Predictive Diagnosis of Ulcero-Necrotizing EnteroColitis in Premature Babies Using an Artificial Intelligence Approach Based on Early Analysis of the Fecal Microbiota
Trial ID: NCT06727877
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2027-06-01
Sponsor: University Hospital, Clermont-Ferrand
Conditions: Necrotizing Enterocolitis (NEC); Healthy Control
Interventions: Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification.
Locations: CHU de Clermont-Ferrand; CHU Grenoble; HFME; Hopital Croix Rousse; CHU Saint Etienne
Countries: France; France; France; France; France
Enrollment: 1000
Age range: None - 1 Day
Sex: ALL

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)

Eligibility criteria:
Inclusion Criteria:

* Child born prematurely (i.e. before 34 weeks of amenorrhea) in one of participating university hospitals and hospitalized in neonatal intensive care units of the AURA region's university hospitals
* Child born outside CHU and transferred before 24h of life to the neonatal intensive care unit of one of thehospital participating in the study
* Affiliated with a Social Security scheme

Exclusion Criteria:

* Child whose guardians are protected by law (guardianship, curatorship, safeguard of justice)
* Children whose parents are under 18 years of age
* Refusal of parental authority to participate

Outcomes:
predictive diagnostic of NEC based on artificial intelligence analysis of fecal microbiota