Unclassifiable-interstitial lung disease: Outcome prediction using CT and functional indices
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] analy...
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| Veröffentlicht in: | Respiratory medicine 2017-09, Vol.130, p.43-51 |
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| creator | Jacob, Joseph Bartholmai, Brian J. Rajagopalan, Srinivasan Egashira, Ryoko Brun, Anne Laure Kokosi, Maria Nair, Arjun Walsh, Simon L.F. Karwoski, Ronald Nicholson, Andrew G. Hansell, David M. Wells, Athol U. |
| description | 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.
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.
On univariate analysis, CPI was the most powerful functional predictor of mortality (p |
| doi_str_mv | 10.1016/j.rmed.2017.07.007 |
| format | Article |
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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.
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.
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.
•At baseline in uILD the composite physiologic index independently predicts survival.•PA diameter and traction bronchiectasis scores also independently predict mortality.•Change in CALIPER fibrosis scores independently predict survival in uILD.•Change in CALIPER fibrosis scores better predicts survival than baseline measures.•CALIPER fibrosis extent independently predicts outcome when FVC decline is marginal.</description><identifier>ISSN: 0954-6111</identifier><identifier>EISSN: 1532-3064</identifier><identifier>DOI: 10.1016/j.rmed.2017.07.007</identifier><identifier>PMID: 29206632</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Aged ; Automation ; Bronchiectasis ; Bronchiectasis - diagnostic imaging ; Carbon monoxide ; Carbon Monoxide - metabolism ; Computed tomography ; Connective Tissue Diseases - diagnostic imaging ; Connective Tissue Diseases - mortality ; Connective Tissue Diseases - physiopathology ; Coronary vessels ; Diagnostic systems ; Emphysema ; Evaluation ; Female ; Fibrosis ; Forced Expiratory Volume - physiology ; Humans ; Idiopathic Pulmonary Fibrosis - diagnostic imaging ; Idiopathic Pulmonary Fibrosis - mortality ; Idiopathic Pulmonary Fibrosis - physiopathology ; Longitudinal analysis ; Lung - diagnostic imaging ; Lung - pathology ; Lung - physiopathology ; Lung diseases ; Lung Diseases, Interstitial - diagnostic imaging ; Lung Diseases, Interstitial - mortality ; Lung Diseases, Interstitial - physiopathology ; Male ; Medical imaging ; Mortality ; Multivariate Analysis ; Outcome Assessment (Health Care) ; Pathology ; Patients ; Pneumonia ; Predictions ; Predictive Value of Tests ; Prognosis ; Pulmonary arteries ; Pulmonary artery ; Pulmonary Artery - anatomy & histology ; Pulmonary Artery - diagnostic imaging ; Pulmonary fibrosis ; Pulmonary hypertension ; Quantitative CT ; Regression analysis ; Respiratory Function Tests - methods ; Sarcoidosis ; Severity of Illness Index ; Tomography, X-Ray Computed - methods ; Unclassifiable interstitial lung disease ; Veins & arteries ; Visual perception ; Vital Capacity - physiology</subject><ispartof>Respiratory medicine, 2017-09, Vol.130, p.43-51</ispartof><rights>2017</rights><rights>Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited Sep 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c428t-78520b851a02d8c772949806b3d515f8ba8c7e53f1cb535b1ed820d554baedfc3</citedby><cites>FETCH-LOGICAL-c428t-78520b851a02d8c772949806b3d515f8ba8c7e53f1cb535b1ed820d554baedfc3</cites><orcidid>0000-0002-8054-2293 ; 0000-0001-7834-6579</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rmed.2017.07.007$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29206632$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jacob, Joseph</creatorcontrib><creatorcontrib>Bartholmai, Brian J.</creatorcontrib><creatorcontrib>Rajagopalan, Srinivasan</creatorcontrib><creatorcontrib>Egashira, Ryoko</creatorcontrib><creatorcontrib>Brun, Anne Laure</creatorcontrib><creatorcontrib>Kokosi, Maria</creatorcontrib><creatorcontrib>Nair, Arjun</creatorcontrib><creatorcontrib>Walsh, Simon L.F.</creatorcontrib><creatorcontrib>Karwoski, Ronald</creatorcontrib><creatorcontrib>Nicholson, Andrew G.</creatorcontrib><creatorcontrib>Hansell, David M.</creatorcontrib><creatorcontrib>Wells, Athol U.</creatorcontrib><title>Unclassifiable-interstitial lung disease: Outcome prediction using CT and functional indices</title><title>Respiratory medicine</title><addtitle>Respir Med</addtitle><description>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.
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.
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.
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.
•At baseline in uILD the composite physiologic index independently predicts survival.•PA diameter and traction bronchiectasis scores also independently predict mortality.•Change in CALIPER fibrosis scores independently predict survival in uILD.•Change in CALIPER fibrosis scores better predicts survival than baseline measures.•CALIPER fibrosis extent independently predicts outcome when FVC decline is marginal.</description><subject>Aged</subject><subject>Automation</subject><subject>Bronchiectasis</subject><subject>Bronchiectasis - diagnostic imaging</subject><subject>Carbon monoxide</subject><subject>Carbon Monoxide - metabolism</subject><subject>Computed tomography</subject><subject>Connective Tissue Diseases - diagnostic imaging</subject><subject>Connective Tissue Diseases - mortality</subject><subject>Connective Tissue Diseases - physiopathology</subject><subject>Coronary vessels</subject><subject>Diagnostic systems</subject><subject>Emphysema</subject><subject>Evaluation</subject><subject>Female</subject><subject>Fibrosis</subject><subject>Forced Expiratory Volume - physiology</subject><subject>Humans</subject><subject>Idiopathic Pulmonary Fibrosis - diagnostic imaging</subject><subject>Idiopathic Pulmonary Fibrosis - mortality</subject><subject>Idiopathic Pulmonary Fibrosis - physiopathology</subject><subject>Longitudinal analysis</subject><subject>Lung - diagnostic imaging</subject><subject>Lung - pathology</subject><subject>Lung - physiopathology</subject><subject>Lung diseases</subject><subject>Lung Diseases, Interstitial - diagnostic imaging</subject><subject>Lung Diseases, Interstitial - mortality</subject><subject>Lung Diseases, Interstitial - physiopathology</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Mortality</subject><subject>Multivariate Analysis</subject><subject>Outcome Assessment (Health Care)</subject><subject>Pathology</subject><subject>Patients</subject><subject>Pneumonia</subject><subject>Predictions</subject><subject>Predictive Value of Tests</subject><subject>Prognosis</subject><subject>Pulmonary arteries</subject><subject>Pulmonary artery</subject><subject>Pulmonary Artery - anatomy & histology</subject><subject>Pulmonary Artery - diagnostic imaging</subject><subject>Pulmonary fibrosis</subject><subject>Pulmonary hypertension</subject><subject>Quantitative CT</subject><subject>Regression analysis</subject><subject>Respiratory Function Tests - methods</subject><subject>Sarcoidosis</subject><subject>Severity of Illness Index</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Unclassifiable interstitial lung disease</subject><subject>Veins & arteries</subject><subject>Visual perception</subject><subject>Vital Capacity - physiology</subject><issn>0954-6111</issn><issn>1532-3064</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMtKxDAUhoMoOl5ewIUU3LjpeJI0vYgbGbyB4MbZCSFNTiWlk45JK_j2ps7MxoVwIHDy_T-Hj5BzCnMKNL9u536FZs6AFnOIA8UemVHBWcohz_bJDCqRpTml9Igch9ACQJVlcEiOWMUgzzmbkfel050KwTZW1R2m1g3ow2AHq7qkG91HYmxAFfAmeR0H3a8wWXs0Vg-2d8kYbCQWb4lyJmlG97uNQesigeGUHDSqC3i2fU_I8uH-bfGUvrw-Pi_uXlKdsXJIi1IwqEtBFTBT6qJgVVaVkNfcCCqaslZxiYI3VNeCi5qiKRkYIbJaoWk0PyFXm9617z9HDINc2aCx65TDfgySVgXPRMErHtHLP2jbjz7ePFGcixIoqyLFNpT2fQgeG7n2dqX8t6QgJ_eylZN7ObmXEAeKGLrYVo_19LeL7GRH4HYDYHTxZdHLoC06HXV61IM0vf2v_we2HpVY</recordid><startdate>201709</startdate><enddate>201709</enddate><creator>Jacob, Joseph</creator><creator>Bartholmai, Brian J.</creator><creator>Rajagopalan, Srinivasan</creator><creator>Egashira, Ryoko</creator><creator>Brun, Anne Laure</creator><creator>Kokosi, Maria</creator><creator>Nair, Arjun</creator><creator>Walsh, Simon L.F.</creator><creator>Karwoski, Ronald</creator><creator>Nicholson, Andrew G.</creator><creator>Hansell, David M.</creator><creator>Wells, Athol U.</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U9</scope><scope>ASE</scope><scope>FPQ</scope><scope>H94</scope><scope>K6X</scope><scope>K9.</scope><scope>M7N</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-8054-2293</orcidid><orcidid>https://orcid.org/0000-0001-7834-6579</orcidid></search><sort><creationdate>201709</creationdate><title>Unclassifiable-interstitial lung disease: Outcome prediction using CT and functional indices</title><author>Jacob, Joseph ; Bartholmai, Brian J. ; Rajagopalan, Srinivasan ; Egashira, Ryoko ; Brun, Anne Laure ; Kokosi, Maria ; Nair, Arjun ; Walsh, Simon L.F. ; Karwoski, Ronald ; Nicholson, Andrew G. ; Hansell, David M. ; Wells, Athol U.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c428t-78520b851a02d8c772949806b3d515f8ba8c7e53f1cb535b1ed820d554baedfc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aged</topic><topic>Automation</topic><topic>Bronchiectasis</topic><topic>Bronchiectasis - diagnostic imaging</topic><topic>Carbon monoxide</topic><topic>Carbon Monoxide - metabolism</topic><topic>Computed tomography</topic><topic>Connective Tissue Diseases - diagnostic imaging</topic><topic>Connective Tissue Diseases - mortality</topic><topic>Connective Tissue Diseases - physiopathology</topic><topic>Coronary vessels</topic><topic>Diagnostic systems</topic><topic>Emphysema</topic><topic>Evaluation</topic><topic>Female</topic><topic>Fibrosis</topic><topic>Forced Expiratory Volume - physiology</topic><topic>Humans</topic><topic>Idiopathic Pulmonary Fibrosis - diagnostic imaging</topic><topic>Idiopathic Pulmonary Fibrosis - mortality</topic><topic>Idiopathic Pulmonary Fibrosis - physiopathology</topic><topic>Longitudinal analysis</topic><topic>Lung - diagnostic imaging</topic><topic>Lung - pathology</topic><topic>Lung - physiopathology</topic><topic>Lung diseases</topic><topic>Lung Diseases, Interstitial - diagnostic imaging</topic><topic>Lung Diseases, Interstitial - mortality</topic><topic>Lung Diseases, Interstitial - physiopathology</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Mortality</topic><topic>Multivariate Analysis</topic><topic>Outcome Assessment (Health Care)</topic><topic>Pathology</topic><topic>Patients</topic><topic>Pneumonia</topic><topic>Predictions</topic><topic>Predictive Value of Tests</topic><topic>Prognosis</topic><topic>Pulmonary arteries</topic><topic>Pulmonary artery</topic><topic>Pulmonary Artery - anatomy & histology</topic><topic>Pulmonary Artery - diagnostic imaging</topic><topic>Pulmonary fibrosis</topic><topic>Pulmonary hypertension</topic><topic>Quantitative CT</topic><topic>Regression analysis</topic><topic>Respiratory Function Tests - methods</topic><topic>Sarcoidosis</topic><topic>Severity of Illness Index</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>Unclassifiable interstitial lung disease</topic><topic>Veins & arteries</topic><topic>Visual perception</topic><topic>Vital Capacity - physiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jacob, Joseph</creatorcontrib><creatorcontrib>Bartholmai, Brian J.</creatorcontrib><creatorcontrib>Rajagopalan, Srinivasan</creatorcontrib><creatorcontrib>Egashira, Ryoko</creatorcontrib><creatorcontrib>Brun, Anne Laure</creatorcontrib><creatorcontrib>Kokosi, Maria</creatorcontrib><creatorcontrib>Nair, Arjun</creatorcontrib><creatorcontrib>Walsh, Simon L.F.</creatorcontrib><creatorcontrib>Karwoski, Ronald</creatorcontrib><creatorcontrib>Nicholson, Andrew G.</creatorcontrib><creatorcontrib>Hansell, David M.</creatorcontrib><creatorcontrib>Wells, Athol U.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Virology and AIDS Abstracts</collection><collection>British Nursing Index</collection><collection>British Nursing Index (BNI) (1985 to Present)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>British Nursing Index</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Respiratory medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jacob, Joseph</au><au>Bartholmai, Brian J.</au><au>Rajagopalan, Srinivasan</au><au>Egashira, Ryoko</au><au>Brun, Anne Laure</au><au>Kokosi, Maria</au><au>Nair, Arjun</au><au>Walsh, Simon L.F.</au><au>Karwoski, Ronald</au><au>Nicholson, Andrew G.</au><au>Hansell, David M.</au><au>Wells, Athol U.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unclassifiable-interstitial lung disease: Outcome prediction using CT and functional indices</atitle><jtitle>Respiratory medicine</jtitle><addtitle>Respir Med</addtitle><date>2017-09</date><risdate>2017</risdate><volume>130</volume><spage>43</spage><epage>51</epage><pages>43-51</pages><issn>0954-6111</issn><eissn>1532-3064</eissn><abstract>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.
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.
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.
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.
•At baseline in uILD the composite physiologic index independently predicts survival.•PA diameter and traction bronchiectasis scores also independently predict mortality.•Change in CALIPER fibrosis scores independently predict survival in uILD.•Change in CALIPER fibrosis scores better predicts survival than baseline measures.•CALIPER fibrosis extent independently predicts outcome when FVC decline is marginal.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>29206632</pmid><doi>10.1016/j.rmed.2017.07.007</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-8054-2293</orcidid><orcidid>https://orcid.org/0000-0001-7834-6579</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Automation Bronchiectasis Bronchiectasis - diagnostic imaging Carbon monoxide Carbon Monoxide - metabolism Computed tomography Connective Tissue Diseases - diagnostic imaging Connective Tissue Diseases - mortality Connective Tissue Diseases - physiopathology Coronary vessels Diagnostic systems Emphysema Evaluation Female Fibrosis Forced Expiratory Volume - physiology Humans Idiopathic Pulmonary Fibrosis - diagnostic imaging Idiopathic Pulmonary Fibrosis - mortality Idiopathic Pulmonary Fibrosis - physiopathology Longitudinal analysis Lung - diagnostic imaging Lung - pathology Lung - physiopathology Lung diseases Lung Diseases, Interstitial - diagnostic imaging Lung Diseases, Interstitial - mortality Lung Diseases, Interstitial - physiopathology Male Medical imaging Mortality Multivariate Analysis Outcome Assessment (Health Care) Pathology Patients Pneumonia Predictions Predictive Value of Tests Prognosis Pulmonary arteries Pulmonary artery Pulmonary Artery - anatomy & histology Pulmonary Artery - diagnostic imaging Pulmonary fibrosis Pulmonary hypertension Quantitative CT Regression analysis Respiratory Function Tests - methods Sarcoidosis Severity of Illness Index Tomography, X-Ray Computed - methods Unclassifiable interstitial lung disease Veins & arteries Visual perception Vital Capacity - physiology |
| title | Unclassifiable-interstitial lung disease: Outcome prediction using CT and functional indices |
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