Internal Evaluation of a Machine Learning-Based Random Forest Model for Clinically Relevant Postoperative Pancreatic Fistula After Pancreaticoduodenectomy: Limited Incremental Value of Inflammatory–Nutritional Markers

Background and Objectives: This study evaluated whether preoperative neutrophil-to-lymphocyte ratio (NLR) improves prediction of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy. Materials and Methods: This single-center retrospective cohort included 120 of 158 patients operated on between January 2019 and March 2026; 38 lacked documented pancreatic duct diameter. CR-POPF followed the 2016 International Study Group on Pancreatic Surgery definition. A random forest incorporating duct diameter, estimated blood loss, and NLR was compared with a model omitting NLR and penalized logistic regression using 10-times-repeated stratified five-fold cross-validation. Post hoc clinicopathological sensitivity analyses added pancreatic texture and tumor origin, then tested inflammatory–nutritional markers individually. Discrimination, average precision, Brier score, calibration, and exploratory decision-curve analysis were assessed.