Objectives: to assess the relevance of the RiboTaxa algorithm coupled with neural network learning based on analysis of vaginal microbiota metagenomic sequencing data for predicting prematurity in an identified at-risk population. Study description: Longitudinal follow-up of a cohort of pregnant women, with collection of biological samples, and a posteriori case-control comparison based on the occurrence of an event (premature birth).
There is currently no reliable clinical or biological diagnosis to predict premature birth. Recent work using metagenomic data analysis coupled with artificial intelligence approaches suggests that there may be a vaginal microbiota signature during pregnancy that correlates with the occurrence of preterm birth. The aim of the study is to use biological samples to confirm the identification of these vaginal microbiota signatures as a means of predicting preterm birth.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
150
Any pregnant woman attending the obstetrics department of Clermont-Ferrand University Hospital who requires a vaginal swab (VS) for her clinical situation (routine care) will be offered the study. If the participant accepts, the usual swab will be doubled and performed simultaneously in a single procedure using two swabs. The first swab will be sent to the laboratory and used for diagnosis. The second swab will be used for microbiota analysis.
CHU de Clermont-Ferrand
Clermont-Ferrand, France
RECRUITINGEarly diagnosis of preterm birth using vaginal microbiota analysis
prematurity yes/no (define by birth \<37WG)
Time frame: at birth
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