Bibliometric and visualized analysis of research trends and knowledge structure in novel pharmacological treatments for hypertrophic cardiomyopathy

Abstract

Background

This study presents a bibliometric and visual analysis of the literature on novel drug therapies for hypertrophic cardiomyopathy (HCM) to explore recent research trends, hotspots, and developmental directions.

Methods

Publications related to novel drug treatments for HCM from 2015 to 2025 were retrieved from the Web of Science Core Collection. CiteSpace and VOSviewer (with standard parameters: time slicing 2015–2025, node selection based on co-occurrence, and pathfinder pruning) were utilized for quantitative and visual analyses.

Results

After screening, 281 publications were included. The annual publication output exhibited an overall upward trend. The United States led in the number of publications, while England played a prominent hub role in international collaboration. The Harvard University system formed a high-yield cluster, and the University of California system served as a key bridge. Co-citation analysis of journals revealed an interdisciplinary knowledge structure spanning basic science to clinical medicine. Both reference and keyword analyses confirmed that the research frontier has shifted from early exploration of the cardiac myosin mechanism to a current focus on myosin inhibitors.

Conclusion

Bibliometric evidence indicates a clear shift toward targeted therapy centered on cardiac myosin inhibitors, marking a transition in research focus. These findings provide a quantitative roadmap for researchers and clinicians to identify key players, collaborative opportunities, and emerging topics. Future studies are expected to deepen focus on non-obstructive HCM, personalized treatment, and long-term efficacy verification.

Introduction

Hypertrophic cardiomyopathy (HCM) is a common inherited myocardial disease characterized by unexplained asymmetric left ventricular hypertrophy that is not secondary to abnormal loading conditions such as hypertension or aortic stenosis [1, 2]. HCM often causes left ventricular outflow tract (LVOT) obstruction, which can subsequently lead to heart failure, malignant arrhythmias, and even sudden cardiac death [3]. Genetic mutations are central to the etiology of HCM, with approximately 40–60% of cases attributed to autosomal dominant variants in genes encoding sarcomeric proteins [4, 5] .To date, more than 1,400 distinct HCM-associated mutations have been reported [6]. Among these, mutations in the β-myosin heavy chain (MYH7) and myosin-binding protein C (MYBPC3) genes account for approximately 40% of HCM cases [7]. For a long time, the focus of HCM management has been on symptom relief and complication prevention. This includes the use of negative inotropic agents, such as beta-blockers and calcium channel blockers, to reduce LVOT obstruction and improve diastolic function [8], as well as invasive interventions like surgical septal myectomy or percutaneous alcohol septal ablation [9–11]. However, these conventional therapies primarily address symptoms and do not directly target the underlying pathophysiological mechanism of HCM—namely, sarcomeric dysfunction leading to myocardial hypercontractility. In recent years, a deeper understanding of the molecular genetics and pathophysiology of HCM has elucidated how mutations in the myosin heavy chain gene enhance the interaction between cardiac myosin and actin. This insight has driven the development of novel cardiac myosin inhibitors(CMIs) [12, 13]. Agents such as mavacamten and aficamten have demonstrated significant improvements in symptoms, exercise capacity, and LVOT gradients in patients with obstructive HCM in clinical trials [14, 15]. Differentiating obstructive HCM stemming from left ventricular outflow tract pressure discrepancies from non-obstructive HCM (nHCM) is crucial for guiding treatment. The 2024 American College of Cardiology and 2023 ESC guidelines suggest reserving cardiac myosin inhibitors for symptomatic oHCM, thereby limiting therapeutic options for nHCM patients. Addressing myocardial remodeling reversal, sudden cardiac death prevention, long-term outcomes, and the potential of gene therapy represents significant unresolved challenges. These gaps underscore the importance of systematically identifying research priorities, for which bibliometric analysis can offer valuable insights. The approval of these novel therapies by regulatory authorities and their incorporation into clinical guidelines mark a pivotal shift in HCM treatment—from conventional symptom management toward precision-targeted intervention [13, 16] —thereby stimulating rapidly growing research interest in this field.

Despite the rapid advancement in research on new pharmacological treatments for HCM, a comprehensive bibliometric analysis focusing on these developments remains lacking. Therefore, a systematic bibliometric study of this emerging and rapidly evolving field is warranted to outline its knowledge structure and predict future trends, which holds significant implications for both clinical practice and research planning. This study aims to employ bibliometric methods to systematically evaluate the developmental trajectory, core themes, and key contributors in the field of novel drug therapies for HCM. Specifically, we aim to reveal development trends and knowledge diffusion patterns, explore the thematic structure and evolution of research topics. Furthermore, we aim to assess the extent and impact of global academic cooperation and to identify potential future research directions and trends in this domain.

