Assessment of functional diversities in patients with Asthma, COPD, Asthma-COPD overlap, and Cystic Fibrosis (CF).

ABSTRACT

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.