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 FEV1/FVC. The combination of CAT item 1 (≤ 2) with mMRC (> 1) and FEV1/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 FEV1/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 .

中文翻译：

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通过肺量计和临床症状预测 COPD 肺气肿：COSYCONET 的结果

肺气肿是慢性阻塞性肺疾病 (COPD) 的重要表型，强烈建议进行 CT 扫描以确定诊断。本研究旨在确定技术资源有限的医生可以改进肺气肿诊断的标准。我们研究了来自 COSYCONNET 队列的 436 名 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、修改后的医学研究委员会 (mMRC) 量表以及来自肺活量测定和 CO 扩散能力的数据都用于构建二元决策树。通过随机森林和 AdaBoost 机器学习算法检查参数的重要性。仅依靠问卷调查时，项目 CAT 1 & 7 和 SGRQ 8 & 12 个子项 3 对于肺气肿与气道主导的表型以及肺活量测量 FEV1/FVC 最重要。CAT 项目 1 (≤ 2) 与 mMRC (> 1) 和 FEV1/FVC 的组合可以将肺气肿的几率提高 7.7 倍。大约 50% 的患者显示的值组合不会显着改变表型的可能性，并且这些值可以很容易地在树中识别出来。包含 CO 扩散能力揭示了转移系数作为主要衡量标准。机器学习的结果与单棵树的结果一致。从综合 COPD 问卷中选择的项目（咳嗽、睡眠、呼吸困难、胸部状况、行走缓慢）结合 FEV1/FVC 可以提高或降低 COPD 患者肺气肿的可能性。简单的，如果有关呼吸系统疾病的诊断资源有限，我们提出的简约方法可能会有所帮助。试验注册 ClinicalTrials.gov，标识符：NCT01245933，2010 年 11 月 18 日注册，https://clinicaltrials.gov/ct2/show/record/NCT01245933。