Gao Ren-Xuan, Wang Xin-Lei, Tian Ming-Jie, Li Xiao-Ming, Zhang Jia-Jia, Wang Jun-Jing, Gao Jing, Zhang Chao, Li Zhi-Ting
Department of Gastroenterology, North China University of Science and Technology Affiliated Hospital, Tangshan 063000, Hebei Province, China.
Department of Gastroenterology, Tangshan Fengrun District People's Hospital, Tangshan 064000, Hebei Province, China.
World J Gastrointest Endosc. 2025 Jul 16;17(7):108307. doi: 10.4253/wjge.v17.i7.108307.
Difficulty of colonoscopy insertion (DCI) significantly affects colonoscopy effectiveness and serves as a key quality indicator. Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.
To evaluate the predictive performance of machine learning (ML) algorithms for DCI by comparing three modeling approaches, identify factors influencing DCI, and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.
This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021. Demographic data, past medical history, medication use, and psychological status were collected. The endoscopist assessed DCI using the visual analogue scale. After univariate screening, predictive models were developed using multivariable logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) algorithms. Model performance was evaluated based on discrimination, calibration, and decision curve analysis (DCA), and results were visualized using nomograms.
A total of 712 patients (53.8% male; mean age 54.5 years ± 12.9 years) were included. Logistic regression analysis identified constipation [odds ratio (OR) = 2.254, 95% confidence interval (CI): 1.289-3.931], abdominal circumference (AC) (77.5-91.9 cm, OR = 1.895, 95%CI: 1.065-3.350; AC ≥ 92 cm, OR = 1.271, 95%CI: 0.730-2.188), and anxiety (OR = 1.071, 95%CI: 1.044-1.100) as predictive factors for DCI, validated by LASSO and RF methods. Model performance revealed training/validation sensitivities of 0.826/0.925, 0.924/0.868, and 1.000/0.981; specificities of 0.602/0.511, 0.510/0.562, and 0.977/0.526; and corresponding area under the receiver operating characteristic curves (AUCs) of 0.780 (0.737-0.823)/0.726 (0.654-0.799), 0.754 (0.710-0.798)/0.723 (0.656-0.791), and 1.000 (1.000-1.000)/0.754 (0.688-0.820), respectively. DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37. The RF model demonstrated superior diagnostic accuracy, reflected by perfect training sensitivity (1.000) and highest validation AUC (0.754), outperforming other methods in clinical applicability.
The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models. This approach supports individualized preoperative optimization, enhancing colonoscopy quality through targeted risk stratification.
结肠镜插入困难（DCI）显著影响结肠镜检查的效果，是一项关键的质量指标。术前预测和评估DCI风险对于优化术中策略至关重要。
通过比较三种建模方法，评估机器学习（ML）算法对DCI的预测性能，识别影响DCI的因素，并使用ML算法开发术前预测模型，以提高结肠镜检查的质量和效率。
这项横断面研究纳入了2020年6月至2021年5月在一家三级医院接受结肠镜检查的712例患者。收集了人口统计学数据、既往病史、用药情况和心理状态。内镜医师使用视觉模拟量表评估DCI。经过单变量筛选后，使用多变量逻辑回归、最小绝对收缩和选择算子（LASSO）回归以及随机森林（RF）算法建立预测模型。基于鉴别力、校准和决策曲线分析（DCA）评估模型性能，并使用列线图将结果可视化。
共纳入712例患者（男性占53.8%；平均年龄54.5岁±12.9岁）。逻辑回归分析确定便秘[比值比（OR）=2.254，95%置信区间（CI）：1.289 - 3.931]、腹围（AC）（77.5 - 91.9 cm，OR = 1.895，95%CI：1.065 - 3.350；AC≥92 cm，OR = 1.271，95%CI：0.730 - 2.188）和焦虑（OR = 1.071，95%CI：1.044 - 1.100）为DCI的预测因素，经LASSO和RF方法验证。模型性能显示训练/验证敏感性分别为0.826/0.925、0.924/0.868和1.000/0.981；特异性分别为0.602/0.511、0.510/0.562和0.977/0.526；相应的受试者操作特征曲线下面积（AUC）分别为0.780（0.737 - 0.823）/0.726（0.654 - 0.799）、0.754（0.710 - 0.798）/0.723（0.656 - 在概率阈值0至0.9和0.05至0.37范围内，DCA显示出最佳净效益。RF模型显示出卓越的诊断准确性，完美的训练敏感性（1.000）和最高的验证AUC（0.754）表明了这一点，在临床适用性方面优于其他方法。
与多变量逻辑回归和LASSO回归模型相比，基于RF的模型对DCI表现出卓越的预测准确性。这种方法支持个体化的术前优化，通过有针对性的风险分层提高结肠镜检查质量。
