Development of a nomogram for predicting anastomotic leakage after rectal cancer surgery incorporating inflammatory indices

Objective Anastomotic leakage (AL) is a common and serious complication after rectal cancer surgery, and there is still a lack of effective prediction tools. This study aimed to build a prediction model for AL after rectal cancer surgery by combining inflammatory indicators. Methods We collected clinical data from 650 patients who underwent anterior resection for rectal cancer in the General Surgery Department of Cang Zhou Hospital of Integrated Traditional Chinese and Western Medicine of Hebei Province between January 2020 and December 2024. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to screen predictors. A multivariate logistic regression was then used to build the prediction model, presented as a nomogram. The model’s performance was evaluated using the Receiver Operating Characteristic (ROC) curve, Calibration curves and Decision Curve Analysis (DCA) further demonstrated acceptable calibration and suggested potential clinical utility. Results Data from 650 rectal cancer patients were included, with an average age of 63.06 ± 9.84 years. Multivariate logistic regression identified six independent predictors for AL: male gender (OR=1.675, 95% CI: 1.020-2.755), low tumor location (tumor location<7cm, OR=1.795, 95% CI: 1.126-2.862), high BMI (OR=2.176, 95% CI: 1.607-2.948), longer operation time (OR=2.697, 95% CI: 1.942-3.745), blood loss (OR=0.520, 95% CI: 0.403-0.671), and high Systemic Inflammation Response Index (SIRI) (OR=1.520, 95% CI: 1.278-1.808). The nomogram model demonstrated good predictive performance in both the training and validation sets, with Area Under the Curve (AUC) values of 0.763 (95% CI: 0.706-0.820) and 0.798 (95% CI: 0.707-0.889), respectively. Calibration curves and Decision Curve Analysis (DCA) indicated acceptable calibration and potential clinical utility. Conclusion This study developed and validated a nomogram prediction model that combines preoperative inflammatory indicators (SIRI) with key clinical features. This model can help clinicians identify high-risk patients early and provides a quantitative tool for implementing targeted perioperative intervention strategies.