Materials and methods

Data sources

Publications related to new drug therapies for HCM were retrieved from the Web of Science Core Collection (WoSCC). The search period covered literature published from January 1, 2015, to November 14, 2025, and the retrieval was conducted on November 14, 2025. The search strategy was as follows: TS=(“Hypertrophic Cardiomyopathy” OR HCM OR HOCM OR oHCM OR nHCM OR “left ventricular outflow tract” OR LVOT) AND TS=(“cardiac myosin inhibit*” OR (“myosin inhibitor*” NEAR/3 cardiac) OR Mavacamten OR “MYK-461” OR Camzyos OR Aficamten OR “CK-274” OR “allosteric myosin inhibitor*” OR “sarcomere modulator*”) AND TS=(treat* OR therap* OR manag* OR random* OR “phase 2” OR “phase 3” OR “double-blind” OR “clinical trial*”) NOT TS=((skelet* NEAR/3 myosin) OR (smooth NEAR/3 myosin) OR omecamtiv OR “myosin activator*”). Myosin activators, like omecamtiv mecarbil and danicamtiv, were disregarded due to their ability to enhance cardiac contractility, which is contrary to the goal of treating HCM by reducing hyperdynamic myocardial movement. Other emerging intervention approaches, such as metabolic regulation, gene therapy, and RNA-based interventions, were omitted because they are still in early clinical stages, lacking sufficient bibliometric data for analysis. To ensure search sensitivity and relevance, we manually screened literature using “left ventricular outflow tract” and “LVOT” as search terms. Studies exclusively centered on surgical septal myectomy, alcohol septal ablation, or diagnostic imaging without drug intervention were excluded. This initial search yielded a dataset of 397 publications. Further exclusions were applied: 67 Meeting Abstracts, 22 Editorial Materials, 13 Letters, 9 Early Access publications, 2 Proceeding Papers, and 2 Corrections. The search was also limited to the English language. Ultimately, 281 relevant publications were identified, comprising 188 Articles and 93 Reviews. The records were saved in Plain Text file format, exporting the ‘Full Record and Cited References’. Figure 1 illustrates the article selection process used in this study.

Fig. 1.

Bibliometric literature screening flowchart

Data analysis

A bibliometric analysis was performed using data visualization and science mapping tools, specifically VOSviewer (version 1.6.20) [17] and CiteSpace (version 6.3.R1). These tools were applied to the WoS core dataset to analyze publication output, countries and institutions, author collaboration, as well as journal citations, co-citations, keywords, and references.

Bibliometric analysis parameters

CiteSpace (Version 6.3.R1) sets the time slice from 2015 to 2025 into 1-year segments. Node types such as country, institution, author, cited journal, reference, and keyword are chosen individually for each analysis, focusing on the top 50 most frequently cited or occurring items within each time frame. Pruning is conducted utilizing the pathfinding algorithm in conjunction with the pruning slice network. Clustering involves the application of latent semantic indexing (LSI) to keyword clusters and the use of Log-likelihood ratio (LLR) for reference clusters.

VOSviewer (Version 1.6.20) was utilized to perform co-author analysis for countries, institutions, and authors. Additionally, co-occurrence analysis was conducted for keywords, while co-citation analysis was carried out for journals and references. Full counting methodology was employed with specific minimum thresholds: a minimum of 5 articles from distinct countries, 5 institutions, and 5 authors, along with at least 10 occurrences of keywords, 50 citations by cited journals, and 20 citations by cited references. Normalization was implemented using the association strength approach. The default layout settings were maintained (attract = 2, Repel = 0), and the cluster resolution was set at 1.00.

Results

This study analyzed 281 publications authored by 1852 researchers from 1397 institutions across 177 countries or regions. These works were published in 129 different journals. The dataset comprised 188 research articles (66.90%) and 93 review articles (33.10%).

Publication output analysis

The annual distribution of publications provides a clear visualization of the development process and research trends within a field. Figure 2 illustrates the annual publication trend in the field of new drug therapies for HCM, outlining a distinct nonlinear growth pattern. From 2015 to 2019, the annual publication output remained in the single digits, fluctuating between 0 and 4 articles, indicating that the field was in its nascent stage with relatively sporadic academic attention. Starting in 2020, the field entered a phase of sustained growth, with the annual publication count rising to 9. The number of publications jumped to 23 in 2021 and reached 26 in 2022. This period likely marks the accumulation of preliminary research findings. The most significant growth occurred from 2023 onward, with publications increasing from 62 in 2023 to 86 in 2024. The publications in 2024 alone accounted for 30.60% of the total output. The data for 2025 (66 publications), albeit potentially incomplete, strongly corroborates the persistence of this high level of research activity. This explosive growth following 2020 aligns with the release of the pivotal EXPLORER-HCM trial in 2020 [21], the FDA’s regulatory approval of mavacamten in 2022, and the subsequent expansion of clinical trials for aficamten.

Fig. 2.

Number of publications per year and growth trend for new drug therapies in hypertrophic cardiomyopathy

Analysis of national or regional collaboration networks

To elucidate the global distribution and collaborative patterns in HCM new drug research, Fig. 3A presents a country/regional collaboration network constructed using CiteSpace. This network comprises 47 nodes and 183 links, with a density of 0.1693, indicating active international collaboration and revealing the distinct structural roles of contributing countries. Figure 3B illustrates the study of international cooperation among countries. When the minimum publication threshold for a country or region was set at 5 documents, a total of 19 countries met this criterion. The United States occupied the most central position in this network map. Table 1 lists the top 10 most productive countries: the United States led significantly with 187 publications, followed by England and Italy, each with 50 publications. China and Spain also showed prominent contributions, with 28 and 22 publications, respectively. Notably, England possessed the highest centrality (0.31), establishing it as the most critical hub in the international collaboration network. Although the Netherlands (centrality 0.15) and France (centrality 0.14) had moderate publication volumes, they also served as important bridging connectors. In contrast, the United States had a relatively lower centrality (0.08), suggesting its collaborative model might be more focused on domestic partnerships. From a temporal perspective, the United States (2016) and England (2019) were early initiators in the field. The recent emergence of active contributors like China (2021) and Scotland (2023) indicates the ongoing global expansion and dynamic evolution of this research domain. This pattern suggests that European countries have played a more significant bridging role in global cooperation compared to the United States, which exhibits higher domestic research output.

Fig. 3.

The network of co-country for the period 2015–2025. (A) Country or regional collaboration network constructed using CiteSpace for the period 2015–2025. (B) International cooperation network among countries (visualized by VOSviewer)

Table 1.

Top 10 productive countries

Rank	Publications	Centrality	First publication year	Countries
1	187	0.08	2016	USA
2	50	0.31	2019	England
3	50	0.13	2020	Italy
4	28	0.07	2021	Peoples r china
5	22	0.06	2020	Spain
6	21	0.07	2019	Germany
7	17	0.01	2020	Poland
8	15	0.01	2023	Scotland
9	13	0.15	2021	Netherlands
10	12	0.14	2019	France

Analysis of research institutions

Table 2 lists the top 10 most productive institutions. Affiliates of the Harvard University system demonstrated prominent performance. Harvard University itself (51 publications), Harvard University Medical Affiliates (49 publications), Brigham and Women’s Hospital (43 publications), and Harvard Medical School (31 publications) all ranked among the top 10 contributing institutions. Notably, although the University of California System produced 31 publications, it possessed the highest centrality (0.16), indicating its crucial role as a primary connector within the global research network. Figure 4 shows the institutional collaboration network map constructed using CiteSpace. This network consists of 223 institutional nodes and 304 collaborative links, with a network density of 0.0123. This suggests that institutional collaborations are not yet widespread, forming a decentralized network composed of multiple distinct yet internally tight clusters. The University of California system’s high centrality (0.16) underscores its significant role as a crucial link between pharmaceutical companies such as Bristol‑Myers Squibb, which has affiliations with UC, and academic medical centers. This intermediary function is supported by the involvement of various UC campuses in pivotal CMI trials like EXPLORER‑HCM and VALOR‑HCM.

Table 2.

Top 10 productive institutions

Rank	Publications	Centrality	First publication year	Institutions
1	51	0.02	2016	Harvard University
2	49	0.13	2016	Harvard University Medical Affiliates
3	43	0.01	2016	Brigham & Women’s Hospital
4	39	0.04	2020	University of Florence
5	38	0.05	2019	University of Pennsylvania
6	31	0.08	2016	Harvard Medical School
7	31	0.16	2016	University of California System
8	31	0.02	2021	Bristol-Myers Squibb
9	29	0.05	2019	Oregon Health & Science University
10	26	0.02	2020	Cleveland Clinic Foundation

Fig. 4.

