Knowledge Mapping for Prediction of Spontaneous Preterm Birth

doi:10.21203/rs.3.rs-5276735/v1

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Knowledge Mapping for Prediction of Spontaneous Preterm Birth

https://doi.org/10.21203/rs.3.rs-5276735/v1

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Background

Preterm birth (PTB) before 37 weeks gestation, especially spontaneous premature birth (sPTB), poses significant global health challenges, with rising rates linked to advanced and multiple pregnancies. Despite efforts to understand sPTB and biomarkers like cervical length and fetal fibronectin, accurate prediction is still difficult to achieve. Recent research in academic journals has focused on sPTB prediction, prompting our bibliometric analysis to understand the current situation and explore the new research direction.

Methods

We used keywords in the Web of Science Core Collection (WoSCC) to search for articles related to sPTB prediction from 2004 to 2023. Subsequently, we primarily employed three distinct software tools (VOSviewer, CiteSpace, and Python) for conducting this bibliometric analysis.

Results

Focusing on sPTB as the primary subject, a total of 647 papers have been published in 136 academic journals. There are the most publications from The United States (n = 245, 39.26%), and the University of London contributed the most publications (n = 57, 8.81%). The American Journal of Obstetrics and Gynecology is the most productive academic journal on sPTB prediction [n = 74, 11.44%; impact factor (IF) = 8.7]. Through the co-occurrence and cluster analysis, we found that recent research has focused on is “pamg-1”, “uterocervical angle”, “twin pregnancy”, “quantitative ultrasound”, and “lactobacillus iners”.

Conclusion

We utilized bibliometric software to perform a comprehensive analysis of the literature concerning sPTB prediction. Broadly, the primary focus of future sPTB prediction lies in the application of novel ultrasound and biological markers, as well as in predicting sPTB in twin pregnancies.

spontaneous premature birth

prediction

bibliometric study

VOSviewer

CiteSpace

The study could provide comprehensive guidance for clinicians and researchers focusing on prediction of sPTB.
This study aids clinicians and researchers in gaining a better understanding of the current primary focus areas in preterm birth prediction.

Preterm birth (PTB) refers to delivery that occurs before 37 gestational weeks, including spontaneous and iatrogenic PTB[1], with spontaneous premature birth (sPTB) accounts for approximately 70%[2]. Recently, the PTB rate has been on the rise due to an increase in advanced pregnancies and multiple pregnancies[3]. It is estimated that 15 million babies are born prematurely every year, and PTB complications are the leading cause of morbidity and mortality under the age of 5 years[4]. Moreover, many survivors are at greater risk of a range of long-term medical complications such as respiratory, gastrointestinal, cardiovascular, and metabolic disorders[5]. PTB, particularly sPTB, are a global problem. Therefore, early screening, precise identification and effective prevention are crucial for reducing the incidence and mortality rate of sPTB.

Some researchers have devoted themselves to exploring the pathogenesis and related biomarkers of sPTB and have made some important contributions[6]. The biomarkers for PTB may be classified as clinical, microbiological, ultrasonographic and biochemical. In clinical, cervical length (CL) and fetal fibronectin (fFN) in the cervicovaginal fluid are the most widely used markers for predicting the risk of sPTB in the second trimester of pregnancy[7]. However, so far, these biomarkers have not been able to reliably predict the risk of developing sPTB, accurate prenatal assessment of the risk of sPTB remains a great challenge[8].

Numerous scholarly publications over the past decade have delved into the prediction of sPTB. However, no attempts have been made to analyze the data on publications systematically to construct knowledge map. Bibliometrics employs statistical methods to quantitatively analyze lots of documents in a specific research field, revealing various facets and research trends[9]. Consequently, we conducted a bibliometric analysis to summarize the knowledge base in that neighborhood of predicting sPTB and to comprehensively outline the relevant research hotspots, so that researchers could understand and explore this field more easily.

