Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid

Abstract

Problem Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activation of an inflammatory response as a result of ascending genital tract infection. Despite various studies on the effects of the vaginal microbiome on PTB, a practical method for its clinical application has yet to be developed. Method of study In this case‐control study, 94 Korean pregnant women with PTB (n = 38) and term birth (TB; n = 56) were enrolled. Their cervicovaginal fluid (CVF) was sampled, and a total of 10 bacteria were analyzed using multiplex quantitative real‐time PCR (qPCR). The PTB and TB groups were compared, and a PTB prediction model was created using bacterial risk scores using machine learning techniques (decision tree and support vector machine). The predictive performance of the model was validated using random subsampling. Results Bacterial risk scoring model showed significant differences (P < 0.001). The PTB risk was low when the Lactobacillus iners ratio was 0.812 or more. In groups with a ratio under 0.812, moderate and high risk was classified as a U. parvum ratio of 4.6 × 10–3. The sensitivity and specificity of the PTB prediction model using bacteria risk score were 71% and 59%, respectively, and 77% and 67%, respectively, when white blood cell (WBC) data were included. Conclusion Using machine learning, the bacterial risk score in CVF can be used to predict PTB.

Ureaplasma and Prevotella Colonization with Lactobacillus Abundance During Pregnancy Facilitates Term Birth

“…Ureaplasma and Prevotella, which were previously considered pathogenic, did not cause complications when coexisting with Lactobacillus. This was consistent with the bacterial risk score model developed by this research team to predict PTB through machine learning using a PCR kit 19 . In this study, Ruminococcus and Gemmiger were suggested to play a protective role against pathogenic bacteria.…”

“…The Mollicutes class, which includes Ureaplasma and Mycoplasma, has also been studied for its potential association with PTB 20,35 . However, there was controversy about colonization in healthy women, and this study revealed that colonization itself was not associated with all infection and pregnancy complications 19 .…”

“…For future research, it can be applied in clinical practice through a method that can more quantitatively evaluate the microbiome relationship or it may be useful to substitute PCR tests that can utilize whether mRNA is expressed from DNA 19 . In order to con rm the biological mechanism, it may be necessary to study the analysis of proteomics and metabolomics in addition to genomics.…”

“…In addition, the effect of the microbiome of pregnant women on the prognosis during pregnancy and PTB has been studied. [17][18][19][20][21] However, according to previous studies, the vaginal microbiome shows differences according to race and place of residence 16,22 , and a study has also reported that there is no relationship between PTB and a speci c microbiome in some races. 23 Ureaplasma spp., which is known to be associated with PTB, colonizes the vagina in women without any pregnancy complications.…”

Contribution of Lactobacillus iners to Vaginal Health and Diseases: A Systematic Review

“…However, most studies did not identify a significant relationship between the Lactobacillusdominant vaginal microbiome and PTB in pregnant black women (Hyman et al, 2014;Nelson et al, 2016;Subramaniam et al, 2016;Stout et al, 2017). Conversely, three studies reported that L. iners was associated with a decreased risk of PTB (Fettweis et al, 2019;Tabatabaei et al, 2019;Park et al, 2021). Therefore, the association between L. iners and PTB risk is controversial (Table 1).…”