The network of co-institution for the period 2015–2025

Analysis of research authors

Table 3 presents the top ten most productive authors. Iacopo Olivotto led with 36 publications, followed by Sara Saberi (25 publications), Ahmad Masri (22 publications), and Milind Y. Desai (21 publications). In contrast to other authors, Amy J. Sehnert exhibited relatively high centrality (0.08). Conversely, highly productive authors Iacopo Olivotto, Sara Saberi, and Ahmad Masri displayed centrality values of 0.00 or close to it. The author collaboration network, co-mapped using CiteSpace and VOSviewer, revealed key contributors in this research field. The CiteSpace network (Fig. 5A) comprised 299 authors and 289 collaborative links, with a network density of 0.0065, indicating limited collaboration among researchers. Analysis of 60 core authors, each with ≥ 5 publications, identified five major research clusters (Fig. 5B). Cluster #1 (20 members) focused on clinical phenotypes and comprehensive management, including Olivotto Iacopo, Masri Ahmad, Maron Martin S, et al. Cluster #2 (19 members) represented a surgical and interventional therapy transition group, containing Desai Milind Y., Wang Andrew, Lakdawala Neal K, et al. Cluster #3 (12 members) was a key clinical trials and bridging cluster, including Saberi Sara, Sehnert Amy J., Spertus John A, et al. Clusters #4 (6 members) and #5 (3 members) were emerging and specialized clusters, represented by Garcia-Pavia Pablo, et al. and Malik Fady I, et al., respectively. The color gradient in the overlay map (from blue to yellow) represents the evolution of the authors’ average publication years, visually demonstrating the active periods of each team and the intergenerational progression of research focus. Top-productivity authors, such as Olivotto, Saberi, and Masri, exhibit low centrality, suggesting their engagement within a cohesive, single-team structure. In contrast, Amy J. Sehnert demonstrates relatively high centrality (0.08), indicative of her function as a bridge among various research groups in her capacity as a clinical trial coordinator. This role aligns with her affiliation with Bristol-Myers Squibb, a sponsor of multiple CMI trials. The differentiation between “productive” and “bridging” authors offers a strategic framework for developing collaboration networks.

Table 3.

Top 10 most productive and top 10 most citation authors

Rank	Publications	Centrality	First publication year	Authors
1	36	0.05	2020	Olivotto, Iacopo
2	25	0.00	2020	Saberi, Sara
3	22	0.00	2023	Masri, Ahmad
4	21	0.02	2020	Desai, Milind Y
5	21	0.08	2020	Sehnert, Amy J
6	20	0.02	2020	Wang, Andrew
7	19	0.00	2019	Heitner, Stephen B
8	16	0.00	2024	Kupfer, Stuart
9	16	0.01	2020	Abraham, Theodore P
10	16	0.00	2024	Maron, Martin S

Fig. 5.

(A) Author collaboration network, (B) Author collaboration overlay visualization

Analysis of journals and co-cited journals

Through journal co-citation analysis, we identified the core knowledge sources in this research field. Data from Table 4 (most productive journals) and Table 5 (most cited journals) collectively provide a comprehensive overview. Frontiers in Cardiovascular Medicine and Journal of the American College of Cardiology (JACC) led with 12 publications each. They were followed by other renowned cardiology journals, such as Journal of the American Heart Association (9 publications), American Journal of Cardiology (8 publications), and JACC: Heart Failure (8 publications).

Table 4.

Top 10 journals with the most publications

Rank	Journals	Publications	citations	IF(2025)
1	frontiers in cardiovascular medicine	12	44	2.9
2	Journal of the American College of Cardiology	12	899	22.3
3	journal of the american heart association	9	63	5.3
4	american journal of cardiology	8	35	2.1
5	jacc-heart failure	8	164	11.8
6	circulation	7	546	38.7
7	american journal of cardiovascular drugs	6	50	3
8	current cardiology reports	6	53	3.3
9	esc heart failure	6	109	3.7
10	journal of clinical medicine	6	69	2.9

Table 5.

