Necrotizing Enterocolitis (NEC)PECUNIANCT06727877
Predictive Diagnosis of Ulcero-Necrotizing EnteroColitis in Premature Babies Using an Artificial Intelligence Approach Based on Early Analysis of the Fecal Microbiota
A study testing Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification. in people with Necrotizing Enterocolitis (NEC) and Healthy Control, run by University Hospital, Clermont-Ferrand at 5 sites in France.
Trial 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. Read more about this study
- Who is it for?
- Ages Up to 1 dayHealthy volunteers can take part
- Conditions the study is for
- Necrotizing Enterocolitis (NEC), Healthy Control
- What is being tested?
- Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification.
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 5 sitesFrance
- Study dates
- 1 Apr 2025to 1 Jun 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 1 day
- All sexes
- Healthy volunteers accepted
- Child
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
France5 sites, 5 recruiting
- Recruiting
CHU de Clermont-Ferrand
Clermont-Ferrand
Lise Laclautre, +33473750573
Maguelonne Pons
- Not yet recruiting
CHU Grenoble
Grenoble
Pierre Louis VEROT
- Not yet recruiting
HFME
Lyon
Marine BUTIN
- Not yet recruiting
Hopital Croix Rousse
Lyon
Jean Charles PICAUD
- Recruiting
CHU Saint Etienne
Saint-Etienne
Antoine GIRAUD
About this study
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.
Read more
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)
Read the 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.
- Conditions
- Necrotizing Enterocolitis (NEC), Healthy Control
- Being tested
- Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification.
- Medical subject headings
- Enterocolitis, Necrotizing
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| NEC | Experimental | diagnosis of NEC according to the Bell classification Receives: 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 Receives: Diagnostic Test: Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification. |
What is being measured
The primary outcome is the main question the study is designed to answer.
predictive diagnostic of NEC based on artificial intelligence analysis of fecal microbiota
percentage of prediction occurrence of NEC
Measured: before day 21
Secondary outcomes (4)
predictive diagnostic of NEC as a function of newborn characteristics
percentage of predictive NEC according to newborn characteristics
Measured: before day 21
caracterization of microbiota in premature babies
measures how many types of species
Measured: before day 21
caracterization of microbiota in premature babies
percentage of different bacteria
Measured: 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
Measured: before day 21
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Dec 2024
- Study start1 Apr 2025
- Primary completion1 Jun 2027 (estimated)
- Study completion1 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Necrotizing Enterocolitis (NEC)Studies of the same condition, open and under way.
- More trials run by University Hospital, Clermont-FerrandThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University Hospital, Clermont-Ferrand
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Maguelonne Pons, Principal investigator, University Hospital, Clermont-Ferrand
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06727877
- Sponsor's protocol ID
- PHRC I 2023 PONS
- Other IDs
- 2024-A01840-47
- Acronym
- PECUNIA
- Status last verified
- Apr 2025
- First submitted
- 26 Nov 2024
- Last updated
- 6 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predictive Diagnosis of Ulcero-Necrotizing EnteroColitis in Premature Babies Using an Artificial Intelligence Approach Based on Early Analysis of the Fecal Microbiota
- ClinicalTrials.gov ID
- NCT06727877
- Acronym / sponsor ID
- PECUNIA / PHRC I 2023 PONS
- Conditions
- Necrotizing Enterocolitis (NEC), Healthy Control
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- University Hospital, Clermont-FerrandUniversity, hospital or other
- Study start
- 1 Apr 2025 (actual)
- Primary completion
- 1 Jun 2027 (estimated)
- Study completion
- 1 Jun 2027 (estimated)
- First posted
- 11 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| NEC | Experimental | Diagnostic Test: Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification.diagnosis of NEC according to the Bell classification |
| control | Other | Diagnostic Test: Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification.children without diagnosis of NEC |
Who may qualify
- Ages
- Up to 1 day
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Diagnostic test | NEC, control |
Primary outcome
predictive diagnostic of NEC based on artificial intelligence analysis of fecal microbiota
Time frame: before day 21
percentage of prediction occurrence of NEC
4 secondary outcomes
predictive diagnostic of NEC as a function of newborn characteristics
Time frame: before day 21
caracterization of microbiota in premature babies
Time frame: before day 21
caracterization of microbiota in premature babies
Time frame: before day 21
correlations between fecal microbiota and complications of prematurity (infectious, neurological, neurovegetative)
Time frame: before day 21
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Apr 2026.
- Individual participant dataSharing planned: No
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RecruitingObservational study
Multidimensional Dynamic Early Warning System for Neonatal Necrotizing Enterocolitis: A Multicenter Clinical Study
NEC - Necrotizing Enterocolitis
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Fecal Microbiota Transplantation (FMT) as a Prophylaxis of Necrotizing Enterocolitis (NEC) - Clinical Study
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Active, not recruitingObservational study
Early Predictors of Necrotizing Enterocolitis in Neonates
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Recruiting
Effects of Enteral Feeding Regimens on NEC, Mortality, and Neurodevelopment in Very Preterm Infants
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Maguelonne Pons principal investigator
University Hospital, Clermont-Ferrand
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Ability of early digestive microbiota analysis (using artificial intelligence) to predict the occurrence of NEC diagnosed according to the Bell classification. in people with Necrotizing Enterocolitis (NEC) and Healthy Control, run by University Hospital, Clermont-Ferrand at 5 sites in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Lise Laclautre (334.73.754.963, promo_interne_drci@chu-clermontferrand.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
predictive diagnostic of NEC based on artificial intelligence analysis of fecal microbiota (time frame: before day 21). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Apr 2026.