Evaluation of surgical strategy for low anterior resection syndrome using preoperative low anterior resection syndrome score in China

摘要

BACKGROUND Despite improved survival rates in rectal cancer treatment, many patients experience low anterior resection syndrome (LARS). The preoperative LARS score (POLARS) aims to address the limitations of LARS assessment by predicting outcomes preoperatively to enhance surgical planning. AIM To investigate the predictive accuracy of POLARS in assessing the occurrence of LARS. METHODS This study enrolled a total of 335 patients who underwent laparoscopic or robotic low anal sphincter-preserving surgery for rectal tumors. Patients were categorized into three groups according to their POLARS score: no LARS (score 0-20), minor LARS (score 21-29), and major LARS (score 30-42). The QLQ-C30/CR29 scores were compared among these groups, and the agreement between POLARS predictions and the actual LARS scores was analyzed. RESULTS The study population was divided into three groups: major LARS (n = 51, 27.42%), minor LARS (n = 109, 58.6%), and no LARS (n = 26, 13.98%). Significant differences in the QLQ-C30 scales of social function, diarrhea, and financial impact were detected between the no LARS and major LARS groups (P < 0.05) and between the minor LARS and major LARS groups (P < 0.05). Similarly, significant differences were detected in the QLQ-CR29 scales for blood and mucus in the stool, fecal incontinence, and stool frequency between the no LARS and minor LARS groups (P < 0.05), as well as between the minor LARS and major LARS groups (P < 0.05). The predictive precision for major LARS using the POLARS score was 82.35% (42/51), with a recall of 35.89% (42/117). The mean absolute error (MAE) between the POLARS score and the actual LARS score was 8.92 ± 5.47. In contrast, the XGBoost (extreme gradient boosting) model achieved a lower MAE of 6.29 ± 4.77, with a precision of 84.39% and a recall of 74.05% for predicting major LARS. CONCLUSION The POLARS score demonstrated effectiveness and precision in predicting major LARS, thereby providing valuable insights into postoperative symptoms and patient quality of life. However, the XGBoost model exhibited superior performance with a lower MAE and higher recall for predicting major LARS compared to the POLARS model.