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

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)

Study Overview

• Status: Recruiting
• Conditions: Healthy Control; Necrotizing Enterocolitis (NEC)
• Intervention / Treatment: Diagnostic test: Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification.

Detailed Description

Systematic collection of stool (excluding meconium) from premature infants up to 21 days of age. Systematic analysis of the first two stools at the MEDiS laboratory: analysis of fecal microbiota by direct metagenomic sequencing (RiboTaxa), coupled with artificial intelligence (deep neural network previously trained on literature data). The test gives us a dichotomous response (yes/no) for each stool.

In the event of discordant analysis between the 2 stools (approximately 35% of cases in our preliminary study), a 3ème stool will be analyzed in order to classify the child as being at risk of NEC or not. The person performing these analyses will not be informed of the child's clinical evolution.

The diagnosis of NEC will be made by the clinician in charge of the child, according to the Bell classification.

Follow-up until return home or transfer to a peripheral center. A telephone call will be made to parents at 3 months of age, to ensure that no NECN has occurred after transfer to a peripheral center.

• Study Type: Interventional
• Enrollment (Estimated): 1000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Lise Laclautre; Phone Number: 334.73.754.963; Email: promo_interne_drci@chu-clermontferrand.fr

Study Locations

• France
  • Clermont-Ferrand, France
    • Recruiting
    • CHU de Clermont-Ferrand
    • Principal Investigator: Maguelonne Pons
    • Contact: Lise Laclautre; Phone Number: +33473750573
  • Grenoble, France
    • Not yet recruiting
    • CHU Grenoble
    • Principal Investigator: Pierre Louis VEROT
  • Lyon, France
    • Not yet recruiting
    • HFME
    • Principal Investigator: Marine BUTIN
  • Lyon, France
    • Not yet recruiting
    • Hopital croix rousse
    • Principal Investigator: Jean Charles PICAUD
  • Saint-Etienne, France
    • Recruiting
    • CHU Saint Etienne
    • Principal Investigator: Antoine GIRAUD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: Yes

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: NEC; 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.; 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.
Other: control; 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.; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
predictive diagnostic of NEC based on artificial intelligence analysis of fecal microbiota	percentage of prediction occurrence of NEC	before day 21

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
predictive diagnostic of NEC as a function of newborn characteristics	percentage of predictive NEC according to newborn characteristics	before day 21
caracterization of microbiota in premature babies	measures how many types of species	before day 21
caracterization of microbiota in premature babies	percentage of different bacteria	before day 21
correlations between fecal microbiota and complications of prematurity (infectious, neurological, neurovegetative)	percentage of different bacteria according to the occurrence or non-occurrence of complications of prematurity (intraventricular hemorrhage, retinopathy, sepsis	before day 21

Collaborators and Investigators

• Sponsor: University Hospital, Clermont-Ferrand
• Investigators: Principal Investigator: Maguelonne Pons, University Hospital, Clermont-Ferrand

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2025
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): June 1, 2027

Study Registration Dates

• First Submitted: November 26, 2024
• First Submitted That Met QC Criteria: December 10, 2024
• First Posted (Actual): December 11, 2024

Study Record Updates

• Last Update Posted (Actual): April 6, 2026
• Last Update Submitted That Met QC Criteria: April 3, 2026
• Last Verified: April 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Intestinal Diseases; Digestive System Diseases; Gastrointestinal Diseases; Gastroenteritis; Enterocolitis; Enterocolitis, Necrotizing
• Other Study ID Numbers: PHRC I 2023 PONS; 2024-A01840-47 (Other Identifier: 2024-A01840-47)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No