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Predictive Diagnosis of Ulcero-Necrotizing EnteroColitis in Premature Babies Using an Artificial Intelligence Approach Based on Early Analysis of the Fecal Microbiota

NCT06727877 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1000

Last updated 2026-04-06

No results posted yet for this study

Summary

Prematurity affects around 7% of births in France. Necrotizing enterocolitis (NEC) is a dreaded digestive complication. It is responsible for a mortality rate ranging from 15 to 40%, a rate that has remained stable in recent years, and for medium- and long-term digestive and neurodevelopmental morbidity.

Its onset is unpredictable and sudden, usually between 10 and 20 days of life, and requires immediate, aggressive management: hemodynamic support, fasting, systemic antibiotic therapy or even surgery.

Prevention is therefore essential, but systematic measures with proven efficacy (breastfeeding, early enteral feeding, multiple probiotics) are few and far between. What's more, these preventive measures cannot be modulated and adapted individually, since it is not possible to finely predict the risk of developing enterocolitis.

Thus, the use of a predictive diagnostic test for NEC would make it possible to identify high-risk premature babies and develop personalized preventive measures.

Changes in the digestive microbiota precede the onset of NEC, but it has not been possible to identify a reproducible and reliable microbial signature. As a result, the limited power of microbiota analysis and interpretation means that it cannot be used in practice to predict ECUN.

Our partner team (MEDiS) has developed a bioinformatics chain (RiboTaxa) to obtain the precise structure of complex microbial communities from direct metagenomic sequencing data. Stool samples from international cohorts (1562 samples, 208 preterm infants) were then mined to train a deep neural network and generate a predictive diagnostic test for NEC. In a local study (10 cases and 10 controls), the predictive diagnostic performance of this test was 90%, with the 1ère stool identified as "at risk" preceding NEC by 8 days (extremes 4 - 17 days), and the 2nde by 2 days (extremes 0-7 days). We would now like to test our predictive diagnostic technique on a larger number of premature babies in the AURA region.

1000 children included, 200 children tested (50 NEC - 150 controls)

Conditions

  • Necrotizing Enterocolitis (NEC)
  • Healthy Control

Interventions

DIAGNOSTIC_TEST

Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification.

The test gives us a dichotomous response (yes/no) for each stool. We will systematically analyze two stools per child, and in the event of a discrepancy, we will analyze a third to classify the child as being at risk of NEC or not. The analysis model consists of a deep neural network that has been trained and optimized on data from international cohorts. In a local pilot study (N=20), it enabled accurate prediction for 90% of newborns.

Sponsors & Collaborators

  • University Hospital, Clermont-Ferrand

    lead OTHER

Principal Investigators

  • Maguelonne Pons · University Hospital, Clermont-Ferrand

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Max Age
1 Day
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-04-01
Primary Completion
2027-06-01
Completion
2027-06-01

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06727877 on ClinicalTrials.gov