Background: Left ventricular diastolic dysfunction (LVDD) is highly prevalent in COPD and conflicting results have emerged regarding the consequences on exercise capacity in the 6MWT. We sought to examine the ventilatory efficiency and variability metrics as the primary endpoint and aerobic capacity (V'O 2) as the secondary endpoint. Methods: Forty subjects were included and submitted to comprehensive lung function tests, detailed pulsed-Doppler echocardiography, and cardiopulmonary exercise testing. Four subjects were excluded due to concomitant cardiac disease and two owing to COPD exacerbation. Results: Seventeen COPD/LVDD+ and seventeen COPD/LVDD-individuals were closely matched for baseline characteristics. Throughout the exercise, there was no difference between-groups for primary (V' E /V'CO 2 slope and V' E /V'CO 2 nadir, p > 0.05 for both) or secondary endpoints (V'O 2peak %pred, p > 0.05). Ventilatory variability remained unchanged. However, after very well age-and sex-matched subgroup analysis, five-moderate and three-mild COPD/LVDD + subjects with elevated left ventricular filling pressure (E/e' > 13, n = 8), presented a downward-shifted V' E /V'CO 2 slope (25.7 ± 5.1 vs 33.4 ± 7.1, p = 0.031) and V' E /V'CO 2 nadir reduction (29.7 ± 3.9 vs 36.3 ± 7.2, p = 0.042) besides significantly better V'O 2peak %pred (92.1 ± 21.6% vs 75.8 ± 13.1%, p = 0.045) compared to 8 COPD/LVDD-controls. Ventilatory variability remained once again unchanged. Conclusions: COPD/LVDD overlap is not associated with worse exercise tolerance and/or wasted ventilation in excess compared to controls, even when suspected for elevated left ventricular filling pressure. Further studies are warranted to study specifically if augmented pulmonary blood transit time can allow better gas-exchange, thus preserving exercise capacity under specific conditions in COPD patients without heart failure.

Abstract.  DNA array technology is an important tool for genomic research due to its capa‐city of measuring simultaneously the expression levels of a great number of genes or fragments of genes in different experimental conditions. An important point in gene expression data analysis is to identify clusters of genes which present similar expression levels. We propose a new procedure for estimating the mixture model for clustering of gene expression data. The proposed method is a posterior split‐merge‐birth MCMC procedure which does not require the specification of the number of components, since it is estimated jointly with component parameters. The strategy for splitting is based on data and on posterior distribution from the previously allocated observations. This procedure defines a quick split proposal in contrary to other split procedures, which require substantial computational effort. The performance of the method is verified using real and simulated datasets.

This supplementary material (SM) presents the acceptance probability for splitmerge movements and some additional results obtained with application of the proposed SDAS algorithm to the simulated datasets.

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Background/Objective The current approach to measuring ventilatory (in)efficiency (V′E‐V′CO2 slope, nadir and intercept) presents critical drawbacks in the evaluation of COPD subjects, owing mainly to mechanical ventilatory constraints. Thus, we aimed to compare the current approach with a new method we have developed for ventilatory efficiency calculation. Methods The new procedure was based on measuring the amount of CO2 cleared by the lungs (V′CO2, L/min) plotted against a predefined range of increase in minute ventilation (V′E) (ten‐fold increase based on semilog scale) during incremental exercise to symptom‐limited maximum tolerance. This value was compared to a hypothetical predicted maximum CO2 output at the predicted maximal voluntary ventilation, defining ventilatory efficiency (ηV′E, %). The results were used to compare 30 subjects with COPD (II‐IV Global Initiative for Chronic Obstructive Lung Disease, GOLD) and 10 non‐COPD smokers, to establish the best discriminative physiological variable for disease severity through logistic multinomial regression. Results The new approach was more sensitive to progressive deterioration of airway obstruction, resulting in worse ηV′E as lung function worsens throughout the GOLD panel (ηV′E (%), p < .001), when compared with V′E‐V′CO2 slope (p = .715) or V′E‐V′CO2 nadir (p = .070), besides showing the best model based on the logistic regression approach. Conclusion Although requiring more complex calculations compared to the current procedure, the new approach is highly sensitive to true ventilatory/gas‐exchange deterioration, even throughout more severe pulmonary lung function in COPD subjects.