Objective: Studies characterizing asthma phenotypes have predominantly included adults or have involved children and adolescents in developed countries. Therefore, their applicability in other populations, such as those of developing countries, remains indeterminate. Our objective was to determine how low-income children and adolescents with asthma in Brazil are distributed across a cluster analysis. Methods: We included 306 children and adolescents (6-18 years of age) with a clinical diagnosis of asthma and under medical treatment for at least one year of follow-up. At enrollment, all the patients were clinically stable. For the cluster analysis, we selected 20 variables commonly measured in clinical practice and considered important in defining asthma phenotypes. Variables with high multicollinearity were excluded. A cluster analysis was applied using a two-step agglomerative test and log-likelihood distance measure. Results: Three clusters were defined for our population. Cluster 1 (n = 94) included subjects with normal pulmonary function, mild eosinophil inflammation, few exacerbations, later age at asthma onset, and mild atopy. Cluster 2 (n = 87) included those with normal pulmonary function, a moderate number of exacerbations, early age at asthma onset, more severe eosinophil inflammation, and moderate atopy. Cluster 3 (n = 108) included those with poor pulmonary function, frequent exacerbations, severe eosinophil inflammation, and severe atopy. Conclusions: Asthma was characterized by the presence of atopy, number of exacerbations, and lung function in low-income children and adolescents in Brazil. The many similarities with previous cluster analyses of phenotypes indicate that this approach shows good generalizability.

Keywords: Asthma/classification; Asthma/etiology; Child; Adolescent.

Objective: To establish reproducible electron microscopic criteria for identifying the four major types of neuroendocrine tumors of the lung: carcinoid; atypical carcinoid; large cell neuroendocrine carcinoma; and small cell carcinoma. Methods: Measurements were made on electron micrographs using a digital image analyzer. Sixteen morphometric variables related to tumor cell differentiation were assessed in 27 tumors. The examination under electron microscopy revealed that all of the tumors could be classified as belonging to one of the four categories listed above. Cluster analysis of the morphometry variables was used to group the tumors into three clusters, and Kaplan-Meier survival function curves were employed in order to draw correlations between each cluster and survival. Results: All three clusters of neuroendocrine carcinomas were found to be associated with survival curves, demonstrating the prognostic significance of electron microscopic features. The tumors fell into three well-defined clusters, which represent the spectrum of neuroendocrine differentiation: typical carcinoid (cluster 1); atypical carcinoid and large cell neuroendocrine carcinoma (cluster 2); and small cell carcinoma (cluster 3). Cluster 2 represents an intermediate step in neuroendocrine carcinogenesis, between typical carcinoid tumors and small cell carcinomas. Conclusions: Our findings confirm that electron microscopy is useful in making the diagnosis and prognosis in cases of lung tumor.

Keywords: Neuroendocrine tumors/lung; Microscopy, electron; Cluster analysis; Survival analysis.

Objective: Lung cancer patients often experience profound physical and psychosocial changes as a result of disease progression or treatment side effects. Fatigue, pain, dyspnea, depression, and sleep disturbances appear to be the most common symptoms in such patients. The objective of the present study was to examine the prevalence of symptoms in lung cancer patients in order to identify subgroups (clusters) of patients, grouped according to the magnitude of the symptoms, as well as to compare the quality of life among the identified subgroups. Methods: A cross-sectional study involving agglomerative hierarchical clustering. A total of 50 lung cancer patients were evaluated in terms of their demographic characteristics and their scores on three quality of life questionnaires, namely the 30-item European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire (EORTC QLQ-C30), the Functional Assessment of Cancer Therapy-Lung, and the Medical Outcomes Study 36-item Short-form Survey. The cluster analysis took into account the magnitude of the most prevalent symptoms as assessed by the EORTC QLQ-C30 symptom scale scores; those symptoms were fatigue, pain, dyspnea, and insomnia. Results: Three clusters (subgroups)_of patients were identified on the basis of the magnitude of the four most prevalent symptoms. The three subgroups of patients were as follows: patients with mild symptoms (n = 30; 60%); patients with moderate symptoms (n = 14; 28%); and patients with severe symptoms (n = 6; 12%). The subgroup of patients with severe symptoms had the worst quality of life, as assessed by the total scores and by the integrated domains of all three instruments. Conclusions: This study highlights the importance of symptom cluster assessment as an important tool to assess the quality of life of patients with chronic diseases, such as lung cancer.

Objective: To determine the characteristics of individuals with asthma who are responsive to aerobic training. Methods: This post hoc analysis of pooled data from previous randomized controlled trials involved 101 individuals with moderate to severe asthma who underwent aerobic training. Participants underwent a maximal cardiopulmonary exercise test and completed the Asthma Control Questionnaire and the Asthma Quality of Life Questionnaire before and after a 24-session aerobic training program. Better and worse responders to aerobic training were identified by cluster analysis. Results: Two clusters were identified according to the improvement in peak VO2 after aerobic training (better and worse responders). Characteristics of the better responder group were being older, being female, having higher BMI, and having higher cardiac reserve at baseline when compared with the worse responder group. Also, better responders had worse clinical control, worse quality of life, and lower physical capacity at baseline. After training, worse responders, in comparison with better responders, showed half the improvement in ?peak VO2 (7.4% vs. 13.6%; 95% CI, -12.1 to -0.92%; p < 0.05) and worse asthma control. A weak, negative, but significant association (r = -0.35; p < 0.05) was observed between clinical control and aerobic fitness only in the better responder group. Both groups showed significant improvement in quality of life. Conclusions: Obese individuals with worse exercise capacity, clinical control, and quality of life showed improvement with aerobic training. Moreover, worse responders also improved with training, but to a lesser extent.

Keywords: Asthma; Exercise therapy; Cluster analysis; Status Asthmaticus; Quality of Life; Rehabilitation.