eprintid: 10054265
rev_number: 38
eprint_status: archive
userid: 608
dir: disk0/10/05/42/65
datestamp: 2018-08-14 17:03:03
lastmod: 2021-12-05 00:37:23
status_changed: 2018-08-15 08:55:15
type: article
metadata_visibility: show
creators_name: Jacob, J
creators_name: Bartholmai, BJ
creators_name: Rajagopalan, S
creators_name: Egashira, R
creators_name: Brun, AL
creators_name: Kokosi, M
creators_name: Nair, A
creators_name: Walsh, SLF
creators_name: Karwoski, R
creators_name: Nicholson, AG
creators_name: Hansell, DM
creators_name: Wells, AU
title: Unclassifiable-interstitial lung disease: Outcome prediction using CT and functional indices
ispublished: pub
divisions: UCL
divisions: B02
divisions: C10
divisions: D17
divisions: K71
keywords: Quantitative CT; unclassifiable interstitial lung disease; longitudinal analysis
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Background:
Unclassifiable-interstitial lung disease (uILD) represents a heterogeneous collection of pathologies encompassing those fibrosing lung diseases which do not fulfill current diagnostic criteria. We evaluated baseline and longitudinal functional and CT (visual and quantitative computer [CALIPER] analysis) variables to identify outcome predictors in uILD. //

Methods:
Consecutive patients with uILD on multidisciplinary review (n = 95) had baseline functional (FVC, DLco, CPI [composite physiologic index]) and CT features (visual evaluation: CT pattern, fibrosis extent, honeycombing presence, traction bronchiectasis severity, pulmonary artery (PA) diameter; CALIPER evaluation: fibrosis extent, pulmonary vessel volume (PVV)) examined in univariate and multivariate Cox regression models. Change in functional and CT variables were examined in a patient subset (n = 37), to identify indicators of outcome. //

Results:
On univariate analysis, CPI was the most powerful functional predictor of mortality (p < 0.0001). Visual traction bronchiectasis (p < 0.0001), PA diameter (p < 0.0001) and honeycombing presence (p = 0.0001) and CALIPER PVV (p = 0.0003) were the strongest CT outcome predictors. 

On multivariate analysis of baseline indices, traction bronchiectasis (p = 0.003), PA diameter (p = 0.003) and CPI (p = 0.0001) independently predicted mortality. Colinearity with functional indices precluded the evaluation of CALIPER PVV in multivariate models.

On evaluation of longitudinal variables, increasing CALIPER fibrosis extent was the strongest outcome predictor, and remained so following adjustment for baseline disease severity, and when FVC declines were marginal.

Conclusions:
In uILD patients, CPI, traction bronchiectasis severity and PA diameter independently predicted outcome at baseline. Increasing fibrosis extent measured by CALIPER was the most powerful index of outcome regardless of baseline disease severity and strongly predicted outcome in patients with marginal FVC declines.
date: 2017-09
date_type: published
official_url: https://doi.org/10.1016/j.rmed.2017.07.007
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
article_type_text: Journal Article
verified: verified_manual
elements_id: 1544146
doi: 10.1016/j.rmed.2017.07.007
lyricists_name: Jacob, Joseph
lyricists_id: JJACO76
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Respiratory Medicine
volume: 130
pagerange: 43-51
issn: 1532-3064
citation:        Jacob, J;    Bartholmai, BJ;    Rajagopalan, S;    Egashira, R;    Brun, AL;    Kokosi, M;    Nair, A;                     ... Wells, AU; + view all <#>        Jacob, J;  Bartholmai, BJ;  Rajagopalan, S;  Egashira, R;  Brun, AL;  Kokosi, M;  Nair, A;  Walsh, SLF;  Karwoski, R;  Nicholson, AG;  Hansell, DM;  Wells, AU;   - view fewer <#>    (2017)    Unclassifiable-interstitial lung disease: Outcome prediction using CT and functional indices.                   Respiratory Medicine , 130    pp. 43-51.    10.1016/j.rmed.2017.07.007 <https://doi.org/10.1016/j.rmed.2017.07.007>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10054265/1/Jacob_Unclassifiable%20ILD%20paper%20clean.pdf