Research hotspots and trends in speckle-tracking echocardiography in hypertension: a bibliometric analysis

Introduction

Hypertension is one of the most prevalent chronic diseases globally (1), and its prevalence increases yearly with lifestyle changes (2). Hypertension is not only a major risk factor for cardiovascular and cerebrovascular diseases, but also closely related to the development of heart failure (3). The prolonged presence of hypertension can trigger the development of heart failure through multiple mechanisms (4), including ventricular remodeling (5), left ventricular hypertrophy (LVH) (6) and myocardial fibrosis (7). In particular, the direct effect of hypertension on the myocardium (8) triggers cardiac decline by increasing cardiac load, which gradually leads to heart failure (9). Early diagnosis of heart failure and assessment of myocardial function are essential for therapeutic intervention (10). Currently, examination techniques to assess myocardial function mainly include traditional echocardiography, magnetic resonance imaging (MRI), and computed tomography (CT) (11). Echocardiography has become a routine method for the assessment of clinical cardiac function due to its noninvasive, real-time, easy-to-use, and relatively low cost (12). However, conventional echocardiography has some limitations in assessing myocardial fine motion, especially in the detection of early myocardial functional changes and microscopic motion induced by hypertension (13). In recent years, speckle-tracking echocardiography (STE), as an emerging cardiac imaging technique, has demonstrated its superiority in the assessment of myocardial function (14). STE is capable of quantitatively assessing microscopic motion information such as myocardial deformation, strain, and rotation by tracking myocardial speckles with higher sensitivity and accuracy (15). Compared with conventional echocardiography, STE is able to reveal subtle changes in early cardiac function more accurately, especially in hypertensive patients, and has a significant advantage in the assessment of myocardial micromotion (16). STE demonstrates significant clinical utility in hypertension management, enabling early detection of subclinical myocardial dysfunction and longitudinal therapeutic monitoring (17). This technology provides quantitative biomarkers for drug efficacy evaluation and predicts adverse cardiovascular outcomes (18), ultimately enhancing patient survival (19). This study addresses the lack of a systematic analytic framework for STE studies in hypertension. Previous review studies have mostly used traditional qualitative methods, which have inherent shortcomings such as lack of objectivity, selective bias and low visualization. Thus, bibliometric methods are introduced to systematically reveal the development trajectory and research hotspots in the field through data-driven analysis techniques (20). As an important branch of scientometrics (21), bibliometrics, through mathematical modeling and statistical analysis methods, can provide a multidimensional analysis of the literature characteristics of a specific subject area (22). This method can not only quantitatively assess the scale and quality of research output, but also construct a knowledge evolution map to reveal the collaborative networks and dissemination paths of disciplinary development (23). In this study, we used CiteSpace (6.4.R1) (24) and VOSviewer (1.6.21) (25) knowledge graph analysis tools to implement co-occurrence network analysis and cluster visualization based on the dataset of STE-related hypertension literature from the Web of Science core citation database [2005–2024]. By establishing time series mapping, keyword co-occurrence network and emergent word detection model, we aim to construct a knowledge evolution framework in this field, which provides evidence-based basis for researchers to grasp the discipline’s development law and identify the cutting-edge direction. We present this article in accordance with the BIBLIO reporting checklist (available at https://amj.amegroups.com/article/view/10.21037/amj-25-49/rc).

Methods

Data collection

The Web of Science Core Collection (WoSCC) (SCI-EXPANDED index) (26) was selected as the sole data source for this bibliometric analysis due to its rigorous journal selection criteria, comprehensive citation network, and compatibility with analytical tools such as CiteSpace and VOSviewer, which are widely recognized as the gold standard for bibliometric research. Although databases like PubMed offer broader coverage, their citation data structure and analytical functionality are less suited for complex network analysis and visualization. The literature search was conducted for the period from November 1, 2005 to January 31, 2025, covering nearly 20 years from the year of the first identified publication on STE in hypertension. The search query was as follows: (((((((((TS=(hypertension)) OR TS=(“hypertensive heart disease”)) OR TS=(“high blood pressure”)) OR TS=(“essential hypertension”)) OR TS=(hypertensive)) OR TS=(hypertention)) OR TS=(renovascular hypertensive)) OR TS=(“primary hypertension”)) AND TS=(“speckle-tracking echocardiography”)) OR TS=(strain). Inclusion criteria restricted to English-language articles/reviews (n=1,012,606 initial records). After duplicate removal (EndNote X20) and PRISMA-guided screening (27) by two independent cardiologists (κ =0.86) (28), 449 publications were retained for analysis (29). The PRISMA flowchart was adopted to enhance the transparency and reproducibility of the screening process, although this study is not a systematic review. The flowchart for search strategy and selection process is presented in Figure 1.

