A nomogram model for predicting risk factors of low anterior resection syndrome after anus-preserving radical resection for rectal cancer

Acta Universitatis Medicinalis Anhui 2021 10 v.56 1632-1636     font:big middle small

Found programs:

Authors:Bu Minchun; Cao Xiandong; Zhou Bo;

Keywords:rectal cancer;low anterior resection syndrome;risk factor;nomogram;predicting model

DOI:10.19405/j.cnki.issn1000-1492.2021.10.024

〔Abstract〕 Objective To investigate the risk factors of low anterior resection syndrome after anus-preserving radical resection for rectal cancer and establish a predictive nomogram model. Methods This was a retrospective case-control study. Patients who had undergone anus-preserving radical resection for rectal cancer at department of general surgery of the first affiliated hospital of Anhui medical university completed a LARS score scale. Then the nomogram model was established according to the risk factors of LARS which were assessed by univariate and multivariate analyses. Results Body mass index( BMI) ≥24 kg/m2( OR = 2. 041,95% CI: 1. 038-4. 013),recovery time≤6 months( OR = 2. 456,95% CI: 1. 339-4. 505),the distance from tumor to anus≤7 cm( OR = 2. 735,95%CI: 1. 480-5. 055),neoadjuvant therapy( OR = 3. 772,95% CI: 1. 109-12. 832),anastomotic leak( OR = 5. 537,95% CI: 1. 103-27. 791) were independent risk factors of LARS. Based on the 5 selected risk factors,a nomogram model was established to predict the risk factors of LARS after anus-preserving radical resection for rectal cancer. The area under ROC curve of the nomogram model was 0. 754( 95% CI: 0. 689-0. 819). After internal verification by Bootstrap self-sampling method,the C-index value of the model was 0. 750 and the calibration curve fitted well with the ideal curve. Conclusion The nomogram model based on the above risk factors can better predict the probability of LARS after anus-preserving radical resection for rectal cancer,which is helpful for early identification of hign-risk population and development of clinical intervention measures.