Published online May 21, 2023. doi: 10.3748/wjg.v29.i19.2979
Peer-review started: February 23, 2023
First decision: March 23, 2023
Revised: April 2, 2023
Accepted: April 25, 2023
Article in press: April 25, 2023
Published online: May 21, 2023
Processing time: 82 Days and 5.3 Hours
Low anterior resection syndrome (LARS) severely impairs patient postoperative quality of life, especially major LARS. However, there are few tools that can accurately predict major LARS in clinical practice.
To develop a machine learning model using preoperative and intraoperative factors for predicting major LARS following laparoscopic surgery of rectal cancer in Chinese populations.
Clinical data and follow-up information of patients who received laparoscopic anterior resection for rectal cancer from two medical centers (one discovery cohort and one external validation cohort) were included in this retrospective study. For the discovery cohort, the machine learning prediction algorithms were developed and internally validated. In the external validation cohort, we evaluated the trained model using various performance metrics. Further, the clinical utility of the model was tested by decision curve analysis.
Overall, 1651 patients were included in the present study. Anastomotic height, neoadjuvant therapy, diverting stoma, body mass index, clinical stage, specimen length, tumor size, and age were the risk factors associated with major LARS. They were used to construct the machine learning model to predict major LARS. The trained random forest (RF) model performed with an area under the curve of 0.852 and a sensitivity of 0.795 (95%CI: 0.681-0.877), a specificity of 0.758 (95%CI: 0.671-0.828), and Brier score of 0.166 in the external validation set. Compared to the previous preoperative LARS score model, the current model exhibited superior predictive performance in predicting major LARS in our cohort (accuracy of 0.772 for the RF model vs 0.355 for the preoperative LARS score model).
We developed and validated a robust tool for predicting major LARS. This model could potentially be used in the clinic to identify patients with a high risk of developing major LARS and then improve the quality of life.
Core Tip: We developed and externally validated a machine learning-based prediction model that integrated preoperative and intraoperative risk factors as input features and showed satisfactory predictive performance in Chinese patients. According to the decision curve analysis, patients with major low anterior resection syndrome (LARS) would have a net benefit superior to “treat all” or “treat none” with a range of threshold probabilities by using the model. This study provides a new tool for predicting major LARS, which can potentially be used for rectal cancer patients to acquire early postoperative consultation and strengthen self-management to improve their quality of life.