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Updated: Jun 23, 2025

Employing the Forced Oscillation Technique for the Assessment of Respiratory Mechanics in Adults
Published on: February 9, 2022
A computerized tool for the systematic visual quality assessment of infant multiple-breath washout measurements
Marc-Alexander Oestreich1, Isabelle Doswald1, Yasmin Salem1
1Division of Paediatric Respiratory Medicine and Allergology, Department of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Background:
Multiple-breath washout (MBW) is a sensitive method for assessing lung volumes and ventilation inhomogeneity in infants, but remains prone to artefacts (e.g., sighs). There is a lack of tools for systematic retrospective analysis of existing datasets, and unlike N2-MBW in older children, there are few specific quality control (QC) criteria for artefacts in infant SF6-MBW.
Aim:
We aimed to develop a computer-based tool for systematic evaluation of visual QC criteria of SF6-MBW measurements and to investigate interrater agreement and effects on MBW outcomes among three independent examiners.
Methods:
We developed a software package for visualization of raw Spiroware (Eco Medics AG, Switzerland) and signal processed WBreath (ndd Medizintechnik AG, Switzerland) SF6-MBW signal traces. Interrater agreement among three independent examiners (two experienced, one novice) who systematically reviewed 400 MBW trials for visual artefacts and the decision to accept/reject the washin and washout were assessed.
Results:
Our tool visualizes MBW signals and provides the user with (i) display options (e.g., zoom), (ii) options for a systematic QC assessment [e.g., decision to accept or reject, identification of artefacts (leak, sigh, irregular breathing pattern, breath hold), and comments], and (iii) additional information (e.g., automatic identification of sighs). Reviewer agreement was good using pre-defined QC criteria (κ 0.637-0.725). Differences in the decision to accept/reject had no substantial effect on MBW outcomes.
Conclusion:
Our visual quality control tool supports a systematic retrospective analysis of existing data sets. Based on predefined QC criteria, even inexperienced users can achieve comparable MBW results.

