{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T01:29:51Z","timestamp":1793410191638,"version":"4.5.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":["www.atsjournals.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Rationale<\/jats:title>\n                    <jats:p>The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machine-learning tool called \u201cSubtype and Stage Inference\u201d (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1\u20134 patients and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of patients with COPD. We confirmed the identified subtypes and progression patterns using ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements and Main Results<\/jats:title>\n                    <jats:p>We identified two trajectories of disease progression in COPD: a \u201cTissue\u2192Airway\u201d subtype (n\u2009=\u20092,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an \u201cAirway\u2192Tissue\u201d subtype (n\u2009=\u2009988, 29.6%), in which large airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV1\/FVC decline (r\u2009=\u2009\u22120.16 [P\u2009&amp;lt;\u20090.001] in the Tissue\u2192Airway group; r\u2009=\u2009\u22120.14 [P\u2009=\u20090.011] in the Airway\u2192Tissue group). SuStaIn placed 30% of smokers with normal lung function at elevated stages, suggesting imaging changes consistent with early COPD. Individuals with early changes were 2.5 times more likely to meet COPD diagnostic criteria at follow-up.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We demonstrate two distinct patterns of disease progression in COPD using SuStaIn, likely representing different endotypes. One third of healthy smokers have detectable imaging changes, suggesting a new biomarker of \u201cearly COPD.\u201d<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1164\/rccm.201908-1600oc","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T11:35:49Z","timestamp":1572262549000},"page":"294-302","update-policy":"https:\/\/doi.org\/10.1164\/crossmarkpolicies","source":"Crossref","is-referenced-by-count":97,"title":["Disease Progression Modeling in Chronic Obstructive Pulmonary Disease"],"prefix":"10.1093","volume":"201","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7772-781X","authenticated-orcid":false,"given":"Alexandra L.","family":"Young","sequence":"first","affiliation":[{"name":"Centre for Medical Image Computing"},{"name":"Department of Computer Science"},{"name":"Department of Medical Physics and Biomedical Engineering, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5910-2966","authenticated-orcid":false,"given":"Felix J. S.","family":"Bragman","sequence":"additional","affiliation":[{"name":"Centre for Medical Image Computing"},{"name":"UCL Respiratory, University College London, London, United Kingdom"},{"name":"Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7017-1575","authenticated-orcid":false,"given":"Bojidar","family":"Rangelov","sequence":"additional","affiliation":[{"name":"Centre for Medical Image Computing"},{"name":"UCL Respiratory, University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9095-4419","authenticated-orcid":false,"given":"MeiLan K.","family":"Han","sequence":"additional","affiliation":[{"name":"Artificial Medical Intelligence Group, School of Biomedical Engineering and Imaging Sciences, King\u2019s College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5596-7487","authenticated-orcid":false,"given":"Craig J.","family":"Galb\u00e1n","sequence":"additional","affiliation":[{"name":"Pulmonary and Critical Care, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Lynch","sequence":"additional","affiliation":[{"name":"Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7151-1762","authenticated-orcid":false,"given":"David J.","family":"Hawkes","sequence":"additional","affiliation":[{"name":"Centre for Medical Image Computing"},{"name":"UCL Respiratory, University College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2439-350X","authenticated-orcid":false,"given":"Daniel C.","family":"Alexander","sequence":"additional","affiliation":[{"name":"Centre for Medical Image Computing"},{"name":"Department of Computer Science"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7246-6040","authenticated-orcid":false,"given":"John R.","family":"Hurst","sequence":"additional","affiliation":[{"name":"Department of Radiology, National Jewish Health, Denver, Colorado"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,2,1]]},"reference":[{"key":"2026030321402797900_bib1","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/S0140-6736(07)61380-4","article-title":"Global burden of COPD: risk factors, prevalence, and future trends","volume":"370","author":"Mannino","year":"2007","journal-title":"Lancet"},{"key":"2026030321402797900_bib2","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1056\/NEJMoa1105482","article-title":"Changes in forced expiratory volume in 1 second over time in COPD","volume":"365","author":"Vestbo","year":"2011","journal-title":"N Engl J Med"},{"key":"2026030321402797900_bib3","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1164\/rccm.201511-2219OC","article-title":"Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease","volume":"194","author":"Bhatt","year":"2016","journal-title":"Am J Respir Crit Care Med"},{"key":"2026030321402797900_bib4","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1136\/thoraxjnl-2013-203601","article-title":"Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema","volume":"69","author":"Castaldi","year":"2014","journal-title":"Thorax"},{"key":"2026030321402797900_bib5","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1183\/09031936.00175109","article-title":"Clinical COPD phenotypes: a novel approach using principal component and cluster analyses","volume":"36","author":"Burgel","year":"2010","journal-title":"Eur Respir J"},{"key":"2026030321402797900_bib6","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1109\/TMI.2016.2608782","article-title":"A Bayesian nonparametric model for disease subtyping: application to emphysema phenotypes","volume":"36","author":"Ross","year":"2017","journal-title":"IEEE Trans Med Imaging"},{"key":"2026030321402797900_bib7","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1164\/rccm.201707-1405OC","article-title":"Longitudinal modeling of lung function trajectories in smokers with and without chronic obstructive pulmonary disease","volume":"198","author":"Ross","year":"2018","journal-title":"Am J Respir Crit Care Med"},{"key":"2026030321402797900_bib8","first-page":"46","article-title":"Manifold learning of COPD","volume-title":"Med Image Comput Comput Assist Interv","author":"Bragman","year":"2017"},{"key":"2026030321402797900_bib9","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.acra.2015.12.021","article-title":"A robust emphysema severity measure based on disease subtypes","volume":"23","author":"Harmouche","year":"2016","journal-title":"Acad Radiol"},{"key":"2026030321402797900_bib10","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1093\/brain\/awu176","article-title":"A data-driven model of biomarker changes in sporadic Alzheimer\u2019s disease","volume":"137","author":"Young","year":"2014","journal-title":"Brain"},{"key":"2026030321402797900_bib11","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1016\/j.neuroimage.2012.01.062","article-title":"An event-based model for disease progression and its application in familial Alzheimer\u2019s disease and Huntington\u2019s disease","volume":"60","author":"Fonteijn","year":"2012","journal-title":"Neuroimage"},{"key":"2026030321402797900_bib12","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1097\/WCO.0000000000000460","article-title":"Imaging plus X: multimodal models of neurodegenerative disease","volume":"30","author":"Oxtoby","year":"2017","journal-title":"Curr Opin Neurol"},{"key":"2026030321402797900_bib13","doi-asserted-by":"crossref","first-page":"S400","DOI":"10.1016\/j.jalz.2013.10.003","article-title":"Estimating long-term multivariate progression from short-term data","volume":"10","author":"Donohue","year":"2014","journal-title":"Alzheimers Dement"},{"key":"2026030321402797900_bib14","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.neuroimage.2016.04.001","article-title":"A multivariate nonlinear mixed effects model for longitudinal image analysis: application to amyloid imaging","volume":"134","author":"Bilgel","year":"2016","journal-title":"Neuroimage"},{"key":"2026030321402797900_bib15","doi-asserted-by":"crossref","article-title":"Unsupervised learning of disease progression models","author":"Wang","DOI":"10.1145\/2623330.2623754"},{"key":"2026030321402797900_bib16","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1038\/s41467-018-05892-0","article-title":"Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference","volume":"9","author":"Young","year":"2018","journal-title":"Nat Commun"},{"key":"2026030321402797900_bib17","first-page":"OA2139","article-title":"Disease progression patterns in COPD","volume-title":"Eur Respir J","author":"Bragman","year":"2018"},{"key":"2026030321402797900_bib18","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3109\/15412550903499522","article-title":"Genetic epidemiology of COPD (COPDGene) study