PT - JOURNAL ARTICLE AU - Peter J Castaldi AU - Marta Benet AU - Hans Petersen AU - Nicholas Rafaels AU - James Finigan AU - Matteo Paoletti AU - H Marike Boezen AU - Judith M Vonk AU - Russell Bowler AU - Massimo Pistolesi AU - Milo A Puhan AU - Josep Anto AU - Els Wauters AU - Diether Lambrechts AU - Wim Janssens AU - Francesca Bigazzi AU - Gianna Camiciottoli AU - Michael H Cho AU - Craig P Hersh AU - Kathleen Barnes AU - Stephen Rennard AU - Meher Preethi Boorgula AU - Jennifer Dy AU - Nadia N Hansel AU - James D Crapo AU - Yohannes Tesfaigzi AU - Alvar Agusti AU - Edwin K Silverman AU - Judith Garcia-Aymerich TI - Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts AID - 10.1136/thoraxjnl-2016-209846 DP - 2017 Nov 01 TA - Thorax PG - 998--1006 VI - 72 IP - 11 4099 - http://thorax.bmj.com/content/72/11/998.short 4100 - http://thorax.bmj.com/content/72/11/998.full SO - Thorax2017 Nov 01; 72 AB - Background COPD is a heterogeneous disease, but there is little consensus on specific definitions for COPD subtypes. Unsupervised clustering offers the promise of ‘unbiased’ data-driven assessment of COPD heterogeneity. Multiple groups have identified COPD subtypes using cluster analysis, but there has been no systematic assessment of the reproducibility of these subtypes.Objective We performed clustering analyses across 10 cohorts in North America and Europe in order to assess the reproducibility of (1) correlation patterns of key COPD-related clinical characteristics and (2) clustering results.Methods We studied 17 146 individuals with COPD using identical methods and common COPD-related characteristics across cohorts (FEV1, FEV1/FVC, FVC, body mass index, Modified Medical Research Council score, asthma and cardiovascular comorbid disease). Correlation patterns between these clinical characteristics were assessed by principal components analysis (PCA). Cluster analysis was performed using k-medoids and hierarchical clustering, and concordance of clustering solutions was quantified with normalised mutual information (NMI), a metric that ranges from 0 to 1 with higher values indicating greater concordance.Results The reproducibility of COPD clustering subtypes across studies was modest (median NMI range 0.17–0.43). For methods that excluded individuals that did not clearly belong to any cluster, agreement was better but still suboptimal (median NMI range 0.32–0.60). Continuous representations of COPD clinical characteristics derived from PCA were much more consistent across studies.Conclusions Identical clustering analyses across multiple COPD cohorts showed modest reproducibility. COPD heterogeneity is better characterised by continuous disease traits coexisting in varying degrees within the same individual, rather than by mutually exclusive COPD subtypes.