Development and validation of a nomogram model for predicting infection after radical resection of gastric cancer.

Objective: To develop and validate a nomogram model for predicting infection after radical resection of gastric cancer (GC). Methods: In this retrospective cohort study clinical data of patients who underwent radical resection of GC in BenQ Medical Center in Nanjing, China from January 2020 to April 2024 was retrospectively selected. Patients were randomly assigned to the training cohort and the validation cohort in a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to analyze the characteristics and screen the independent risk factors of infection after radical resection of GC to construct a predictive nomogram model. The prediction performance and clinical utility of the nomogram model were evaluated by drawing the receiver operating characteristic (ROC) and calculating the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results: Records of 581 patients with GC after radical resection were included in this study. The incidence of postoperative infection was 19.1% (111/581). The nomogram model that included age, hypertension, open surgery, operation duration, lymphocyte count, and prognostic nutritional index (PNI) showed sufficient prediction accuracy, with the AUC of the training set and validation set of 0.833 (95% CI: 0.778-0.888) and 0.859 (0.859; 0.777-0.941), respectively. The calibration curve showed that the model's predicted value is basically consistent with the actual value, and the calibration effect is good. DCA also shows that the predictive model has good clinical utility. Conclusions: The established nomogram model has a good predictive value in predicting infection after radical resection of GC in this study, which may be a reliable tool for clinicians to identify patients with GC at high risk of infection after radical gastrectomy.