Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy

Abstract

Abstract Background Clinically relevant postoperative pancreatic fistula (CR-POPF) following laparoscopic pancreaticoduodenectomy (LPD) is a critical complication that significantly worsens patient outcomes. However, the heterogeneity of its risk factors and the clinical utility of predictive models remain to be fully elucidated. This study aims to systematically analyze the risk factors for CR-POPF and develop an optimized predictive model using machine learning algorithms, providing an evidence-based approach for individualized risk assessment in patients undergoing LPD. Methods A retrospective study was conducted, including 210 patients with periampullary cancer who underwent laparoscopic pancreaticoduodenectomy (LPD) at the Hepatobiliary Surgery Center, Olympic Stadium Campus, Shandong Provincial Hospital Affiliated to Shandong First Medical University, from January 2017 to January 2024. Patients were classified into the clinically relevant pancreatic fistula (CR-POPF) group (n = 34) and the non-clinically relevant pancreatic fistula (non-CR-POPF) group (n = 176) according to the 2016 criteria of the International Study Group of Pancreatic Surgery (ISGPS). Potential risk factors were identified through intergroup comparisons, and independent risk factors were determined using univariate and multivariate logistic regression analyses. Based on these findings, a predictive model for CR-POPF was developed using machine learning algorithms. Results CR-POPF was associated with higher BMI, monocyte levels, platelet count, total bilirubin, AST, ALT, and lower albumin. Pathological diagnosis of ampullary carcinoma and soft pancreatic texture were significantly more common in the CR-POPF group. Multivariate analysis identified soft pancreatic texture as an independent predictor (OR = 4.99, 95% CI: 1.93–12.86). Among all models, the random forest model showed the best performance (AUC = 0.747, sensitivity = 0.917, specificity = 0.574), using only preoperative variables such as age, gender, BMI, hypertension, diabetes, hemoglobin, platelets, AST, and ALT. Conclusion Soft pancreatic texture was identified as an independent risk factor for postoperative pancreatic fistula following laparoscopic pancreaticoduodenectomy (LPD). The random forest model based on preoperative clinical variables enables individualized risk prediction, offering value for preoperative planning and postoperative care.

Keywords

• Laparoscopic pancreaticoduodenectomy
• Clinically relevant postoperative pancreatic fistula
• Postoperative complications
• Machine learning
• Predictive model