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. 2022 Oct 14;17(10):e0275866.
doi: 10.1371/journal.pone.0275866. eCollection 2022.

Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children

Marc-Alexander Oestreich  1   2 , Florian Wyler  1 , Bettina S Frauchiger  1 , Philipp Latzin  1 , Kathryn A Ramsey  1
Affiliations

Breath detection algorithms affect multiple-breath washout outcomes in pre-school and school age children

Marc-Alexander Oestreich et al. PLoS One. .

Abstract

Background: Accurate breath detection is essential for the computation of outcomes in the multiple-breath washout (MBW) technique. This is particularly important in young children, where irregular breathing is common, and the designation of inspirations and expirations can be challenging.

Aim: To investigate differences between a commercial and a novel breath-detection algorithm and to characterize effects on MBW outcomes in children.

Methods: We replicated the signal processing and algorithms used in Spiroware software (v3.3.1, Eco Medics AG). We developed a novel breath detection algorithm (custom) and compared it to Spiroware using 2,455 nitrogen (N2) and 325 sulfur hexafluoride (SF6) trials collected in infants, children, and adolescents.

Results: In 83% of N2 and 32% of SF6 trials, the Spiroware breath detection algorithm rejected breaths and did not use them for the calculation of MBW outcomes. Our custom breath detection algorithm determines inspirations and expirations based on flow reversal and corresponding CO2 elevations, and uses all breaths for data analysis. In trials with regular tidal breathing, there were no differences in outcomes between algorithms. However, in 10% of pre-school children tests the number of breaths detected differed by more than 10% and the commercial algorithm underestimated the lung clearance index by up to 21%.

Conclusion: Accurate breath detection is challenging in young children. As the MBW technique relies on the cumulative analysis of all washout breaths, the rejection of breaths should be limited. We provide an improved algorithm that accurately detects breaths based on both flow reversal and CO2 concentration.

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Conflict of interest statement

All authors are in regular contact with ndd Medizintechnik AG (Zurich, Switzerland) and Eco Medics AG (Duernten, Switzerland). The authors contacted both companies in the process of this study to obtain further information on current breath detection algorithms and to present results. There were no changes to the manuscript by either company. Florian Wyler was hired from July to August 2022 to work on an unrelated project for ndd Medizintechnik AG. The authors declare no conflict of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Prof. Latzin: personal fees from Vertex, Novartis, Roche, Polyphor, Vifor, Gilead, Schwabe, Zambon, Santhera, grants from Vertex, all outside this work. All other authors declare no conflicts of interest.

Figures

Fig 1
Fig 1. Invalid breathing as detected by Spiroware analysis software.
Shown are flow and carbon dioxide traces of intervals of inspirations, expirations, and invalid breathing before re-classification (A) or rejection (B, C, D). The shaded areas indicate reclassified intervals. Blue area: expiration; red area: invalid breathing; green area: inspiration.
Fig 2
Fig 2. Relative difference [%] in total breaths detected.
Comparison of the Spiroware and custom breath-dection algorithms by age group.
Fig 3
Fig 3. Bland-Altman plots of absolute difference between the commercial and custom breath detection algorithm.
Shown are absolute differences in main MBW outcomes (FRC, CEV, LCI) for pre-school and school-age subjects with a difference in total breaths detected >10%. Abbreviations: FRC: functional residual capacity; CEV: cumulative expired volume; LCI: lung clearance index.
See this image and copyright information in PMC

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