Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid.
Lactobacillus iners
Ureaplasma parvum
machine learning
prediction model
preterm birth
vaginal microbiome
Journal
American journal of reproductive immunology (New York, N.Y. : 1989)
ISSN: 1600-0897
Titre abrégé: Am J Reprod Immunol
Pays: Denmark
ID NLM: 8912860
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
revised:
04
04
2021
received:
04
01
2021
accepted:
22
04
2021
pubmed:
28
4
2021
medline:
27
1
2022
entrez:
27
4
2021
Statut:
ppublish
Résumé
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. 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. 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 Using machine learning, the bacterial risk score in CVF can be used to predict PTB.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e13435Informations de copyright
© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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