Automated and impact-based quality control in multiple-breath washout

Introduction: Quality control (QC) is an important but challenging step in the correct interpretation of multiple-breath washout (MBW) tests, as irregular behaviors can lead to biased outcomes. Methods: We developed an automated QC algorithm to produce estimates of the impact of irregular behaviors on MBW outcomes. It compares results between simulated measurements containing either ideal target behaviors and the observed irregular behaviors of the measurement. Differences in main MBW outcomes (Lung clearance index (LCI), functional residual capacity) between simulations served as behavior-specific QC outcomes. We applied the automated QC algorithm to 2471 measurements of 87 children with cystic fibrosis (CF), and compared the automated QC with that performed by experienced raters (expert QC) in a dataset of 100 measurements of healthy children and children with CF. Results: 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 overlap between automated and expert QC was moderate but comparable to the overlap between two separate instances of expert QC. The factors with the largest effect on MBW outcomes were found to be irregular increases in expired N2 concentration (leaks, trapped gas, in 31% of cases), changes in end-expiratory lung volume (23%) and variations in breath size around the end-of-test (19%). Discussion: We developed a novel automated QC tool to produce estimates for the accuracy of MBW outcomes, providing a fast, reproducible and impact-based method to perform QC for MBW measurements.