Top 10 journals with the most citations

Rank	Journals	citations	total link strength
1	Journal of the American College of Cardiology	1570	100,987
2	circulation	1293	90,584
3	eur heart j	581	43,639
4	lancet	417	18,440
5	new engl j med	413	30,482
6	eur j heart fail	387	39,536
7	am j cardiol	336	38,155
8	circ res	290	14,096
9	circ-heart fail	274	22,264
10	jama cardiol	252	18,283

Analysis of citation frequencies identified the journals exerting the greatest influence on the field. Journal of the American College of Cardiology (JACC) and Circulation emerged as the two most authoritative knowledge sources, with 1,570 and 1,293 citations respectively, far surpassing other journals. The journal co-citation network map (Fig. 6), integrating this information, displays the interrelationships among these knowledge sources. The map reveals four distinct clusters. Cluster #1 is the largest, containing 23 journals. High-impact journals such as Nature, Science, Nature Communications, and PNAS co-exist with specialized journals like Circulation Research, Cardiovascular Research, Journal of Molecular and Cellular Cardiology, and Journal of Biological Chemistry, clearly defining this cluster’s focus. Cluster #2 contains 19 journals, including the most authoritative clinical journals in the field, such as Journal of the American College of Cardiology (JACC), Circulation, European Heart Journal, and JAMA Cardiology. This cluster also includes imaging journals (e.g., JACC: Cardiovascular Imaging, Circulation: Cardiovascular Imaging) and electrophysiology journals (e.g., Heart Rhythm, Europace). Cluster #3 contains 9 journals. It brings together leading heart failure sub-specialty journals (European Journal of Heart Failure, JACC: Heart Failure, ESC Heart Failure, Journal of Cardiac Failure) with world-renowned general medical journals (The New England Journal of Medicine, The Lancet, Nature Medicine). This fusion indicates that research on new HCM drugs is not only a concern for cardiologists but is also recognized as a significant topic by the broader medical community. Co-citation cluster #3, linking heart failure journals with general medical journals such as NEJM and The Lancet, indicates that the clinical significance of CMIs has surpassed disciplinary confines. The publication of the EXPLORER-HCM trial in The Lancet, followed by guidelines in Circulation and JACC, elucidates the substantial citation rates of these journals. Cluster #4 contains only the highly influential journal JAMA.

Fig. 6.

Journal co-citation network. Red represents cluster #1, green represents cluster #2, blue represents cluster #3, and yellow represents cluster #4

Analysis of references

Table 6 lists the top ten most frequently cited references. The reference titled “Mavacamten for treatment of symptomatic obstructive hypertrophic cardiomyopathy (EXPLORER-HCM): a randomised, double-blind, placebo-controlled, phase 3 trial” was the most influential, with a citation count of 216. This work established Mavacamten as a foundational therapy for symptomatic obstructive HCM. Other key trials have further consolidated its evidence base in both obstructive and nHCM. Notably, the early trial by Heitner et al. (2019) exhibited the highest betweenness centrality (0.39), identifying it as a crucial conceptual bridge linking earlier research to the modern targeted therapy era. The appearance of recent major clinical guidelines among the highly cited references underscores their immediate impact on reshaping clinical practice. The timeline visualization of reference clusters (Fig. 7A) vividly illustrates the shift in research paradigms. Prior to 2020, the field was predominantly dominated by traditional approaches such as #3 septal reduction. A pivotal transition began around 2020–2021, marked by the emergence and consolidation of the #6 cardiac myosin inhibitors cluster. Since then, these themes have defined the research frontier. Citation burst detection (Fig. 7B) provides strong quantitative evidence for this paradigm shift. Foundational scientific papers from 2016 to 2018 exhibited strong, sustained bursts (e.g., Green et al., 2016, Strength 17.66), which elucidated the molecular mechanisms of myosin inhibition. Subsequently, bursts corresponding to the landmark Mavacamten clinical trials appeared between 2020 and 2023 (e.g., Heitner et al., 2019; Maron et al., 2020). The citation burst timeline clearly separates fundamental scientific breakthroughs (2016–2018, exemplified by Green et al.‘s study on mouse models) from clinical implementation (2020–2023). The most significant surge (Green et al., intensity: 17.66) underscores the foundational role of this basic research in the field of HCM drug therapy. Following this, Heitner et al.‘s burst in 2019, possessing the highest centrality (0.39), serves as a crucial link connecting preclinical investigations to human trials.

Table 6.

