151. Disease Progression Modeling in Chronic Obstructive Pulmonary Disease
- Author
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Young, Alexandra L, Bragman, Felix JS, Rangelov, Bojidar, Han, MeiLan K, Galbán, Craig J, Lynch, David A, Hawkes, David J, Alexander, Daniel C, Hurst, John R, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Castaldi, Peter J, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Qiao, Dandi, Wan, Emily S, Won, Sungho, Sakornsakolpat, Phuwanat, Prokopenko, Dmitry, Al Qaisi, Mustafa, Coxson, Harvey O, Gray, Teresa, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Newell, John D, Ross, James C, Estepar, Raul San Jose, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Washko, George, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Moore, Camille, Strand, Matt, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A, Bhatt, Surya, Bon, Jessica, Martinez, Carlos, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Martinez, Carlos H, Pernicano, Perry G, Hanania, Nicola, Alapat, Philip, Atik, Mustafa, Bandi, Venkata, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Nachiappan, Arun, Parulekar, Amit, Barr, R Graham, Austin, John, D’Souza, Belinda, Pearson, Gregory DN, Rozenshtein, Anna, Thomashow, Byron, MacIntyre, Neil, McAdams, H Page, Washington, Lacey, McEvoy, Charlene, and Tashjian, Joseph
- Subjects
Biomedical Imaging ,Lung ,Clinical Research ,Chronic Obstructive Pulmonary Disease ,Aetiology ,4.2 Evaluation of markers and technologies ,Detection ,screening and diagnosis ,4.1 Discovery and preclinical testing of markers and technologies ,2.1 Biological and endogenous factors ,Respiratory ,Aged ,Disease Progression ,Female ,Humans ,Male ,Middle Aged ,Models ,Theoretical ,Pulmonary Disease ,Chronic Obstructive ,Tomography ,X-Ray Computed ,clustering ,CT imaging ,emphysema ,bronchitis ,chronic obstructive pulmonary disease ,COPDGene Investigators ,Medical and Health Sciences ,Respiratory System - 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 machine-learning 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 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.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 = -0.16 [P
- Published
- 2020