Data Collection

Data spanning from 2004 to 2023 in the field were retrieved from the Web of Science Core Collection (WoSCC) database on April 10, 2024. The database source was limited to Science Citation Index Expanded (SCIE) and publication types to “article”. The main search topics were “sPTB”, and “prediction”. The detailed search strategy is presented in the Supplementary Material 1. To prevent database update bias, all searches were conducted on the same day, followed by the download of raw data from WoSCC as plain text files containing complete records.

Data Analysis

A bibliometric program, VOSviewer (Van Eck and Walt-man, Leiden University, Leiden, The Netherlands) is utilized to perform the co-occurrence of authors/institutes/countries/journals. CiteSpace 5.6 (Chaomei Chen, Drexel University, USA) is a bibliometric tool that reveals trends and dynamics within the literature, highlighting key points in a specific field.[10, 11]. The CiteSpace parameters were as follows: link retaining factor (LRF = 3), look back years (LBY = 8), e for top N (e = 2.0), time span (2004–2023), years per slice (1), links (strength: cosine, scope: within slices), selection criteria (g-index: k = 25), and minimum duration (MD = 1). Python (Python Software Foundation, Wilmington, DE) was employed to draw the world map showing the country distribution of publications. Microsoft Office Excel 2019 (Microsoft, Redmond, Washington, USA) was used to manage the data and analyzed the publication trend.

Analysis of Publication Trend and Country Distribution

A total of 647 articles related to the “prediction of sPTB” published between 2004 and 2023 were retrieved from the WOS database. A trend indicating a rise in the number of publications from 2004 to 2023 was noted. The number of publications reached a peak (66 articles) in 2021 (Fig. 1). Figure 2a displayed the top 10 most productive countries, while Fig. 2b showed the world map of publications distribution. The United States emerged as the most productive country, with 245 articles published, followed by United Kingdom (n = 93) and China (n = 43). The VOSviewer software was used to analyze the network visualization of cooperating relationships between countries. Only countries with a minimum of 5 articles were included. Among the 34 countries meeting the threshold, the United States was at the center of research on prediction of sPTB and was in close collaboration with England, Italy, and South Korea (Fig. 2c). Figure 2d showed that the articles from China were mainly published after 2020.

Contribution of Institutions

The top 10 institutions are mainly located in United States (n = 8), and United Kingdom (n = 2) in Fig. 3a. A network map was generated for institutions with five or more publications (T = 5). In the Fig. 3b, there is extensive cooperation between institutions in various countries. For example, the King's College London not only has full cooperation with domestic research institutions, but also maintains close cooperation with the United States and other countries.

Distribution of Authors and Co-Cited Authors

More than 3,000 authors and 7,000 co-cited authors contributed to the investigation on prediction of sPTB. Co-cited authors refer to authors who are cited together[12]. Table 1 showed the top 10 authors and co-cited authors. Of the top 10 contributing authors, Berghella V (n = 27) was ranked first, followed by Iams JD (n = 24) and Shennan AH (n = 23). Among the top 10 co-cited authors, four authors had co-citations over 200. Goldenberg RL (302 co-citations) was ranked first, followed by Iams JD (283 co-citations), Romero R (233 co-citations) and Berghella V (206 co-citations). The network map of co-authorship and co-cited relationship were constructed by VOSviewer (Fig. 4). In the Fig. 4, each node on the network map represents an author, where the size of the node denotes the number of articles, and the thickness of the line between nodes indicates collaborative intensity[13]. Berghella V and Iams JD were not only the prolific authors and active co-cited authors but also exhibited close co-cited relationships with other active co-cited authors in Table 1 and Fig. 4.

