术前炎症负荷指数联合缺氧诱导因子1α构建胆囊癌淋巴结转移预测模型的价值
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山东省临沂市中心医院 胆胰脾外科,山东 临沂 276400

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王江,山东省临沂市中心医院主治医师,主要从事胆囊癌方面的研究。

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山东省医药卫生科技发展计划基金资助项目 202303020109山东第二医科大学附属医院科研发展基金资助项目 2024FYQ066山东省医药卫生科技发展计划基金资助项目(202303020109);山东第二医科大学附属医院科研发展基金资助项目(2024FYQ066)。


Predictive value of preoperative inflammatory burden index combined with hypoxia-inducible factor 1α for lymph node metastasis in gallbladder cancer
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Department of Hepatopancreatobiliary and Splenic Surgery, Linyi Central Hospital, Linyi, Shandong 276400, China

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    摘要:

    背景与目的 淋巴结转移(LNM)是影响胆囊癌(GBC)患者预后的重要因素,术前准确评估LNM对于制定个体化治疗策略具有重要意义。本研究探讨术前炎症负荷指数(IBI)联合缺氧诱导因子1α(HIF-1α)预测GBC患者LNM的价值及二者的交互作用。方法 回顾性分析2020年1月—2025年3月山东省临沂市中心医院收治的106例GBC患者临床资料,根据术后病理结果分为转移组(44例)和未转移组(62例)。比较两组IBI及HIF-1α水平差异,采用多因素Logistic回归分析LNM的独立危险因素;构建联合预测模型并采用受试者工作特征曲线评价预测效能;应用断点回归设计(RDD)分析关键阈值;采用相加及相乘模型评价IBI与HIF-1α对LNM的交互作用;进一步分析不同分化程度及T分期亚组中的预测效能。结果 转移组患者IBI和HIF-1α水平均显著高于未转移组(均P<0.001)。多因素Logistic回归分析显示,T分期、IBI及HIF-1α均为GBC患者LNM的独立危险因素(均P<0.05)。联合模型预测LNM的曲线下面积为0.847(95% CI=0.782~0.902),高于单项指标。RDD显示,当IBI<33.21及HIF-1α<9.65 pg/mL时,LNM发生风险明显下降。交互作用分析显示,IBI与HIF-1α在相加和相乘尺度上均存在协同作用。亚组分析显示,联合检测在不同分化程度及T分期患者中均具有较好的预测效能,其中在中低分化及T3~T4期患者中的预测效能更优。结论 术前IBI和HIF-1α是GBC患者LNM的独立危险因素,两者联合检测可显著提高LNM的预测效能,并且两者存在协同促进作用,可为GBC患者术前风险分层及个体化治疗决策提供参考依据。

    Abstract:

    Background and Aims Lymph node metastasis (LNM) is a major determinant of prognosis in patients with gallbladder carcinoma (GBC). Accurate preoperative assessment of LNM is essential for individualized treatment planning. This study investigated the predictive value of the inflammatory burden index (IBI) combined with hypoxia-inducible factor 1α (HIF-1α) for LNM and evaluated their interaction in patients with GBC.Methods Clinical data from 106 patients with pathologically confirmed GBC treated at Linyi Central Hospital between January 2020 and March 2025 were retrospectively analyzed. Patients were divided into an LNM group (n=44) and a non-LNM group (n=62) according to postoperative pathological findings. Differences in IBI and HIF-1α levels were compared between groups. Independent risk factors for LNM were identified using multivariate Logistic regression analysis. Receiver operating characteristic curve analysis was used to evaluate predictive performance. Regression discontinuity design (RDD) was applied to determine threshold effects. Additive and multiplicative interaction analyses were performed to assess the synergistic effects of IBI and HIF-1α. Subgroup analyses were conducted according to tumor differentiation and T stage.Results Patients with LNM exhibited significantly higher IBI and HIF-1α levels than those without LNM (both P<0.001). Multivariate Logistic regression analysis identified T stage, IBI, and HIF-1α as independent risk factors for LNM (all P<0.05). The combined model demonstrated good predictive performance with an area under the curve of 0.847 (95% CI=0.782-0.902), outperforming either marker alone. RDD analysis identified significant thresholds at IBI<33.21 and HIF-1α<9.65 pg/mL, below which the risk of LNM decreased significantly. Significant synergistic interactions between IBI and HIF-1α were observed on both additive and multiplicative scales. Subgroup analyses showed that the combined model maintained superior predictive performance across different differentiation grades and T stages, particularly in poorly differentiated tumors and T3-T4 stage disease.Conclusion Preoperative IBI and HIF-1α are independent predictors of LNM in GBC. Their combined assessment significantly improves predictive accuracy and demonstrates synergistic effects, providing a useful tool for preoperative risk stratification and individualized therapeutic decision-making for GBC patients.

    图1 Logistic回归模型有效性评价 A:ROC曲线;B:校准曲线;C:决策曲线Fig.1 Performance evaluation of the Logistic regression prediction model A: ROC curve; B: Calibration curve; C: Decision curve analysis
    图2 各指标预测LNM的ROC曲线Fig.2 ROC curves of different indicators for predicting LNM
    图3 IBI、HIF-1α水平在截断点时患者发生LNM的风险Fig.3 Risk of LNM at different cutoff values of IBI and HIF-1α in the regression discontinuity design
    图4 McCrary检验的密度函数图Fig.4 McCrary density test plots in the regression discontinuity design
    表 2 各变量赋值情况Table 2 Assignment of variables included in Logistic regression analysis
    表 3 多因素Logistic回归分析Table 3 Multivariate Logistic regression analysis
    表 4 RDD中相关协变量的连续性检验Table 4 Continuity test of covariates in the regression discontinuity design
    表 5 RDD稳健性检验Table 5 Robustness analysis of the RDD
    表 6 不同IBI与HIF-1α水平下GBC患者LNM的风险Table 6 Risk of lymph node metastasis according to different levels of IBI and HIF-1α
    表 7 IBI与HIF-1α对GBC患者LNM的交互作用效应Table 7 Interaction effects of IBI and HIF-1α on LNM in patients with GBC
    表 8 不同分化程度患者血清IBI、HIF-1α水平对LNM的预测效能Table 8 Predictive performance of serum IBI and HIF-1α for LNM according to tumor differentiation
    表 9 不同T分期患者血清IBI、HIF-1α水平对LNM的预测效能Table 9 Predictive performance of serum IBI and HIF-1α for LNM according to T stage
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王江,孙文胜,许壮志,于秀涛.术前炎症负荷指数联合缺氧诱导因子1α构建胆囊癌淋巴结转移预测模型的价值[J].中国普通外科杂志,2026,35(8):1542-1552.
DOI:10.7659/j. issn.1005-6947.250711

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  • 收稿日期:2025-12-19
  • 最后修改日期:2026-04-17
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  • 在线发布日期: 2026-09-29
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