Knowledge mapping for prediction of spontaneous preterm birth

Abstract

Introduction

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 the pathogenesis and related biomarkers of sPTB, 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.