• 1. Department of Anesthesiology, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu 610041, P. R. China;
  • 2. West China School of Medicine, Sichuan University, Chengdu 610041, P. R. China;
  • 3. Center of Biostatistics, Design, Measurement and Evaluation (CBDME), Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
  • 4. Department of General Surgery Ⅱ, Huaxi Shangjin Hospital, Sichuan University / Chengdu Shangjin Nanfu Hospital, Chengdu 611700, P. R. China;
  • 5. Center of Colorectal Cancer, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
  • 6. Day Surgery Center, General Practice Medical Center, West China Hospital, Sichuan University / West China School of Nursing, Sichuan University, Chengdu 610041, P. R. China;
HUANG Mingjun, Email: huangmingiun@wchscu.cn
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Objective  To investigate the key risk factors for low anterior resection syndrome (LARS) within 6 months after rectal cancer surgery and to construct a risk prediction model based on the random forest algorithm, providing a reference for early clinical intervention. Methods  A retrospective study was conducted on patients who underwent rectal cancer surgery at the West China Hospital of Sichuan University from January 2020 to August 2021. A prediction model for the occurrence of LARS within 6 months after rectal cancer surgery was constructed using the random forest algorithm. The dataset was divided into a training set and a test set in an 8∶2 ratio. The model performance was evaluated by accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis (DCA). Results  A total of 394 patients were enrolled. Among the 394 patients, 106 developed LARS within 6 months after surgery, with an incidence rate of 26.9%. According to the importance ranking in the random forest algorithm, the key predictive factors were: distance from the inferior tumor margin to the dentate line, body mass index (BMI), tumor size, time to first postoperative flatus, operation time, age, neoadjuvant therapy, and TNM stage. The prediction model constructed using these key factors achieved the accuracy of 73.4%, sensitivity of 75.0%, specificity of 72.7%, AUC (95% confidence interval) of 0.801 (0.685, 0.916), and the Brier score of 0.198. DCA showed that the model provided favorable clinical benefit when the threshold probability was between 25% and 64%. Conclusions  The results of this study suggest that patients with a shorter distance from the tumor to the dentate line, higher BMI, and larger tumor size are at higher risk of developing LARS. The risk prediction model constructed in this study demonstrates a good predictive performance and may provide a useful reference for early identification of high-risk patients after rectal cancer surgery.

Citation: ZHANG Chun, LIN Yuxin, LI Lin, LI Li, HUANG Mingjun. Construction of a risk prediction model for postoperative low anterior resection syndrome in patients with rectal cancer: Based on a random forest algorithm. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2025, 32(7): 801-807. doi: 10.7507/1007-9424.202504144 Copy

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