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Published in: BMC Pulmonary Medicine 1/2017

Open Access 01-12-2017 | Research article

Chronic hypersensitivity pneumonitis: identification of key prognostic determinants using automated CT analysis

Authors: Joseph Jacob, Brian J. Bartholmai, Ryoko Egashira, Anne Laure Brun, Srinivasan Rajagopalan, Ronald Karwoski, Maria Kokosi, David M. Hansell, Athol U. Wells

Published in: BMC Pulmonary Medicine | Issue 1/2017

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Abstract

Background

Chronic hypersensitivity pneumonitis (CHP) has a variable disease course. Computer analysis of CT features was used to identify a subset of CHP patients with an outcome similar to patients with idiopathic pulmonary fibrosis (IPF).

Methods

Consecutive patients with a multi-disciplinary team diagnosis of CHP (n = 116) had pulmonary function tests (FEV1, FVC, DLco, Kco, and a composite physiologic index [CPI]) and CT variables predictive of mortality evaluated by analysing visual and computer-based (CALIPER) parenchymal features: total interstitial lung disease (ILD) extent, honeycombing, reticular pattern, ground glass opacities, pulmonary vessel volume (PVV), emphysema, and traction bronchiectasis. Mean survival was compared between both CHP and IPF patients (n = 185).

Results

In CHP, visual/CALIPER measures of reticular pattern, honeycombing, visual traction bronchiectasis, and CALIPER ILD extent were predictive of mortality (p < 0 · 05) on univariate analysis. PVV was strongly predictive of mortality on univariate (p < 0 · 0001) and multivariate analysis independent of age, gender and disease severity (represented by the CPI [p < 0 · 01]). CHP patients with a PVV threshold >6 · 5% of the lung had a mean survival (35 · 3 ± 6 · 1 months; n = 20/116 [17%]) and rate of disease progression that closely matched IPF patients (38 · 4 ± 2 · 2 months; n = 185).

Conclusions

Pulmonary vessel volume can identify CHP patients at risk of aggressive disease and a poor IPF-like prognosis.
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Metadata
Title
Chronic hypersensitivity pneumonitis: identification of key prognostic determinants using automated CT analysis
Authors
Joseph Jacob
Brian J. Bartholmai
Ryoko Egashira
Anne Laure Brun
Srinivasan Rajagopalan
Ronald Karwoski
Maria Kokosi
David M. Hansell
Athol U. Wells
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Pulmonary Medicine / Issue 1/2017
Electronic ISSN: 1471-2466
DOI
https://doi.org/10.1186/s12890-017-0418-2

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