Table 1

The top 10 authors/co-cited authors involved in research on prediction of sPTB.
Rank	Authors	Count	Co-authors	Count
1	Berghella V	27	Goldenberg RL	302
2	Iams JD	24	Iams JD	283
3	Shennan AH	23	Romero R	233
4	Seed PT	22	Berghella V	206
5	Park KH	20	TO MS	104
6	Nicolaides KH	18	Conde-Agudelo A	101
7	Rebarber A	15	Blencowe H	90
8	Tribe RM	15	Honest H	84
9	Fox NS	14	Owen J	84
10	Kim YM	14	Hassan SS	82

Distribution of Journals

In total, 136 academic journals have published research investigating the prediction of sPTB. In the Table 2, American Journal of Obstetrics and Gynecology (n = 74, impact factor (IF) = 8.7) published the highest number of papers, followed by Journal of Maternal Fetal Neonatal Medicine (n = 71, IF = 1.7), Ultrasound in Obstetrics Gynecology (n = 49, IF = 6.1). Fig.S1 displayed the dual-map overlay of journals. The left and right sides corresponded to the citing and cited journals, respectively. The label represents the subject covered by the journal and colored curves reflect the citation paths[14]. Two green citation paths indicated that articles from Molecular/Biology/Genetics journals and Health/Nursing/Medicine journals were commonly cited in articles from the Medical/Medicine/Clinical journals.

Table 2

The top 10 journals in the field of prediction of sPTB from 2004 to 2023.
Rank	Journal	Count	Percent	Country	IF	JCR
1	American Journal of Obstetrics and Gynecology	74	11.44	United States	8.7	Q1
2	Journal Of Maternal Fetal Neonatal Medicine	71	10.97	United Kingdom	1.7	Q3
3	Ultrasound In Obstetrics Gynecology	49	7.57	United States	6.1	Q1
4	European Journal of Obstetrics Gynecology and Reproductive Biology	26	4.02	Netherlands	2.1	Q2
5	Obstetrics And Gynecology	25	3.86	United States	5.7	Q1
6	American Journal of Perinatology	24	3.71	United States	1.5	Q3
7	Acta Obstetricia Et Gynecologica Scandinavica	22	3.40	United Kingdom	3.5	Q1
8	Plos One	18	2.78	United States	2.9	Q1
9	Bjog An International Journal of Obstetrics and Gynaecology	17	2.63	United Kingdom	4.7	Q1
10	Journal Of Perinatal Medicine	17	2.63	Switzerland	1.7	Q3

Reference Co-Citation Analysis

Table 3 presents the top 10 co-cited references related to research exploring prediction of sPTB. Co-cited of references refers to the relationship between two references cited simultaneously by a third reference. Among them, the article entitled “Epidemiology and causes of preterm birth” published in Lancet by Goldenberg RL et al., the article entitled “Vaginal progesterone for preventing preterm birth and adverse perinatal outcomes in singleton gestations with a short cervix: a meta-analysis of individual patient data” published in American Journal of Obstetrics and Gynecology by Romero R et al. and the article entitled “National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications” published in lancet by Blencowe H et al. have been co-cited more than 40 times, indicating their significant impact in the field of predicting sPTB. Additionally, VOSviewer was used to create the network map of co-cited references (Fig. 5a).

Table 3

The top 10 co-cited references related to prediction of sPTB.
Rank	Count	Year	Co-cited references	Journal	IF
1	58	2008	Goldenberg RL, 2008, LANCET, V371, P75, DOI 10.1016/S0140-6736(08)60074-4	Lancet	98.4
2	40	2018	Romero R, 2018, AM J OBSTET GYNECOL, V218, P161, DOI 10.1016/j.ajog.2017.11.576	American Journal of Obstetrics and Gynecology	8.7
3	40	2012	Blencowe H, 2012, LANCET, V379, P2162, DOI 10.1016/S0140-6736(12)60820-4	Lancet	98.4
4	37	2017	Esplin MS, 2017, JAMA-J AM MED ASSOC, V317, P1047, DOI 10.1001/jama.2017.1373	JAMA	63.1
5	35	2014	Romero R, 2014, SCIENCE, V345, P760, DOI 10.1126/science.1251816	Science	44.7
6	35	2011	Hassan SS, 2011, ULTRASOUND OBST GYN, V38, P18, DOI 10.1002/uog.9017	Ultrasound in Obstetrics & Gynecology	6.1
7	31	2019	Chawanpaiboon S, 2019, LANCET GLOB HEALTH, V7, P0, DOI 10.1016/S2214-109X(18)30451-0	Lancet Global Health	19.9
8	30	2007	Fonseca EB, 2007, NEW ENGL J MED, V357, P462, DOI 10.1056/NEJMoa067815	New England Journal of Medicine	96.2
9	29	2013	Abbott DS, 2013, AM J OBSTET GYNECOL, V208, P0, DOI 10.1016/j.ajog.2012.10.890	American Journal of Obstetrics and Gynecology	8.7
10	27	2012	Goya M, 2012, LANCET, V379, P1800, DOI 10.1016/S0140-6736(12)60030-0	Lancet	98.4

