Evidence map›Paper›PMID 41318965›Full record

ArticleJournal of pediatric gastroenterology and nutrition2026

Using machine learning to predict clinical remission with exclusive enteral nutrition in pediatric Crohn disease.

Ricardo G Suarez Suarez, Daniel G McClement, Roberto Vega, Hien Q Huynh, Ayub Shaikh, Anthony Otley, Kevan Jacobson, Mary Sherlock, David R Mack, Colette Deslandres and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pediatric gastroenterology and nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Ricardo G Suarez SuarezDepartment of Paediatrics, University of Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0003-2609-7894
Daniel G McClementDepartment of Paediatrics, University of Alberta, Edmonton, Alberta, Canada.
Roberto VegaDepartment of Computing Science, University of Alberta, Edmonton, Alberta, Canada.
Hien Q HuynhDepartment of Paediatrics, University of Alberta, Edmonton, Alberta, Canada.
Ayub ShaikhSickKids Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, The Hospital for Sick Children (SickKids), Toronto, Ontario, Canada.
Anthony OtleyDepartment of Pediatrics, Division of Gastroenterology, Dalhousie University, Halifax, Nova Scotia, Canada.
Kevan JacobsonDepartment of Paediatrics, Division of Gastroenterology, Hepatology and Nutrition, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Mary SherlockDivision of Paediatrics, Faculty of Health Sciences McMaster University, Hamilton, Ontario, Canada.
David R MackDepartment of Pediatrics, Children's Hospital of Eastern Ontario and University of Ottawa, Ottawa, Ontario, Canada.
Colette DeslandresDepartment of Paediatrics, Division of Gastroenterology, Hepatology and Nutrition, CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada.
Wael El-MataryDepartment of Paediatrics, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.
Eileen CrowleyDepartment of Paediatrics, Children Hospital of Western Ontario, Western University, London, Ontario, Canada.
Jennifer deBruynDepartment of Paediatrics, Cumming School of Medicine University of Calgary, Calgary, Alberta, Canada.
Thomas WaltersSickKids Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, The Hospital for Sick Children (SickKids), Toronto, Ontario, Canada.
Anne M GriffithsSickKids Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, The Hospital for Sick Children (SickKids), Toronto, Ontario, Canada.
Russell GreinerDepartment of Computing Science, University of Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0001-8327-934X
Eytan WineDepartment of Paediatrics, University of Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0002-3458-4142
Canadian Children IBD Network

Funding

Canadian Institutes of Health Research (CIHR) 426160Children with Intestinal and Liver Disorders (Ch.I.L.D.) FoundationThe Canadian Children Inflammatory Bowel Disease Network (CIDsCaNN)Women & Children's Health Research Institute (WCHRI)
6 · The paper itself

Abstract

objectivesExclusive enteral nutrition (EEN) is a first-line treatment for induction of remission in luminal pediatric Crohn disease (pCD). However, as the efficacy of EEN varies from patient to patient, there is a need to distinguish between responders and nonresponding patients. This study had two aims. First, to develop a model to predict EEN-induced clinical remission (weighted pediatric CD activity index [wPCDA] ≤ 12.5) using baseline clinical information. Second, to develop a model to predict corticosteroid-free sustained clinical remission post-EEN induction (wPCDA ≤ 12.5, for ≥36 weeks after EEN).

methodsWe applied machine learning to clinical and laboratory data from a prospectively followed cohort of pCD patients who received EEN as their first treatment for CD (n = 308). This learning algorithm used feature selection and k-fold (internal) cross-validation to systematically find the model with the best combination of features and hyperparameter settings. To estimate the quality of the learned model, we used k-fold (external) cross-validation.

resultsClinical, laboratory, and treatment data were compiled into two different datasets: EEN clinical remission at the end of EEN treatment (mean of 60 days; n = 114) and corticosteroid-free sustained clinical remission post-EEN induction (n = 206). Our resulting models were effective, with external area under the curves of 0.65

conclusionOur models, based on accessible clinical features, were able to effectively predict EEN success above chance. This supports the plausibility of building clinical tools to assist precision therapy for pCD patients.

Indexed as

Crohn DiseaseEnteral NutritionMachine LearningAdolescentChildFemaleHumansMaleProspective StudiesRemission InductionTreatment Outcomeclassification taskmodel selecting learnernutritional therapypersonalized medicinetreatment response

Identifiers

PMID41318965
PMCPMC12864170

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.