Fang Changzhong, Shi Wenbin, Qiao Yu, Deng Shuwen, Liang Gen, Huang Binbin, Gao Wenjuan, Lian Jiming, Yu Nanhui
Department of Gastrointestinal Surgery, The Second Xiangya Hospital of Central South University, Changsha, 410000, China.
Nanchang Institute of Technology, Nanchang, 330000, China.
J Robot Surg. 2025 Aug 1;19(1):441. doi: 10.1007/s11701-025-02611-y.
This study aimed to assess the impact of various clinical and surgical factors on the occurrence of postoperative complications in elderly patients with colorectal cancer and to use various machine learning models to predict the risk of these complications. This study included 109 elderly patients with colorectal cancer who underwent robot-assisted surgery and natural orifice specimen extraction (NOSE) at the Second Xiangya Hospital of Central South University from March 2016 to March 2024. The patients were divided into a no-complication group (77 patients) and a complication group (32 patients) based on whether complications occurred within 30 days after surgery. The clinical and surgical data were collected and analysed using various machine learning models to evaluate the importance of each variable, and SHAP values were used to interpret the model's predictions. The average age of patients in the complication group was significantly greater than that in the no-complication group (77.75 vs. 72.55 years, P < 0.001), and their BMI was also greater (24.22 vs. 22.51 kg/m, P = 0.010). The preoperative haemoglobin levels and albumin levels were lower in the complication group. The duration of surgery and amount of intraoperative blood loss significantly longer and greater, respectively, and the postoperative hospital stay and time to first normal diet were longer in the complication group. Multivariate logistic regression analysis revealed that lymph node metastasis (N1 stage: OR = 20.64, P < 0.001; N2 stage: OR = 6.31, P = 0.002); and a history of abdominal surgery (OR = 4.74, P = 0.001), hypertension (OR = 4.07, P < 0.001), diabetes (OR = 7.70, P < 0.001), or coronary heart disease (OR = 19.07, P < 0.001) significantly increased the risk of complications. The XGBoost and logistic regression models performed best in terms of clinical applicability and prediction accuracy. SHAP interpretation of the XGBoost model revealed that age, a history of coronary heart disease, preoperative haemoglobin level, and ASA grade were the main influencing factors. Various clinical and surgical factors significantly affect the occurrence of postoperative complications in elderly patients with colorectal cancer. The XGBoost model performed excellently in predicting postoperative complications and has high clinical application potential. The results can provide a basis for preoperative risk assessment and postoperative management.
本研究旨在评估各种临床和手术因素对老年结直肠癌患者术后并发症发生情况的影响，并使用各种机器学习模型预测这些并发症的风险。本研究纳入了2016年3月至2024年3月在中南大学湘雅二医院接受机器人辅助手术及经自然腔道取标本手术（NOSE）的109例老年结直肠癌患者。根据术后30天内是否发生并发症，将患者分为无并发症组（77例）和并发症组（32例）。收集临床和手术数据，并使用各种机器学习模型进行分析，以评估每个变量的重要性，并使用SHAP值来解释模型的预测结果。并发症组患者的平均年龄显著高于无并发症组（77.75岁对72.55岁，P<0.001），其体重指数也更高（24.22对22.51kg/m²，P=0.010）。并发症组术前血红蛋白水平和白蛋白水平较低。并发症组手术时间和术中出血量分别显著更长和更多，术后住院时间和首次正常饮食时间也更长。多因素logistic回归分析显示，淋巴结转移（N1期：OR=20.64，P<0.001；N2期：OR=6.31，P=0.002）；以及腹部手术史（OR=4.74，P=0.001）、高血压（OR=4.07，P<0.001）、糖尿病（OR=7.70，P<0.001）或冠心病（OR=19.07，P<0.001）显著增加并发症风险。XGBoost模型和logistic回归模型在临床适用性和预测准确性方面表现最佳。对XGBoost模型的SHAP解释显示，年龄、冠心病史、术前血红蛋白水平和美国麻醉医师协会（ASA）分级是主要影响因素。各种临床和手术因素显著影响老年结直肠癌患者术后并发症的发生。XGBoost模型在预测术后并发症方面表现出色，具有较高的临床应用潜力。研究结果可为术前风险评估和术后管理提供依据。
