Development of a Nomogram to Predict Clinically Relevant Postoperative Pancreatic Fistula After Pancreaticoduodenectomy on the Basis of Visceral Fat Area and Magnetic Resonance Imaging

Abstract

Background: The aim of this study was to develop a nomogram to predict the risk of developing clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy (PD) using preoperative clinical and imaging data.

Methods: The data of 205 patients were retrospectively analyzed, randomly divided into training (n = 125) and testing groups (n = 80). The patients' preoperative laboratory indicators, preoperative clinical baseline data, and preoperative imaging data [enhanced computed tomography (CT), enhanced magnetic resonance imaging (MRI)] were collected. Univariate analyses combined with multivariate logistic regression were used to identify the independent risk factors for CR-POPF. These factors were used to train and validate the model and to develop the risk nomogram. The area under the curve (AUC) was used to measure the predictive ability of the models. The integrated discrimination improvement index (IDI) and decision curve analysis (DCA) were used to assess the clinical feasibility of the nomogram in relation to five other models established in literature.

Results: CT visceral fat area (P = 0.014), the pancreatic spleen signal ratio on T1 fat-suppressed MRI sequences (P < 0.001), and CT main pancreatic duct diameter (P = 0.001) were identified as independent prognostic factors and used to develop the model. The final nomogram achieved an AUC of 0.903. The IDI and DCA showed that the nomogram outperformed the other five CR-POPF models in the training and testing cohorts.

Conclusion: The nomogram achieved a superior predictive ability for CR-POPF following PD than other models described in literature. Clinicians can use this simple model to optimize perioperative planning according to the patient's risk of developing CR-POPF.

Keywords: Decision curve analysis; Nomogram; Preoperative pancreatic fistula prediction model; Visceral fat area.