Turning subtypes into disease axes to improve prediction of COPD progression. Issue 9 (12th June 2019)

• Record Type: Journal Article
• Authors: Chen, Junxiang; Cho, Michael; Silverman, Edwin K; Hokanson, John E; Kinney, Greg L; Crapo, James D; Rennard, Stephen; Dy, Jennifer; Castaldi, Peter
• Is Part Of: Thorax. Volume 74:Issue 9(2019)
• Page Start: 906
• Page End: 909
• Publication Date: 2019-06-12
• Subjects: copd epidemiology -- emphysema; Chest -- Diseases -- Periodicals; Thorax; Chest -- Diseases; Periodicals; Periodicals; 617.54
• DOI: 10.1136/thoraxjnl-2018-213005
• Languages: English
• ISSNs: 0040-6376

Abstract

Abstract : Chronic obstructive pulmonary disease (COPD) is an umbrella definition encompassing multiple disease processes. COPD heterogeneity has been described as distinct subgroups of individuals (subtypes) or as continuous measures of COPD variability (disease axes). There is little consensus on whether subtypes or disease axes are preferred, and the relative value of disease axes and subtypes for predicting COPD progression is unknown. Using a propensity score approach to learn disease axes from pairs of subtypes, we demonstrate that these disease axes predict prospective forced expiratory volume in 1 s decline and emphysema progression more accurately than the subtype pairs from which they were derived.