ArticleBMC gastroenterology2022
Development and evaluation of a Japanese prediction model for low anterior resection syndrome after rectal cancer surgery.
Article in BMC gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 7 citations in OpenAlex.
- Exploring Symptom Domains of Postoperative Bowel Dysfunction by Integrating LARS and Wexner Scores: A Longitudinal Factor-Analytic Study.Annals of gastroenterological surgery · 2026Article
- Development and validation of web-based prediction models for major low anterior resection syndrome at 3 and 6 months after sphincter-preserving surgery in patients with rectal cancer.International journal of colorectal disease · 2026Article
- Predictive factors of major low anterior resection syndrome after surgery for rectal tumors.La Tunisie medicale · 2024Article
- Predicting bowel function after diverting stoma closure in patients with rectal cancer.Journal of gastrointestinal oncology · 2024Article
- The effect of robotic surgery on low anterior resection syndrome in patients with lower rectal cancer: a propensity score-matched analysis.Surgical endoscopy · 2024Article
- Validity assessment of the POLARS score tool in the prediction of post rectal cancer surgery LARS score in a population-based Swedish cohort.BMJ open gastroenterology · 2024Article
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLow anterior resection syndrome (LARS) is the most common complication after rectal cancer resection. We aimed to identify LARS' predictive factors and construct and evaluate a predictive model for LARS.
methodsThis retrospective study included patients with rectal cancer more than 1 year after laparoscopic or robotic-assisted surgery. We administered a questionnaire to evaluate the degree of LARS. In addition, we examined clinical characteristics with univariate and multivariate analysis to identify predictive factors for major LARS. Finally, we divided the obtained data into a learning set and a validation set. We constructed a predictive model for major LARS using the learning set and assessed the predictive accuracy of the validation set.
resultsWe reviewed 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 the learning set was well-calibrated.
conclusionsWe 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.
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