Figure 1 The flowchart illustrating the search strategy and selection process.

Data processing

Quality control

We implemented a modified DEAN framework (30) with the following steps:

• Automated deduplication (CiteSpace 6.4.R1) was performed. For thematic analysis, keyword data underwent refinement through: (I) semantic validation: synonymous terms (e.g., “STE” and “speckle tracking echocardiography”) were merged based on a predefined mapping table prior to co-occurrence calculation; (II) low-frequency filtering: for the main keyword network visualization , a threshold of minimum three occurrences was applied; (III) network pruning: only keywords belonging to the largest connected subnetwork were retained in the final visualization to elucidate core research themes. The complete and pruned keyword networks are provided in Figures S1,S2, respectively. All refinement steps were applied during the analytical processing, and the original retrieved data remain unchanged.
• Semantic validation of keywords (MeSH term mapping): synonyms and closely related terms were manually merged (e.g., “speckle tracking echocardiography” with “STE”, “arterial hypertension” with “hypertension”, “global longitudinal strain” with “GLS”) based on MeSH term mapping and expert review to ensure conceptual consistency.
• Low-frequency term filtering (occurrence <5).

Bibliometric profiling

• Temporal trends: annual publication counts visualized through epidemic curves (Excel 2010).
• Collaboration networks: institutional/country co-authorship maps generated with VOSviewer 1.6.20 (minimum three documents per node).

Conceptual evolution: CiteSpace 6.4.R1 time-zone analysis (2005–2025 slicing, pathfinder pruning) (Table 1).

Results

Publication trends

The 449 bibliographic papers used in this study came from 2,573 authors from 671 institutions in 58 countries, published in 99 journals, and cited 8,657 citations from 1,186 journals. The amount of scholarly output in a subject area in a specific time period can effectively reveal the dynamic and changing characteristics of research activities in the field (31). Within the search period (2005–2024), the literature on STE and hypertension was first indexed in 2005. The annual number of publications showed a fluctuating growth trend, fitting the curve: y = 3.4725X^1.6363 (R²=0.9983), The field experienced a slow start before 2008, entered a phase of steady growth from 2008, with a faster annual growth rate between 2012 and 2014 (approximately 64.1% per year), and reached an explosive period in 2022, followed by a slight decline after 2023 (Figure 2).

Figure 2 Trends in the growth of publications and the number of citations from 2005 to 2024.

Author and institutions network analysis

Analyzing authors and institutions in the literature can help identify the core scholars and major research forces in the field of study. Author Collaboration Network Analysis reveals underdeveloped scholarly cohesion in STE and hypertension research. Per Price’s Law (32), core authors (contributing 50% of publications) should approximate the square root of total authors (formula: ∑m+1In(x)=N; parameters: m =5, I =49, N=2,573). Calculations show 42 core authors (≥5 publications) produced only 428 papers (12.094% of total output), significantly below Price’s 50% threshold (Table S1). Leading contributors include Tadic, Marijana (Univ Hosp Dr Dragisa Misovic Dedinje, Germany; 403 papers, 4,658 citations) and Cuspidi, Cesare (Univ Milano-Bicocca, Italy) (Table S2). VOSviewer co-occurrence networks demonstrate: Node size reflects publication volume, line thickness indicates collaboration intensity. Tadic, Marijana forms a central cluster with Cuspidi, Cesare and Mancia, Giuseppe, secondary collaboration groups: Celic, Vera, Marwick, Thomas H, Yaman, Halil (Figure 3A). The central cluster primarily focuses on myocardial strain and prognosis in hypertension, while the group led by Celic, Vera emphasizes left atrial function, and Marwick’s group contributes to technology standardization and validation studies. The institutional collaboration network (Figure 3B) indicates that more than half of the top research institutions are located in Italy, highlighting its leading role.

Figure 3 Collaboration networks among (A) authors and (B) institutions in hypertension and STE research. STE, speckle-tracking echocardiography.

Distribution of countries/regions

Bibliometric analysis (Table 2) identifies Italy (90 publications), China (70 publications), and the USA (69 publications) as the leading contributors. The UK (29 publications) and the USA demonstrate the highest betweenness centrality (0.28 and 0.16, respectively), underscoring their pivotal collaborative roles (Figure 4A). Notably, China’s high output contrasts with its low centrality (0.01), suggesting potential for enhanced international collaboration. VOSviewer clustering delineates four primary collaboration clusters (Figure 4B).

Figure 4 Cooperation map of countries/regions. (A) CiteSpace network (purple rings indicate betweenness centrality). (B) VOSviewer network showing collaboration clusters.

