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Our goal was to identify subgroups of adults with cystic fibrosis (CF) at low risk of death within 10 years.Factor analysis for mixed data followed by Ward's cluster analysis was conducted using 25 variables from 1572 French CF adults in 2005. Rates of death by subgroups were analysed over 10 years. An algorithm was developed using CART (classification and regression tree) analysis to provide rules for the identification of subgroups of CF adults with low rates of death within 10 years. This algorithm was validated in 1376 Canadian CF adults.Seven subgroups were identified by cluster analysis in French CF adults, including two subgroups with low (∼5%) rates of death at 10 years: one subgroup (22% of patients) was composed of patients with nonclassic CF, the other subgroup (17% of patients) was composed of patients with classic CF but low rates of Pseudomonas aeruginosa infection and diabetes. An algorithm based on CART analysis of data in 2005 allowed us to identify most French adults with low rates of death. When tested using data from Canadian CF adults in 2005, the algorithm identified 287 out of 1376 (21%) patients at low risk (10-year death: 7.7%).Large subgroups of CF adults share low risk of 10-year mortality., Competing Interests: Conflict of interest: P-R. Burgel reports grants and personal fees from Boehringer Ingelheim, and personal fees from AstraZeneca, Chiesi, GSK, Novartis, Vertex, Zambon and MSD, outside the submitted work. Conflict of interest: L. Lemonnier has nothing to disclose. Conflict of interest: C. Dehillotte has nothing to disclose. Conflict of interest: J. Sykes has nothing to disclose. Conflict of interest: S. Stanojevic has nothing to disclose. Conflict of interest: A.L. Stephenson receives a stipend from Cystic Fibrosis Canada for her work as Director of the Canadian CF Registry, outside the submitted work. Conflict of interest: J-L. Paillasseur has nothing to disclose., (Copyright ©ERS 2019.)

This study aimed to identify simple rules for allocating chronic obstructive pulmonary disease (COPD) patients to clinical phenotypes identified by cluster analyses.Data from 2409 COPD patients of French/Belgian COPD cohorts were analysed using cluster analysis resulting in the identification of subgroups, for which clinical relevance was determined by comparing 3-year all-cause mortality. Classification and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative.Cluster analysis identified five subgroups of COPD patients with different clinical characteristics (especially regarding severity of respiratory disease and the presence of cardiovascular comorbidities and diabetes). The CART-based algorithm indicated that the variables relevant for patient grouping differed markedly between patients with isolated respiratory disease (FEV 1 , dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV 1 and body mass index). Application of this algorithm to the 3CIA cohorts confirmed that it identified subgroups of patients with different clinical characteristics, mortality rates (median, from 4% to 27%) and age at death (median, from 68 to 76 years).A simple algorithm, integrating respiratory characteristics and comorbidities, allowed the identification of clinically relevant COPD phenotypes., Competing Interests: Conflict of interest: Disclosures can be found alongside this article at erj.ersjournals.com, (Copyright ©ERS 2017.)

Classification of chronic obstructive pulmonary disease (COPD) is usually based on the severity of airflow limitation, which may not reflect phenotypic heterogeneity. Here, we sought to identify COPD phenotypes using multiple clinical variables. COPD subjects recruited in a French multicentre cohort were characterised using a standardised process. Principal component analysis (PCA) was performed using eight variables selected for their relevance to COPD: age, cumulative smoking, forced expiratory volume in 1 s (FEV(1)) (% predicted), body mass index, exacerbations, dyspnoea (modified Medical Research Council scale), health status (St George's Respiratory Questionnaire) and depressive symptoms (hospital anxiety and depression scale). Patient classification was performed using cluster analysis based on PCA-transformed data. 322 COPD subjects were analysed: 77% were male; median (interquartile range) age was 65.0 (58.0-73.0) yrs; FEV(1) was 48.9 (34.1-66.3)% pred; and 21, 135, 107 and 59 subjects were classified in Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1, 2, 3 and 4, respectively. PCA showed that three independent components accounted for 61% of variance. PCA-based cluster analysis resulted in the classification of subjects into four clinical phenotypes that could not be identified using GOLD classification. Importantly, subjects with comparable airflow limitation (FEV(1)) belonged to different phenotypes and had marked differences in age, symptoms, comorbidities and predicted mortality. These analyses underscore the need for novel multidimensional COPD classification for improving patient care and quality of clinical trials.