Delayed gastric emptying risk stratification in patients with pancreatic ductal adenocarcinoma after pancreatoduodenectomy: An international validation cohort study.

Abstract

Background: Currently, there is still a lack of an accurate predictive model for delayed gastric emptying (DGE) following pancreaticoduodenectomy (PD) in patients with pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to develop a concise model that could effectively predict the risk of DGE.

Methods: This retrospective cohort study included a training cohort of 1251 consecutive PDAC patients who underwent PD from the US multicenter ACS-NSQIP database. Additionally, a validation cohort of 934 consecutive PDAC patients who underwent PD was included from the National Cancer Center in China. A total of 46 perioperative indicators were incorporated in the analysis. The DGE risk stratification (DGERS) model was then developed and validated using Lasso-logistic regression.

Results: After screening using Lasso-logistic regression, we identified four independent predictors that were significantly correlated with DGE: days to pancreatic drain removal (HR, 1.05; 95% CI, 1.02-1.08; p < 0.001), pancreatic fistula (HR, 2.61; 95% CI, 1.65-4.12; p < 0.001), sepsis/septic shock (HR, 2.46; 95% CI, 1.52-3.91; p < 0.001), and reoperation (HR, 4.16; 95% CI, 2.27-7.57; p < 0.001). Based on these factors, we developed a nomogram to predict postoperative DGE. The model demonstrated excellent calibration and optimal performance in the validation cohorts (AUC, 0.73; 95% CI, 0.67-0.73). In the validation cohort, the DGERS exhibited significant risk stratification ability, with AUC values of 0.7, 0.61, and 0.74 for the low-, moderate-, and high-risk groups, respectively.

Conclusions: This study identified four factors that independently increased the occurrence of DGE in patients with PDAC after PD, including days to pancreatic drain removal, pancreatic fistula, sepsis/septic shock, and reoperation. Based on these findings, we developed a personalized and straightforward DGERS that enables dynamic and precise prediction of DGE risk, allowing for effective stratification of individuals based on their risk profiles.