Journal distribution and citation analysis

Table 3 and Figure 5 show that journal of Hypertension published the highest number of articles (n=51), followed by Echocardiography (n=32) and the International Journal of Cardiovascular Imaging (n=22). Notably, despite a lower publication count, Hypertension (20 articles) and the Journal of the American Society of Echocardiography (JASE) (15 articles) received exceptionally high average citations per document (39.35 and 98.20, respectively), indicating their pivotal role in publishing highly influential research in this field. Among the co-cited journals (Table S3), Circulation had the highest impact factor (IF) (IF 2023: 8.42), followed by the European Heart Journal (7.41). Eleven of the top co-cited journals were in Journal Citation Reports (JCR) Q1.

Figure 5 Collaboration networks among (A) source journals and (B) co-cited journals.

Co-cited references and references bursts

CiteSpace used the nodes in purple circles to measure the importance of documents and identify the top 11 most cited articles (Table S4). The co-citation analysis revealed that two co-cited documents appeared in the reference list of the third document, thus establishing a co-citation relationship between them (Figure 6A). The most cited document was the 2005 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) recommendations for chamber quantification (33), followed by the 2015 update (34) and the 2016 recommendations for diastolic function evaluation (35). The article with the highest betweenness centrality (0.15) was by Mondillo et al. on early left atrial strain detection (36), Citation burst analysis (Figure 6B) showed the strongest burst for the 2015 chamber quantification guidelines (strength: 18.51), followed by the 2016 diastolic function guidelines (14.27) (Figure 6C). These foundational guidelines represent key knowledge turning points, driving research on ventricular quantification and mechanical assessment (37-39).

Figure 6 Collaboration networks among references co-citation in hypertension and STE. (A) VOSviewer network of co-cited references. (B) CiteSpace network of co-cited references (purple rings indicate high betweenness centrality). (C) Top 25 references with the strongest citation bursts.

Analysis of research hotspots and frontiers

The top 25 keywords by frequency are listed in Table 4. Besides “hypertension” and “echocardiography”, high-frequency keywords included “strain”, “left ventricular hypertrophy”, “global longitudinal strain”, and “myocardial work”. The keyword co-occurrence network formed eight clusters representing main research directions (Figure 7A). The time-zone view (Figure 7B) visually depicts the evolution of research focus: studies concentrated on LVH before 2016, shifted to ventricular quantification [2016–2018], and then to mechanics and functional assessment (after 2018). The overlay visualization (Figure 7C) shows current hotspots (yellow spectrum) focused on myocardial work and strain analysis.

Figure 7 Co-occurrence analysis of keywords (2005–2024). (A) Keyword co-occurrence network. (B) Timezone view of keyword distribution. (C) Overlay visualization showing keyword average publication year. (D) Top 25 keywords with the strongest citation bursts (strength ≥3.29). (E) Timeline view of co-cited reference clusters (modularity and silhouette scores indicated in the plot).

Burst detection analysis (Figure 7D) identified “european association” as the strongest burst (12.85). For clarity, we present the top 5 bursts: european association (12.85), american society (11.18), myocardial work (7.43), update (7.01), and validation (6.78). Terms like “left ventricular hypertrophy” and “tissue Doppler” appeared earlier, while “myocardial work”, “global longitudinal strain”, and “update” represent current frontiers.

The timeline view of co-cited literature clustering (Figure 7E) revealed 16 clusters. The clustering quality was good, with a modularity Q value of 0.782 and a mean silhouette S value of 0.912. Analysis of clusters over time shows that early clusters like “#2 LV torsion” were active but have faded, while clusters like “#0 myocardial work” and “#3 three-dimensional echocardiography” have emerged more recently and remain active, indicating persistent research interest in functional and technological advancement.

Discussion

This study provides a comprehensive bibliometric overview of STE research in hypertension over the past two decades. The data, sourced from the WoSCC, demonstrate a consistent growth in publication volume, reflecting the increasing clinical and research interest in STE as a tool for early myocardial assessment in hypertension.

Our analysis highlights the central role of Italy, the USA, and China in terms of productivity. Italy and the USA not only have high output but also strong centrality in collaboration networks and publish in high-impact journals, indicating leadership in producing influential research. Countries like China, Serbia, and Turkey, while productive, show lower network centrality, suggesting opportunities to enhance international collaboration. China, in particular, ranks second in output but has a betweenness centrality of only 0.01, indicating a need to strengthen its integrative role in the global knowledge network. This could be achieved by fostering deeper collaborations with leading research groups in Italy and the USA, who have pioneered advancements in STE standardization, multicenter validation studies, and the integration of artificial intelligence for strain analysis. These technologies demonstrate high clinical value in risk stratification and treatment monitoring.

