Averaging Tidal Flow-volume Loops: A tale of two methods

Abstract

Introduction: A standard practice in cardiopulmonary exercise testing (CPET) is calculating the average flow and volume data across multiple breaths. Currently, many laboratories find average flow-volume loops by taking equal increments of the tidal volume and averaging the respective increments of flow for each breath. However, it has been proposed that using equal increments of total time to find an average flow-volume loop could more accurately capture the shape of the loop, which could be critical to determining expiratory flow limitation (EFL) when placed within a maximal flow-volume loop. Purpose: To compare the use of time vs. volume bins on producing averaged flow-volume loops and evaluate whether the results could affect the interpretation of EFL. Methods: Time, volume, and flow data of consecutive breaths at rest and during exercise were obtained for four individuals: one younger healthy man, one older healthy woman, one older man with heart failure, and one child with obesity. The data were separated into individual breaths using zero flow points, then total time and tidal volume were calculated for each full breath. For the time bin method as proposed by M. Younes, equal intervals of time were calculated for both the inspiratory and expiratory phases of each breath. Linear interpolation between surrounding data points was performed to obtain volume and flow values at these intervals. Volume data were then normalized to the tidal volume of the breath by taking the value as a percentage of the total tidal volume. From here, the volume and flow data at the corresponding intervals for each breath were averaged to create the final average flow-volume loop. For the volume bin method, equal intervals of volume were determined based on the tidal volume. Linear interpolation was then used to calculate the respective flow values at these intervals. The volume and flow data at the corresponding intervals for each breath were then averaged to generate the average flow-volume loop data. Standard deviation at each averaged point was also calculated for both methods. Results: Minimal difference was visible between the time bin and volume bin methods at 20 intervals. As the number of intervals used increased, the difference became less apparent. At 250 intervals, the volume bin method depicted the average flow-volume loop as effectively as the time bin method. Addition of standard deviations gave a better view of the variability of flow at any volume. Conclusion: Despite the inherent incrementation associated with the use of time bins vs. volume bins the average flow-volume loops looked very similar. However, the addition of the standard deviations of flow and volume at each data point could yield different results for determining EFL. Also, the time bin method may yield more resolution at rapid transition points at the beginning of inspiration and expiration. Overall, the observed similarities between time bin and volume bin methods are likely due to increased computer capabilities and the ability to create more intervals (i.e., 20 vs. 250), making a constant unit of measurement less necessary. Funding: NIH R01 HL136643, NIH R01 AG070262, NIH P01 HL137630, King Charitable Foundation Trust, Susan Lay Atwell Annuity Trust for Pulmonary Research, Cain Foundation, and Texas Health Presbyterian Hospital Dallas. Dr. Daniel Wilhite was funded by an NIH Administrative Supplement to Promote Diversity in Health-Related Research (HL136643-01S1). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.