Centre of Pulmonary Medicine, Hirslanden Hospital Group, Salem-Hospital, Bern, Switzerland.
Department of Paediatrics, University of Bern, Bern, Switzerland.
PLoS One. 2024 Feb 20;19(2):e0292270. doi: 10.1371/journal.pone.0292270. eCollection 2024.
The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (70%) and even more for ACO (60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.
本研究的目的是评估肺功能参数(包括肺活量计和体积描记法)的区分能力,以区分哮喘、ACO、COPD 的诊断,并定义功能特征以更精确地分类阻塞性肺疾病。从 4 个中心的数据库中,共收集了 756 项肺功能测试(194 名健康受试者、175 名哮喘患者、71 名 ACO 患者、78 名 COPD 患者和 238 名 CF 患者),并分别对肺活量计(1 秒用力呼气量:FEV1;FEV1/用力肺活量:FEV1/FVC;25-75%用力肺活量时的呼气流速:FEF25-75)和体积描记法(有效气道阻力:sReff;静息呼吸时的呼吸功:sWOB)的目标参数组合之间的梯度进行了评估,包括吸气和呼气(sReffIN、sReffEX、sWOBin、sWOBex)以及静态肺容量(肺总量:TLC;功能残气量:FRCpleth;残气量:RV)、呼吸控制(口腔阻断压:P0.1;平均吸气流量:VT/TI;吸气时间与总时间比:TI/Ttot)和吸气阻抗(Zinpleth = P0.1/VT/TI)。线性判别分析(LDA)用于通过递归分区决策树识别判别函数和分类规则。LDA 对健康受试者、COPD 和 CF 的分类准确率(敏感性和特异性>90%)较高。对于哮喘(70%)甚至 ACO(60%),准确性下降。决策树显示,P0.1、sRtot 和 VT/TI 可将健康与哮喘(68.9%)、COPD(82.1%)和 CF(60.6%)区分开来。此外,使用 sWOBex 和 Zinpleth 可以将 ACO 与哮喘和 COPD 区分开来(60%)。因此,如果使用特定的肺活量计和体积描记法参数,可以更好地理解阻塞性肺疾病的功能复杂性。此外,新描述的气道动力学参数和包括 Zinpleth 在内的中枢呼吸控制可能是精准医学领域有前途的功能标志物。