基于机器学习的腹腔镜胰十二指肠切除术后胰瘘预测。

IF 1.6 3区 医学 Q2 SURGERY
Qianchang Wang, Zhe Wang, Fangfeng Liu, Zhengjian Wang, Qingqiang Ni, Hong Chang
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引用次数: 0

摘要

背景:腹腔镜胰十二指肠切除术(LPD)后临床相关的术后胰瘘(CR-POPF)是显著恶化患者预后的关键并发症。然而,其风险因素的异质性和预测模型的临床应用仍有待充分阐明。本研究旨在系统分析CR-POPF的危险因素,并利用机器学习算法建立优化的预测模型,为LPD患者的个体化风险评估提供循证方法。方法:回顾性研究2017年1月至2024年1月在山东第一医科大学附属山东省医院奥林匹克体育场校区肝胆外科中心行腹腔镜胰十二指肠切除术(LPD)的壶腹周围癌患者210例。根据国际胰腺外科研究组(ISGPS) 2016年的标准,将患者分为临床相关胰瘘(CR-POPF)组(n = 34)和非临床相关胰瘘(non-CR-POPF)组(n = 176)。通过组间比较确定潜在危险因素,通过单因素和多因素logistic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了CR-POPF的预测模型。结果:CR-POPF与较高的BMI、单核细胞水平、血小板计数、总胆红素、AST、ALT和较低的白蛋白有关。CR-POPF组壶腹癌和胰腺软质病变病理诊断明显增多。多变量分析确定胰腺软质是独立预测因子(OR = 4.99, 95% CI: 1.93-12.86)。在所有模型中,随机森林模型在仅考虑年龄、性别、BMI、高血压、糖尿病、血色素、血小板、AST、alt等术前变量时表现最佳(AUC = 0.747,灵敏度= 0.917,特异性= 0.574)。结论:胰腺质地软是腹腔镜胰十二指肠切除术(LPD)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可实现个体化风险预测,为术前规划和术后护理提供价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

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.

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来源期刊
BMC Surgery
BMC Surgery SURGERY-
CiteScore
2.90
自引率
5.30%
发文量
391
审稿时长
58 days
期刊介绍: BMC Surgery is an open access, peer-reviewed journal that considers articles on surgical research, training, and practice.
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