Knowledge mapping for prediction of spontaneous preterm birth

Area of Science:

• Obstetrics and Gynecology
• Reproductive Health
• Biomedical Informatics

Background:

• Spontaneous preterm birth (sPTB) before 37 weeks gestation presents global health challenges, with increasing rates.
• Accurate prediction of sPTB remains difficult despite research into pathogenesis and biomarkers.
• Recent academic focus on sPTB prediction necessitates a bibliometric analysis to guide future research.

Purpose of the Study:

• To conduct a bibliometric analysis of research on spontaneous preterm birth (sPTB) prediction.
• To identify current trends, key publications, and emerging research directions in sPTB prediction.
• To understand the global research landscape and influential institutions in sPTB prediction.

Main Methods:

• Bibliometric analysis of 647 papers published between 2004 and 2023 from the Web of Science Core Collection.
• Utilized VOSviewer, CiteSpace, and Python for data analysis, including co-occurrence and cluster analysis.
• Keywords focused on spontaneous preterm birth (sPTB) prediction.

Main Results:

• The United States led publications (39.26%), with the University of London being the most prolific institution.
• The American Journal of Obstetrics and Gynecology published the most papers (11.44%) on sPTB prediction.
• Emerging research themes include "pamg-1", "uterocervical angle", "twin pregnancy", "quantitative ultrasound", and "lactobacillus iners".

Conclusions:

• Bibliometric analysis reveals key trends in spontaneous preterm birth (sPTB) prediction research.
• Future prediction efforts will likely involve novel ultrasound techniques and biological markers.
• Predicting sPTB in twin pregnancies is a significant area for future research focus.