Whitfield, C.A., Horsley, A., Jensen, O.E. et al. (4 more authors) (2022) Model-based Bayesian inference of the ventilation distribution in patients with cystic fibrosis from multiple breath washout, with comparison to ventilation MRI. Respiratory Physiology & Neurobiology, 302. 103919. ISSN 1569-9048
Abstract
Background
Indices of ventilation heterogeneity (VH) from multiple breath washout (MBW) have been shown to correlate well with VH indices derived from hyperpolarised gas ventilation MRI. Here we report the prediction of ventilation distributions from MBW data using a mathematical model, and the comparison of these predictions with imaging data.
Methods
We developed computer simulations of the ventilation distribution in the lungs to model MBW measurement with 3 parameters: determining the extent of VH; , the lung volume; and , the dead-space volume. These were inferred for each individual from supine MBW data recorded from 25 patients with cystic fibrosis (CF) using approximate Bayesian computation. The fitted models were used to predict the distribution of gas imaged by 3He ventilation MRI measurements collected from the same visit.
Results
The MRI indices measured (, the fraction of pixels below one-third of the mean intensity and , the coefficient of variation of pixel intensity) correlated strongly with those predicted by the MBW model fits ( respectively). There was also good agreement between predicted and measured MRI indices (mean bias limits of agreement: and Fitted model parameters were robust to truncation of MBW data.
Conclusion
We have shown that the ventilation distribution in the lung can be inferred from an MBW signal, and verified this using ventilation MRI. The Bayesian method employed extracts this information with fewer breath cycles than required for LCI, reducing acquisition time required, and gives uncertainty bounds, which are important for clinical decision making.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Multiple breath washout; Cystic fibrosis; Ventilation distribution; Mathematical; modelling; Bayesian parameter estimation; Computational modelling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Sheffield Teaching Hospitals |
Funding Information: | Funder Grant number Medical Research Council MR/M008894/1 NIHR Academy NIHR-RP-R3-12-027 National Institute for Health Research ICA-CDRF-2015-01-027 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Jul 2022 11:16 |
Last Modified: | 11 Jul 2022 11:16 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.resp.2022.103919 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188854 |