The journal analysis underscores the importance of targeting high-quality venues. While the Journal of Hypertension leads in volume, Hypertension and JASE stand out for their exceptional citation impact per article, marking them as key platforms for disseminating seminal work. Researchers should consider these journals for submitting high-impact studies.

Keyword and cluster analyses reveal a clear thematic evolution. The field has progressed from an early focus on anatomical changes (LVH) (40-44) to quantification methods, and more recently to sophisticated functional and mechanical assessments [myocardial work, global longitudinal strain (GLS)] (45-48). Prolonged hypertension can also indirectly increase the burden on the right ventricle by affecting the pulmonary circulation (resulting in pulmonary hypertension), leading to right heart hypoplasia (49-52). With LVH and diastolic dysfunction, blood flows back into the left atrium, leading to left atrial dilatation. Left atrial dilatation may lead to electrophysiologic abnormalities that increase the risk of atrial fibrillation (AF). AF further aggravates the cardiac load, creating a vicious cycle (53). Compared with the traditional left ventricular ejection fraction (LVEF), myocardial strain and work done (54) can reflect small changes in the heart more sensitively, and the movement of various parts of the heart, including the left ventricle, right ventricle, and atria, can be quantitatively assessed by strain. Myocardial work done integrates the pressure-volume relationship of the heart, providing an overall picture of the heart’s performance during systole and diastole (55). The strong citation bursts of guidelines (“european association”, “american society”, “update”) and emerging terms (“myocardial work”, “three-dimensional echocardiography”) confirm that standardization and technological innovation are current driving forces (56,57).

Despite the progress, several challenges remain. First, standardization of STE acquisition and analysis protocols across vendors and centers is still evolving, which may limit comparability. Second, there is a need for more large-scale, prospective studies to validate the prognostic value of STE parameters in diverse hypertensive populations, including those with comorbidities. Third, research on the application of STE in special populations (58-60), such as those with gestational hypertension or resistant hypertension, is relatively limited. Future efforts should prioritize multicenter collaborations to establish normative data, explore the cost-effectiveness of STE in routine care, and investigate its role in guiding personalized therapy in hypertension (61-63).

Furthermore, STE has shown promise beyond chronic hypertension. For instance, studies have utilized STE to detect transient impairment of myocardial strain parameters in gestational hypertension, even when LVEF remains normal. This highlights STE’s sensitivity in identifying subclinical cardiac dysfunction under acute or transient hemodynamic stress, suggesting its potential utility in monitoring cardiovascular adaptation during pregnancy and predicting adverse outcomes (62).

Despite the progress, several challenges remain. First, standardization of STE acquisition and analysis protocols across vendors and centers is still evolving, which may limit comparability. Second, there is a need for more large-scale, prospective studies to validate the prognostic value of STE parameters in diverse hypertensive populations, including those with comorbidities. Third, research on the application of STE in special populations, such as those with gestational hypertension or resistant hypertension, is relatively limited. Future efforts should prioritize multicenter collaborations to establish normative data, explore the cost-effectiveness of STE in routine care, and investigate its role in guiding personalized therapy in hypertension.

Limitations

Our study has several limitations. First, we relied solely on the WoSCC database. While the WoSCC database is widely recognized as the most critical data source for conducting bibliometric analyses (63), some relevant publications indexed in other databases (e.g., Scopus, PubMed) may have been missed. Second, the search strategy, although refined through quality control steps, may not have captured all synonyms, potentially affecting completeness. Third, bibliometric analysis is inherently reliant on citation metrics, which favor older publications and may not fully represent the immediate impact of recent high-quality work (64). Fourth, author name disambiguation and institution attribution can be challenging. Despite these limitations, we believe our analysis robustly captures the major trends, contributors, and intellectual structure of STE research in hypertension.

Conclusions

This bibliometric analysis maps the knowledge landscape of STE application in hypertension over the past 20 years. The field has grown significantly, driven by international collaboration and guided by key consensus documents. Research hotspots have evolved from LVH assessment to advanced myocardial mechanics and work analysis. While countries like Italy and the USA lead in influential output, there is substantial global engagement. Future research should address standardization, validate clinical utility in diverse settings, and explore STE’s potential in guiding precision medicine for hypertensive heart disease.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://amj.amegroups.com/article/view/10.21037/amj-25-49/rc

Peer Review File: Available at https://amj.amegroups.com/article/view/10.21037/amj-25-49/prf

Funding: This work was supported by the National Natural Science Foundation of China (Nos. 81960315 and 82360354) and the Guizhou Science and Technology Department {qiankehejichu-ZK[2022]yiban359 and qiankehejichu-ZK[2024]zhongdian045}.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://amj.amegroups.com/article/view/10.21037/amj-25-49/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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