Development and validation of a nomogram for predicting clinically relevant delayed gastric emptying in patients undergoing total pancreatectomy.

Background: Current research on delayed gastric emptying (DGE) after pancreatic surgery is predominantly focused on pancreaticoduodenectomy (PD), with little exploration into DGE following total pancreatectomy (TP). This study aims to investigate the risk factors for DGE after TP and develop a predictive model.

Methods: This retrospective cohort study included 106 consecutive cases of TP performed between January 2013 and December 2023 at Peking Union Medical College Hospital (PUMCH). After applying the inclusion criteria, 96 cases were selected for analysis. These patients were randomly divided into a training set (n = 67) and a validation set (n = 29) in a 7:3 ratio. LASSO regression and multivariate logistic regression analyses were used to identify factors associated with clinically relevant DGE (grades B/C) and to construct a predictive nomogram. The ROC curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were employed to evaluate the model's prediction accuracy.

Results: The predictive model identified end-to-side gastrointestinal anastomosis, intraoperative blood transfusion, and venous reconstruction as risk factors for clinically relevant DGE after TP. The ROC was 0.853 (95%CI 0.681-0.900) in the training set and 0.789 (95%CI 0.727-0.857) in the validation set. The calibration curve, DCA, and CIC confirmed the accuracy and practicality of the nomogram.

Conclusion: We developed a novel predictive model that accurately identifies potential risk factors associated with clinically relevant DGE in patients undergoing TP.