Serum nutritional predictive biomarkers and risk assessment for anastomotic leakage after laparoscopic surgery in rectal cancer patients
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Anastomotic leakage (AL) is one of the severest complications after laparoscopic surgery for middle/low rectal cancer, significantly impacting patient outcomes. Identifying reliable predictive factors for AL remains a clinical challenge. Serum nutritional biomarkers have been implicated in surgical outcomes but are underexplored as predictive tools for AL in this setting. Our study hypothesizes that preoperative serum levels of prealbumin (PA), albumin (ALB), and transferrin (TRF), along with surgical factors, can accurately predict AL risk. AIM: To determine the predictive value of preoperative serum nutritional biomarkers for rectal cancer AL following laparoscopic surgery. METHODS: In the retrospective cohort study carried out at a tertiary cancer center, we examined 560 individuals who underwent laparoscopic procedures for rectal cancer from 2018 to 2022. Preoperative serum levels of PA, ALB, and TRF were measured. We employed multivariate logistic regression to determine the independent risk factors for AL, and a predictive model was constructed and evaluated using receiver operating characteristic curve analysis. RESULTS: = 0.031), respectively. Tumor location (< 7 cm from anal verge) and intraoperative bleeding ≥ 300 mL also increased AL risk. The predictive model demonstrated an excellent accuracy, achieving an area under the receiver operating characteristic curve of 0.942, a sensitivity of 0.844, and a specificity of 0.922, demonstrating an excellent ability to discriminate. CONCLUSION: Preoperative serum nutritional biomarkers, combined with surgical factors, reliably predict anastomotic leakage risk after rectal cancer surgery, highlighting their importance in preoperative assessment.
Authors: Paerhati Shayimu, Maitisaidi Awula, Chang-Yong Wang, Rexida Jiapaer, Yi-Peng Pan, Zhi-Min Wu, Yi Chen, Ze-Liang Zhao
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4402876145
