Turning subtypes into disease axes to improve prediction of COPD progression

Junxiang Chen, Michael Cho, Edwin K. Silverman, John E. Hokanson, Greg L. Kinney, James D. Crapo, Stephen Rennard, Jennifer Dy, Peter Castaldi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)906-909
Number of pages4
JournalThorax
Volume74
Issue number9
DOIs
StatePublished - Sep 1 2019

Keywords

  • COPD epidemiology
  • emphysema

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

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