Abstract
OBJECTIVE: To construct a nomogram prediction model for ileus after radical gastrectomy for gastric cancer. METHODS: The 424 gastric cancer patients were assigned into a modeling group of 297 cases and a validation group of 127 cases. The modeling group was assigned into the ileus group of 54 cases and the non ileus group of 243 cases based on the occurrence of postoperative ileus. LASSO regression analysis and multivariate logistic regression analysis were used to screen the influencing factors of ileus after radical gastrectomy for gastric cancer. The nomogram model was constructed for predicting ileus after radical gastrectomy for gastric cancer. The calibration curve was used to evaluate the calibration degree of the model. ROC curve was used to evaluate the model discrimination. The clinical decision curve was used to analyze the clinical applicability of the model. RESULTS: LASSO regression analysis and multivariate logistic regression analysis showed that age (OR=4.999), previous abdominal surgery (OR=3.836), operative time (OR=3.541), IL-6 (OR=1.339), and TNF-α (OR=8.254) were all independent risk factors for ileus after radical gastrectomy for gastric cancer (P<0.05). The deviation correction curves of the modeling group and the validation group were both close to the ideal curve, with Hosmer-Lemeshow test showed x(2)=7.785, 6.145, P=0.236, 0.372; the AUC of the ROC curve was 0.940 (95% CI: 0.898~0.981) and 0.904 (95% CI: 0.855~0.954). Both the modeling group and the validation group had high net returns within the threshold probability ranges of 0.02~0.86 and 0.02~0.80. CONCLUSION: The nomogram prediction model constructed in this study can serve as an early warning indicator for ileus after radical gastrectomy for gastric cancer, guiding clinical decision-making.