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中国防痨杂志 ›› 2026, Vol. 48 ›› Issue (6): 760-768.doi: 10.19982/j.issn.1000-6621.20250441

• 论著 • 上一篇    下一篇

结核性气道狭窄介入治疗后再狭窄风险预测模型的构建及验证

徐勇1, 周腾2(), 刘俊佳1   

  1. 1 苏州市第五人民医院结核科, 苏州 215000
    2 苏州市第五人民医院重症医学科, 苏州 215000
  • 收稿日期:2025-11-12 出版日期:2026-06-10 发布日期:2026-05-25
  • 通信作者: 周腾 E-mail:396103272@qq.com
  • 基金资助:
    苏州市科技计划项目(SYW2024138)

Construction and validation of a risk prediction model for restenosis after interventional treatment of tuberculous airway stenosis

Xu Yong1, Zhou Teng2(), Liu Junjia1   

  1. 1 Department of Tuberculosis, Suzhou Fifth People’s Hospital, Suzhou 215000, China
    2 Department of Critical Care Medicine, Suzhou Fifth People’s Hospital, Suzhou 215000, China
  • Received:2025-11-12 Online:2026-06-10 Published:2026-05-25
  • Contact: Zhou Teng E-mail:396103272@qq.com
  • Supported by:
    Suzhou Science and Technology Plan Project(SYW2024138)

摘要:

目的: 分析结核性气道狭窄介入治疗后再狭窄的危险因素,构建风险预测模型,并验证其预测性能。方法: 回顾性收集2021年4月至2024年10月苏州市第五人民医院收治并接受介入治疗的结核性气道狭窄患者350例作为研究对象,采用留出法,以4∶1比例分层随机分为建模组和验证组,收集患者临床资料,采用LASSO回归分析筛选关键变量,进行多因素分析,基于独立危险因素构建列线图预测模型,并分别采用受试者工作特征(ROC)曲线、Hosmer-Lemeshow检验、决策曲线分析(DCA)对模型性能进行验证。结果: 纳入患者介入治疗后再狭窄发生率为41.71%(146/350)。LASSO回归共筛选出10个关键变量,对其进行多因素分析,结果显示,并发糖尿病(OR=3.126,95%CI:1.017~9.604)、镜下活动期(OR=2.646,95%CI:1.197~5.851)、狭窄长度>3cm(OR=5.437,95%CI:2.682~11.022)、系统免疫炎症指数升高(OR=1.008,95%CI:1.006~1.011)均为结核性气道狭窄治疗后再狭窄的危险因素;治疗前接受全身抗结核治疗(OR=0.326,95%CI:0.150~0.707)为保护因素。ROC曲线分析结果显示,建模组和验证组AUC分别为0.878(95%CI:0.832~0.924)和0.867(95%CI:0.771~0.962);建模组和验证组Hosmer-Lemeshow检验(χ2值分别为5.419和6.331,P值分别为0.712和0.610);DCA结果显示,在高风险阈值0.1~0.8范围内,采用列线图模型对患者进行预防性干预,相比于均干预和均不干预,可获得更高的标准化净效益。结论: 结核性气道狭窄患者行介入治疗后再狭窄与并发糖尿病、镜下活动期、狭窄长度>3cm、系统免疫炎症指数升高等因素有关,基于相关构建列线图预测模型,可实现再狭窄的风险评估及早期预测,可为患者介入治疗后的疾病管理方案提供参考依据。

关键词: 结核, 气管狭窄, 支气管镜, 预后, 列线图

Abstract:

Objective: To analyze the risk factors for restenosis after interventional treatment of tuberculous airway stenosis, construct a risk prediction model, and verify its predictive performance. Methods: A total of 350 patients with tuberculous airway stenosis who received interventional therapy in the Suzhou Fifth People’s Hospital from April 2021 to October 2024 were retrospectively collected as the research subjects. The patients were randomly divided into a modeling group and a validation group at a ratio of 4∶1 using the set-away method. Clinical data of patients was collected and LASSO regression was performed to identify key variables and do multivariable analysis, and a nomogram prediction model was constructed based on independent risk factors. Receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and decision curve analysis (DCA) were used to verify performance of the model. Results: The incidence of restenosis after interventional treatment in patients included in this study was 41.71% (146/350). Ten key variables were screened by LASSO regression for multivariable analysis. The results showed that diabetes (OR=3.126, 95%CI:1.017-9.604), microscopic active period (OR=2.646, 95%CI:1.197-5.851), stenosis length >3 cm (OR=5.437, 95%CI:2.682-11.022), and increased systemic immune inflammation index (OR=1.008, 95%CI:1.006-1.011) were all risk factors for restenosis after treatment of tuberculous airway stenosis, while receiving systemic antituberculosis treatment before treatment (OR=0.326, 95%CI:0.150-0.707) was protective factor. The ROC curve analysis showed that the AUCs of the modeling group and the validation group were 0.878 (95%CI:0.832-0.924) and 0.867 (95%CI:0.771-0.962), respectively; The Hosmer Lemeshow test results for the modeling group and validation group were 5.419 (P=0.712) and 6.331 (P=0.610), respectively; The DCA results showed that within the high-risk threshold range of 0.1-0.8, using a nomogram model for preventive intervention in patients could achieve higher standardized net benefit compared to average intervention and no intervention. Conclusion: The restenosis of patients with tuberculous airway stenosis after interventional treatment is related to diabetes, microscopic active period, stenosis length >3 cm, and increased systemic immune inflammation index factors. Based on the relevant factors, a nomogram prediction model can be constructed to conduct risk assessment and early prediction of restenosis, which can provide reference for disease management plans for patients after interventional treatment.

Key words: Tuberculosis, Tracheal stenosis, Bronchoscopy, Prognosis, Nomograms

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