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结核与肺部疾病杂志 ›› 2025, Vol. 6 ›› Issue (3): 335-342.doi: 10.19983/j.issn.2096-8493.20250036

• 论著 • 上一篇    下一篇

基于三种分类模型的肺结核患者诊断延迟影响因素分析

张蕾洁1, 李培2, 王丹1(), 李惠平3, 朱妮4   

  1. 1.西安市莲湖区疾病预防控制中心传染病预防控制科,西安 710077
    2.西安市莲湖区疾病预防控制中心结核病防治科,西安 710077
    3.西北大学医学院流行病与卫生统计学教研室,西安 710068
    4.陕西省疾病预防控制中心传染病预防控制所,西安 710054
  • 收稿日期:2025-02-24 出版日期:2025-06-20 发布日期:2025-06-12
  • 通信作者: 王丹,Email:diianer@163.com
  • 基金资助:
    陕西省创新能力支撑计划(2022PT-26)

Influencing factors of diagnostic delay in pulmonary tuberculosis patients: a comparative study using three classification models

Zhang Leijie1, Li Pei2, Wang Dan1(), Li Huiping3, Zhu Ni4   

  1. 1. Department of Infectious Disease Prevention and Control, Lianhu Center for Disease Control and Prevention, Xi’an 710077, China
    2. Department of Tuberculosis Prevention and Control, Lianhu Center for Disease Control and Prevention, Xi’an 710077, China
    3. Department of Epidemiology and Health Statistics, School of Medical, Northwest University, Xi’an 710068, China
    4. Department of Infectious Disease Prevention and Control, Shaanxi Provincial Center for Disease Control and Prevention, Xi’an 710054, China
  • Received:2025-02-24 Online:2025-06-20 Published:2025-06-12
  • Contact: Wang Dan,Email:diianer@163.com
  • Supported by:
    Shaanxi Province Innovation Capability Support Plan(2022PT-26)

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摘要：

目的:了解西安市莲湖区肺结核患者诊断延迟情况,并基于3种分类模型分析其影响因素,为调整结核病防治策略提供依据。方法:通过“中国疾病预防控制信息系统”子系统“结核病信息管理系统”收集2023—2024年西安市莲湖区登记的642例资料完整的肺结核患者信息,采用描述性流行病学方法分析患者的诊断延迟情况,采用多因素logistic回归模型、决策树模型和Bayes判别模型分析诊断延迟的影响因素,并采用受试者工作特征曲线(ROC曲线)对3种模型的性能进行评估。结果:2023—2024年西安市莲湖区肺结核患者就诊延迟时间[M(Q1,Q3)]为16(5,35)d,就诊延迟率为51.40%(330/642);诊断延迟时间[M(Q1,Q3)]为2(0,9)d,诊断延迟率为17.76%(114/642)。3种模型筛选出的诊断延迟影响因素一致,2024年就诊(OR=1.882,95%CI:1.221~2.901)和患者现住址为本市其他县(区)(OR=3.798,95%CI:1.760~8.198)均是诊断延迟的危险因素;有结核病相关症状(OR=0.334,95%CI:0.215~0.518)和存在就诊延迟(OR=0.559,95%CI:0.365~0.858)均是诊断延迟的保护因素。logistic回归模型、决策树模型和Bayes判别模型对肺结核患者影响因素诊断的ROC曲线下面积(AUC)分别为0.709、0.696和0.706,敏感度分别为75.44%、61.40%和80.70%,特异度分别为57.01%、68.00%和50.19%,约登指数分别为0.325、0.294和0.309。结论:2023—2024年西安市莲湖区肺结核患者就诊和诊断延迟情况较为普遍,基于3种分类模型筛选的诊断延迟影响因素一致,3种模型总体预测能力相当,但在特定指标上各有侧重。

关键词: 结核, 肺, 诊断延迟, 因素分析,统计学, Logistic模型, 决策树

Abstract:

Objective: To investigate the diagnostic delay status of pulmonary tuberculosis (PTB) patients in Lianhu District, Xi’an City, and analyze its influencing factors based on three classification models, providing a basis for adjusting prevention and control strategies. Methods: Data of 642 PTB patients registered in Lianhu District from 2023 to 2024 were collected through the “Tuberculosis Information Management System”, a subsystem of the “China Information for Disease Control and Prevention”. Descriptive epidemiological methods were used to analyze patients’ baseline characteristics and diagnostic delay. Multivariable logistic regression, decision tree, and Bayes discriminant model were applied to identify the influencing factors of diagnostic delay of PTB patients. Their performances were evaluated using ROC curve analysis. Results: The median healthcare-seeking delay in PTB patients of Lianhu District was 16 (5, 35) days, with a delay rate of 51.40% (330/642). The median diagnostic delay was 2 (0, 9) days, with a delay rate of 17.76% (114/642). All three models consistently identified the following factors: seeking healthcare in 2024 (OR=1.882, 95%CI: 1.221-2.901) and residing in other districts of Xi’an city (OR=3.798, 95%CI: 1.760-8.198) as risk factors for diagnostic delay, while having TB-related symptoms (OR=0.334, 95%CI: 0.215-0.518) and experiencing healthcare-seeking delay (OR=0.559, 95%CI: 0.365-0.858) were protective factors. The AUC values for logistic regression, decision tree, and Bayes discriminant model were 0.709, 0.696 and 0.706, respectively. Sensitivity values were 75.44%,61.40% and 80.70%, specificity values were 57.01%, 68.00% and 50.19%, and Youden indices were 0.325, 0.294 and 0.309, respectively. Conclusion: Delay in healthcare-seeking and diagnosis among PTB patients in Lianhu District are prevalent. The three classification models consistently identified influencing factors for diagnostic delay, with comparable overall predictive performance, but each model had its specific focus on certain indicators.

Key words: Tuberculosis, pulmonary, Delayed diagnosis, Factor analysis, statistical, Logistic models, Decision tree

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