RESUMO
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.
Assuntos
Teste de Esforço/métodos , Tolerância ao Exercício/fisiologia , Doença Pulmonar Obstrutiva Crônica/fisiopatologia , Feminino , Humanos , Pulmão/fisiopatologia , Masculino , Pessoa de Meia-IdadeRESUMO
A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.