Analyzing key elements of breathing patterns, deriving remaining variables, and identifying cutoff values in...

Pulmonary physiology encompasses intricate breathing patterns (BPs), characterized by breathing frequency (Bf), volumes, and flows. The complexities intensify in the presence of interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD), especially during exercise. This study seeks to identify pivotal factors driving changes among these variables and establish cutoff values, comparing their efficacy in differentiating BPs to traditional methods, specifically a breathing reserve (BR) of 30% and a Bf of 50 bpm.

Screening 267 subjects revealed 23 with ILD, 126 with COPD, 33 healthy individuals, and the exclusion of 85 subjects. Lung function tests and ramp-pattern cardiopulmonary exercise testing (CPET) were conducted, identifying crucial BP elements. Changes were compared between groups at peak exercise. The area under the receiver operating characteristic curve (AUC) analysis determined cutoff values.

Inspiratory time (TI) remained constant at peak exercise for all subjects (two-group comparisons, all p=NS). Given known differences in expiratory time (TE) and tidal volume (VT) among ILD, COPD, and healthy states, constant TI could infer patterns for Bf, total breathing cycle time (TTOT=60/Bf), I:E ratio, inspiratory duty cycle (IDC, TI/TTOT), rapid shallow breathing index (Bf/VT), tidal inspiratory and expiratory flows (VT/TI and VT/TE), and minute ventilation (V̇E=Bf×VT) across conditions. These inferences aligned with measurements, with potential type II errors causing inconsistencies. RSBI of 23 bpm/L and VT/TI of 104 L/min may differentiate ILD from control, while V̇E of 54 L/min, BR of 30%, and VT/TE of 108 may differentiate COPD from control. BR of 21%, TE of 0.99 s, and IDC of .45 may differentiate ILD from COPD. The algorithm outperformed traditional methods (AUC 0.84-0.91 versus 0.59-0.90).

The quasi-fixed TI, in conjunction with TE and VT, proves effective in inferring time-related variables of BPs. The findings have the potential to significantly enhance medical education in interpreting cardiopulmonary exercise testing. Moreover, the study introduces a novel algorithm for distinguishing BPs among individuals with ILD, COPD, and those who are healthy.