Li Hui-Yuan, Zhou Jiang-Tao, Wang Ya-Nan, Zhang Ning, Wu Shao-Fen
Department of General Surgery, Jincheng People's Hospital of Shanxi Province, Jincheng 048026, Shanxi Province, China.
Department of Gastroenterology, Jincheng People's Hospital of Shanxi Province, Jincheng 048026, Shanxi Province, China.
World J Gastrointest Surg. 2023 Oct 27;15(10):2201-2210. doi: 10.4240/wjgs.v15.i10.2201.
Anastomotic leakage (AL) occurs frequently after sphincter-preserving surgery for rectal cancer and has a significant mortality rate. There are many factors that influence the incidence of AL, and each patient's unique circumstances add to this diversity. The early identification and prediction of AL after sphincter-preserving surgery are of great significance for the application of clinically targeted preventive measures. Developing an AL predictive model coincides with the aim of personalised healthcare, enhances clinical management techniques, and advances the medical industry along a more precise and intelligent path.
To develop nomogram, decision tree, and random forest prediction models for AL following sphincter-preserving surgery for rectal cancer and to evaluate the predictive efficacy of the three models.
The clinical information of 497 patients with rectal cancer who underwent sphincter-preserving surgery at Jincheng People's Hospital of Shanxi Province between January 2017 and September 2022 was analyzed in this study. Patients were divided into two groups: AL and no AL. Using univariate and multivariate analyses, we identified factors influencing postoperative AL. These factors were used to establish nomogram, decision tree, and random forest models. The sensitivity, specificity, recall, accuracy, and area under the receiver operating characteristic curve (AUC) were compared between the three models.
AL occurred in 10.26% of the 497 patients with rectal cancer. The nomogram model had an AUC of 0.922, sensitivity of 0.745, specificity of 0.966, accuracy of 0.936, recall of 0.987, and accuracy of 0.946. The above indices in the decision tree model were 0.919, 0.833, 0.862, 0.951, 0.994, and 0.955, respectively and in the random forest model were 1.000, 1.000, 1.000, 0.951, 0.994, and 0.955, respectively. The DeLong test revealed that the AUC value of the decision-tree model was lower than that of the random forest model ( < 0.05).
The random forest model may be used to identify patients at high risk of AL after sphincter-preserving surgery for rectal cancer owing to its strong predictive effect and stability.
直肠癌保肛手术后吻合口漏（AL）发生率较高，且死亡率显著。影响AL发生率的因素众多，每位患者的独特情况进一步增加了这种多样性。直肠癌保肛手术后AL的早期识别和预测对于临床针对性预防措施的应用具有重要意义。开发AL预测模型符合个性化医疗的目标，可提升临床管理技术，并推动医疗行业朝着更精准、智能的方向发展。
建立直肠癌保肛手术后AL的列线图、决策树和随机森林预测模型，并评估这三种模型的预测效能。
本研究分析了2017年1月至2022年9月在山西省晋城市人民医院接受保肛手术的497例直肠癌患者的临床资料。将患者分为两组：发生AL组和未发生AL组。通过单因素和多因素分析，确定影响术后AL的因素。利用这些因素建立列线图、决策树和随机森林模型。比较三种模型的灵敏度、特异度、召回率、准确率以及受试者工作特征曲线下面积（AUC）。
497例直肠癌患者中，AL发生率为10.26%。列线图模型AUC为0.922，灵敏度为0.745，特异度为0.966，准确率为0.936，召回率为0.987，精确率为0.946。决策树模型上述指标分别为0.919、0.833、0.862、0.951、0.994和0.955，随机森林模型分别为1.000、1.000、1.000、0.951、0.994和0.955。DeLong检验显示，决策树模型的AUC值低于随机森林模型（<0.05）。
随机森林模型因其强大的预测效果和稳定性，可用于识别直肠癌保肛手术后发生AL的高危患者。
