Postoperative Pancreatic Fistula Following Pancreaticoduodenectomy

Several risk prediction models have been developed to estimate the likelihood of CR-POPF, including the Fistula Risk Score (FRS), the Alternative Fistula Risk Score (a-FRS) and updated alternative Fistula Risk Score (ua-FRS). However, these models rely predominantly on intraoperative variables, such as pancreatic texture and main pancreatic duct diameter, limiting their value in preoperative risk stratification and patient counselling .

Despite the robustness of these scoring systems, certain aspects require further refinement. In particular, assessment of pancreatic texture remains largely subjective and dependent on intraoperative surgeon palpation, highlighting the need for more objective and quantitative methods. [3,4] Furthermore, elastography has emerged as a promising non-invasive technique for quantitative characterization of pancreatic tissue stiffness and has recently been investigated for preoperative prediction of clinically relevant postoperative pancreatic fistula; however, it has not yet been incorporated into the widely used fistula risk scoring systems.

Onodera's prognostic nutritional index (PNI) is calculated from serum albumin concentration and peripheral blood lymphocyte (PBL) count. It indicates the patient's nutritional and immunological status and is used to predict risk of several kinds of complications after surgery. Moreover, several preoperative nomograms and machine-learning models have demonstrated promising predictive performance for POPF. However, PNI has not been included in any of these scores.

Given the absence of an internally validated preoperative prediction model in the Egyptian population, this study aims to develop and internally validate a preoperative risk model for clinically relevant postoperative pancreatic fistula by incorporation of preoperative PNI and pancreatic texture assessment by elastography with previously emerged scores in patients undergoing pancreaticoduodenectomy. The proposed model may facilitate individualized risk stratification, optimize perioperative planning, improve patient counselling, and support evidence-based surgical decision-making