中国普通外科杂志2026，Vol.35Issue(3)：480-487,8.DOI:10.7659/j.issn.1005-6947.260122
多算法机器学习模型在胰十二指肠切除术后胰瘘风险预测中的构建与比较
Construction and comparison of multi-algorithm machine learning models for predicting postoperative pancreatic fistula after pancreaticoduodenectomy
摘要
Abstract
Background and Aims:Clinically relevant postoperative pancreatic fistula(CR-POPF)remains a major complication after pancreaticoduodenectomy(PD),significantly affecting patient outcomes.Conventional risk models have limitations in capturing complex nonlinear relationships.This study aimed to identify independent risk factors for CR-POPF and to develop and compare multiple machine learning-based prediction models. Methods:A total of 334 patients who underwent PD at the Hepatobiliary and Pancreatic Center of Zhongda Hospital,Southeast University between January 2016 and December 2025 were retrospectively analyzed.Independent risk factors were identified using univariate and multivariate logistic regression analyses.The dataset was randomly divided into training and validation sets at a 7∶3 ratio.Prediction models were developed using Logistic regression(LR),artificial neural network(ANN),decision tree(DT),random forest(RF),and support vector machine(SVM).Model performance was evaluated using AUC,sensitivity,specificity,positive predictive value,negative predictive value,F1-score,and accuracy. Results:Multivariate analysis identified increased BMI(OR=1.167),diabetes mellitus(OR=3.826),hypertension(OR=2.232),history of abdominal surgery(OR=2.599),lower preoperative albumin(OR=0.625),elevated postoperative white blood cell count(OR=1.091),and pancreatic-origin lesions(OR=2.945)as independent risk factors for CR-POPF(all P<0.05).Among the models,the ANN model demonstrated superior performance,with an AUC of 0.866,sensitivity of 0.745,specificity of 0.914,and F1-score of 0.768. Conclusion:CR-POPF after PD is influenced by multiple clinical factors.The ANN-based model shows strong predictive performance and may serve as a valuable tool for early identification of high-risk patients and implementation of individualized interventions.关键词
胰十二指肠切除术/胰腺瘘/机器学习/危险因素Key words
Pancreaticoduodenectomy/Pancreatic Fistula/Machine Learning/Risk Factor分类
医药卫生引用本文复制引用
王珂璇,金晓灵..多算法机器学习模型在胰十二指肠切除术后胰瘘风险预测中的构建与比较[J].中国普通外科杂志,2026,35(3):480-487,8.基金项目
东南大学附属中大医院护理科研课题(KJZC-HL-202417) （KJZC-HL-202417）
国家临床重点专科建设基金资助项目. （）