The timeline view in CiteSpace could visually display the changes of research trends over time. In the timeline view, nodes on the right represent references with more recent publication time, while nodes on the left represent references with earlier publication time. Furthermore, the rightmost label is the cluster label, reflecting the theme of the research field and ordered by the number of co-cited references from largest to smallest. Moreover, in the timeline view, the more nodes there are, the more important the research topic is. Reference with citation bursts refers to the reference that is commonly cited by the articles related to the research filed over a period of time. CiteSpace was employed to search for references with strong citation burst and construct the timeline view of co-cited references (Fig. 5b and Fig. 5c). Among the top 20 references with citation bursts (Fig. 5b), the paper entitled “Epidemiology and causes of preterm birth” published in Lancet by Goldenberg RL et al., which is the reference with the strongest citation burst(strength = 18.77). As shown in the timeline view (Fig. 5c), The clusters were relatively important and have received continued attention include “#0 pamg-1”, “#2 uterocervical angle”, “#5 twin pregnancy”, “#8 quantitative ultrasound”, and “#10 lactobacillus iners”.

Analysis of keywords and burst keywords

Based on the keyword co-occurrence analysis in VOSviewer, 12,263 keywords were extracted and the network map of keywords was constructed (Fig. 5d). Figure 5d showed that the top 5 keywords with the highest rate of occurrence included “prediction”, “preterm birth”, “risk”, “cervical length” and “women”. In addition, keywords with strong citation bursts were identified through CiteSpace (Fig. 5e). Recently, keywords exhibiting strong citation bursts include ‘‘quantitative fetal fibronectin’’ (2016–2020, strength 3.97), ‘‘uterocervical angle’’ (2018–2021, strength 4.12), ‘‘multiple protein’’ (2020–2020, strength 3.47) and “interleukin 6’’ (2021–2022, strength 3.36).

General information

Our research indicated that 647 papers have been published across 136 academic journals, involving 3214 authors and 1010 institutions from 65 countries/regions. We observed a consistent upward trend in publications related to the prediction of sPTB from 2004 to 2023. This growing number of articles revealed that the level of teamwork and funds in the field is constantly improving and many achievements have been made. This study will be helpful in guiding researchers in selecting relevant topics and in finding suitable teams to collaborate with and research platforms to use. The United States, as the world leader in both economics and science, has contributed the highest number of articles on the prediction of sPTB (n = 245, 39.26%). While, about 80% of top institutions were from the United States. Close collaborations were observed between countries/regions and institutions, but the cooperation among agencies is closer than between countries, suggesting international collaborations need to be further developed.

The top 10 academic journals published 343 articles, accounting for 53.01% of all articles. The American Journal of Obstetrics and Gynecology (n = 74) leaded in the number of publications among all journals, followed by the Journal of Journal of Maternal Fetal Neonatal Medicine (n = 71) and Ultrasound in Obstetrics Gynecology (n = 49), suggesting a significant interest in sPTB prediction research in these journals. These data are valuable for future scholars in choosing journals when submitting manuscripts related to the prediction of sPTB. By analyzing authors and co-cited authors, we identified Berghella V (n = 27 and 206 co-citations) and Iams JD (n = 24 and 283 co-citations) as the most productive and influential researchers. BerghellaV is an outstanding expert in obstetrics and gynecology, and has developed the maternal-fetal evidence-based guidelines. In addition, BerghellaV is committed to the application of CL in predicting sPTB. [15]. At the same time, exploring the application of biological markers (e.g.: fFN, granulocyte colony-stimulating factor and alpha-fetoprotein) in predicting sPTB is Iams JD‘s main research direction[16–18].

Research Hotspots

By using CiteSpace to capture burst keywords and build the timeline view, we further explored and excavated emerging research hotspots. The recent research hotspots mainly focus on three aspects. On the one hand, the role of new ultrasound markers in predicting sPTB; on the other hand, the application of new biological markers in predicting sPTB, and finally, predicting sPTB in twin pregnancies.

Ultrasound is widely recognized as the primary method for predicting premature delivery in pregnant women[19], and the measurement of CL is the most common predictor[15, 20]. Our research indicated that the uterocervical angle (UCA) and quantitative ultrasound have emerged as research hotspots in recent years. Related studies have found that UCA performed better than CL in predicting sPTB, and UCA can serve as a useful ultrasound marker for the predicting PTB in high-risk patients[21, 22]. Conversely, one study showed that UCA was unable to predict PTB in women at risk between 20 and 31 weeks of pregnancy, while CL exhibited notable accuracy in predicting PTB[23]. Furthermore, a recent study suggested that measuring the UCA combined with CL is more accurate than measuring them separately for predicting the risk of PTB[24]. Quantitative ultrasound is a method that uses quantitative ultrasound data to reflect changes in tissue microstructure before the onset of clinical symptoms[25]. Therefore, quantitative ultrasound features could detect early cervical microstructure changes[26, 27]. And researches have demonstrated the robust consistency and repeatability of quantitative ultrasound measurements[28, 29]. A prospective study found that the quantitative ultrasound feature provided a small but statistically significant improvement in PTB risk assessment[25]. Burgos-Artizzu XP et al. found that the combination of quantitative ultrasound and CL significantly enhanced the predictive performance[30]. Overall, further validation of the predictive efficacy of these new ultrasound indicators is still needed through multicenter prospective cohort studies.

In addition to ultrasonic markers, many researchers are also committed to finding biological markers to predict sPTB. Measurement of fFN concentration in the cervicovaginal fluid is a known biological predictor of sPTB[31]. Nevertheless, several studies have indicated that fFN has limited predictive capacity for PTB, necessitating further research into alternative screening modalities[32, 33]. Our research revealed that PAMG-1 and lactobacillus iners have been the research hotspots in recent years. Transudation of PAMG‐1 occurs through chorioamniotic pores in fetal membranes during uterine contractions. Additionally, the degradation of the extracellular matrix of fetal membranes is due to the inflammatory process of labor and/or infection could allow PAMG‐1 to permeate[34]. Therefore, PAMG‐1 may be a more accurate predictor of imminent delivery. Certain researchers have observed that the predictive accuracy of PAMG-1 is comparable to that of fFN[35, 36]. However, unlike fFN, it is unaffected by recent sexual intercourse or contamination with urine, and it offers advantages of lower costs and faster testing[37]. Moreover, in the prediction of sPTB within 7 days of testing in women with signs and symptoms of preterm labor, the positive predictive value (PPV) of PAMG-1 surpassed that of fFN and CL[34, 38]. Furthermore, the combination of PAMG-1 with CL demonstrated high predictability for impending spontaneous preterm delivery in women with threatened preterm labor[36, 39]. Moreover, the vaginal microbiome plays an important role in the health of the female reproductive tract, and a Lactobacillus species-dominated microbiome has been considered important to maintain a healthy state by producing lactic acid and lowering the vaginal pH. The presence of three Lactobacillus species―L. crispatus, L. jensenii, and L. gasseri were associated with reduced risk of PTB, while the presence of L. iners was associated with increased risk of PTB[40]. Studies from different countries also showed a significant association between L. iners and an increased prevalence of sPTB[41]. In fact, there is a lot of controversy over whether L. iners is advantageous or detrimental for the host microbiota. Some studies suggested that when there is an abundance of L. iners because of its low production of lactic acid, the vaginal microflora does not maintain a low enough pH to be protective. This condition allows a higher diversity of pathological species, which leads to a higher risk of PTB[42]. Conversely, a minority of researchers found that the majority of women with an L. iners-dominated vaginal community will deliver at term without experiencing adverse pregnancy outcome[43, 44]. Due to the significant complex and diverse individual discrepancies of the vaginal microbiota, more studies are required to make clear whether L. iners can be used as a novel biomarker to detect the presence of sPTB.

Rencently, research hotspots have focused on predicting sPTB in twin pregnancies, due to the increase in the number of twin pregnancies caused by older reproductive age and the use of assisted reproductive technologies [45, 46]. The incidence of PTB in twins is ten times greater than in singleton pregnancies[47]. Consequently, precise prediction of PTB will greatly reduce the incidence of PTB in twin pregnancy and improve the physical quality of perinatal infants. Certain studies have proposed that a single measurement of CL at 20–24 weeks of gestation is a good predictor of PTB at < 28, <32, and < 34 weeks of gestation in asymptomatic women with twin gestations. However, in patients with symptoms of preterm labor, the accuracy of CL measurement in predicting PTB is limited[48, 49]. A meta-analysis found that fFN has low to moderate accuracy in predicting sPTB in both asymptomatic and symptomatic women with twin gestations[50]. In addition, the combination of fFN and CL did not notably enhance predictive performance compared to either CL or fFN alone[50]. In summary, no single screening modality for sPTB can be universally applied for women with twin pregnancies. We still need to develop new markers to improve the accuracy of predicting sPTB in women with twin pregnancies.

Overall, our study systematically analyzed the field of predicting sPTB using bibliometric tools and forecasted future research trends. This study not only aids researchers in gaining a comprehensive understanding of the advancements and evolution in predicting sPTB over the past two decades but also offers insights for their future research direction. Inevitably, our study has some limitations. Primarily, we only analyzed English-language data from WoSCC, omitting information from other crucial search engines (such as PubMed and Ovid) and different languages. Secondly, despite ongoing updates to the WoSCC database, our study encompasses the majority of articles related to predicting sPTB between 2004 and 2023, suggesting that new data might not significantly impact the outcomes. Finally, most of the information was extracted by bibliometrics software, so our results may also be biased. For example, we cannot rule out the possibility that some authors have the same acronym and some keywords have different expressions.

We used bibliometrics software to conduct a relatively comprehensive and objective analysis of the landscape concerning the prediction of sPTB, and explored the frontiers of research in this field. Broadly speaking, the application of new ultrasound markers and biological markers, and twin pregnancies, are the research focus of predicting sPTB in the future.

Funding

Funding by the National Scientific Foundation Committee of China (82171938, 82202156) and Science and Technology Projects in Guangzhou (202201011238).

Competing interests

No competing interests exists for any of the authors.

Authors Contributions

Hui Shen and Lihe Zhang: manuscript writing, data management, data analysis, and manuscript editing. Han Bai and Lihong Wu: data collection and data management. Liu Du and Hongning Xie: project development, data management and manuscript writing.

Ethics approval

Not applicable.

Authors' information

Hui Shen and Lihe Zhang contributed equally to this work.

Hongning Xie and Liu Du contributed equally to this work.

Authors and Affiliations

Department of Ultrasonic Medicine, Fetal Medical Centre, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.

Hui Shen, Lihe Zhang, Han Bai, Lihong Wu, Liu Du, Hongning Xie

Correspondence to:

Correspondence to Hongning Xie

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No competing interests reported.

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Knowledge Mapping for Prediction of Spontaneous Preterm Birth

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Abstract

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Background

Materials and Methods

Data Collection

Data Analysis

Results

Analysis of Publication Trend and Country Distribution

Contribution of Institutions

Distribution of Authors and Co-Cited Authors

Distribution of Journals

Analysis of keywords and burst keywords

Discussion

General information

Research Hotspots

Conclusion

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