ArticleSurgical endoscopy2025
Prediction of endoscopic restenosis after endoscopic balloon dilation in patients with Crohn's disease: a machine learning approach.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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.
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Who cites it
1 citing paper in PubMed.
- Utility of endoscopic ileocecal valve-plasty for Crohn's disease: a propensity-matched retrospective study.Surgical endoscopy · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundEndoscopic balloon dilation (EBD) is recognized as a minimally invasive and effective procedure for managing intestinal stenosis in patients with Crohn's disease (CD). It offers an alternative to surgery and has been shown to improve the quality of life for these patients by reducing the need for more aggressive interventions. This study aimed to evaluate factors associated with endoscopic restenosis after EBD and construct a prognostic model.
methodsWe retrospectively collected and analyzed data on patients receiving EBD treatment at the Sixth Affiliated Hospital of Sun Yat-sen University from 2013 to 2024. Seven machine learning (ML) algorithms were used to construct prognostic models. Subsequently, we conducted comparative tests on the performance of the models to ensure accuracy and reliability.
resultsA total of 135 patients were included in the statistical analysis. 53% occurred endoscopic restenosis, with an average restenosis time of 183 days. COX and logistic regression analysis showed that 4 features including ever-use glucocorticoids, stenosis position, technical success, and albumin level were associated with restenosis risk. When comparing different ML models, CoxPH and LASSO models performed better on various evaluation metrics, including C-index which was greater than 0.7 in the train and test set. Based on SHapley Additive exPlanations (SHAP), stenosis position, balloon diameter, and albumin level were identified as the top 3 important features associated with prognosis.
conclusionThe ML-based prognostic model has good predictive performance and can accurately assess the risk of endoscopic restenosis after EBD treatment.
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