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Objectives: To investigate the incidence of active tuberculosis (TB) among COPD patients using fluticasone/salmeterol or budesonide/formoterol, and to identify any differences between these two groups of patients.Methods: The study enrolled COPD patients from Taiwan NHIRD who received treatment with fluticasone/salmeterol or budesonide/formoterol for > 90 days between 2004 and 2011. The incidence of active TB was the primary outcome.Results: Among the intention-to-treat population prior to matching, the incidence rates of active TB were 0.94 and 0.61% in the fluticasone/salmeterol and budesonide/formoterol groups, respectively. After matching, the fluticasone/salmeterol group had significantly higher rates of active TB (adjusted HR, 1.41, 95% CI, 1.17-1.70) compared with the budesonide/formoterol group. The significant difference between these two groups remained after a competing risk analysis (HR, 1.45, 95% CI, 1.21-1.74). Following propensity score matching, the fluticasone/salmeterol group had significantly higher rates of active TB compared with the budesonide/formoterol group (adjusted HR, 1.45, 95% CI, 1.14-1.85). A similar trend was observed after a competing risk analysis (HR, 1.44, 95% CI, 1.19-1.75). A higher risk of active TB was observed in the fluticasone/salmeterol group compared with the budesonide/formoterol group across all subgroups, but some differences did not reach statistical significance.Conclusion: Fluticasone/salmeterol carried a higher risk of active TB compared with budesonide/formoterol among COPD patients. [ABSTRACT FROM AUTHOR]

Background: This paper proposes a semantic segmentation algorithm that provides the spatial distribution patterns of pulmonary ground-glass nodules with solid portions in computed tomography (CT) images., Methods: The proposed segmentation algorithm, anatomy packing with hierarchical segments (APHS), performs pulmonary nodule segmentation and quantification in CT images. In particular, the APHS algorithm consists of two essential processes: hierarchical segmentation tree construction and anatomy packing. It constructs the hierarchical segmentation tree based on region attributes and local contour cues along the region boundaries. Each node of the tree corresponds to the soft boundary associated with a family of nested segmentations through different scales applied by a hierarchical segmentation operator that is used to decompose the image in a structurally coherent manner. The anatomy packing process detects and localizes individual object instances by optimizing a hierarchical conditional random field model. Ninety-two histopathologically confirmed pulmonary nodules were used to evaluate the performance of the proposed APHS algorithm. Further, a comparative study was conducted with two conventional multi-label image segmentation algorithms based on four assessment metrics: the modified Williams index, percentage statistic, overlapping ratio, and difference ratio., Results: Under the same framework, the proposed APHS algorithm was applied to two clinical applications: multi-label segmentation of nodules with a solid portion and surrounding tissues and pulmonary nodule segmentation. The results obtained indicate that the APHS-generated boundaries are comparable to manual delineations with a modified Williams index of 1.013. Further, the resulting segmentation of the APHS algorithm is also better than that achieved by two conventional multi-label image segmentation algorithms., Conclusions: The proposed two-level hierarchical segmentation algorithm effectively labelled the pulmonary nodule and its surrounding anatomic structures in lung CT images. This suggests that the generated multi-label structures can potentially serve as the basis for developing related clinical applications.