Respiratory physiology & neurobiology · 2025
Spirometric and exercise ventilatory predictors of expiratory flow-limitation in adults with normal and mildly-obstructed airways.
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Haverkamp Hans Christian, Luu Peter, Molgat-Seon Yannick, Petrics Gregory, McPherson Sterling
Аннотация
PURPOSE: We determined the resting spirometric and exercise ventilatory variables that are predictive of exercise expiratory flow limitation (EFL) in a group of adults in whom airway function varied from mildly obstructed to above predicted values.
METHODS: We studied n = 25 adults (16/9 M/F; age, 31 years) in whom forced expiratory volume in 1 s (FEV1) ranged from 63 % to 149 %-predicted. Subjects completed an incremental exercise test. Tidal expiratory flow-volume (TEFV) curves were placed within the maximal expiratory flow-volume (MEFV) curve and %-EFL was determined based on the overlap between the TEFV and MEFV curves. Multivariate linear (MRlin) and logistic regression (MRlog) were performed using spirometric and exercise measurements as predictor variables and the presence and magnitude of EFL as the outcome variable.
RESULTS: A set of spirometric variables explained 75 % and 73 % of the variance in %-EFL and the presence of EFL using MRlin and MRlog, respectively. A set of inputs consisting of both spirometric and exercise ventilatory variables improved the predictive ability of the models. Backward stepwise modeling, using both MRlin and MRlog, resulted in a total of seven final variables in both analyses. The final variables represented a combination of spirometric and exercise ventilatory inputs, and R2 values of 0.93 and 1.00 for MRlin and MRlog.
CONCLUSIONS: Given the difficulty of measuring EFL during exercise, our findings suggest that integrating additional measurements reflecting ventilatory capacity (i.e., spirometry) and demand (exercise measurements), including dynamic operating lung volumes, in clinical exercise testing will improve assessment of exercise ventilatory constraint.
METHODS: We studied n = 25 adults (16/9 M/F; age, 31 years) in whom forced expiratory volume in 1 s (FEV1) ranged from 63 % to 149 %-predicted. Subjects completed an incremental exercise test. Tidal expiratory flow-volume (TEFV) curves were placed within the maximal expiratory flow-volume (MEFV) curve and %-EFL was determined based on the overlap between the TEFV and MEFV curves. Multivariate linear (MRlin) and logistic regression (MRlog) were performed using spirometric and exercise measurements as predictor variables and the presence and magnitude of EFL as the outcome variable.
RESULTS: A set of spirometric variables explained 75 % and 73 % of the variance in %-EFL and the presence of EFL using MRlin and MRlog, respectively. A set of inputs consisting of both spirometric and exercise ventilatory variables improved the predictive ability of the models. Backward stepwise modeling, using both MRlin and MRlog, resulted in a total of seven final variables in both analyses. The final variables represented a combination of spirometric and exercise ventilatory inputs, and R2 values of 0.93 and 1.00 for MRlin and MRlog.
CONCLUSIONS: Given the difficulty of measuring EFL during exercise, our findings suggest that integrating additional measurements reflecting ventilatory capacity (i.e., spirometry) and demand (exercise measurements), including dynamic operating lung volumes, in clinical exercise testing will improve assessment of exercise ventilatory constraint.