Nomogram for predicting early complications after distal gastrectomy

Abstract

BACKGROUND Reducing or preventing postoperative morbidity in patients with gastric cancer (GC) is particularly important in perioperative treatment plans. AIM To identify risk factors for early postoperative complications of GC post-distal gastrectomy and to establish a nomogram prediction model. METHODS This retrospective study included 131 patients with GC who underwent distal gastrectomy at the Second Hospital of Shandong University between January 2019 and February 2023. The factors influencing the development of complications after distal gastrectomy in these patients were evaluated using univariate and multivariate logistic regression analysis. Based on the results obtained, a predictive nomogram was established. The nomogram was validated using internal and external (n = 45) datasets. Its sensitivity and specificity were established by receiver operating characteristic curve analysis. Decision curve (DCA) analysis was used to determine its clinical benefit and ten-fold overfitting was used to establish its accuracy and stability. RESULTS Multivariate logistic regression analysis showed that hypertension, diabetes, history of abdominal surgery, and perioperative blood transfusion were independent predictors of postoperative complications of distal gastrectomy. The modeling and validation sets showed that the area under the curve was 0.843 [95% confidence interval (CI): 0.746-0.940] and 0.877 (95%CI: 0.719-1.000), the sensitivity was 0.762 and 0.778, respectively, and the specificity was 0.809 and 0.944, respectively, indicating that the model had good sensitivity and specificity. The C-indexes of the modeling and validation datasets were 0.843 (95%CI: 0.746-0.940) and 0.877 (95%CI: 0.719-1.000), respectively. The calibration curve (Hosmer Lemeshow test: χ 2 = 7.33) showed that the model had good consistency. The results of the DCA analysis indicated that this model offered good clinical benefits. The accuracy of 10-fold cross-validation was 0.878, indicating that the model had good accuracy and stability. CONCLUSION The nomogram prediction model based on independent risk factors related to postoperative complications of distal gastrectomy can facilitate perioperative intervention for high-risk populations and reduce the incidence of postoperative complications.