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

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, Sh

SUBMITTER: Wang Q 

PROVIDER: S-EPMC12045007 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature