恶性胆道梗阻金属支架再狭窄的预测模型构建与验证
作者:
通讯作者:
作者单位:

1.河北省邢台市人民医院 放射介入科,河北 邢台 054000;2.河北省邢台市人民医院 中医内科,河北 邢台 054000;3.河北省邢台市人民医院 内分泌科,河北 邢台 054000;4.河北省邢台市人民医院 肝胆外科,河北 邢台 054000;5.河北省邢台市人民医院 妇二科,河北 邢台 054000

作者简介:

戴守方,河北省邢台市人民医院副主任医师,主要从事恶性肿瘤介入治疗方面的研究。

基金项目:

河北省邢台市重点研发计划基金资助项目(2023ZC050)。


Construction and validation of a predictive model for metal stent restenosis in malignant biliary obstruction
Author:
Affiliation:

1.Department of Interventional Radiology, Xingtai People's Hospital, Xingtai, Hebei 054000, China;2.Department of Traditional Chinese Medicine, Xingtai People's Hospital, Xingtai, Hebei 054000, China;3.Department of Endocrinology, Xingtai People's Hospital, Xingtai, Hebei 054000, China;4.Department of Hepatobiliary Surgery, Xingtai People's Hospital, Xingtai, Hebei 054000, China;5.Second Department of Gynecology, Xingtai People's Hospital, Xingtai, Hebei 054000, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 音频文件
  • |
  • 视频文件
    摘要:

    背景与目的 金属支架和125I粒子腔内照射是恶性胆道梗阻非手术治疗的首选方法,疗效确切,能改善患者生存预后,但金属支架再狭窄发生率较高,导致其发生的因素尚不明确,且缺乏可靠的预测模型。因此,本研究探讨恶性胆道梗阻金属支架再狭窄的影响因素,建立其预测模型并验证性能。方法 选取2019年1月—2022年3月河北省邢台市人民医院收治的110例初次接受金属支架和125I粒子腔内照射恶性胆道梗阻患者,根据术后12个月内金属支架是否再狭窄分为再狭窄组与无再狭窄组。采用单因素、LASSO回归初步筛选金属支架再狭窄的特征变量,Logistic回归进一步分析金属支架再狭窄的相关影响因素,并以R语言绘制列线图预测模型,分别采用一致性指数(C指数)、Hosmer-Lemeshow拟合优度、受试者工作特征曲线下面积(AUC)、临床影响曲线分别评价所构建恶性胆道梗阻金属支架再狭窄预测模型的价值;另选同一中心不同时期的50例初次接受金属支架和125I粒子腔内照射恶性胆道梗阻患者作为外部验证数据集,采用κ检验比较列线图预测再狭窄与临床实际的符合率。结果 110例患者中,术后12个月内共发生再狭窄58例。单因素分析,糖尿病、总胆红素(TBIL)、糖类抗原19-9(CA19-9)、术后胆系感染、胆结石、白蛋白、射频消融、光动力治疗可能与再狭窄有关(均P<0.05)。Logistic回归分析,TBIL、CA19-9、术后胆系感染、胆结石、射频消融、光动力治疗是恶性胆道梗阻金属支架再狭窄的独立相关因素(均P<0.05);基于Logistic回归绘制列线图预测模型显示,其C指数为0.838,Hosmer-Lemeshow拟合优度检验显示,模型预测值与实际观测值之间的差异无统计学意义(χ2=2.796,P=0.803);所构建的恶性胆道梗阻金属支架再狭窄的列线图预测模型的AUC为0.838(95% CI=0.762~0.913),绘制临床影响曲线显示,在各个阈概率下,被所构建的恶性胆道梗阻金属支架再狭窄的列线图预测模型划分为高风险的人数与实际情况的符合度较高;采用列线图预测模型对外部验证数据集进行预测显示,列线图预测再狭窄的发生率与实际情况符合率为94.00%,κ值为0.880。结论 TBIL、CA19-9、术后胆系感染、胆结石、射频消融、光动力治疗是恶性胆道梗阻金属支架再狭窄的独立相关因素,基于以上因素的预测模型具有良好的预测能力,能为临床早期识别高风险人群提供一定的参考。

    Abstract:

    Background and Aims Metal stents and intraluminal irradiation with 125I seeds are the preferred non-surgical treatments for malignant biliary obstruction, with proven efficacy in improving patient survival outcomes. However, the incidence of restenosis in metal stents remains high, and the factors contributing to this are not well understood, with no reliable predictive models currently available. Therefore, this study investigated the factors influencing metal stent restenosis in malignant biliary obstruction, developed a predictive model, and validated its performance.Methods A total of 110 patients with malignant biliary obstruction who received metal stents and 125I seed intraluminal irradiation for the first time between January 2019 and March 2022 were selected. Patients were divided into restenosis and non-restenosis groups based on whether restenosis occurred within 12 months after operation. Univariate analysis and LASSO regression were initially used to screen for characteristic variables associated with stent restenosis, followed by Logistic regression to further analyze the related influencing factors. A nomogram predictive model was constructed using R language, and its value was assessed using the concordance index (C-index), Hosmer-Lemeshow goodness-of-fit test, the area under the receiver operating characteristic curve (AUC), and clinical impact curves. An additional 50 patients with malignant biliary obstruction from the same center, who received metal stents and 125I seed intraluminal irradiation for the first time during a different period, were selected as an external validation dataset, and the κ statistic was used to compare the concordance rate between the nomogram prediction of restenosis and clinical reality.Results Of the 110 patients, 58 cases of restenosis occurred within 12 months after operation. Univariate analysis showed that diabetes, total bilirubin (TBIL), carbohydrate antigen 19-9 (CA19-9), postoperative biliary infection, gallstones, albumin level, radiofrequency ablation, and photodynamic therapy were associated with restenosis (all P<0.05). Logistic regression analysis identified TBIL, CA19-9, postoperative biliary infection, gallstones, radiofrequency ablation, and photodynamic therapy as independent factors associated with metal stent restenosis in malignant biliary obstruction (all P<0.05). The nomogram predictive model based on Logistic regression had a C-index of 0.838, and the Hosmer-Lemeshow goodness-of-fit test indicated no significant difference between the predicted and observed values (χ2=2.796, P=0.803). The AUC of the constructed nomogram for predicting metal stent restenosis in malignant biliary obstruction was 0.838 (95% CI=0.762-0.913), and the clinical impact curve showed a high concordance between the predicted high-risk group and actual outcomes across various threshold probabilities. Using the nomogram predictive model to predict the external validation dataset showed a concordance rate of 94.00% between the predicted restenosis rate and the actual one, with a κ value of 0.880.Conclusion TBIL, CA19-9, postoperative biliary infection, gallstones, radiofrequency ablation, and photodynamic therapy are independent factors associated with metal stent restenosis in malignant biliary obstruction. The predictive model based on these factors demonstrates good predictive ability and may provide a reference for early clinical identification of high-risk patients.

    表 2 恶性胆道梗阻金属支架再狭窄影响因素的Logistic回归分析Table 2 Logistic regression analysis of factors for metal stent restenosis in malignant biliary obstruction
    表 3 列线图预测模型的外部验证结果(n)Table 3 External validation results of the nomogram prediction model (n)
    图1 主要介入步骤图 A:经皮经肝穿刺肝左叶肝管,置管造影示:肝左右管贯通显影,胆总管上段截断;B:经皮肝穿刺道置入8 F导管鞘后,在导丝引导下引入6 F造影导管,使用超滑导丝探查胆总管上段,通过狭窄段后,引入造影导管,退管造影示:胆总管中上段重度狭窄,符合胆管癌表现,狭窄段长度约3 cm;C:通过造影导管引入双导丝后,撤出导管,留置双导丝;D:通过其中任1根导丝引入8 mm×60 mm裸支架1枚,通过另1根导丝引入造影导管,均留置于狭窄段;E:明确狭窄段及支架位置后,将装载好的粒子链通过造影导管预留于狭窄段,释放支架同时退管并通过导丝推出导管内的粒子链,使粒子链留置于支架与狭窄段之间;F:留置导丝,退出8 F导管鞘,在导丝引导下,引入8.5 F胆道外引流管,固定引流管,局部包扎,外接引流袋,结束治疗Fig.1 Main interventional procedure steps A: Percutaneous transhepatic puncture of the left hepatic duct, and catheterization and imaging show continuity between the left and right hepatic ducts, with the upper segment of the common bile duct severed; B: After inserting an 8 F sheath through the percutaneous hepatic puncture tract, a 6 F angiographic catheter is guided by a guidewire, using a super-smooth guidewire, the upper segment of the common bile duct is probed and navigated through the stenotic segment, and imaging with the catheter withdrawn reveals severe stenosis in the middle and upper segments of the common bile duct, consistent with cholangiocarcinoma, with the stenotic segment approximately 3 cm in length; C: After introducing dual guidewires through the angiographic catheter, the catheter is withdrawn, leaving the dual guidewires in place; D: An 8 mm × 60 mm bare-metal stent is introduced over one guidewire, and an angiographic catheter is introduced over the other, both positioned at the stenotic segment; E: After confirming the location of the stenotic segment and the stent, a loaded seed chain is advanced through the angiographic catheter and positioned in the stenotic segment, the stent is released, and simultaneously the catheter is withdrawn, deploying the seed chain between the stent and the stenotic segment; F: A guidewire is left in place, the 8 F sheath is withdrawn, and an 8.5 F external biliary drainage catheter is introduced under guidewire guidance, the drainage catheter is secured, the area is bandaged, and an external drainage bag is connected, concluding the procedure
    图2 恶性胆道梗阻金属支架再狭窄特征变量分析 A:LASSO回归筛选变量动态过程;B:交叉验证最佳参数λ的选择过程Fig.2 Analysis of characteristic variables for metal stent restenosis in malignant biliary obstruction A: Dynamic process of variable selection using LASSO regression; B: Selection process for the optimal λ parameter through cross-validation
    图3 恶性胆道梗阻金属支架再狭窄的列线图预测模型Fig.3 Nomogram predictive model for metal stent restenosis in malignant biliary obstruction
    图4 列线图的预测效能分析 A:ROC曲线;B:临床影响曲线Fig.4 Analysis of the predictive performance of the nomogram A: ROC curve; B: Clinical impact curve
    参考文献
    相似文献
    引证文献
引用本文

戴守方,张丽晓,王瑞锋,李蕾,王继涛,连晓静,尹永超,徐晓,陈威.恶性胆道梗阻金属支架再狭窄的预测模型构建与验证[J].中国普通外科杂志,2024,33(8):1220-1229.
DOI:10.7659/j. issn.1005-6947.2024.08.003

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
历史
  • 收稿日期:2024-05-23
  • 最后修改日期:2024-07-18
  • 录用日期:
  • 在线发布日期: 2024-09-05