School of Medicine, Institute of General Practice and Health Services Research, Technische Universität München/Klinikum Rechts der Isar, Orleansstr. 47, 81667, Munich, Germany.
Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Comprehensive Pneumology Center Munich (CPC-M), Ludwig-Maximilians-Universität München, Ziemssenstr. 1, 80336, Munich, Germany.
Respir Res. 2021 Sep 9;22(1):242. doi: 10.1186/s12931-021-01837-2.
Lung emphysema is an important phenotype of chronic obstructive pulmonary disease (COPD), and CT scanning is strongly recommended to establish the diagnosis. This study aimed to identify criteria by which physicians with limited technical resources can improve the diagnosis of emphysema.
We studied 436 COPD patients with prospective CT scans from the COSYCONET cohort. All items of the COPD Assessment Test (CAT) and the St George's Respiratory Questionnaire (SGRQ), the modified Medical Research Council (mMRC) scale, as well as data from spirometry and CO diffusing capacity, were used to construct binary decision trees. The importance of parameters was checked by the Random Forest and AdaBoost machine learning algorithms.
When relying on questionnaires only, items CAT 1 & 7 and SGRQ 8 & 12 sub-item 3 were most important for the emphysema- versus airway-dominated phenotype, and among the spirometric measures FEV/FVC. The combination of CAT item 1 (≤ 2) with mMRC (> 1) and FEV/FVC, could raise the odds for emphysema by factor 7.7. About 50% of patients showed combinations of values that did not markedly alter the likelihood for the phenotypes, and these could be easily identified in the trees. Inclusion of CO diffusing capacity revealed the transfer coefficient as dominant measure. The results of machine learning were consistent with those of the single trees.
Selected items (cough, sleep, breathlessness, chest condition, slow walking) from comprehensive COPD questionnaires in combination with FEV/FVC could raise or lower the likelihood for lung emphysema in patients with COPD. The simple, parsimonious approach proposed by us might help if diagnostic resources regarding respiratory diseases are limited. Trial registration ClinicalTrials.gov, Identifier: NCT01245933, registered 18 November 2010, https://clinicaltrials.gov/ct2/show/record/NCT01245933 .
肺气肿是慢性阻塞性肺疾病（COPD）的一个重要表型，强烈建议通过 CT 扫描来建立诊断。本研究旨在确定资源有限的医生可以改善肺气肿诊断的标准。
我们研究了来自 COSYCONET 队列的 436 例 COPD 患者的前瞻性 CT 扫描。COPD 评估测试（CAT）和圣乔治呼吸问卷（SGRQ）的所有项目、改良的医学研究委员会（mMRC）量表以及肺活量和 CO 弥散能力的数据均用于构建二叉决策树。参数的重要性通过随机森林和 AdaBoost 机器学习算法进行检查。
仅依靠问卷时，CAT 项目 1 和 7 以及 SGRQ 项目 8 和 12 的第 3 项对于以气腔为主的表型和肺活量测定中的 FEV/FVC 是最重要的。CAT 项目 1（≤2）与 mMRC（>1）和 FEV/FVC 的组合可使肺气肿的可能性增加 7.7 倍。约 50%的患者表现出不会明显改变表型可能性的组合值，这些值在树中很容易识别。CO 弥散能力的纳入显示出转移系数是主要的测量指标。机器学习的结果与单个树的结果一致。
综合 COPD 问卷中的选定项目（咳嗽、睡眠、呼吸困难、胸部状况、行走缓慢）与 FEV/FVC 结合使用，可提高或降低 COPD 患者发生肺肺气肿的可能性。我们提出的这种简单、简约的方法在呼吸系统疾病诊断资源有限的情况下可能会有所帮助。
ClinicalTrials.gov，标识符：NCT01245933，于 2010 年 11 月 18 日注册，https://clinicaltrials.gov/ct2/show/record/NCT01245933。
