Wang Qianchang, Wang Zhe, Liu Fangfeng, Wang Zhengjian, Ni Qingqiang, Chang Hong
Shandong First Medical University, Jinan, Shandong, China.
Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital, Jinan, Shandong, China.
BMC Surg. 2025 Apr 30;25(1):191. doi: 10.1186/s12893-025-02935-4.
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.
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.
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.
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.
腹腔镜胰十二指肠切除术(LPD)后临床相关的术后胰瘘(CR-POPF)是一种严重并发症,会显著恶化患者预后。然而,其危险因素的异质性以及预测模型的临床实用性仍有待充分阐明。本研究旨在系统分析CR-POPF的危险因素,并使用机器学习算法开发优化的预测模型,为接受LPD的患者提供基于证据的个体化风险评估方法。
进行一项回顾性研究,纳入2017年1月至2024年1月在山东第一医科大学附属山东省立医院奥体中心院区肝胆外科中心接受腹腔镜胰十二指肠切除术(LPD)的210例壶腹周围癌患者。根据国际胰腺手术研究组(ISGPS)2016年标准,将患者分为临床相关胰瘘(CR-POPF)组(n = 34)和非临床相关胰瘘(非CR-POPF)组(n = 176)。通过组间比较确定潜在危险因素,并使用单因素和多因素逻辑回归分析确定独立危险因素。基于这些发现,使用机器学习算法开发CR-POPF预测模型。
CR-POPF与较高的BMI、单核细胞水平、血小板计数、总胆红素、AST、ALT以及较低的白蛋白相关。壶腹癌的病理诊断和胰腺质地柔软在CR-POPF组中明显更常见。多因素分析确定胰腺质地柔软为独立预测因素(OR = 4.99,95% CI:1.93 - 12.86)。在所有模型中,随机森林模型表现最佳(AUC = 0.747,灵敏度 = 0.917,特异度 = 0.574),仅使用年龄、性别、BMI、高血压、糖尿病、血红蛋白、血小板、AST和ALT等术前变量。
胰腺质地柔软被确定为腹腔镜胰十二指肠切除术(LPD)后术后胰瘘的独立危险因素。基于术前临床变量的随机森林模型能够进行个体化风险预测,为术前规划和术后护理提供价值。