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, dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV 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.

Inhalational challenges with inflammatory mediators may provoke lung function disturbances similar to those shown in spontaneous acute asthma. Cysteinyl leukotrienes (CysLTs) have recently been established as mediators of bronchoconstriction in asthma but their effects on pulmonary gas exchange in asthma have not been assessed. We therefore investigated the effects of leukotriene D(4) (LTD(4)) challenge resulting in a significant decrease in FEV(1) (mean +/- SE, by 32 +/- 3%) in 13 nonsmoking, mild asthmatics. Respiratory system resistance (Rrs), and respiratory and inert gases were measured before and immediately after, and at 15 and 45 min after challenge. After bronchoprovocation, Rrs increased (by 106 +/- 12%), Pa(O(2)) decreased (by 25 +/- 4 mm Hg), and ventilation-perfusion distributions moderately to severely deteriorated, as shown by increases in the dispersions of pulmonary blood flow (Log SDQ, by 59 +/- 12%) and alveolar ventilation (Log SDV, by 65 +/- 20%) (p < 0.05 each). Sputum eosinophils (p < 0.05) and urinary LTE(4) (p < 0.005) increased after challenge. Despite the lack of mathematical correlations between spirometric and Rrs changes and gas exchange indices, the pattern of improvement of the functional variables after challenge ran in parallel. These findings support the evidence that CysLTs, in addition to being potent bronchoconstrictors, also provoke profound disturbances of pulmonary gas exchange in asthma.

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BackgroundExternal validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD.MethodsWe relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores.ResultsDepending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile–3rd quartile = 0.655–0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUCADO – AUCBODE = 0.015 [95% confidence interval (CI) = −0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated – AUCBODE = 0.008 [95% CI = −0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency.ConclusionsOur analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1013-y) contains supplementary material, which is available to authorized users.