A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s disease

ObjectivesAccording to the updated Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE-II), mucosal healing (MH) is the long-term therapeutic target for Crohn’s disease (CD). Capsule endoscopy (CE) is effective in evaluating small bowel mucosal inflammation. This research seeks to co...

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Main Authors: Bingxia Chen, Huiwen Li, Hongli Wang, Lu Ren, Liya Xiong, Yang Cheng, Rui Li, Meiwan Cao, Zihuan Zeng, Sitang Gong, Peiyu Chen, Lanlan Geng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Medicine
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Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1582238/full
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author Bingxia Chen
Huiwen Li
Hongli Wang
Lu Ren
Liya Xiong
Yang Cheng
Rui Li
Meiwan Cao
Zihuan Zeng
Sitang Gong
Peiyu Chen
Lanlan Geng
author_facet Bingxia Chen
Huiwen Li
Hongli Wang
Lu Ren
Liya Xiong
Yang Cheng
Rui Li
Meiwan Cao
Zihuan Zeng
Sitang Gong
Peiyu Chen
Lanlan Geng
author_sort Bingxia Chen
collection DOAJ
description ObjectivesAccording to the updated Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE-II), mucosal healing (MH) is the long-term therapeutic target for Crohn’s disease (CD). Capsule endoscopy (CE) is effective in evaluating small bowel mucosal inflammation. This research seeks to construct a simple tool for predicting small bowel MH in pediatric CD to aid clinical decision-making.MethodsData from the medical records of patients with CD who underwent CE at the Guangzhou Women and Children’s Medical Center between November 2017 and July 2022 were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was applied to identify predictive factors for small bowel MH. A nomogram incorporating these factors was constructed to predict the probability of MH in this population.ResultsIn total, 143 CE examinations performed in 91 pediatric CD patients (median age, 11 years) were included. Based on the Lewis scores, the CD patients were divided into “MH” (42 cases) and “non-MH” groups (101 cases). LASSO regression analysis identified erythrocyte sedimentation rate, albumin levels, aspartate transaminase levels, C-reactive protein levels, platelet count, and lymphocyte percentage as the most significant predictors; and thus, these factors were incorporated into the predictive nomogram model. The area under the receiver-operating characteristic (ROC) curve of the predictive nomogram model was 0.855 (95% confidence interval, 0.783–0.926), suggesting a high discrimination power.ConclusionA nomogram was constructed to predict small bowel MH in pediatric CD patients. This nomogram model can enable accurate and simple attentive observation of small bowel inflammation in CD patients.
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spelling doaj-art-0eccd00357da46368c962ffd0a38c48a2025-08-20T03:29:34ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15822381582238A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s diseaseBingxia ChenHuiwen LiHongli WangLu RenLiya XiongYang ChengRui LiMeiwan CaoZihuan ZengSitang GongPeiyu ChenLanlan GengObjectivesAccording to the updated Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE-II), mucosal healing (MH) is the long-term therapeutic target for Crohn’s disease (CD). Capsule endoscopy (CE) is effective in evaluating small bowel mucosal inflammation. This research seeks to construct a simple tool for predicting small bowel MH in pediatric CD to aid clinical decision-making.MethodsData from the medical records of patients with CD who underwent CE at the Guangzhou Women and Children’s Medical Center between November 2017 and July 2022 were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was applied to identify predictive factors for small bowel MH. A nomogram incorporating these factors was constructed to predict the probability of MH in this population.ResultsIn total, 143 CE examinations performed in 91 pediatric CD patients (median age, 11 years) were included. Based on the Lewis scores, the CD patients were divided into “MH” (42 cases) and “non-MH” groups (101 cases). LASSO regression analysis identified erythrocyte sedimentation rate, albumin levels, aspartate transaminase levels, C-reactive protein levels, platelet count, and lymphocyte percentage as the most significant predictors; and thus, these factors were incorporated into the predictive nomogram model. The area under the receiver-operating characteristic (ROC) curve of the predictive nomogram model was 0.855 (95% confidence interval, 0.783–0.926), suggesting a high discrimination power.ConclusionA nomogram was constructed to predict small bowel MH in pediatric CD patients. This nomogram model can enable accurate and simple attentive observation of small bowel inflammation in CD patients.https://www.frontiersin.org/articles/10.3389/fmed.2025.1582238/fullCrohn’s diseasecapsule endoscopymucosal healingnomogramprediction model
spellingShingle Bingxia Chen
Huiwen Li
Hongli Wang
Lu Ren
Liya Xiong
Yang Cheng
Rui Li
Meiwan Cao
Zihuan Zeng
Sitang Gong
Peiyu Chen
Lanlan Geng
A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s disease
Frontiers in Medicine
Crohn’s disease
capsule endoscopy
mucosal healing
nomogram
prediction model
title A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s disease
title_full A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s disease
title_fullStr A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s disease
title_full_unstemmed A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s disease
title_short A nomogram for predicting small bowel mucosal healing in pediatric Crohn’s disease
title_sort nomogram for predicting small bowel mucosal healing in pediatric crohn s disease
topic Crohn’s disease
capsule endoscopy
mucosal healing
nomogram
prediction model
url https://www.frontiersin.org/articles/10.3389/fmed.2025.1582238/full
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