Development and evaluation of a Japanese prediction model for low anterior resection syndrome after rectal cancer surgery

Abstract

Background: Low anterior resection syndrome (LARS) is the most common complication after rectal cancer resection. We aimed to identify predictive factors for LARS and to construct and evaluate a predictive model for LARS.Methods: This retrospective study included patients with rectal cancer more than one year after laparoscopic or robotic-assisted surgery. A questionnaire was administered to evaluate the degree of LARS. Clinical characteristics were examined with univariate and multivariate logistic regressions to identify predictive factors for major LARS. The obtained data was divided into learning set and validation set. A predictive model for major LARS was constructed using the learning set, and predictive accuracy of the validation set was assessed.Results: We reviewed a total of 160 patients with rectal cancer and divided them into a learning set (n = 115) and a validation set (n = 45). Univariate and multivariate analyses in the learning set showed that male (odds ratio [OR]: 2.88, 95% confidence interval [95%CI]: 1.11-8.09, p = 0.03), age <75 years (OR: 5.87, 95%CI: 1.14-47.25, p = 0.03) and tumors located <8.5 cm from the AV (OR: 7.20, 95%CI: 2.86-19.49, p <0.01) were significantly related to major LARS. A prediction model based on the patients in learning set was well calibrated.Conclusions: We found that sex, age and tumor location were independent predictors of major LARS in Japanese patients that underwent rectal cancer surgery. Our predictive model for major LARS could aid medical staff in educating and treating patients with rectal cancer before and after surgery.