Early Diagnosis of Premature Births by Analysis of the Vaginal Microbiota

NCT ID: NCT06265740

Last Updated: 2025-02-12

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

150 participants

Study Classification

INTERVENTIONAL

Study Start Date

2024-03-29

Study Completion Date

2025-11-30

Brief Summary

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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).

Detailed Description

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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.

Conditions

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Premature Birth Vaginal Flora

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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cohort of pregnant women at -risk of preterm delivery

vaginal swab

Group Type OTHER

vaginal swab

Intervention Type OTHER

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.

Interventions

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vaginal swab

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.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Pregnant women Admitted to the Clermont-Ferrand University Hospital maternity ward from 14 weeks' gestation onwards
* For threat of preterm birth (PTB) characterized by contractile activity and/or cervical changes, or for premature rupture of fetal membranes (PROM)
* And in need of vaginal swabbing
* Single or multiple pregnancy
* Able to understand and object to the study
* Covered by a French social security scheme.
* Give informed consent for the study

Exclusion Criteria

* Patient under guardianship, curatorship or safeguard of justice
* Patient having received antibiotic therapy in the 2 weeks prior to admission
Minimum Eligible Age

18 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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University Hospital, Clermont-Ferrand

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Denis Gallot

Role: PRINCIPAL_INVESTIGATOR

University Hospital, Clermont-Ferrand

Locations

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CHU de Clermont-Ferrand

Clermont-Ferrand, , France

Site Status RECRUITING

Countries

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France

Central Contacts

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Lise Laclautre

Role: CONTACT

334.73.754.963

Facility Contacts

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Lise Laclautre

Role: primary

Other Identifiers

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2023-A02466-39

Identifier Type: OTHER

Identifier Source: secondary_id

RBHP 2023 GALLOT (PREMABIOTE)

Identifier Type: -

Identifier Source: org_study_id

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