Abstract
Respiratory mechanics analysis provides valuable clinical and physiological data but relies on costly software or manual analysis. We developed RespMech, an automated, free, open-source and citable Python-based alternative enabling robust analysis and ease of use.
We retrospectively assessed key respiratory mechanical variables at rest, 40W, highest within-group iso-work rate, and peak exercise from CPETs with oesophageal manometry-coupled open-circuit spirometry in 17 mature (8m:9f; age 62±9y) healthy controls and 10 patients with COPD (4m:6f; age 68±7y; FEV1=56±21%pred) using RespMech vs. custom software (GNAR; National Instruments LabView) that has been published extensively.
Agreement between RespMech and GNAR was excellent, with ICC 0.9-1.0 (Table 1). Bland-Altman analysis additionally established limits of agreement between the methods to be well within acceptable ranges.
RespMech is non-inferior to GNAR with the advantages of being automated, free, open-source, and citable. It additionally enables customizable quality control and optional concurrent automated analysis of diaphragmatic electromyography signals (RMS, integral, and sample entropy) including ECG artefact removal.
Footnotes
Cite this article as Eur Respir J 2022; 60: Suppl. 66, 2737.
This article was presented at the 2022 ERS International Congress, in session “-”.
This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
- Copyright ©the authors 2022
