A computerized tool for the systematic visual quality assessment of infant multiple-breath washout measurements.

Oestreich, Marc-Alexander; Doswald, Isabelle; Salem, Yasmin; Künstle, Noëmi; Wyler, Florian; Frauchiger, Bettina S; Kentgens, Anne-Christianne; Latzin, Philipp; Yammine, Sophie (2024). A computerized tool for the systematic visual quality assessment of infant multiple-breath washout measurements. Frontiers in Pediatrics, 12 Frontiers 10.3389/fped.2024.1393291

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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.

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6 Citations in Scopus

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