Wen Rongbo, Zheng Kuo, Zhang Qihang, Zhou Leqi, Liu Qizhi, Yu Guanyu, Gao Xianhua, Hao Liqiang, Lou Zheng, Zhang Wei
Department of Colorectal Surgery, Changhai Hospital, Shanghai, China.
School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
J Gastrointest Oncol. 2021 Jun;12(3):921-932. doi: 10.21037/jgo-20-436.
Anastomotic leakage (AL) is one of the commonest and most serious complications after rectal cancer surgery. The previous analyses on predictors for AL included small-scale patients, and their prediction models performed unsatisfactorily.
Clinical data of 5,220 patients who underwent anterior resection for rectal cancer were scrutinized to create a prediction model via random forest classifier. Additionally, data of 836 patients served as the test dataset. Patients diagnosed with AL within 6 months' follow-up were recorded. A total of 20 candidate factors were included. Receiver operating characteristic (ROC) curve was conducted to determine the clinical efficacy of our model, and compare the predictive performance of different models.
The incidence of AL was 6.2% (326/5,220). A multivariate logistic regression analysis and the random forest classifier indicated that sex, distance of tumor from the anal verge, bowel stenosis or obstruction, preoperative hemoglobin, surgeon volume, diabetes, neoadjuvant chemoradiotherapy, and surgical approach were significantly associated with AL. After propensity score matching, the temporary stoma was not identified as a protective factor for AL (P=0.58). Contrastingly, the first year of performing laparoscopic surgery was a predictor (P=0.009). We created a predictive random forest classifier based on the above predictors that demonstrated satisfactory prediction efficacy. The area under the curve (AUC) showed that the random forest had higher efficiency (AUC =0.87) than the nomogram (AUC =0.724).
Our findings suggest that eight factors may affect the incidence of AL. Our random forest classifier is an innovative and practical model to effectively predict AL, and could provide rational advice on whether to perform a temporary stoma, which might reduce the rate of stoma and avoid the ensuing complications.
吻合口漏（AL）是直肠癌手术后最常见且最严重的并发症之一。以往对AL预测因素的分析纳入的患者规模较小，其预测模型表现不尽人意。
对5220例行直肠癌前切除术患者的临床数据进行详细审查，通过随机森林分类器建立预测模型。另外，836例患者的数据用作测试数据集。记录在6个月随访期内诊断为AL的患者。共纳入20个候选因素。绘制受试者工作特征（ROC）曲线以确定我们模型的临床疗效，并比较不同模型的预测性能。
AL的发生率为6.2%（326/5220）。多因素逻辑回归分析和随机森林分类器表明，性别、肿瘤距肛缘距离、肠管狭窄或梗阻、术前血红蛋白、术者手术量、糖尿病、新辅助放化疗以及手术方式与AL显著相关。倾向评分匹配后，未发现临时造口是AL的保护因素（P = 0.58）。相反，开展腹腔镜手术的第一年是一个预测因素（P = 0.009）。我们基于上述预测因素创建了一个预测性随机森林分类器，其显示出令人满意的预测效果。曲线下面积（AUC）表明，随机森林的效率（AUC = 0.87）高于列线图（AUC = 0.724）。
我们的研究结果表明，八个因素可能影响AL的发生率。我们的随机森林分类器是一种创新且实用的模型，可有效预测AL，并可为是否进行临时造口提供合理建议，这可能会降低造口率并避免随之而来的并发症。
