Knowledge mapping of disease-modifying therapy (DMT) in multiple sclerosis (MS): A bibliometrics analysis

Abstract

Background: Multiple sclerosis (MS) is a heterogeneous autoimmune disease, with a rapidly evolving body of literature on disease-modifying therapy (DMT) that urgently needs to be synthesized and regularized.

Methods: The original material used for the analysis was obtained from the Web of Science Core Collection (WoSCC) in the Science Citation Index Expanded Edition (SCI-E). The data material was accessed through VOSviewer, Citespace, R package "Bibliometrix", and Scimago Graphica for data analysis and visualization. Among them, the clustering algorithm based on the Largest Likelihood Ratio (LLR) and the burst citation algorithm is the key.

Results: As of November 6th, 2022, 4142 publications related to emerging disease-modifying therapies (e-DMT) for MS, 6521 publications related to traditional disease-modifying therapies (t-DMT) for MS, and 1793 publications in cross-cutting disease-modifying therapies (I-DMT) for MS were included in the analysis, respectively. Publications related to DMT in MS were analyzed descriptively (for three subjects: country, institution, and author) and predictively (for two subjects: keywords and references) separately according to three sections: e-DMT, t-DMT, and I-DMT. Topics that still have relevant reference output as of 2022 include the safety of Coronavirus disease 2019 (COVID-19) mRNA vaccination, therapeutic inertia (TI), cladribine tablets, autologous hematopoietic stem cell transplantation (aHSCT), progressive multiple sclerosis, and pediatric multiple sclerosis.

Conclusion: The future research focus for MS DMT is the combination trial or cross-trial of various treatment methods to improve the development of individualized treatment plans for MS patients. The exact contents of the research frontiers are included but not limited to ocrelizumab, fingolimod and other monoclonal antibodies, fumaric acid ester, cladribine tablet, aHSCT, and other interventions of randomized controlled trials (RCTs); the impact of mRNA COVID-19 vaccination on MS patients; TI, patient adherence, and other medical management issues; and continued exploration of biomarkers for more accurate disease classification based on the existing clinical indication classification.

Keywords: Bibliometric; Bibliometrix; CiteSpace; Emerging/traditional disease-modifying therapy; Multiple sclerosis; VOSviewer.

Fig. 1

Schematic representation of the literature search formula and the time of the first appearance of keywords. (A) Retrieval strategy and logical thought. #1 indicates retrieval of MS-related content. #2 denotes the retrieval of e-DMT-related content with “targeted therapy, Ocrelizumab, Natalizumab, Fingolimod, natalizumab, alemtuzumab, rituximab, ofatumumab, Siponimod, daclizumab, Masitinib, Ublituximab, Evobrutinib, Tolebrutinib, Fenebrutinib, Orelabrutinib, Daclizumab, Tocilizumab, Amiselimod " as keywords for the search. #3 Indicates retrieval of MS e-DMT related literature. #4 Indicates retrieval of t-DMT related content with the keywords “diseases modify treatment(s), Dimethyl fumarate (DMF), Teriflunomide, Interferon beta (IFN-β), Glatiramer acetate, mitoxantrone, cladribine, diroximel fumarate(DRF), Ozanimod, Oral Myelin, Nanocrystalline gold (CNM-AU8), CS-0777, Ibudilast, Vidofludimus (CNM-AU8), Ibudilast (CNM-AU8), Ibudilast (CNM-AU8), Ibudilast (CNM-AU8) Vidofludimus (IMU-838)" were searched. #5 indicates the acquisition of MS t-DMT-related literature. #6 indicates that the acquired MS e-DMT-related literature needs to be removed from containing MS t-DMT-related literature to be the true e-DMT-related literature for our target analysis. #7 denotes that the MS t-DMT-related literature obtained needs to be removed from the MS e-DMT-related literature to be the true t-DMT-related literature that we aim to analyze. #8 Indicates that the MS t-DMT-related literature acquired needs to be combined with the MS e-DMT-related literature to be the true I-DMT-related literature for our target analysis. The pink color block indicates that we first describe the overall characteristics of e-DMT, t-DMT, and I-DMT in Fig. 2. The orange color block indicates that we will analyze author-related information in Fig. 3. The yellow color block indicates that we will show the results of co-citation and cluster analysis of references based on CiteSpace for visual presentation in Fig. 4, Fig. 5, Fig. 6. The contents of the green color block and cyan color block have been explained in detail in Fig. (B) To make it easier for the reader to understand and get an immediate sense of when the various interventions first appeared, we searched PubMed for the keywords used in the search formula and visualized when the keywords first appeared. Detailed information and reference information we place in Table S5.

Fig. 2

Evaluate the overall trend of the three factors from twelve indicators. (A) e-DMT; (B) t-DMT; (C) I-DMT. The first content was the time span of these studies. E-DMT began in 1999, t-DMT began in 1991, and I-DMT started in 2003, consistent with clinical practice because t-DMT relieved the patient's symptoms significantly ahead of any other DMTs. Around the 21st century, targeted therapy and other e-MDT have also been added to the treatment of MS with the development of targeted drugs. Their comparison was naturally conducted after basic research on both t-DMT and e-DMT therapy. Since t-DMT-related treatment strategies appeared earliest, they contained the most significant number of journals (1133). Since e-DMT had just been developed in the last 20 years, it included an intermediate number of journal types (903, between 346 and 1133). Since I-DMT grew in the past ten years, I-DMT held the least number of journals (346). The number of published articles for this three-factor was 4,142, 6,521, and 1793. The annual growth rate of published articles was 23.17%, 16.68%, and 21.06%, respectively. The number of authors in this three-factor was 18,598, 22,309, 7706; the number of articles published by a single author was 161, 323, 121. The proportion of international cooperation publications was 28.17%, 24.69%, and 30.9%, respectively. The average number of authors per article was 7.8, 7.04, and 8.46. The keywords given by the authors were 5,949, 22,309, and 7706. The cited references were 87,632, 122,614, and 37,689. The average life of each article from publication to citation reduction was 5.78, 11, and 5.86. Since most of the references on e-DMT and I-DMT had emerged in the last two decades, the average life expectancy was influenced by the time of publication, and therefore, there would be a mistake in predicting the average life for three-factor. Finally, the average citation times of each article were 29.82, 36.07, and 33.27.

Fig. 3

Evaluation of the performance of the three factors in the number of publications from the four levels of the country, institution, researcher, and journal (A) e-DMT; (B) t-DMT; (C) I-DMT.

Fig. 4

Co-citation and cluster analysis of related references of e-DMT based on Citespace (A) Clustering network graph. Sampling and data analysis were performed based on Citespace using the parameters identified in the figure, where a circle indicates a piece of literature and the same color indicates attribution to a cluster, and cluster labels were obtained based on the words in the titles and abstracts of the literature under that cluster using the maximum likelihood ratio method. (B) Clustering timeline map. The purple numerical labels indicate when the literature appeared. A circle in the figure represents a piece of literature, and a circle with purple color indicates that the literature has a high mediational centrality, which can be interpreted as literature that has had a profound impact on the research of MS DMT. The earlier appearance of the circle indicates that the related research subclass of the cluster was developed earlier, and the later appearance of the circle indicates that the related research subclass of the cluster is still in a hot research state in recent years.

Fig. 5

Co-citation and cluster analysis of related references of t-DMT based on Citespace.

Fig. 6

Co-citation and cluster analysis of related references of I-DMT based on Citespace.

Fig. 7

Prediction model of a keyword lifetime.

Fig. 8

Venn diagram for keywords with burst citation characteristics.