TY - JOUR T1 - Interpretation of multiple breath washout (MBW) measurements of lung function using mathematical modelling and hyperpolarised 3He gas MRI JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.congress-2020.4340 VL - 56 IS - suppl 64 SP - 4340 AU - Carl Whitfield AU - Laurie Smith AU - Oliver Jensen AU - Noreen West AU - Martin Wildman AU - Guilhem Collier AU - Jim Wild AU - Alex Horsley Y1 - 2020/09/07 UR - http://erj.ersjournals.com/content/56/suppl_64/4340.abstract N2 - Background: MBW is an established method for the sensitive measurement of ventilation heterogeneity (VH) in obstructive lung conditions, but indices do not directly tell us about the distribution of ventilation that can be observed in imaging. We have developed an algorithm to fit an efficient model of MBW to measured data and infer the distribution of ventilation in the lung. We verified these predictions against MR images acquired on the same day.Method: Supine MBW (3 test repeats with SF6 tracer gas) in 24 cystic fibrosis (CF) volunteers (10F, 14M, age: 8—43, LCI: 6.8—16.8) was used. The software estimated realistic ranges for the three model parameters (lung volume: VFRC, dead-space: VD, and VH: DV). Then, approximate Bayesian computation (ABC-SMC) was used to predict likely parameter distributions and the 3He MRI measured ventilation distribution.Results: The predicted coefficient of variation (a measure of VH) of the MRI lung image intensity (ICV) correlated strongly with measurements (r = 0.85, p < 10-6) and agreed well. In 17 cases (68%) ICV values were found to agree within the predicted 95% CI.Conclusions: The algorithm predictions showed good agreement and strong correlation with the observed MRI data in this CF cohort. The uncertainty in estimated measurements appeared to explain most, but not all, of the prediction error. Prediction uncertainty also increases with predicted VH, suggesting that the model better describes mild-moderate disease. This algorithm enables clearer interpretation of MBW outcomes by quantitatively estimating the distribution of ventilation with robust bounds of certainty on predictions.FootnotesCite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4340.This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only). ER -