Quality control in multiple-breath washout measurements: an automated, model-based approach quantifies effects on primary outcomes

Quality control (QC) is an important but challenging step in the correct interpretation of multiple-breath washout (MBW) tests, as irregular behaviors are frequent, particularly among young and ill patients, and can lead to biased outcomes. We developed a model-based, automated QC algorithm to estimate the effect of irregular behaviors on MBW outcomes. It compares results between simulated measurements containing either ideal target behaviors or the observed irregular behaviors of the measurement. Differences in primary outcomes - lung clearance index (LCI), functional residual capacity - between simulations served as behavior-specific QC outcomes. We validated the automated QC algorithm against a dataset of 2,471 measurements of 87 children with cystic fibrosis (CF). We compared the results of automated QC with the ones performed by experienced raters in a second dataset of 100 measurements of healthy children and children with CF. The test could be applied successfully to all measurements. We found that the impact of QC-related factors on MBW outcomes explained around 45% of the within-visit variability of LCI in children with CF. The largest effect on LCI was caused by leaks or trapped gas (in 31% of measurements), changes in end-expiratory lung volume (23%), and variations in breath size around the end-of-test (19%). For the second dataset, the agreement between automated QC and QC by experienced raters was moderate but comparable to QC performed by two experienced raters. Our novel automated QC tool estimates the accuracy of MBW outcomes, providing a fast, reproducible and impact-based method to perform QC for MBW measurements.