Nomogram and a predictive model for postoperative hemorrhage in preoperative patients of laparoscopic pancreaticoduodectomy

Abstract

AbstractTo develop a predictive model and a nomogram for predicting postoperative hemorrhage in preoperative patients undergoing laparoscopic pancreaticoduodenectomy (LPD). A total of 409 LPD patients that underwent LPD by the same surgical team between January 2014 and December 2020 were included as the training cohort. The preoperative data of patients were statistically compared and analyzed for exploring factors correlated with postoperative hemorrhage. The predictive model was developed by multivariate logistic regression and stepwise (stepAIC) selection. A nomogram based on the predictive model was developed. The discriminatory ability of the predictive model was validated using the receiver operating characteristic (ROC) curve and leave-one-out method. The statistical analysis was performed using R 3.5.1 (www.r-project.org). The predictive model including the risk-associated factors of postoperative hemorrhage was as follows: 2.695843 − 0.63056 × (Jaundice = 1) − 1.08368 × (DM = 1) − 2.10445 × (Hepatitis = 1) + 1.152354 × (Pancreatic tumor = 1) + 1.071354 × (Bile duct tumor = 1) − 0.01185 × CA125 − 0.04929 × TT − 0.08826 × APTT + 26.03383 × INR − 1.9442 × PT + 1.979563 × WBC − 2.26868 × NEU − 2.0789 × LYM − 0.02038 × CREA + 0.00459 × AST. A practical nomogram based on the model was obtained. The internal validation of ROC curve was statistically significant (AUC = 0.7758). The validation by leave-one-out method showed that the accuracy of the model and the F measure was 0.887 and 0.939, respectively. The predictive model and nomogram based on the preoperative data of patients undergoing LPD can be useful for predicting the risk degree of postoperative hemorrhage.