Clinical Features and Outcomes of Paediatric Patients With Isolated Colonic Crohn Disease

Induction of Remission With Exclusive Enteral Nutrition in Children With Crohn's Disease: Determinants of Higher Adherence and Response

BACKGROUND

Exclusive enteral nutrition (EEN) is the first choice to induce remission and promote mucosal healing in pediatric Crohn's disease (CD). However, full adherence to EEN treatment may be problematic for children with CD.

METHODS

The goal of the current multicenter retrospective study was to define predictive factors of nonadherence to treatment and nonremission at the end of induction treatment. Those data together were analyzed with the ultimate goal of trying to define an individualized induction treatment for children with CD.

RESULTS

Three hundred seventy-six children with CD from 14 IBD pediatric referral centers were enrolled in the study. The rate of EEN adherence was 89%. Colonic involvement and fecal calprotectin >600 μg/g at diagnosis were found to be associated with a reduced EEN adherence. Exclusive enteral nutrition administered for 8 weeks was effective for inducing clinical remission in 67% of the total cohort. Factors determining lower remission rates were age >15 years and Pediatric Crohn's Disease Activity Index >50.

CONCLUSION

Although EEN is extremely effective in promoting disease remission, several patients' related factors may adversely impact EEN adherence and response. Personalized treatments should be proposed that weigh benefits and risks based on the patient's disease location, phenotype, and disease activity and aim to promote a rapid control of inflammation to reduce long-term bowel damage.

Machine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classification

Antibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn's disease (CD) and ulcerative colitis (UC). Whether modern modeling techniques such as supervised and unsupervised machine learning are of value for finer distinction of subtypes such as IBD-unclassified (IBD-U) is not known. We determined the antibody profile of 100 adult IBD patients from the Swiss IBD cohort study with known subtype (50 CD, 50 UC) as well as of 76 IBD-U patients. We included ASCA IgG and IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements for computing different antibody panels as well as machine learning models. The AUC of an optimized antibody panel was 85% (95%CI, 78-92%) to distinguish CD from UC patients. The antibody profile of IBD-U patients was closely related to UC. No specific antibody profile was predictive for IBD-U nor for re-classification. The panel diagnostic was in favor of UC reclassification prediction with a correct assignment rate of 69.2-73.1% depending on the cut-off applied. Supervised machine learning could not distinguish between CD, UC, and IBD-U. More so, unsupervised machine learning suggested only two distinct clusters as a likely number of IBD subtypes. Antibodies in IBD are supportive in confirming clinical determined subtypes CD and UC but have limited capacity to predict IBD-U and reclassification during follow-up. In terms of antibody profiles, IBD-U is not a distinct subtype of IBD.