Published online Jan 27, 2025. doi: 10.4240/wjgs.v17.i1.100910
Revised: October 3, 2024
Accepted: October 22, 2024
Published online: January 27, 2025
Processing time: 119 Days and 7.1 Hours
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.
To investigate the predictive accuracy of POLARS in assessing the occurrence of LARS.
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.
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.
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.
Core Tip: The study evaluated the predictive accuracy of the preoperative low anterior resection syndrome score (POLARS) in predicting low anterior resection syndrome (LARS) among 335 patients. Patients were categorized into no LARS, minor LARS, and major LARS groups based on their POLARS scores. Significant differences in the quality of life metrics were identified between the groups. While POLARS effectively predicted major LARS with an 82.35% precision and a mean absolute error (MAE) of 8.92, the XGBoost model outperformed it with an MAE of 6.29 and better recall, highlighting its superior predictive capabilities.