A new era of quality control in multiple-breath washout measurements: an automated, model-based approach quantifies effects on the primary outcome LCI

Nitrogen multiple-breath washout (N 2 MBW) is a sensitive method for assessing ventilation inhomogeneity. However, irregular behaviors during the measurement are frequent, particularly among young and ill patients, and can lead to large variability and biased outcomes. Irregular behaviors include irregular breathing, variation in end-expiratory lung volume, and leaks. Quality control (QC) is thus an important step in ensuring the correct interpretation of MBW tests but QC by experts is time-consuming, technically demanding, and subjective. Aim: Presentation, application and validation of the novel automated QC on a set of N 2 MBW measurements of children with cystic fibrosis (cwCF) and comparison to manual quality control by experts. Methods: A two-compartment lung model adjusted to the measured lung volumes and washout progress was used to calculate the main outcome, the lung clearance index (LCI). This model calculation was performed repeatedly for each measurement individually with and without different irregular behaviors to determine the effect of each irregularity as a difference in LCI. Irregular behavior in a measurement was considered acceptable if all effects were below a predefined threshold. We validated the automated QC algorithm against a first data set of 2471 measurements from 87 cwCF. We compared the results of automated QC with the ones performed by experienced raters (expert QC) on a second dataset of 100 measurements of healthy children and cwCF. Results: Automated QC was successfully applied to all measurements. Applying QC to the first data set resulted in an acceptance ratio of 76%, and reduced the within-visit variance of LCI by 46% (from 0.72 to 0.39). A strong association of the measured within-visit variability of LCI with estimated variability confirmed the validity of the approach (R 2 = 0.45). The largest effect on LCI among cwCF 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 and expert QC was moderate but comparable to that between two separate expert QC. Discussion: Our novel approach to QC uses a fast, reproducible method to provide detailed insights into measured variation of LCI related to the patient's irregular behavior during the measurement. Rather than a simple accept/reject QC output, our method quantifies irregularities as an expected difference in LCI. Publication History Article published online: 26 February 2026 © 2026. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany