Disease progression modeling in chronic obstructive pulmonary disease

Alexandra L. Young, Felix J.S. Bragman, Bojidar Rangelov, Meilan K. Han, Craig J. Galbán, David A. Lynch, David J. Hawkes, Daniel C. Alexander, John R. Hurst, James D. Crapo, Edwin K. Silverman, Barry J. Make, Elizabeth A. Regan, Terri Beaty, Ferdouse Begum, Peter J. Castaldi, Michael Cho, Dawn L. DeMeo, Adel R. Boueiz, Marilyn G. ForemanEitan Halper-Stromberg, Lystra P. Hayden, Craig P. Hersh, Jacqueline Hetmanski, Brian D. Hobbs, John E. Hokanson, Nan Laird, Christoph Lange, Sharon M. Lutz, Merry Lynn McDonald, Margaret M. Parker, Dandi Qiao, Elizabeth A. Regan, Edwin K. Silverman, Emily S. Wan, Sungho Won, Phuwanat Sakornsakolpat, Dmitry Prokopenko, Mustafa Al Qaisi, Harvey O. Coxson, Teresa Gray, Meilan K. Han, Eric A. Hoffman, Stephen Humphries, Francine L. Jacobson, Philip F. Judy, Ella A. Kazerooni, Alex Kluiber, David A. Lynch, John D. Newell, Elizabeth A. Regan, James C. Ross, Raul San Jose Estepar, Joyce Schroeder, Jered Sieren, Douglas Stinson, Berend C. Stoel, Juerg Tschirren, Edwin Van Beek, Bram Van Ginneken, Eva Van Rikxoort, George Washko, Carla G. Wilson, Robert Jensen, Douglas Everett, Jim Crooks, Camille Moore, Matt Strand, Carla G. Wilson, John E. Hokanson, John Hughes, Gregory Kinney, Sharon M. Lutz, Katherine Pratte, Kendra A. Young, Surya Bhatt, Jessica Bon, Meilan K. Han, Barry J. Make, Carlos Martinez, Susan Murray, Elizabeth A. Regan, Xavier Soler, Carla G. Wilson, Russell P. Bowler, Katerina Kechris, Farnoush Banaei-Kashani, Jeffrey L. Curtis, Carlos H. Martinez, Perry G. Pernicano, Nicola Hanania, Philip Alapat, Mustafa Atik, Venkata Bandi, Aladin Boriek, Kalpatha Guntupalli, Elizabeth Guy, Arun Nachiappan, Amit Parulekar, Dawn L. DeMeo, Craig P. Hersh, Francine L. Jacobson, George Washko, R. Graham Barr, John Austin, Belinda D'Souza, Gregory D.N. Pearson, Anna Rozenshtein, Byron Thomashow, Neil MacIntyre, H. Page McAdams, Lacey Washington, Charlene McEvoy, Joseph Tashjian, Robert Wise, Robert Brown, Nadia N. Hansel, Karen Horton, Allison Lambert, Nirupama Putcha, Richard Casaburi, Alessandra Adami, Matthew Budoff, Hans Fischer, Janos Porszasz, Harry Rossiter, William Stringer, Amir Sharafkhaneh, Charlie Lan, Christine Wendt, Brian Bell, Marilyn G. Foreman, Eugene Berkowitz, Gloria Westney, Russel P. Bowler, David A. Lynch, Richard Rosiello, David Pace, Gerard Criner, David Ciccolella, Francis Cordova, Chandra Dass, Gilbert D'Alonzo, Parag Desai, Michael Jacobs, Steven Kelsen, Victor Kim, A. James Mamary, Nathaniel Marchetti, Aditi Satti, Kartik Shenoy, Robert M. Steiner, Alex Swift, Irene Swift, Maria Elena Vega-Sanchez, Mark Dransfield, William Bailey, Surya Bhatt, Anand Iyer, Hrudaya Nath, J. Michael Wells, Joe Ramsdell, Paul Friedman, Xavier Soler, Andrew Yen, Alejandro P. Comellas, Karin F. Hoth, John D. Newell, Brad Thompson, Meilan K. Han, Ella A. Kazerooni, Carlos H. Martinez, Joanne Billings, Abbie Begnaud, Tadashi Allen, Frank Sciurba, Jessica Bon, Divay Chandra, Carl Fuhrman, Joel Weissfeld, Antonio Anzueto, Sandra Adams, Diego Maselli-Caceres, Mario E. Ruiz

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Rationale: The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging. Objectives: To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machinelearning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD. Methods: We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-4 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 COPDLongitudinally 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. Measurements and Main Results: We identified two trajectories of disease progression in COPD: a "Tissue→Airway" subtype (n = 2,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an "Airway→Tissue" subtype (n = 988, 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 =20.16 [P<0.001] in the Tissue→Airway group; r =20.14 [P = 0.011] in the Airway→Tissue 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. Conclusions: 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 "early COPD".

Original languageEnglish (US)
Pages (from-to)294-302
Number of pages9
JournalAmerican journal of respiratory and critical care medicine
Volume201
Issue number3
DOIs
StatePublished - Feb 1 2020

Bibliographical note

Funding Information:
*Joint first authors. ‡M.K.H. is Associate Editor of AJRCCM. Her participation complies with American Thoracic Society requirements for recusal from review and decisions for authored works. Supported by Engineering and Physical Sciences Research Council (EPSRC) grants EP/H046410/1 and EP/K502959/1 (F.J.S.B.); University College London Hospitals (UCLH) NIHR Research Capability Funding Senior Investigator Award under grant RCF107/DH/2014 (F.J.S.B. and D.J.H.); EPSRC Doctoral Prize Fellowship and MRC Skills Development Fellowship (A.L.Y.); EPSRC Centre For Doctoral Training in Medical Imaging grant EP/L016478/1 and Industrial Fellowship from the Royal Commission for the Exhibition of 1851 (B.R.); an industrial CASE studentship with funding from GlaxoSmithKline Research and Development, Agreement BIDS3000032413 (B.R.); the European Union’s Horizon 2020 research and innovation program under grant agreement 666992 (D.C.A.); EPSRC grants M020533, M006093, and J020990 (D.C.A.). This work was supported by the UCLH NIHR Biomedical Research Centre. The COPDGene Study was supported by Award U01 HL089897 and Award U01 HL089856 from the NHLBI. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHLBI or the NIH. The COPDGene project is also supported by the Chronic Obstructive Pulmonary Disease Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion. The ECLIPSE study was sponsored by GlaxoSmithKline. The study sponsor did not place any restrictions regarding statements made in this manuscript. A Steering Committee and a Scientific Committee comprising academic and sponsor representatives developed the original ECLIPSE study design, had full access to the study data, and were responsible for decisions regarding publications.

Keywords

  • Bronchitis
  • CT imaging
  • Chronic obstructive pulmonary disease
  • Clustering
  • Emphysema

Fingerprint Dive into the research topics of 'Disease progression modeling in chronic obstructive pulmonary disease'. Together they form a unique fingerprint.

Cite this