Clinical trial NCT06727877: Predictive Diagnosis of Ulcero-Necrotizing EnteroColitis in Premature Babies Using an Artificial Intelligence Approach Based on Early Analysis of the Fecal Microbiota
Official title: Predictive Diagnosis of Ulcero-Necrotizing EnteroColitis in Premature Babies Using an Artificial Intelligence Approach Based on Early Analysis of the Fecal Microbiota
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Necrotizing Enterocolitis (NEC)
Healthy Control
Lead sponsor: University Hospital, Clermont-Ferrand
Sponsor class: OTHER
First posted: 2024-12-11
Enrollment: 1000
Sex: ALL
Maximum age (years): 1
Standard ages: CHILD
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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)

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.

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

Primary outcomes:
measure: predictive diagnostic of NEC based on artificial intelligence analysis of fecal microbiota
timeFrame: before day 21
Secondary outcomes:
measure: predictive diagnostic of NEC as a function of newborn characteristics
timeFrame: before day 21
measure: caracterization of microbiota in premature babies
timeFrame: before day 21
measure: caracterization of microbiota in premature babies
timeFrame: before day 21
measure: correlations between fecal microbiota and complications of prematurity (infectious, neurological, neurovegetative)
timeFrame: before day 21
Other outcomes:
Locations:
city: Clermont-Ferrand
country: France
facility: CHU de Clermont-Ferrand
city: Grenoble
country: France
facility: CHU Grenoble
city: Lyon
country: France
facility: HFME
city: Lyon
country: France
facility: Hopital Croix Rousse
city: Saint Etienne
country: France
facility: CHU Saint Etienne
Study officials:
affiliation: University Hospital, Clermont-Ferrand
name: Maguelonne Pons
role: PRINCIPAL_INVESTIGATOR
References: