study has several inherent limitations that should be acknowledged. First, the analysis was restricted to the top 100 highly cited TAVR-related publications from the Web of Science Core Collection, which introduces citation bias. This focus prioritizes foundational landmark studies and established research teams, potentially underrepresenting emerging clinically valuable research topics and contributions from early-career researchers or smaller institutions with fewer cumulative citations. Though citation density was used as a complementary metric, recent publications (2022–2025) still lack sufficient citation accumulation to be included in core network analyses, limiting insights into the latest research frontiers. Second, the bibliometric analysis was confined to a single database. While Web of Science was selected for its standardized citation indexing and CiteSpace compatibility, it may not capture all TAVR-related literature, such as non-English language studies and research in niche non-indexed journals, leading to potential underrepresentation of research from non-English-speaking regions and emerging economies. Third, the core network and clustering analyses were truncated at 2021 due to citation lag, despite the initial temporal scope of 2010–2025. This truncation ensured the robustness of co-citation and collaboration network calculations but excludes the rapid evolution of TAVR research in the post-2021 period, including novel device developments, updated clinical guidelines, and emerging hotspots that have not yet accumulated sufficient high citations. Fourth, this bibliometric study is descriptive in nature and cannot establish causal relationships. For example, while North American institutions dominate TAVR research output and collaboration, this study cannot determine whether this is driven by resource availability, early clinical adoption of TAVR, or other contextual factors, nor link specific collaborative networks to improved clinical trial design or patient outcomes. To address the above limitations and further deepen the exploration of the TAVR research field, we propose targeted future research directions. In future related research, we plan to conduct a more comprehensive analysis: Firstly, we will conduct a separate analysis of the recent high-impact publications determined based on journal impact factors, peer recognition, and research innovation (such as publications from the past 3 to 5 years), rather than merely based on citation counts. Secondly, we will expand the data source to a multi-database integration framework, incorporating MEDLINE, Scopus, Embase and other authoritative academic databases, and include high-quality non-English language research and regional core journals. This will complement the research evidence from emerging economies and non-English-speaking regions, and construct a more comprehensive and global TAVR research collaboration network and thematic evolution map. Thirdly, we will carry out in-depth stratified and subgroup analyses based on publication type, research region and study design. We will compare the collaborative patterns and academic influence of original research, clinical guidelines and systematic reviews, and analyze the differences in research focus and collaboration characteristics of TAVR research in different regions. This will provide more nuanced and targeted insights for the development of TAVR research in different regions and research directions. Finally, we will combine bibliometric analysis with qualitative research methods, such as integrating clinical practice guideline interpretation, expert interview and real-world clinical outcome data. We will further explore the causal relationship between research collaboration patterns, academic influence and clinical practice transformation, and provide more practical and actionable research suggestions for the clinical application and research innovation of TAVR technology. In conclusion, by focusing on long-term outcomes, technical refinements and global cooperative research, the TAVR field is well positioned to continue its remarkable evolution. Addressing these future challenges and questions will not only fill current research gaps but also ensure that TAVR realises its full potential as a durable, safe and widely accessible therapy for valvular heart disease. Each new insight and collaborative effort will help sustain the momentum of TAVR research, guiding the next generation of improvements in patient care. Conclusion The development of transcatheter aortic valve replacement research is progressing with the technological progress and the expansion of clinical indications. North America and Europe are the core research forces in this field, and the close cooperation between core authors and institutions is the key to promote the development of this field. The research in this field has gradually shifted from the initial feasibility verification of surgery to clinical outcomes, postoperative complications and long-term management. Artificial intelligence, valve durability, and repeat TAVR are the emerging research frontiers. Continuous international cooperation, in-depth research on long-term postoperative outcomes, device durability, and prevention and control of complications, as well as exploration of emerging research populations and research directions, are the core to optimize the treatment effect of TAVR and promote the sustainable development of the field. At the same time, it is necessary to fill the research evidence gaps in low - and middle-income countries and different ethnic groups, and build a more inclusive global research network to make TAVR technology accessible to a wider patient population. Supplementary Material Below is the link to the electronic supplementary material. Supplementary Material 1 (14.6KB, docx) Supplementary Material 2 (14.9KB, docx) Supplementary Material 3 (19.3KB, docx) Supplementary Material 4 (428.7KB, docx) Acknowledgements Not applicable. Author contributions Ma B and Xiao JJ conceived of the study, and Yuan L, Yu Q, Wang LL and Xia JJ participated in its design and data analysis and statistics and Yuan L and Yu Q helped to draft the manuscript. All authors read and approved the final manuscript. Funding This study did not receive any funding in any form. Data availability All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author. Declarations Ethics approval and consent to participate An ethics statement is not applicable because this study is based exclusively on published literature. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Jingju Xia, Email: xiajingjjmn@21cn.com. Bo Ma, Email: Mabbomuo@163.com. References 1. Green P, Woglom AE, Genereux P, Daneault B, Paradis JM, Schnell S, et al. The impact of frailty status on survival after transcatheter aortic valve replacement in older adults with severe aortic stenosis: a single-center experience. JACC Cardiovasc Interv. 2012;5(9):974–81. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Abdel-Wahab M, Mehilli J, Frerker C, Neumann FJ, Kurz T, Tölg R, et al. Comparison of balloon-expandable vs self-expandable valves in patients undergoing transcatheter aortic valve replacement: the CHOICE randomized clinical trial. JAMA. 2014;311(15):1503–14. [ DOI ] [ PubMed ] [ Google Scholar ] 3. 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Supplementary Materials Supplementary Material 1 (14.6KB, docx) Supplementary Material 2 (14.9KB, docx) Supplementary Material 3 (19.3KB, docx) Supplementary Material 4 (428.7KB, docx) Data Availability Statement All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author. Articles from Journal of Cardiothoracic Surgery are provided here courtesy of BMC ACTIONS View on publisher site PDF (2.5 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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