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

NCT06727877 | Recruiting

• 2 other identifiers: PHRC I 2023 PONS2024-A01840-47
• Study Type: interventional
• Target: 1,000
• Locations: 1 country
• Sites: 5

Brief 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

Outcome Measures

Primary Outcomes (1)

• predictive diagnostic of NEC based on artificial intelligence analysis of fecal microbiota

  percentage of prediction occurrence of NEC

  before day 21

Secondary Outcomes (4)

• predictive diagnostic of NEC as a function of newborn characteristics

  before day 21

• caracterization of microbiota in premature babies

  before day 21

• caracterization of microbiota in premature babies

  before day 21

• correlations between fecal microbiota and complications of prematurity (infectious, neurological, neurovegetative)

  before day 21

Study Arms (2)

NEC

EXPERIMENTAL

diagnosis of NEC according to the Bell classification

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

control

OTHER

children without diagnosis of NEC

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

Interventions

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

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.

NEC; control

Eligibility Criteria

• Age: Up to 1 Day
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• University Hospital, Clermont-Ferrand: lead

Study Sites (5)

CHU de Clermont-Ferrand

Clermont-Ferrand, France

RECRUITING

CHU Grenoble

Grenoble, France

NOT YET RECRUITING

HFME

Lyon, France

NOT YET RECRUITING

Hopital Croix Rousse

Lyon, France

NOT YET RECRUITING

CHU Saint Etienne

Saint-Etienne, France

RECRUITING

MeSH Terms

Conditions

Enterocolitis, Necrotizing

Condition Hierarchy (Ancestors)

Enterocolitis; Gastroenteritis; Gastrointestinal Diseases; Digestive System Diseases; Intestinal Diseases

Study Officials

• Maguelonne Pons

  University Hospital, Clermont-Ferrand

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Lise Laclautre

CONTACT

334.73.754.963; promo_interne_drci@chu-clermontferrand.fr

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 26, 2024
• First Posted: December 11, 2024
• Study Start: April 1, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: April 15, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

FR (5)