Prevalence of Major Comorbidities in Chronic Obstructive Pulmonary Disease Caused by Biomass Smoke or Tobacco.

BACKGROUND: Comorbidities are very common in chronic obstructive pulmonary disease (COPD), contributing to the overall severity of the disease. The relative prevalence of comorbidities in COPD caused by biomass smoke (B-COPD), compared with COPD related to tobacco (T-COPD), is not well known. OBJECTIVES: To establish if both types of COPD are associated with a different risk for several major comorbidities. METHOD: The prevalence of comorbidities was compared in 863 subjects with B-COPD (n = 179, 20.7%) or T-COPD (n = 684, 79.2%). Multivariate analysis was carried out to explore the independent relationship between comorbidities and type of exposure. RESULTS: Three comorbidities were more frequent in T-COPD than in B-COPD: ischemic heart disease (11.5 vs. 5.0%, respectively, p = 0.01), peripheral vascular disease (9.2 vs. 2.7%, p = 0.006), and peptic ulcer disease (4.8% vs. 0, p = 0.005). After correcting for potential confounding variables, the risk of ischemic heart disease was lower in B-COPD than in T-COPD (OR: 0.33, 95% CI: 0.16-0.69, p = 0.003). CONCLUSIONS: The prevalence of ischemic heart disease is significantly lower in B-COPD than in T-COPD, suggesting a different systemic effect of both types of smoke in COPD patients.

Prognostic Indices and Mortality Prediction in COPD Caused by Biomass Smoke Exposure.

INTRODUCTION: Little is known about survival and prognostic factors in chronic obstructive pulmonary disease (COPD) due to biomass smoke exposure (BS-COPD). OBJECTIVES: (1) To determine the value of two indices of COPD severity: BODEx (body mass index, obstruction, dyspnea, and previous severe exacerbations) and Global Initiative for Chronic Obstructive Lung Disease (GOLD) categories system (ABCD) to predict all-cause mortality in BS-COPD, compared with COPD due to tobacco (T-COPD); (2) to verify the usefulness of 2 comorbidity indices, Charlson index and COTE (COPD comorbidity index); and (3) to put side by side the value of these indices. METHODS: 612 consecutive COPD patients were retrospectively studied. Prognostic factors were evaluated taking into account the exposure to biomass or tobacco smoke. The relative predictive values of the prognostic indices were compared using receiver-operating characteristic analysis. RESULTS: Mortality in the BS-COPD and T-COPD groups was not significantly different, when sex was taken into account. BODEx, Charlson and COTE indices-but not type of exposure-predicted mortality in multivariate analysis. The value of the C-statistic for the BODEx index was not different than that of GOLD ABCD for BS-COPD, but was higher for T-COPD. The discriminatory value of the Charlson index was higher than that of COTE for BS-COPD, but no significant differences were found for T-COPD. CONCLUSIONS: Multidimensional indices of COPD severity and comorbidity predict all-cause mortality in BS-COPD. The behavior of the different indices is different for BS-COPD and T-COPD.

Multivariate analysis in thoracic research.

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.