Diarrhea Infantile
Conditions
Brief summary
This study will establish a clinical cohort of children with congenital diarrhea and enteropathy (CODE), mine biomarkers of CODE through multi-omics technology and construct a clinical risk prediction model.
Detailed description
This study will establish a clinical cohort and a clinical phenotype database of children with congenital diarrhea and enteropathy (CODE), The investigator will mine biomarkers of CODE through multi-omics technology. This study is designed to construct a clinical risk prediction model by combining artificial intelligence technology.
Interventions
None listed
Sponsors
Children's Hospital of Fudan University
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
1 Months to 3 Years
Healthy volunteers
Yes
Inclusion criteria
* Patients with chronic diarrhea lasting greater than 2 months or greater than 1 month in patients younger than 2 months of age * Patients with consent from parents or legal guardians
Exclusion criteria
* Chronic diarrhea caused by specific infections, i.e. CMV, Clostridioides difficile * Chronic diarrhea with necrotizing enterocolitis, short bowel syndrome * Functional diarrhea * Patients with poor compliance
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Clinical phenotype of congenital diarrhea and enteropathy in China | Within approximately 2 years of enrollment | Describe the clinical phenotype(Birth status, family history, clinical features of diarrhea, laboratory examination, endoscopic and histological evaluation results, growth and development indicators, previous treatment and effect were collected) of congenital diarrhea and enteropathy in China,We will use our own mobile application or to collect the relevant data, which will be filled in by the parents of the child. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Biomarkers of congenital diarrhea and enteropathy with diagnostic value through microbiome, metabolome and proteome features | Within approximately 2 years of enrollment | Plasma and stool were collected from patients and healthy control children for multi-omics screening to identify biomarkers, of which differential expression were mined through proteome(olink), microbiome(metagenomic sequencing) and metabolome( untargeted metabolomics)，relevant statistical analyses were performed using non-parametric tests, such as the Wilcoxon signed-rank test. |
| Cinical risk prediction model for congenital diarrhea and enteropathy built by artificial intelligence and machine learning | Within approximately 30 months of enrollment | Using artificial intelligence and machine learning to construct predictive models for congenital diarrhea and enteropathy by combining children's clinical phenotypes and multi-omics results，such as the random forest model |
Countries
China
Contacts
Primary ContactYing Huang, MD,PHD
Backup ContactYanqiu Wang, MD
Outcome results
None listed