Top ten most local cited papers in 2015–2025

Rank	Count	Centrality	DOI	References
1	216	0.02	https://doi.org/10.1016/S0140-6736(20)31792-X	Mavacamten for treatment of symptomatic obstructive hypertrophic cardiomyopathy (EXPLORER-HCM): a randomised, double-blind, placebo-controlled, phase 3 trial
2	103	0.02	https://doi.org/10.1016/j.jacc.2020.03.064	Evaluation of Mavacamten in Symptomatic Patients With Nonobstructive Hypertrophic Cardiomyopathy
3	102	0.03	https://doi.org/10.1016/j.jacc.2022.04.048	Myosin Inhibition in Patients With Obstructive Hypertrophic Cardiomyopathy Referred for Septal Reduction Therapy
4	82	0.39	https://doi.org/10.7326/M18-3016	Mavacamten Treatment for Obstructive Hypertrophic Cardiomyopathy: A Clinical Trial
5	69	0.00	https://doi.org/10.1016/S0140-6736(21)00763-7	Mavacamten for treatment of symptomatic obstructive hypertrophic cardiomyopathy (EXPLORER-HCM): health status analysis of a randomised, double-blind, placebo-controlled, phase 3 trial
6	69	0.00	https://doi.org/10.1093/eurheartj/ehad194	2023 ESC Guidelines for the management of cardiomyopathies
7	62	0.02	https://doi.org/10.1016/j.jacc.2022.10.020	Phase 2 Study of Aficamten in Patients With Obstructive Hypertrophic Cardiomyopathy
8	56	0.00	https://doi.org/10.1161/CIR.0000000000001250	2024 AHA/ACC/AMSSM/HRS/PACES/SCMR Guideline for the Management of Hypertrophic Cardiomyopathy: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines
9	56	0.00	https://doi.org/10.1161/CIRCULATIONAHA.120.052359	Mavacamten Favorably Impacts Cardiac Structure in Obstructive Hypertrophic Cardiomyopathy: EXPLORER-HCM Cardiac Magnetic Resonance Substudy Analysis
10	56	0.13	https://doi.org/10.1021/acs.jmedchem.1c01290	Discovery of Aficamten (CK-274), a Next-Generation Cardiac Myosin Inhibitor for the Treatment of Hypertrophic Cardiomyopathy

Fig. 7.

Reference analysis for new drug therapies in HCM from 2015–2025. (A) Timeline view of reference clusters. (B) The top 25 references with the strongest citation bursts

Keywords analysis

Table 7 lists the top ten high-frequency keywords. Among them, “Hypertrophic cardiomyopathy” was the core node with the highest frequency and centrality. Keywords such as “Mavacamten”, “double blind”, and “EXPLORER-HCM” ranked high, directly reflecting the profound impact of specific drugs and their landmark clinical trials on the research landscape. A relationship network was constructed by calculating the co-occurrence frequency of keywords in the literature of this research field to explore disciplinary hotspots and evolutionary trends. Figure 8A displays the clustering results of the keyword co-occurrence network. These clusters represent different research subfields and thematic modules. Among them, #0 obstructive hypertrophic cardiomyopathy was the largest cluster, forming the core of the entire research field. Closely related clusters included those focusing on pathophysiological mechanisms, such as #2 diastolic function, #3 cardiac myosin, and #7 left ventricular hypertrophy, as well as those concerned with clinical outcomes, like #5 sudden cardiac death and #8 heart failure. The timeline view of keyword clusters (Fig. 8B) provides a longitudinal perspective on the evolution of research themes. Themes that emerged early and persist to the present include #6 heart failure and #7 subaortic stenosis, which focus on core clinical manifestations and traditional interventions. A pivotal shift was marked by the rise of the #3 cardiac myosin cluster and the recent emergence of the #8 cardiac myosin inhibitor cluster. Figure 8C is a keyword overlay visualization generated by VOSviewer, with the minimum keyword occurrence threshold set to 10, resulting in a co-occurrence network of 981 keywords, of which 50 met the threshold. The overlay map again confirms the network structure centered around “hypertrophic cardiomyopathy” and highlights the pivotal position of emerging high-frequency keywords like “Mavacamten” within the entire knowledge network. Burst detection analysis (Fig. 8D) provides quantitative evidence for these shifts, indicating the evolution of research hotspots and the dynamics of the academic frontier. Early keyword bursts (around 2018), such as “cardiac myosin” and “atp turnover”, signified a deepening focus on sarcomere biology. This was followed by bursts related to clinical guidelines, like “task force”. Most notably, the keyword “myosin inhibitor” exhibited the strongest and most recent burst (Strength = 3.29), which began in 2024 and is projected to continue into 2025. This unequivocally identifies it as the current most active research frontier. The emergence of the term “myosin inhibitor” as the most prominent keyword (strength 3.29) starting in 2024 coincides with the FDA’s approval of aficamten’s phase 3 trial results (SEQUOIA-HCM, presented in 2023) and the expansion of CMI indications. The exclusion of “non-obstructive HCM” from the leading keywords underscores a significant evidence gap, despite the existing clinical need. Consequently, keyword bursts function as real-time indicators of translational progress and unmet medical needs.

Table 7.

Top ten most frequent keywords in 2015–2025

Rank	Number of occurrences	Centrality	First appearance year	Keywords
1	162	0.12	2015	hypertrophic cardiomyopathy
2	101	0.02	2021	mavacamten
3	74	0.01	2021	double blind
4	60	0.03	2019	diagnosis
5	51	0.02	2021	explorer hcm
6	45	0.11	2015	heart failure
7	42	0.05	2020	management
8	35	0.03	2020	disease
9	34	0.03	2020	outcome
10	30	0.02	2021	obstructive hypertrophic cardiomyopathy

Occurrences: the number of times the keyword appeared in the 281 publications included in this analysis. First appearance year: the earliest year the keyword appeared in the analyzed literature (2015–2025)

Fig. 8.

Keywords related to new drug therapies for hypertrophic cardiomyopathy, 2015–2025. (A) Keyword clusters. (B) Timeline view of keyword clusters. (C) Co-occurrence network of keywords appearing ≥ 10 times. (D) The top 7 keywords with the strongest citation bursts

Discussion

The treatment landscape of HCM has recently been transformed by CMIs. Visual analysis of this field using CiteSpace and VOSviewer can depict its current status, research hotspots, and frontiers, methodologies now widely applied in medical research [18] Between 2015 and 2025, the annual publication output on novel drug therapies for HCM showed an overall upward trend. The period from 2015 to 2019 was characterized by low output, aligning with the phase of intensive investigation into the mechanisms of sarcomeric hypercontractility and the cardiac myosin target, representing a crucial accumulation period for targeted drug development [12, 19]. A significant increase in publications occurred after 2020, with an explosive growth phase notably in 2023–2024. This surge closely coincides with the publication of clinical trial results and regulatory approvals for CMIs like Mavacamten [13, 20, 21]. Studies such as EXPLORER-HCM not only validated the efficacy of Mavacamten in improving symptoms and reducing LVOT obstruction but also mechanistically confirmed the feasibility of treating HCM by inhibiting myosin hypercontractility [20]. The United States, as the country with the highest publication output, demonstrates its strong productivity in HCM novel drug research. However, closer collaboration was observed among European nations like England, the Netherlands, and France, facilitating the cross-border dissemination and translation of mechanistic research findings. In contrast, international collaboration involving China in this field appears relatively limited, suggesting a need for China to enhance global cooperation to elevate its research influence.

Keyword cluster analysis revealed that #0 obstructive hypertrophic cardiomyopathy constituted the core focus of research. Mavacamten, as the first targeted drug addressing myosin hyperfunction in HCM, marks the transition of HCM treatment from a ‘symptom-oriented’ to an ‘etiology-targeting’ era [7, 22]. The emergence of burst terms like “cardiac myosin” and “myosin inhibitor,” alongside highly cited references (e.g., Heitner et al., 2019; Maron et al., 2020), clearly points to (CMIs) as the core mechanism driving research progress [20, 23]. The rapid updates and high citation rates of the 2023 ESC and 2024 AHA/ACC guidelines reflect the high clinical recognition of the mechanisms and efficacy of these novel drugs, highlighting the efficient translation of research findings in this field [24]. Aficamten (CK-274), a next-generation CMI [25], emerged following the success of Mavacamten. It aims to optimize pharmacokinetic properties and widen the therapeutic window, thereby enhancing safety—particularly by reducing the impact on ejection fraction—while maintaining efficacy [26, 27]. Its successful phase III clinical trial (SEQUOIA-HCM) positions it as a drug under regulatory review and currently the most closely watched investigational therapeutic [26]. High-impact journals such as JACC and Circulation serve as primary platforms for disseminating mechanistic explorations and clinical advancements in this field, whose broad reach significantly facilitates the widespread assimilation of new knowledge [24, 25, 28].