design","volume":"7","author":"Regan","year":"2010","journal-title":"COPD"},{"key":"2026030321402797900_bib19","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1183\/09031936.00111707","article-title":"Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE)","volume":"31","author":"Vestbo","year":"2008","journal-title":"Eur Respir J"},{"key":"2026030321402797900_bib20","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1038\/nm.2971","article-title":"Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression","volume":"18","author":"Galb\u00e1n","year":"2012","journal-title":"Nat Med"},{"key":"2026030321402797900_bib21","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1164\/rccm.200407-874OC","article-title":"The prediction of small airway dimensions using computed tomography","volume":"171","author":"Nakano","year":"2005","journal-title":"Am J Respir Crit Care Med"},{"key":"2026030321402797900_bib22","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S2213-2600(13)70006-7","article-title":"The presence and progression of emphysema in COPD as determined by CT scanning and biomarker expression: a prospective analysis from the ECLIPSE study","volume":"1","author":"Coxson","year":"2013","journal-title":"Lancet Respir Med"},{"key":"2026030321402797900_bib23","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/0002-9343(68)90234-9","article-title":"Chronic obstructive bronchopulmonary disease: II. Oxygen transport in two clinical types","volume":"44","author":"Filley","year":"1968","journal-title":"Am J Med"},{"key":"2026030321402797900_bib24","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1056\/NEJM196806202782501","article-title":"Site and nature of airway obstruction in chronic obstructive lung disease","volume":"278","author":"Hogg","year":"1968","journal-title":"N Engl J Med"},{"key":"2026030321402797900_bib25","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jaci.2016.05.011","article-title":"Inflammatory mechanisms in patients with chronic obstructive pulmonary disease","volume":"138","author":"Barnes","year":"2016","journal-title":"J Allergy Clin Immunol"},{"key":"2026030321402797900_bib26","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1136\/bmj.1.6077.1645","article-title":"The natural history of chronic airflow obstruction","volume":"1","author":"Fletcher","year":"1977","journal-title":"BMJ"},{"key":"2026030321402797900_bib27","doi-asserted-by":"crossref","first-page":"95","DOI":"10.2147\/COPD.S27480","article-title":"Lung function decline in COPD","volume":"7","author":"Tantucci","year":"2012","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"key":"2026030321402797900_bib28","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1016\/S0140-6736(15)60647-X","article-title":"Early chronic obstructive pulmonary disease: definition, assessment, and prevention","volume":"385","author":"Rennard","year":"2015","journal-title":"Lancet"},{"key":"2026030321402797900_bib29","first-page":"60","article-title":"Emphysema in heavy smokers with normal chest radiography: detection and quantification by HCRT","volume":"43","author":"Sashidhar","year":"2002","journal-title":"Acta Radiol"},{"key":"2026030321402797900_bib30","first-page":"522","article-title":"Comparison of high-resolution computed tomography with pulmonary function testing in symptomatic smokers","volume":"86","author":"Srinakarin","year":"2003","journal-title":"J Med Assoc Thai"},{"key":"2026030321402797900_bib31","first-page":"133","article-title":"Quantitative computed tomography in COPD: possibilities and limitations","volume":"190","author":"Mets","year":"2012","journal-title":"Hai"}],"container-title":["American Journal of Respiratory and Critical Care Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ajrccm\/article-pdf\/201\/3\/294\/67179382\/ajrccm_201_3_294.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ajrccm\/article-pdf\/201\/3\/294\/67179382\/ajrccm_201_3_294.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T02:40:37Z","timestamp":1772592037000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ajrccm\/article\/201\/3\/294\/8496473"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,1]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2,1]]},"published-print":{"date-parts":[[2020,2,1]]}},"URL":"https:\/\/doi.org\/10.1164\/rccm.201908-1600oc","relation":{},"ISSN":["1073-449X","1535-4970"],"issn-type":[{"value":"1073-449X","type":"print"},{"value":"1535-4970","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,2]]},"published":{"date-parts":[[2020,2,1]]},"assertion":[{"value":"2019-08-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-10-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-01-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}