Prediction of endoscopic restenosis after endoscopic balloon dilation in patients with Crohn's disease: a machine learning approach.

Background: Endoscopic 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.

Methods: We 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.

Results: A 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.

Conclusion: The ML-based prognostic model has good predictive performance and can accurately assess the risk of endoscopic restenosis after EBD treatment.