Mavacamten, a promising treatment for obstructive HCM, faces limitations, such as a dose-dependent reduction in LVEF, necessitating careful monitoring. Its long-term safety beyond 2–3 years remains unknown, and its efficacy in nHCM is yet to be proven. Current evidence primarily stems from oHCM patients, with limited data on broader populations. The focus on oHCM arises from its quantifiable endpoint, the LVOT gradient, which facilitates trials. In contrast, nHCM lacks such markers, complicating research and therapy development. Recognizing this bias is crucial for future research prioritization. Although our bibliometric analysis cannot directly assess publication bias, the prevalence of industry-affiliated authors and rapid citation increases following positive trial outcomes indirectly suggest commercial funding’s influence on research trends. Readers should consider this context when interpreting the prevalence of CMIs in the literature. Future bibliometric studies could explicitly compare outputs from industry-funded and non-industry-funded research. Novel therapeutic strategies, such as gene editing targeting specific mutations, metabolic modulators to enhance cardiac function, and RNA-based therapies, are emerging. While promising in preclinical studies, their clinical application remains distant. These alternative strategies are not yet prevalent in published research, justifying the current focus on cardiac myosin inhibitors. Future bibliometric updates are encouraged to monitor the emergence of these new research fronts.

This study, employing bibliometric methods, analyzed the research hotspots and developmental trajectory in the field of novel HCM drug therapies, providing new insights for subsequent research directions. However, limitations exist. The data source was restricted to the Web of Science Core Collection, potentially introducing database bias by omitting publications from other databases or in non-English languages. Furthermore, the rapid evolution of this field and the incomplete data for 2025 might result in a certain lag in capturing the very latest trends. The bibliometric approach utilized in this study is not restricted to HCM new drug investigations but can be expanded to other biomedical domains experiencing rapid paradigm shifts. Subsequent studies could employ similar techniques to monitor the progression of targeted therapy in different cardiovascular conditions (e.g., HF with preserved ejection fraction, transthyretin amyloidosis cardiomyopathy) or alternative treatment approaches (e.g., gene therapy, RNA-based therapy, etc.). Additionally, as research in HCM drug therapy advances, periodic revisions to this bibliometric analysis will allow scholars to observe the advancement of myocardial myosin inhibitors beyond clinical trials. Integrating citation analysis with supplementary metrics (e.g., social media visibility, policy references, etc.) can offer a more comprehensive evaluation of research impact. Lastly, the collaborative network patterns identified in this study (e.g., England’s intermediary role and the concentration of highly productive authors) could offer valuable insights for early-career researchers aiming to establish effective global collaborations.

Based on our findings and identified gaps, we suggest the following specific future research directions: (1). Further research on non-obstructive hypertrophic cardiomyopathy (nHCM) should extend beyond LVOT gradient to encompass patient-reported outcomes, diastolic function markers, and cardiac imaging metrics. (2). Comparative studies between mavacamten and aficamten are essential for regulatory approval, focusing on efficacy, safety, tolerability, and drug interactions. (3). Long-term real-world evidence from post-marketing registries should evaluate the safety of cardiac myosin inhibitors (CMIs), particularly their impact on LVEF changes over time and outcomes such as sudden death and heart failure hospitalization. (4). Regular updates and expansion of bibliometric analyses should include other emerging HCM therapies such as gene editing and metabolic modulators as publications accumulate. (5). Subsequent bibliometric studies should explore the influence of industry funding on citation patterns and research impact using regression or network-based methods.

Conclusion

Research on novel pharmacological treatments for HCM has significantly increased over the past five years, with a focus on CMIs. This shift in research attention is driven by publication trends rather than direct clinical evidence. The United States leads in output, while European countries like England play crucial collaborative roles. Non‑obstructive HCM remains notably underrepresented, indicating a key evidence gap. Future research should prioritize: (1) nHCM trials using alternative endpoints (e.g., patient‑reported outcomes, imaging); (2) long‑term real‑world safety and efficacy studies of CMIs; and (3) personalized treatment strategies based on genetic and phenotypic profiles. These recommendations aim to enhance the scientific rigor and clinical relevance of future studies.

Author contributions

Yu Li, Zhen Chen, and Fen Ai contributed to conception and design; Zhen Chen and Yu Li contributed to data analysis and interpretation; Jinlei Li, Weihao Cheng and Bingxin Cheng contributed to data collection. All authors have read and agreed to the published version of the manuscript.

Funding

No.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

This study is a bibliometric analysis of publicly available scientific literature and did not involve direct interaction with human subjects or access to private health information. Therefore, ethical approval and informed consent were not required for this study.

Competing interests

The authors declare no competing interests.