Wang Fang-Tao, Lin Yin, Gao Ren-Yuan, Wu Xiao-Cai, Wu Tian-Qi, Jiao Yi-Ran, Li Ji-Yuan, Yin Lu, Chen Chun-Qiu
Department of Abdominal Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Department of General Surgery, Yangpu Hospital, Tongji University School of Medicine, Shanghai, 200090, China.
BMC Gastroenterol. 2025 Feb 25;25(1):117. doi: 10.1186/s12876-025-03668-7.
Crohn's disease (CD) often necessitates surgical intervention, with temporary stoma creation after intestinal resection (IR) being a crucial decision. This study aimed to construct novel models based on machine learning (ML) to predict temporary stoma formation after IR for CD.
Patient data who underwent IR for CD at our center between July 2017 and March 2023 were collected for inclusion in this retrospective study. Eligible CD patients were randomly divided into training and validation cohorts. Feature selection was executed using the least absolute shrinkage and selection operator. We employed three ML algorithms including traditional logistic regression, novel random forest and XG-Boost to create prediction models. The area under the curve (AUC), accuracy, sensitivity, specificity, precision, recall, and F1 score were used to evaluate these models. SHapley Additive exPlanation (SHAP) approach was used to assess feature importance.
A total of 252 patients with CD were included in the study, 150 of whom underwent temporary stoma creation after IR. Eight independent predictors emerged as the most valuable features. An AUC between 0.886 and 0.998 was noted among the three ML algorithms. The random forest (RF) algorithms demonstrated the most optimal performance (0.998 in the training cohort and 0.780 in the validation cohort). By employing the SHAP method, we identified the variables that contributed to the model and their correlation with temporary stoma formation after IR for CD.
The proposed RF model showed a good predictive ability for identifying patients at high risk for temporary stoma formation after IR for CD, which can assist in surgical decision-making in CD management, provide personalized guidance for temporary stoma formation, and improve patient outcomes.
克罗恩病（CD）常需手术干预，肠道切除术后创建临时造口是一项关键决策。本研究旨在构建基于机器学习（ML）的新型模型，以预测CD患者肠道切除术后临时造口的形成。
收集2017年7月至2023年3月在本中心接受CD肠道切除术的患者数据，纳入本回顾性研究。符合条件的CD患者被随机分为训练组和验证组。使用最小绝对收缩和选择算子进行特征选择。我们采用了三种ML算法，包括传统逻辑回归、新型随机森林和XG - Boost来创建预测模型。使用曲线下面积（AUC）、准确性、敏感性、特异性、精确性、召回率和F1分数来评估这些模型。采用夏普利值加法解释（SHAP）方法评估特征重要性。
本研究共纳入252例CD患者，其中150例在肠道切除术后进行了临时造口。八个独立预测因子成为最有价值的特征。三种ML算法的AUC在0.886至0.998之间。随机森林（RF）算法表现出最佳性能（训练组为0.998，验证组为0.780）。通过使用SHAP方法，我们确定了对模型有贡献的变量及其与CD患者肠道切除术后临时造口形成的相关性。
所提出的RF模型在识别CD患者肠道切除术后临时造口形成高风险患者方面具有良好的预测能力，可协助CD管理中的手术决策，为临时造口形成提供个性化指导，并改善患者预后。
