基于肺部不同影像学特征构建耐多药肺结核患者预测模型

doi:10.19871/j.cnki.xfcrbzz.2023.06.003

摘要/Abstract

摘要： 目的通过对耐多药肺结核（multidrug-resistant pulmonary tuberculosis,MDR-PTB）患者肺部影像学特征分析,构建临床预测模型,为MDR-PTB诊断提供影像学诊断依据。方法回顾性收集2018年1月至2020年12月深圳市慢性病防治中心门诊部收治的264例肺结核患者影像学特征数据,根据患者耐药情况分为MDR-PTB组（110例）与药物敏感性肺结核（drug-sensitive pulmonary tuberculosis,DS-PTB）组（154例）,对比分析两组肺部病灶影像学特征、肺空洞特征差异,通过单因素、多因素Logistic回归分析筛选模型纳入变量,分析MDR-PTB及DS-PTB组间基线资料、CT征象及肺空洞特征,利用年龄、患者类型、树芽征或小叶中心型结节、钙化、累及胸部肺野指标构建model1,肺空洞内径最小值构建model2,联合肺空洞内径最小值与树芽征或小叶中心型结节构建model3,并验证模型。结果 MDR-PTB组肺空洞发生率（64.5%）较高,薄壁空洞数[（0.8±2.4）个]、厚壁空洞数[（1.8±3.1）个]均高于DS-PTB组[（0.1±0.3）个、（0.3±0.7）个];MDR-PTB组肺空洞累及肺叶段数[（1.0±1.1）个]高于DS-PTB组[（0.3±0.5）个],MDR-PTB组肺空洞以右上、下肺叶,左上肺叶尖前及后段,左下肺叶为主,两组空洞数和累及部位差异均有统计学意义。MDR-PTB组更易发生树芽征或小叶中心型结节及支气管扩张,且这些征象更易累及更多的肺叶段和肺野,差异具有统计学意义。将年龄、患者类型、树芽征或小叶中心型结节、钙化、累及胸部（肺野）指标纳入预测模型,模型具有较高的诊断价值,曲线下面积为0.829（95%CI 0.779~0.879）。筛选具有肺空洞的MDR-PTB组及DS-PTB组病例并进行倾向性匹配后结果显示,肺空洞外径最大值、内径最大值、内径最小值、最大空洞壁厚度、最小空洞壁厚度在两组间存在差异,且差异具有统计学意义,上述因素纳入Logistics回归分析仅肺空洞内径最小值差异具有统计学意义,OR值为0.34（95%CI 0.17~0.68,P=0.002）。结论本研究构建的3个模型能够较好地筛查诊断MDR-PTB,尤其在病原学及耐药性诊断证据不足时,模型可以提供一定的诊断依据。

关键词: 肺结核, 耐多药, 计算机断层扫描, 预测模型

Abstract: Objective Different clinical prediction models were constructed to provide an imaging basis for the diagnosis of multidrug-resistant pulmonary tuberculosis (MDR-PTB) by characterizing the lung imaging of MDR-PTB patients. Method Data on imaging characteristics of 110 MDR-PTB patients and 154 drug sensitive pulmonary tuberculosis drug-sensitive pulmonary tuberculosis (DS-PTB) patients admitted to the outpatient department of Shenzhen Center for Chronic Disease Control from January 2018 to December 2020, were retrospectively collected, in order to compare and analyze the differences in the lung imaging characteristics and cavity characteristics between MDR-PTB group and DS-PTB group. Different prediction models were constructed by logistic regression method and then further validated. We analyzed the baseline data, CT imaging characteristics between DS-PTB and MDR-PTB groups. The age, patient type, bud sign OR lobule-centered nodule, calcification, and involvement of the lung field were used to construct model1, the minimum internal diameter of lung cavities was used to construct model2, and the minimum internal diameter and bud sign OR lobule-centered nodule were used to construct model3. Result The incidence rate of pulmonary cavities was higher in the MDR-PTB group (64.5%), and both the number of thin-walled and thick-walled pulmonary cavities were higher than that of the DS-PTB group [(0.8±2.4) vs (0.1±0.3), (1.8±3.1) vs (0.3±0.7)], and the number of lobular segments affected by cavities was higher in the MDR-PTB group than that of the DS-PTB group [(1.0±1.1) vs (0.3±0.5)], with a predominance of the right upper and lower lobes, the antero-posterior and posterior apical segments of the left upper lobe, and the left lower lobe. All differences above were statistically different. In MDR-PTB group, tree bud sign or lobule-centered nodule and bronchiectasis were more likely to occur, and these signs were more likely to involve more lung segments and lung fields, with statistical significance. When factors of age, patient type, tree bud sign or lobule-centered nodule, calcification, and involvement of the lung fields were included in the prediction model, the model achieved a higher diagnostic performance with an area under the curve of 0.829 (95%CI 0.779-0.879). We selected cases with lung cavities of the DS-PTB and MDR-PTB groups and performed propensity matching and one-factor logistic regression analysis for them. The results showed that there were significant differences in the maximum outer diameter, the maximum inner diameter, the minimum inner diameter, the maximum wall thickness and the minimum wall thickness of lung cavities between the two groups. However, only one factor of minimum inner diameter of lung cavities was significantly different in the multivariate logistic regression analysis. The OR was 0.34(95%CI 0.17-0.68, P=0.002). Conclusion In this study, the three constructed models can provide a better performance on MDR-PTB diagnosis, especially when there is insufficient evidence of TB etiology and drug resistance test, providing a certain diagnostic basis.

Key words: Tuberculosis, Multidrug-resistant, Computed tomography, Logistic models

中图分类号:

R522
R654.7

许铎耀, 杨翰, 权申文, 罗伟军, 钟铖, 郭琳, 郑秋婷. 基于肺部不同影像学特征构建耐多药肺结核患者预测模型[J]. 新发传染病电子杂志, 2023, 8(6): 14-21.

Xu Duoyao, Yang Han, Quan Shenwen, Luo Weijun, Zhong Cheng, Guo Lin, Zheng Qiuting. Construction of multidrug-resistant pulmonary tuberculosis patient prediction model based on different CT imaging characteristics[J]. Electronic Journal of Emerging Infectious Diseases, 2023, 8(6): 14-21.

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